Repository: GoogleCloudPlatform/analytics-componentized-patterns
Branch: master
Commit: 3c57f520e13f
Files: 83
Total size: 4.3 MB

Directory structure:
gitextract_tju_xjf6/

├── .gitignore
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── gaming/
│   └── propensity-model/
│       └── bqml/
│           ├── README.md
│           └── bqml_ga4_gaming_propensity_to_churn.ipynb
└── retail/
    ├── clustering/
    │   └── bqml/
    │       ├── README.md
    │       └── bqml_scaled_clustering.ipynb
    ├── ltv/
    │   └── bqml/
    │       ├── README.md
    │       ├── notebooks/
    │       │   └── bqml_automl_ltv_activate_lookalike.ipynb
    │       └── scripts/
    │           ├── 00_procedure_persist.sql
    │           ├── 10_procedure_match.sql
    │           ├── 20_procedure_prepare.sql
    │           ├── 30_procedure_train.sql
    │           ├── 40_procedure_predict.sql
    │           ├── 50_procedure_top.sql
    │           └── run.sh
    ├── propensity-model/
    │   └── bqml/
    │       ├── README.md
    │       └── bqml_kfp_retail_propensity_to_purchase.ipynb
    ├── recommendation-system/
    │   ├── bqml/
    │   │   ├── README.md
    │   │   └── bqml_retail_recommendation_system.ipynb
    │   ├── bqml-mlops/
    │   │   ├── README.md
    │   │   ├── dockerfile
    │   │   ├── kfp_tutorial.ipynb
    │   │   ├── part_2/
    │   │   │   ├── Dockerfile
    │   │   │   ├── README.md
    │   │   │   ├── cloudbuild.yaml
    │   │   │   ├── dockerbuild.sh
    │   │   │   └── pipeline.py
    │   │   └── part_3/
    │   │       ├── Dockerfile
    │   │       ├── README.md
    │   │       ├── dockerbuild.sh
    │   │       └── vertex_ai_pipeline.ipynb
    │   └── bqml-scann/
    │       ├── .gitignore
    │       ├── 00_prep_bq_and_datastore.ipynb
    │       ├── 00_prep_bq_procedures.ipynb
    │       ├── 01_train_bqml_mf_pmi.ipynb
    │       ├── 02_export_bqml_mf_embeddings.ipynb
    │       ├── 03_create_embedding_lookup_model.ipynb
    │       ├── 04_build_embeddings_scann.ipynb
    │       ├── 05_deploy_lookup_and_scann_caip.ipynb
    │       ├── README.md
    │       ├── ann01_create_index.ipynb
    │       ├── ann02_run_pipeline.ipynb
    │       ├── ann_grpc/
    │       │   ├── match_pb2.py
    │       │   └── match_pb2_grpc.py
    │       ├── ann_setup.md
    │       ├── embeddings_exporter/
    │       │   ├── __init__.py
    │       │   ├── pipeline.py
    │       │   ├── runner.py
    │       │   └── setup.py
    │       ├── embeddings_lookup/
    │       │   └── lookup_creator.py
    │       ├── index_builder/
    │       │   ├── builder/
    │       │   │   ├── __init__.py
    │       │   │   ├── indexer.py
    │       │   │   └── task.py
    │       │   ├── config.yaml
    │       │   └── setup.py
    │       ├── index_server/
    │       │   ├── Dockerfile
    │       │   ├── cloudbuild.yaml
    │       │   ├── lookup.py
    │       │   ├── main.py
    │       │   ├── matching.py
    │       │   └── requirements.txt
    │       ├── perf_test.ipynb
    │       ├── requirements.txt
    │       ├── sql_scripts/
    │       │   ├── sp_ComputePMI.sql
    │       │   ├── sp_ExractEmbeddings.sql
    │       │   └── sp_TrainItemMatchingModel.sql
    │       ├── tfx01_interactive.ipynb
    │       ├── tfx02_deploy_run.ipynb
    │       └── tfx_pipeline/
    │           ├── Dockerfile
    │           ├── __init__.py
    │           ├── bq_components.py
    │           ├── config.py
    │           ├── item_matcher.py
    │           ├── lookup_creator.py
    │           ├── pipeline.py
    │           ├── runner.py
    │           ├── scann_evaluator.py
    │           ├── scann_indexer.py
    │           └── schema/
    │               └── schema.pbtxt
    └── time-series/
        └── bqml-demand-forecasting/
            ├── README.md
            └── bqml_retail_demand_forecasting.ipynb

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
.ipynb_checkpoints/
.DS_Store
.vscode/
**/*.cpython-37..pyc
**/*.sqllite
**/*.tar.gz
retail/recommendation-system/bqml-scann/vocabulary.txt


================================================
FILE: CONTRIBUTING.md
================================================
# How to Contribute

We'd love to accept your patches and contributions to this project. There are
just a few small guidelines you need to follow.

## Contributor License Agreement

Contributions to this project must be accompanied by a Contributor License
Agreement. You (or your employer) retain the copyright to your contribution;
this simply gives us permission to use and redistribute your contributions as
part of the project. Head over to <https://cla.developers.google.com/> to see
your current agreements on file or to sign a new one.

You generally only need to submit a CLA once, so if you've already submitted one
(even if it was for a different project), you probably don't need to do it
again.

## Code reviews

All submissions, including submissions by project members, require review. We
use GitHub pull requests for this purpose. Consult
[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
information on using pull requests.

## Community Guidelines

This project follows [Google's Open Source Community
Guidelines](https://opensource.google/conduct/).


================================================
FILE: LICENSE
================================================

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================================================
FILE: README.md
================================================
> [!NOTE]
> This repository has been archived and is no longer actively maintained.

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)

## Analytics Componentized Patterns

From sample dataset to activation, these componentized patterns are designed to help you get the most out of [BigQuery ML](https://cloud.google.com/bigquery-ml/docs) and other Google Cloud products in production.

### Retail use cases
* Recommendation systems:
  * How to build an end to end recommendation system with CI/CD MLOps pipeline on hotel data using BigQuery ML. ([Code][bqml_mlops_code] | [Blogpost][bqml_scann_guide])
  * How to build a recommendation system on e-commerce data using BigQuery ML. ([Code][recomm_code] | [Blogpost][recomm_blog] | [Video][recomm_video])
  * How to build an item-item real-time recommendation system on song playlists data using BigQuery ML. ([Code][bqml_scann_code] | [Reference Guide][bqml_scann_guide])
* Propensity to purchase model:
  * How to build an end-to-end propensity to purchase solution using BigQuery ML and Kubeflow Pipelines. ([Code][propen_code] | [Blogpost][propen_blog])
* Activate on Lifetime Value predictions: 
  * How to predict the monetary value of your customers and extract emails of the top customers to use in Adwords for example to create similar audiences. Automation is done by a combination of BigQuery Scripting, Stored Procedure and bash script. ([Code][ltv_code])
* Clustering:
  * How to build customer segmentation through k-means clustering using BigQuery ML. ([Code][clustering_code] | [Blogpost][clustering_blog])
* Demand Forecasting:
  * How to build a time series demand forecasting model using BigQuery ML ([Code][demandforecasting_code] | [Blogpost][demandforecasting_blog] | [Video][demandforecasting_video])


### Gaming use cases
* Propensity to churn model:
  * Churn prediction for game developers using Google Analytics 4 (GA4) and BigQuery ML. ([Code][gaming_propen_code] | [Blogpost][gaming_propen_blog] | [Video][gaming_propen_video])

### Financial use cases
* Fraud detection
  * How to build a real-time credit card fraud detection solution. ([Code][ccfraud_code] | [Blogpost][ccfraud_techblog] | [Video][ccfraud_video])


[gaming_propen_code]: gaming/propensity-model/bqml
[gaming_propen_blog]: https://cloud.google.com/blog/topics/developers-practitioners/churn-prediction-game-developers-using-google-analytics-4-ga4-and-bigquery-ml
[gaming_propen_video]: https://www.youtube.com/watch?v=t5a0gwPM4I8
[recomm_code]: retail/recommendation-system/bqml
[recomm_blog]: https://medium.com/google-cloud/how-to-build-a-recommendation-system-on-e-commerce-data-using-bigquery-ml-df9af2b8c110
[recomm_video]: https://youtube.com/watch?v=sEx8RwvT_-8
[bqml_scann_code]: retail/recommendation-system/bqml-scann
[bqml_mlops_code]: retail/recommendation-system/bqml-mlops
[bqml_scann_guide]: https://cloud.google.com/solutions/real-time-item-matching
[propen_code]: retail/propensity-model/bqml
[propen_blog]: https://medium.com/google-cloud/how-to-build-an-end-to-end-propensity-to-purchase-solution-using-bigquery-ml-and-kubeflow-pipelines-cd4161f734d9
[ltv_code]: retail/ltv/bqml
[clustering_code]: retail/clustering/bqml
[clustering_blog]: https://towardsdatascience.com/how-to-build-audience-clusters-with-website-data-using-bigquery-ml-6b604c6a084c
[demandforecasting_code]: retail/time-series/bqml-demand-forecasting
[demandforecasting_blog]: https://cloud.google.com/blog/topics/developers-practitioners/how-build-demand-forecasting-models-bigquery-ml
[demandforecasting_video]: https://www.youtube.com/watch?v=dwOt68CevYA
[ccfraud_code]: https://gitlab.qdatalabs.com/uk-gtm/patterns/cc_fraud_detection/tree/master
[ccfraud_techblog]: https://cloud.google.com/blog/products/data-analytics/how-to-build-a-fraud-detection-solution
[ccfraud_video]: https://youtu.be/qQnxq3COr9Q




## Questions? Feedback?
If you have any questions or feedback, please open up a [new issue](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/issues).

## Disclaimer
This is not an officially supported Google product.

All files in this repository are under the [Apache License, Version 2.0](LICENSE.txt) unless noted otherwise.


================================================
FILE: gaming/propensity-model/bqml/README.md
================================================
## License
```
Copyright 2021 Google LLC

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

     https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
```
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)

# Churn prediction for game developers using Google Analytics 4 (GA4) and BigQuery ML

This notebook showcases how you can use BigQuery ML to run propensity models on Google Analytics 4 data from your gaming app to determine the likelihood of specific users returning to your app.

Using this notebook, you'll learn how to:

- Explore the BigQuery export dataset for Google Analytics 4
- Prepare the training data using demographic and behavioural attributes
- Train propensity models using BigQuery ML
- Evaluate BigQuery ML models
- Make predictions using the BigQuery ML models
- Implement model insights in practical implementations

## Architecture Diagram

![Architecture Diagram](images/workflow.PNG?raw=true "Architecture Diagram")

## More resources

If you’d like to learn more about any of the topics covered in this notebook, check out these resources:

- [BigQuery export of Google Analytics data]
- [BigQuery ML quickstart]
- [Events automatically collected by Google Analytics 4]

## Questions? Feedback?
If you have any questions or feedback, please open up a [new issue](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/issues).

[BigQuery export of Google Analytics data]: https://support.google.com/analytics/answer/9358801
[BigQuery ML quickstart]: https://cloud.google.com/bigquery-ml/docs/bigqueryml-web-ui-start
[Events automatically collected by Google Analytics 4]: https://support.google.com/analytics/answer/9234069


================================================
FILE: gaming/propensity-model/bqml/bqml_ga4_gaming_propensity_to_churn.ipynb
================================================
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    "# Copyright 2020 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tTt2Oe0szW0M"
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   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://console.cloud.google.com/ai-platform/notebooks/deploy-notebook?name=Churn%20prediction%20for%20game%20developers%20using%20Google%20Analytics%204%20%28GA4%29%20and%20BigQuery%20ML%20Notebook&download_url=https%3A%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fanalytics-componentized-patterns%2Fmaster%2Fgaming%2Fpropensity-model%2Fbqml%2Fbqml_ga4_gaming_propensity_to_churn.ipynb\">\n",
    "      <img src=\"https://cloud.google.com/images/products/ai/ai-solutions-icon.svg\" alt=\"AI Platform Notebooks\">Run on AI Platform Notebooks</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/blob/master/gaming/propensity-model/bqml/bqml_ga4_gaming_propensity_to_churn.ipynb\">\n",
    "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
    "      View on GitHub\n",
    "    </a>\n",
    "  </td>\n",
    "</table>"
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  },
  {
   "cell_type": "markdown",
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    "id": "DWW_k7u_zaER"
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   "source": [
    "# Overview\n",
    "This notebook shows you how you can train, evaluate, and deploy a propensity model in BigQuery ML to predict user retention on a mobile game, based on app measurement data from Google Analytics 4.\n",
    "\n",
    "#### Propensity modeling in the mobile gaming industry\n",
    "According to a [2019 study](https://gameanalytics.com/reports/mobile-gaming-industry-analysis-h1-2019) on 100K mobile games by the Mobile Gaming Industry Analysis, most mobile games only see a 25% retention rate for users after the first 24 hours, and any game \"below 30% retention generally needs improvement\". In light of this, using machine learning -- to identify the propensity that a user churn after day 1 -- can allow app developers to incentivize users at higher risk of churning to return.\n",
    "\n",
    "To predict the propensity (a.k.a. likelihood) that a user will return vs churn, you can use classification algorithms, like logistic regression, XGBoost, neural networks, or AutoML Tables, all of which are available with BigQuery ML.\n",
    "\n",
    "#### Propensity modeling in BigQuery ML\n",
    "With BigQuery ML, you can train, evaluate and deploy our models directly within BigQuery using SQL, which saves time from needing to manually configure ML infrastructure. You can train and deploy ML models directly where the data is already stored, which also helps to avoid potential issues around data governance.\n",
    "\n",
    "Using classification models that you train and deploy in BigQuery ML, you can predict propensity using the output of the models. The models outputs provide a probability score between 0 and 1.0 -- how likely the model predicts that the user will churn (1) or not churn (0).\n",
    "\n",
    "Using the probability (propensity) scores, you can then, for example, target users who may not return on their own, but could potentially return if they are provided with an incentive or notification.\n",
    "\n",
    "#### Not just churn -- propensity modeling for any behavior\n",
    "Propensity modeling is not limited to predicting churn. In fact, you can calculate a propensity score for any behavior you may want to predict. For example, you may want to predict the likelihood a user will spend money on in-app purchases. Or, perhaps you can predict the likelihood of a user performing \"stickier\" behaviors such as adding and playing with friends, which could lead to longer-term retention and organic user growth. Whichever the case, you can easily modify this notebook to suit your needs, as the overall workflow will still be the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t44p6IQUzrY4"
   },
   "source": [
    "## Scope of this notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nznL3qK4z8Jm"
   },
   "source": [
    "### Dataset\n",
    "\n",
    "This notebook uses [this public BigQuery dataset](https://console.cloud.google.com/bigquery?p=firebase-public-project&d=analytics_153293282&t=events_20181003&page=table), contains raw event data from a real mobile gaming app called Flood It! ([Android app](https://play.google.com/store/apps/details?id=com.labpixies.flood), [iOS app](https://itunes.apple.com/us/app/flood-it!/id476943146?mt=8)). The [data schema](https://support.google.com/analytics/answer/7029846) originates from Google Analytics for Firebase, but is the same schema as [Google Analytics 4](https://support.google.com/analytics/answer/9358801); this notebook applies to use cases that use either Google Analytics for Firebase or Google Analytics 4 data.\n",
    "\n",
    "Google Analytics 4 (GA4) uses an [event-based](https://support.google.com/analytics/answer/9322688) measurement model. Events provide insight on what is happening in an app or on a website, such as user actions, system events, or errors. Every row in the dataset is an event, with various characteristics relevant to that event stored in a nested format within the row. While Google Analytics logs many types of events already by default, developers can also customize the types of events they also wish to log.\n",
    "\n",
    "Note that as you cannot simply use the raw event data to train a machine learning model, in this notebook, you will also learn the important steps of how to pre-process the raw data into an appropriate format to use as training data for classification models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hxrwZlF2PYQ8"
   },
   "source": [
    "#### Using your own GA4 data?\n",
    "If you are already using a Google Analytics 4 property, follow [this guide]((https://support.google.com/analytics/answer/9823238) to learn how to export your GA4 data to BigQuery. Once the GA4 data is in BigQuery, there will be two tables:\n",
    "\n",
    "* `events_`\n",
    "* `events_intraday_`\n",
    "\n",
    "For this notebook, you can replace the table in the `FROM` clause in SQL queries with your `events_` table that is updated daily. The `events_intraday_` table contains streaming data for the current day.\n",
    "\n",
    "Note that if you use your own GA4 data, you may need to slightly modify some of the scripts in this notebook to predict a different output behavior or the types events in the training data that are specific to your use case. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KJB3rOtfrBIl"
   },
   "source": [
    "#### Using data from other non-Google Analytics data collection tools?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9corrB3IrPeH"
   },
   "source": [
    "While this notebook provides code based on a Google Analytics dataset, you can also use your own dataset from other non-Google Analytics data collection tools. The overall concepts and process of propensity modeling will be the same, but you may need to customize the code in order to prepare your dataset into the training data format described in this notebook.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sH0CZGku0BPp"
   },
   "source": [
    "### Objective and Problem Statement\n",
    "\n",
    "The goal of this notebook is to provide an end-to-end solution for propensity modeling to predict user churn on GA4 data using BigQuery ML. Using the \"Flood It!\" dataset, based on a user's activity within the first 24 hrs of app installation, you will try various classification models to predict the propensity to churn (1) or not churn (0).\n",
    "\n",
    "By the end of this notebook, you will know how to:\n",
    "* Explore the export of Google Analytics 4 data on BigQuery\n",
    "* Prepare the training data using demographic, behavioral data, and the label (churn/not-churn)\n",
    "* Train classification models using BigQuery ML\n",
    "* Evaluate classification models using BigQuery ML\n",
    "* Make predictions on which users will churn using BigQuery ML\n",
    "* Activate on model predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IStlwydL0NWC"
   },
   "source": [
    "### Costs \n",
    "\n",
    "There is no cost associated with using the free version of Google Analytics and using the BigQuery Export feature. This tutorial uses billable components of Google Cloud Platform (GCP):\n",
    "\n",
    "* BigQuery\n",
    "* BigQuery ML\n",
    "\n",
    "Learn about [BigQuery pricing](https://cloud.google.com/bigquery/pricing), [BigQuery ML\n",
    "pricing](https://cloud.google.com/bigquery-ml/pricing) and use the [Pricing\n",
    "Calculator](https://cloud.google.com/products/calculator/)\n",
    "to generate a cost estimate based on your projected usage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "coMAAaOH0Tcl"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NUlqCGWz0VGL"
   },
   "source": [
    "### PIP Install Packages and dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MujO3LNG0W26"
   },
   "outputs": [],
   "source": [
    "!pip install google-cloud-bigquery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oRab23dd0aeC"
   },
   "outputs": [],
   "source": [
    "# Automatically restart kernel after installs\n",
    "import IPython\n",
    "app = IPython.Application.instance()\n",
    "app.kernel.do_shutdown(True)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8PpWu5MR0bPf"
   },
   "source": [
    "### Set up your GCP project\n",
    "\n",
    "_The following steps are required, regardless of your notebook environment._\n",
    "\n",
    "1. [Select or create a GCP project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
    "\n",
    "1. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n",
    "\n",
    "1. [Enable the AI Platform APIs and Compute Engine APIs.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component)\n",
    "\n",
    "1. Enter your project ID and region in the cell below. Then run the  cell to make sure the\n",
    "Cloud SDK uses the right project for all the commands in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROJECT_ID = \"YOUR-PROJECT-ID\" #replace with your project id\n",
    "REGION = 'US'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mm5cGuC00mHI"
   },
   "source": [
    "### Import libraries and define constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DRYImlXa0mld"
   },
   "outputs": [],
   "source": [
    "from google.cloud import bigquery\n",
    "import pandas as pd\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BlfwBj4P0rHD"
   },
   "source": [
    "### Create a BigQuery dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X9TLkb8f0tCE"
   },
   "source": [
    "In this notebook, you will need to create a dataset in your project called `bqmlga4`. To create it, run the following cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "d8LyhoGy0ttK"
   },
   "outputs": [],
   "source": [
    "DATASET_NAME = \"bqmlga4\"\n",
    "!bq mk --location=$REGION --dataset $PROJECT_ID:$DATASET_NAME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_sOVqTXM0-Wc"
   },
   "source": [
    "## The dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4LMr6Q3MQt0I"
   },
   "source": [
    "### Using the sample gaming event data from Flood it!\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wV7wi8X51eNw"
   },
   "source": [
    "The sample dataset contains raw event data, as shown in the next cell:\n",
    "\n",
    "_Note_: Jupyter runs cells starting with %%bigquery as SQL queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 551
    },
    "id": "wwz_N3Kh1f9l",
    "outputId": "8b162c57-12a6-4262-e482-6e4d0ae3b47c"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "SELECT \n",
    "    *\n",
    "FROM\n",
    "  `firebase-public-project.analytics_153293282.events_*`\n",
    "    \n",
    "TABLESAMPLE SYSTEM (1 PERCENT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PYBMN963Qydl"
   },
   "source": [
    "It may be helpful to take a look at the overall schema used in Google Analytics 4. As mentioned earlier, Google Analytics 4 uses an event based measurement model and each row in this dataset is an event. [Click here](https://support.google.com/analytics/answer/7029846) to view the complete schema and details about each column. As you can see above, certain columns are nested records and contain detailed information:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IaM8co6eRsOp"
   },
   "source": [
    "\n",
    "* `app_info`\n",
    "* `device`\n",
    "* `ecommerce`\n",
    "* `event_params`\n",
    "* `geo`\n",
    "* `traffic_source`\n",
    "* `user_properties`\n",
    "* `items`*\n",
    "* `web_info`*\n",
    "\n",
    "_* present by default in GA4 datasets_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dLUv-7xNRhAj"
   },
   "source": [
    "As we can see below, there are 15K users and 5.7M events in this dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 77
    },
    "id": "MjqKMGVDRPyZ",
    "outputId": "6c0d76c7-ad92-40de-a689-365867b23281"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "SELECT \n",
    "    COUNT(DISTINCT user_pseudo_id) as count_distinct_users,\n",
    "    COUNT(event_timestamp) as count_events\n",
    "FROM\n",
    "  `firebase-public-project.analytics_153293282.events_*`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3iHaV9-q1k1i"
   },
   "source": [
    "### Preparing the training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P358Jo_s8WC0"
   },
   "source": [
    "You cannot simply use raw event data to train a machine learning model as it would not be in the right shape and format to use as training data. So in this section, you will learn how to pre-process the raw data into an appropriate format to use as training data for classification models.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eXl1kSh1yPXk"
   },
   "source": [
    "To predict which user is going to _churn_ or _return_, the ideal training data format for classification should look like the following:  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xv8ibjMNy_bV"
   },
   "source": [
    "|User ID|User demographic data|User behavioral data|Churned|\n",
    "|-|-|-|-|\n",
    "|User1|(e.g., country, device_type)|(e.g., # of times they did something within a time period)|1\n",
    "|User2|(e.g., country, device_type)|(e.g., # of times they did something within a time period)|0\n",
    "|User3|(e.g., country, device_type)|(e.g., # of times they did something within a time period)|1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HydYCB2jzrzn"
   },
   "source": [
    "Characteristics of the training data:\n",
    "- each row is a separate unique user ID\n",
    "- feature(s) for **demographic data**\n",
    "- feature(s) for **behavioral data**\n",
    "- the actual **label** that you want to train the model to predict (e.g., 1 = churned, 0 = returned)\n",
    "\n",
    "You can train a model with only demographic data or behavioral data, but having a combination of both will likely help you create a more predictive model. For this reason, in this section, you will learn how to pre-process the raw data to follow this training data format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ICpTsrfg2-Cw"
   },
   "source": [
    "The following sections will walk you through preparing the demographic data, behavioral data, and the label before joining them all together as the training data.\n",
    "\n",
    "1. Identifying the label for each user (churned or returned)\n",
    "1. Extracting demographic data for each user\n",
    "1. Extracting behavioral data for each user\n",
    "1. Combining the label, demographic and behavioral data together as training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZYHefnNx21lO"
   },
   "source": [
    "#### Step 1: Identifying the label for each user"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qt6a5Kv-25iq"
   },
   "source": [
    "The raw dataset doesn't have a feature that simply identifies users as \"churned\" or \"returned\", so in this section, you will need to create this label based on some of the existing columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lgqxD30Hl6FM"
   },
   "source": [
    "There are many ways to define user churn, but for the purposes of this notebook, you will predict 1-day churn as users who do not come back and use the app again after 24 hr of the user's first engagement. \n",
    "\n",
    "In other words, after 24 hr of a user's first engagement with the app:\n",
    "- if the user _shows no event data thereafter_, the user is considered **churned**. \n",
    "- if the user _does have at least one event datapoint thereafter_, then the user is considered **returned**\n",
    "\n",
    "You may also want to remove users who were unlikely to have ever returned anyway after spending just a few minutes with the app, which is sometimes referred to as \"bouncing\". For example, we can say want to build our model only on users who spent at least 10 minutes with the app (users who didn't bounce).\n",
    "\n",
    "So your updated definition of a **churned user** for this notebook is:\n",
    "> \"any user who spent at least 10 minutes on the app, but after 24 hour from when they first engaged with the app, never used the app again\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_YyxwMQQ1uQW"
   },
   "source": [
    "In SQL, since the raw data contains all of the events for every user, from their first touch (app installation) to their last touch, you can use this information to create two columns: `churned` and `bounced`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d_JCCtuZVzne"
   },
   "source": [
    "Take a look at the following SQL query and the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 531
    },
    "id": "_QQO3POV2EQ4",
    "outputId": "8369d0bd-8527-42bd-d1c6-aa8e4c380cb0"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID \n",
    "\n",
    "CREATE OR REPLACE VIEW bqmlga4.returningusers AS (\n",
    "  WITH firstlasttouch AS (\n",
    "    SELECT\n",
    "      user_pseudo_id,\n",
    "      MIN(event_timestamp) AS user_first_engagement,\n",
    "      MAX(event_timestamp) AS user_last_engagement\n",
    "    FROM\n",
    "      `firebase-public-project.analytics_153293282.events_*`\n",
    "    WHERE event_name=\"user_engagement\"\n",
    "    GROUP BY\n",
    "      user_pseudo_id\n",
    "\n",
    "  )\n",
    "  SELECT\n",
    "    user_pseudo_id,\n",
    "    user_first_engagement,\n",
    "    user_last_engagement,\n",
    "    EXTRACT(MONTH from TIMESTAMP_MICROS(user_first_engagement)) as month,\n",
    "    EXTRACT(DAYOFYEAR from TIMESTAMP_MICROS(user_first_engagement)) as julianday,\n",
    "    EXTRACT(DAYOFWEEK from TIMESTAMP_MICROS(user_first_engagement)) as dayofweek,\n",
    "\n",
    "    #add 24 hr to user's first touch\n",
    "    (user_first_engagement + 86400000000) AS ts_24hr_after_first_engagement,\n",
    "\n",
    "#churned = 1 if last_touch within 24 hr of app installation, else 0\n",
    "IF (user_last_engagement < (user_first_engagement + 86400000000),\n",
    "    1,\n",
    "    0 ) AS churned,\n",
    "\n",
    "#bounced = 1 if last_touch within 10 min, else 0\n",
    "IF (user_last_engagement <= (user_first_engagement + 600000000),\n",
    "    1,\n",
    "    0 ) AS bounced,\n",
    "  FROM\n",
    "    firstlasttouch\n",
    "  GROUP BY\n",
    "    1,2,3\n",
    "    );\n",
    "\n",
    "SELECT \n",
    "  * \n",
    "FROM \n",
    "  bqmlga4.returningusers \n",
    "LIMIT 100;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FOoqPb2J2Q5f"
   },
   "source": [
    "For the `churned` column, `churned=0` if the user performs an action after 24 hours since their first touch, otherwise if their last action was only within the first 24 hours, then `churned=1`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sC3sIc0C2a4Z"
   },
   "source": [
    "For the `bounced` column, `bounced=1` if the user's last action was within the first ten minutes since their first touch with the app, otherwise `bounced=0`. We can use this column to filter our training data later on, by conditionally querying for users where `bounced = 0`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ulbfb8SY2fSM"
   },
   "source": [
    "You might wonder how many of these 15k users bounced and returned? You can run the following query to check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "id": "gC32zyIE2olw",
    "outputId": "6c51e523-0a4f-4a8d-a4c4-e0f7f0131925"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "SELECT\n",
    "    bounced,\n",
    "    churned, \n",
    "    COUNT(churned) as count_users\n",
    "FROM\n",
    "    bqmlga4.returningusers\n",
    "GROUP BY 1,2\n",
    "ORDER BY bounced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z29RsKJi2uwO"
   },
   "source": [
    "For the training data, you will only end up using data where `bounced = 0`. Based on the 15k users, you can see that 5,557 (\\~41%) users bounced within the first ten minutes of their first engagement with the app, but of the remaining 8,031 users, 1,883 users (\\~23%) churned after 24 hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 77
    },
    "id": "ZStzVtgEIkzh",
    "outputId": "15b2abb5-f966-4363-f7f6-b676f6b521b8"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "SELECT\n",
    "    COUNTIF(churned=1)/COUNT(churned) as churn_rate\n",
    "FROM\n",
    "    bqmlga4.returningusers\n",
    "WHERE bounced = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "daSQViux_XWR"
   },
   "source": [
    "#### Step 2. Extracting demographic data for each user"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qcC2JJ6W_-sb"
   },
   "source": [
    "This section is focused on extracting the demographic information for each user. Different demographic information about the user is available in the dataset already, including `app_info`, `device`, `ecommerce`, `event_params`, `geo`. Demographic features can help the model predict whether users on certain devices or countries are more likely to churn.\n",
    "\n",
    "For this notebook, you can start just with `geo.country`, `device.operating_system`, and `device.language`. If you are using your own dataset and have joinable first-party data, this section is a good opportunity to add any additional attributes for each user that may not be readily available in Google Analytics 4.\n",
    "\n",
    "Note that a user's demographics may occasionally change (e.g. moving from one country to another). For simplicity, you will just use the demographic information that Google Analytics 4 provides when the user first engaged with the app as indicated by `MIN(event_timestamp)`. This enables every unique user to be represented by a single row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 343
    },
    "id": "gc47WFyM_5nQ",
    "outputId": "5c545aef-eaa8-451e-8443-38461d2c9923"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "CREATE OR REPLACE VIEW bqmlga4.user_demographics AS (\n",
    "\n",
    "  WITH first_values AS (\n",
    "      SELECT\n",
    "          user_pseudo_id,\n",
    "          geo.country as country,\n",
    "          device.operating_system as operating_system,\n",
    "          device.language as language,\n",
    "          ROW_NUMBER() OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp DESC) AS row_num\n",
    "      FROM `firebase-public-project.analytics_153293282.events_*`\n",
    "      WHERE event_name=\"user_engagement\"\n",
    "      )\n",
    "  SELECT * EXCEPT (row_num)\n",
    "  FROM first_values\n",
    "  WHERE row_num = 1\n",
    "  );\n",
    "\n",
    "SELECT\n",
    "  *\n",
    "FROM\n",
    "  bqmlga4.user_demographics\n",
    "LIMIT 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nxv9yaTt2zD1"
   },
   "source": [
    "#### Step 3. Extracting behavioral data for each user"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c_9qPkVAfmpY"
   },
   "source": [
    "Behavioral data in the raw event data spans across multiple events -- and thus rows -- per user. The goal of this section is to aggregate and extract behavioral data for each user, resulting in one row of behavioral data per unique user.\n",
    "\n",
    "But what kind of behavioral data will you need to prepare? Since the end goal of this notebook is to predict, based on a user's activity within the first 24 hrs since app installation, whether that user will churn or return thereafter, then you will want to use behavioral data from the first 24 hrs in your training data. Later on, we can also extract some extra time-related features from `user_first_engagement`, such as the month or day of the first engagement.\n",
    "\n",
    "Google Analytics automatically collects [certain events](https://support.google.com/analytics/answer/6317485) that you can use to analyze behavior. In addition, there are certain recommended [events for games](https://support.google.com/analytics/answer/6317494). \n",
    "\n",
    "\n",
    "As a first step, you can explore all the unique events that exist in this dataset, based on `event_name`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "XsXBNmeAf3fI",
    "outputId": "da0f4a32-83ba-42e5-c381-49178c44f5f1"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "SELECT\n",
    "    event_name,\n",
    "    COUNT(event_name) as event_count\n",
    "FROM\n",
    "    `firebase-public-project.analytics_153293282.events_*`\n",
    "GROUP BY 1\n",
    "ORDER BY\n",
    "   event_count DESC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "smsCdPFpmJpT"
   },
   "source": [
    "For this notebook, to predict whether a user will churn or return, you can start by counting the number of times a user engages in the following event types:\n",
    "\n",
    "* `user_engagement`\n",
    "* `level_start_quickplay`\n",
    "* `level_end_quickplay`\n",
    "* `level_complete_quickplay`\n",
    "* `level_reset_quickplay`\n",
    "* `post_score`\n",
    "* `spend_virtual_currency`\n",
    "* `ad_reward`\n",
    "* `challenge_a_friend`\n",
    "* `completed_5_levels`\n",
    "* `use_extra_steps`\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D9vIqhuj_qFW"
   },
   "source": [
    "In SQL, you can aggregate the behavioral data by calculating the total number of times when each of the above `event_names` occurred in the data set per user.\n",
    "\n",
    "If you are using your own dataset, you may have different event types that you can aggregate and extract. Your app may be sending very different `event_names` to Google Analytics so be sure to use events most suitable to your scenario."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "nzbVtI6G_Y9p",
    "outputId": "8ae82387-01ff-4bbe-b629-659e4754ac81"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "CREATE OR REPLACE VIEW bqmlga4.user_aggregate_behavior AS (\n",
    "WITH\n",
    "  events_first24hr AS (\n",
    "    #select user data only from first 24 hr of using the app\n",
    "    SELECT\n",
    "      e.*\n",
    "    FROM\n",
    "      `firebase-public-project.analytics_153293282.events_*` e\n",
    "    JOIN\n",
    "      bqmlga4.returningusers r\n",
    "    ON\n",
    "      e.user_pseudo_id = r.user_pseudo_id\n",
    "    WHERE\n",
    "      e.event_timestamp <= r.ts_24hr_after_first_engagement\n",
    "    )\n",
    "SELECT\n",
    "  user_pseudo_id,\n",
    "  SUM(IF(event_name = 'user_engagement', 1, 0)) AS cnt_user_engagement,\n",
    "  SUM(IF(event_name = 'level_start_quickplay', 1, 0)) AS cnt_level_start_quickplay,\n",
    "  SUM(IF(event_name = 'level_end_quickplay', 1, 0)) AS cnt_level_end_quickplay,\n",
    "  SUM(IF(event_name = 'level_complete_quickplay', 1, 0)) AS cnt_level_complete_quickplay,\n",
    "  SUM(IF(event_name = 'level_reset_quickplay', 1, 0)) AS cnt_level_reset_quickplay,\n",
    "  SUM(IF(event_name = 'post_score', 1, 0)) AS cnt_post_score,\n",
    "  SUM(IF(event_name = 'spend_virtual_currency', 1, 0)) AS cnt_spend_virtual_currency,\n",
    "  SUM(IF(event_name = 'ad_reward', 1, 0)) AS cnt_ad_reward,\n",
    "  SUM(IF(event_name = 'challenge_a_friend', 1, 0)) AS cnt_challenge_a_friend,\n",
    "  SUM(IF(event_name = 'completed_5_levels', 1, 0)) AS cnt_completed_5_levels,\n",
    "  SUM(IF(event_name = 'use_extra_steps', 1, 0)) AS cnt_use_extra_steps,\n",
    "FROM\n",
    "  events_first24hr\n",
    "GROUP BY\n",
    "  1\n",
    "  );\n",
    "\n",
    "SELECT\n",
    "  *\n",
    "FROM\n",
    "  bqmlga4.user_aggregate_behavior\n",
    "LIMIT 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EO1zcaiK_I4S"
   },
   "source": [
    "Note that in addition to frequency of performing an action, you can also include other behavioral features in this step such as the total amount of in-game currency they spent, or if they reached certain app-specifc milestones that may be more relevant to your app (e.g., gained a certain threshold amount of XP or leveled up at least 5 times). This is an opportunity for you to extend this notebook to suit your needs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PrWx_WQQBitA"
   },
   "source": [
    "#### Step 4: Combining the label, demographic and behavioral data together as training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o54qf5U6ik2l"
   },
   "source": [
    "In this section, you can now combine these three intermediary views (label, demographic, and behavioral data) into the final training data. Here you can also specify `bounced = 0`, in order to limit the training data only to users who did not \"bounce\" within the first 10 minutes of using the app."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 531
    },
    "id": "2i4WeTqLB1mC",
    "outputId": "c6669996-83c4-4fb5-fcde-c3135ffd7705"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "CREATE OR REPLACE VIEW bqmlga4.train AS (\n",
    "    \n",
    "  SELECT\n",
    "    dem.*,\n",
    "    IFNULL(beh.cnt_user_engagement, 0) AS cnt_user_engagement,\n",
    "    IFNULL(beh.cnt_level_start_quickplay, 0) AS cnt_level_start_quickplay,\n",
    "    IFNULL(beh.cnt_level_end_quickplay, 0) AS cnt_level_end_quickplay,\n",
    "    IFNULL(beh.cnt_level_complete_quickplay, 0) AS cnt_level_complete_quickplay,\n",
    "    IFNULL(beh.cnt_level_reset_quickplay, 0) AS cnt_level_reset_quickplay,\n",
    "    IFNULL(beh.cnt_post_score, 0) AS cnt_post_score,\n",
    "    IFNULL(beh.cnt_spend_virtual_currency, 0) AS cnt_spend_virtual_currency,\n",
    "    IFNULL(beh.cnt_ad_reward, 0) AS cnt_ad_reward,\n",
    "    IFNULL(beh.cnt_challenge_a_friend, 0) AS cnt_challenge_a_friend,\n",
    "    IFNULL(beh.cnt_completed_5_levels, 0) AS cnt_completed_5_levels,\n",
    "    IFNULL(beh.cnt_use_extra_steps, 0) AS cnt_use_extra_steps,\n",
    "    ret.user_first_engagement,\n",
    "    ret.month,\n",
    "    ret.julianday,\n",
    "    ret.dayofweek,\n",
    "    ret.churned\n",
    "  FROM\n",
    "    bqmlga4.returningusers ret\n",
    "  LEFT OUTER JOIN\n",
    "    bqmlga4.user_demographics dem\n",
    "  ON \n",
    "    ret.user_pseudo_id = dem.user_pseudo_id\n",
    "  LEFT OUTER JOIN \n",
    "    bqmlga4.user_aggregate_behavior beh\n",
    "  ON\n",
    "    ret.user_pseudo_id = beh.user_pseudo_id\n",
    "  WHERE ret.bounced = 0\n",
    "  );\n",
    "\n",
    "SELECT\n",
    "  *\n",
    "FROM\n",
    "  bqmlga4.train\n",
    "LIMIT 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Co90TkTsCk9p"
   },
   "source": [
    "## Training the propensity model with BigQuery ML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "stwX9Np9CyWG"
   },
   "source": [
    "In this section, using the training data you prepared, you will now train machine learning models in SQL using BigQuery ML. The remainder of the notebook will only use logistic regression, but you can also follow the optional code below to train other model types."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FQTRryQ9Fr_l"
   },
   "source": [
    "**Choosing the model:**\n",
    "As this is a binary classification task, for simplicity, you can start with [logistic regression](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create), but you can also train other classification models like [XGBoost](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-boosted-tree), [deep neural networks](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-dnn-models) and [AutoML Tables](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-automl) in BigQuery ML to calculate propensity scores. Each of these models will output a probability score (propensity) between 0 and 1.0 of how likely the model prediction is based on the training data. In this notebook, the model predicts whether the user will churn (1) or return (0) after 24 hours of the user's first engagement with the app.\n",
    "\n",
    "\n",
    "|Model| model_type| Advantages | Disadvantages|\n",
    "|-|-|-|-|\n",
    "|**Logistic Regression**| `LOGISTIC_REG` ([documentation](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create))| Fast to train vs. other model types | May not have the highest model performance |\n",
    "|**XGBoost**| `BOOSTED_TREE_CLASSIFIER` ([documentation](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-boosted-tree))| Higher model performance. Can inspect feature importance. | Slower to train vs. `LOGISTIC_REG`.|\n",
    "|**Deep Neural Networks**| `DNN_CLASSIFIER` ([documentation](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-dnn-models))| Higher model performance | Slower to train vs. `LOGISTIC_REG`.|\n",
    "|**AutoML Tables**| `AUTOML_CLASSIFIER` ([documentation](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-automl))| Very high model performance | May take at least a few hours to train, not easy to explain how the model works. |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W326AA24FyPF"
   },
   "source": [
    "**There's no need to split your data into train/test:**\n",
    "- When you run the `CREATE MODEL` statement, BigQuery ML will automatically split your data into training and test, so you can evaluate your model immediately after training (see the [documentation](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create#data_split_method) for more information or how to specify the split manually).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zfNMGKb_GAPM"
   },
   "source": [
    "**Hyperparameter tuning:**\n",
    "Note that you can also tune hyperparameters for each model, although it is beyond the scope of this notebook. See the [BigQuery ML documentation for CREATE MODEL](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create) for further details on the available hyperparameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X5Ei0HB_HTAT"
   },
   "source": [
    "**`TRANSFORM()`:** \n",
    "It may also be useful to extract features from datetimes/timestamps as one simple example of additional feature preprocessing before training. For example, we can extract the month, day of year, and day of week from `user_first_engagement`.  [`TRANSFORM()`](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create#transform) allows the model to remember the extracted values so you won't need to extract them again when making predictions using the model later on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cu-xHARS90xN"
   },
   "source": [
    "#### Train a logistic regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5A_PGKG5Ob8u"
   },
   "source": [
    "The following code trains a logistic regression model. This should only take a minute or two to train.\n",
    "\n",
    "For more information on the default hyperparameters used, you can read the documentation:  \n",
    "[CREATE MODEL statement](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 31
    },
    "id": "1WRGHLIIC-RL",
    "outputId": "32339cf6-5548-4239-e211-002d1c5743a7"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "CREATE OR REPLACE MODEL bqmlga4.churn_logreg\n",
    "\n",
    "OPTIONS(\n",
    "  MODEL_TYPE=\"LOGISTIC_REG\",\n",
    "  INPUT_LABEL_COLS=[\"churned\"]\n",
    ") AS\n",
    "\n",
    "SELECT\n",
    "  *\n",
    "FROM\n",
    "  bqmlga4.train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZBX-46-Q94tI"
   },
   "source": [
    "#### Train an XGBoost model (optional)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uqewIJBoOnAZ"
   },
   "source": [
    "The following code trains an XGBoost model. This may take several minutes to train.\n",
    "\n",
    "For more information on the default hyperparameters used, you can read the documentation:  \n",
    "[CREATE MODEL statement for Boosted Tree models using XGBoost](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-boosted-tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3XNDx5YoEFmY"
   },
   "outputs": [],
   "source": [
    "# %%bigquery --project $PROJECT_ID\n",
    "\n",
    "# CREATE OR REPLACE MODEL bqmlga4.churn_xgb\n",
    "\n",
    "# OPTIONS(\n",
    "#   MODEL_TYPE=\"BOOSTED_TREE_CLASSIFIER\",\n",
    "#   INPUT_LABEL_COLS=[\"churned\"]\n",
    "# ) AS\n",
    "\n",
    "# SELECT\n",
    "#   * EXCEPT(user_pseudo_id)\n",
    "# FROM\n",
    "#   bqmlga4.train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0tSEDUcl98eE"
   },
   "source": [
    "#### Train a deep neural network (DNN) model  (optional)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FPpfxT6ZOslN"
   },
   "source": [
    "The following code trains a deep neural network. This may take several minutes to train.\n",
    "\n",
    "For more information on the default hyperparameters used, you can read the documentation:  \n",
    "[CREATE MODEL statement for Deep Neural Network (DNN) models](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-dnn-models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1o99jDldEGbT"
   },
   "outputs": [],
   "source": [
    "# %%bigquery --project $PROJECT_ID\n",
    "\n",
    "# CREATE OR REPLACE MODEL bqmlga4.churn_dnn\n",
    "\n",
    "# OPTIONS(\n",
    "#   MODEL_TYPE=\"DNN_CLASSIFIER\",\n",
    "#   INPUT_LABEL_COLS=[\"churned\"]\n",
    "# ) AS\n",
    "\n",
    "# SELECT\n",
    "#   * EXCEPT(user_pseudo_id)\n",
    "# FROM\n",
    "#   bqmlga4.train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X-DJU4-IkTsP"
   },
   "source": [
    "### Train an AutoML Tables model  (optional)\n",
    "\n",
    "[AutoML Tables](https://cloud.google.com/automl-tables) enables you to automatically build state-of-the-art machine learning models on structured data at massively increased speed and scale. AutoML Tables automatically searches through Google’s model zoo for structured data to find the best model for your needs, ranging from linear/logistic regression models for simpler datasets to advanced deep, ensemble, and architecture-search methods for larger, more complex ones.\n",
    "\n",
    "You can train an [AutoML model directly with BigQuery ML](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-automl), as in the code below.\n",
    "\n",
    "Note that the `BUDGET_HOURS` parameter is for AutoML Tables training, specified in hours. The default value is 1.0 hour and must be between 1.0 and 72.0. The total query processing time can be greater than the budgeted hours specified in the query.\n",
    "\n",
    "**Note:** This may take a few hours to train.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-tcfIVLZEHXU"
   },
   "outputs": [],
   "source": [
    "# %%bigquery --project $PROJECT_ID\n",
    "\n",
    "# CREATE OR REPLACE MODEL bqmlga4.churn_automl\n",
    "\n",
    "# OPTIONS(\n",
    "#   MODEL_TYPE=\"AUTOML_CLASSIFIER\",\n",
    "#   INPUT_LABEL_COLS=[\"churned\"],\n",
    "#   BUDGET_HOURS=1.0\n",
    "# ) AS\n",
    "\n",
    "# SELECT\n",
    "#   * EXCEPT(user_pseudo_id)\n",
    "# FROM\n",
    "#   bqmlga4.train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s_t0zxoeE1d7"
   },
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b69YK1iX_ksq"
   },
   "source": [
    "To evaluate the model, you can run [`ML.EVALUATE`](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate) on a model that has finished training to inspect some of the metrics.\n",
    "\n",
    "The metrics are based on the test sample data that was automatically split during model creation ([documentation](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create#data_split_method))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 77
    },
    "id": "p16-00xjE3JQ",
    "outputId": "cd9db9bf-2211-4152-e84e-5c299354663d"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "SELECT\n",
    "  *\n",
    "FROM\n",
    "  ML.EVALUATE(MODEL bqmlga4.churn_logreg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ejdRfRYeATL-"
   },
   "source": [
    "`ML.EVALUATE` generates the `precision`, `recall`, `accuracy` and `f1_score` using the default classification threshold of 0.5, which can be modified by using the optional [`THRESHOLD`](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate#eval_threshold) parameter.\n",
    "\n",
    "Generally speaking, you can use the `log_loss` and `roc_auc` metrics to compare  model performance.\n",
    "\n",
    "The `log_loss` ranges between 0 and 1.0, and the closer the `log_loss` is the zero, the closer the predicted labels were to the actual labels.\n",
    "The `roc_auc` ranges between 0 and 1.0, and the closer the `roc_auc` is to 1.0, the better the model is at distinguishing between the classes.\n",
    "\n",
    "For more information on these metrics, you can read through the definitions on [precision and recall](https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall), [accuracy](https://developers.google.com/machine-learning/crash-course/classification/accuracy), [f1-score](https://en.wikipedia.org/wiki/F-score), [log_loss](https://en.wikipedia.org/wiki/Loss_functions_for_classification#Logistic_loss) and [roc_auc](https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "glkKlvoqP7Uf"
   },
   "source": [
    "#### Confusion matrix: predicted vs actual values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AYB1xZ9oQDOv"
   },
   "source": [
    "In addition to model evaluation metrics, you may also want to use a confusion matrix to inspect how well the model predicted the labels, compared to the actual labels.\n",
    "\n",
    "With the rows indicating the actual labels, and the columns as the predicted labels, the resulting format for ML.CONFUSION_MATRIX for binary classification looks like:\n",
    "\n",
    "| | Predicted_0 | Predicted_1|\n",
    "|-|-|-|\n",
    "|Actual_0| True Negatives | False Positives|\n",
    "|Actual_1| False Negatives | True Positives|\n",
    "\n",
    "For more information on confusion matrices, you can read through a detailed explanation [here](https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 106
    },
    "id": "Db5M8U8QQgyi",
    "outputId": "2ea7e3c1-e411-43aa-e32c-6a29a2175c14"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "SELECT\n",
    "  expected_label,\n",
    "  _0 AS predicted_0,\n",
    "  _1 AS predicted_1\n",
    "FROM\n",
    "  ML.CONFUSION_MATRIX(MODEL bqmlga4.churn_logreg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ix5q8Onw1aYs"
   },
   "source": [
    "#### ROC Curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XleHv73dIZx-"
   },
   "source": [
    "You can plot the AUC-ROC curve by using `ML.ROC_CURVE` to return the metrics for different threshold values for the model ([documentation](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-roc))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "p_iG9vOfbgX6"
   },
   "outputs": [],
   "source": [
    "%%bigquery df_roc --project $PROJECT_ID\n",
    "SELECT * FROM ML.ROC_CURVE(MODEL bqmlga4.churn_logreg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "GoHGUcC7bzvX",
    "outputId": "c809aec7-5153-4a8b-bae8-14103c996c61"
   },
   "outputs": [],
   "source": [
    "df_roc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s_zu9zZXJVNG"
   },
   "source": [
    "Plot the AUC-ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "id": "jSRTXr6ub2ty",
    "outputId": "46f6cf64-9108-4c8d-d148-ecbd2036ed2d"
   },
   "outputs": [],
   "source": [
    "df_roc.plot(x=\"false_positive_rate\", y=\"recall\", title=\"AUC-ROC curve\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oyeAzheVFUxU"
   },
   "source": [
    "## Model prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "02ZQPKIebo7y"
   },
   "source": [
    "You can run [`ML.PREDICT`](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-predict) to make predictions on the propensity to churn. The following code returns all the information from `ML.PREDICT`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 590
    },
    "id": "229TPkhUFe23",
    "outputId": "81495cbc-754a-4845-8e74-cd9ef0a49ede"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "SELECT\n",
    "  *\n",
    "FROM\n",
    "  ML.PREDICT(MODEL bqmlga4.churn_logreg,\n",
    "  (SELECT * FROM bqmlga4.train)) #can be replaced with a test dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4WinvkfYdE1q"
   },
   "source": [
    "For propensity modeling, the most important output is the probability of a behavior occuring. The following query returns the probability that the user will return after 24 hrs. The higher the probability and closer it is to 1, the more likely the user is predicted to churn, and the closer it is to 0, the more likely the user is predicted to return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "92eOP8Sw7zO_",
    "outputId": "a98b4dd5-39d4-43ff-ed39-7d1cdbb9a33f"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "SELECT\n",
    "  user_pseudo_id,\n",
    "  churned,\n",
    "  predicted_churned,\n",
    "  predicted_churned_probs[OFFSET(0)].prob as probability_churned\n",
    "  \n",
    "FROM\n",
    "  ML.PREDICT(MODEL bqmlga4.churn_logreg,\n",
    "  (SELECT * FROM bqmlga4.train)) #can be replaced with a proper test dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TCGAfP9DKLDH"
   },
   "source": [
    "### Exporting the predictions out of Bigquery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EMETxq7CKtTQ"
   },
   "source": [
    "##### Reading the predictions directly from BigQuery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6_BUvnJvKQQP"
   },
   "source": [
    "With the predictions from `ML.PREDICT`, you can export the data into a Pandas dataframe using the BigQuery Storage API (see [documentation and code samples](https://cloud.google.com/bigquery/docs/bigquery-storage-python-pandas#download_table_data_using_the_client_library)). You can also use other [BigQuery client libraries](https://cloud.google.com/bigquery/docs/reference/libraries).\n",
    "\n",
    "Alternatively you can also export directly into pandas in a notebook using the %%bigquery <variable name> as in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0qB2DsQkKgEH"
   },
   "outputs": [],
   "source": [
    "%%bigquery df --project $PROJECT_ID\n",
    "\n",
    "SELECT\n",
    "  user_pseudo_id,\n",
    "  churned,\n",
    "  predicted_churned,\n",
    "  predicted_churned_probs[OFFSET(0)].prob as probability_churned\n",
    "  \n",
    "FROM\n",
    "  ML.PREDICT(MODEL bqmlga4.churn_logreg,\n",
    "  (SELECT * FROM bqmlga4.train)) #can be replaced with a proper test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-yJAFuUOKl1i"
   },
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wzEsO_oPK2vv"
   },
   "source": [
    "##### Export predictions table to Google Cloud Storage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xxgKXizcK5Cy"
   },
   "source": [
    "There are several ways to export the predictions table to Google Cloud Storage (GCS), so that you can use them in a separate service. Perhaps the easiest way is to export directly to GCS using SQL ([documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/other-statements#export_data_statement))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5iah7a0KK9lv"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "EXPORT DATA OPTIONS (\n",
    "uri=\"gs://mybucket/myfile/churnpredictions.csv\", \n",
    "  format=CSV\n",
    ") AS \n",
    "SELECT\n",
    "  user_pseudo_id,\n",
    "  churned,\n",
    "  predicted_churned,\n",
    "  predicted_churned_probs[OFFSET(0)].prob as probability_churned\n",
    "FROM\n",
    "  ML.PREDICT(MODEL bqmlga4.churn_logreg,\n",
    "  (SELECT * FROM bqmlga4.train)) #can be replaced with a proper test dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kDdj9jDZFl5z"
   },
   "source": [
    "## Activate on model predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WfWm7WFdmMN7"
   },
   "source": [
    "Once you have the model predictions, there are different steps you can take based on your business objective.\n",
    "\n",
    "In our analysis, we used `user_pseudo_id` as the user identifier. However, ideally, your app should send back the `user_id` from your app to Google Analytics. This will help you to:\n",
    "\n",
    "* join any first-party data you have for model predictions\n",
    "* joins the model predictions with your first-party data\n",
    "\n",
    "Once you have this join capability, you can:\n",
    "\n",
    "* Export the model predictions back into Google Analytics as user attribute. This can be done using [Data Import feature](https://support.google.com/analytics/answer/10071301) in Google Analytics 4.\n",
    "    * Based on the prediction values you can [Create and edit audiences](https://support.google.com/analytics/answer/2611404) and also do [Audience targeting](https://support.google.com/optimize/answer/6283435). For example, an audience can be users with prediction probability between 0.4 and 0.7, to represent users who are predicted to be \"on the fence\" between churning and returning.\n",
    "* Adjust the user experience for targeted users within your app. For Firebase Apps, you can use the [Import segmentments](https://firebase.google.com/docs/projects/import-segments) feature. You can tailor user experience by targeting your identified users through Firebase services such as Remote Config, Cloud Messaging, and In-App Messaging. This will involve importing the segment data from BigQuery into Firebase. After that you can send notifications to the users, configure the app for them, or follow the user journeys across devices.\n",
    "* Run targeted marketing campaigns via CRMs like Salesforce, e.g. send out reminder emails.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ODjAEK2cmf9S"
   },
   "source": [
    "## Further resources: \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u-K-dCfJKSpi"
   },
   "source": [
    "As you collect more data from your users, you may want to regularly evaluate your model on fresh data and re-train the model if you notice that the model quality is decaying.\n",
    "\n",
    "Continuous evaluation—the process of ensuring a production machine learning model is still performing well on new data—is an essential part in any ML workflow. Performing continuous evaluation can help you catch model drift, a phenomenon that occurs when the data used to train your model no longer reflects the current environment. \n",
    "\n",
    "To learn more about how to do continous model evaluation and re-train models, you can read the blogpost: [Continuous model evaluation with BigQuery ML, Stored Procedures, and Cloud Scheduler](https://cloud.google.com/blog/topics/developers-practitioners/continuous-model-evaluation-bigquery-ml-stored-procedures-and-cloud-scheduler)"
   ]
  }
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================================================
FILE: retail/clustering/bqml/README.md
================================================
A common marketing analytics challenge is to understand consumer behavior and develop customer attributes or archetypes. As organizations get better at tackling this problem, they can activate marketing strategies to incorporate additional customer knowledge into their campaigns. Building customer profiles is now easier than ever with BigQuery ML. In this notebook, you’ll learn how to create segmentation and how to use these audiences for marketing activation.

The notebook can be found [here](bqml_scaled_clustering.ipynb)

## Questions? Feedback?
If you have any questions or feedback, please open up a [new issue](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/issues).

================================================
FILE: retail/clustering/bqml/bqml_scaled_clustering.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ur8xi4C7S06n"
   },
   "outputs": [],
   "source": [
    "# Copyright 2020 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eHLV0D7Y5jtU"
   },
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/blob/master/retail/clustering/bqml/bqml_scaled_clustering.ipynb\">\n",
    "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
    "      View on GitHub\n",
    "    </a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tvgnzT1CKxrO"
   },
   "source": [
    "# How to build k-means clustering models for market segmentation using BigQuery ML\n",
    "\n",
    "A common marketing analytics challenge is to understand consumer behavior and develop customer attributes or archetypes.  As organizations get better at tackling this problem, they can activate marketing strategies to incorporate additional customer knowledge into their campaigns.  \n",
    "\n",
    "Clustering algorithms are a common vehicle to address this challenge. They allow businesses to better segment and understand their customers and users.  In the field of Machine Learning, which is a combination of both art and science, unsupervised learning may require more art compared to supervised learning algorithms.  By definition, unsupervised learning has no single metric to guide the algorithm's learning process. Instead, the data science team will need to work hand in hand with business owners to determine feature selection, optimal number of clusters (the number of clusters is often abbreviated as k), and most importantly, to gain a deeper understanding of what each cluster represents.    \n",
    "\n",
    "### How can clustering algorithms help businesses succeed?\n",
    "\n",
    "Clustering algorithms can help companies identify groups of similar customers that can be used for targeting in advertising campaigns. This is paramount as we are breathing a prediction era where customers expect personalization from brands. \n",
    " \n",
    "Using a public sample Google Analytics 360 e-commerce dataset on BigQuery, you will learn how to create and deploy clustering algorithms in production. You will also get an example of how to navigate unsupervised learning.  Keep in mind, your clusters will be even more meaningful when you bring additional data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6XbaFW4SQxGD"
   },
   "source": [
    "# Objective\n",
    "\n",
    "By the end of this notebook, you will know how to:\n",
    "* Explore features to understand what might be interesting for a clustering model\n",
    "* Pre-process data into the correct format needed to create a clustering model using BigQuery ML\n",
    "* Train (and deploy) the k-means model in BigQuery ML\n",
    "* Evaluate the model\n",
    "* Make predictions using the model\n",
    "* Write the results to be used for batch prediction, for example, to send ads based on segmentation\n",
    "\n",
    "## Dataset\n",
    "\n",
    "The [Google Analytics Sample](https://console.cloud.google.com/marketplace/details/obfuscated-ga360-data/obfuscated-ga360-data?filter=solution-type:dataset) dataset, which is hosted publicly on BigQuery, is a dataset that provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the [Google Merchandise Store](https://www.googlemerchandisestore.com/), a real e-commerce store that sells Google-branded merchandise.\n",
    "\n",
    "\n",
    "## Costs \n",
    "\n",
    "This tutorial uses billable components of Google Cloud Platform:\n",
    "\n",
    "* BigQuery\n",
    "* BigQuery ML\n",
    "\n",
    "Learn about [BigQuery pricing](https://cloud.google.com/bigquery/pricing), [BigQuery ML\n",
    "pricing](https://cloud.google.com/bigquery-ml/pricing) and use the [Pricing\n",
    "Calculator](https://cloud.google.com/products/calculator/)\n",
    "to generate a cost estimate based on your projected usage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i7EUnXsZhAGF"
   },
   "source": [
    "## PIP install packages and dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install google-cloud-bigquery\n",
    "!pip install google-cloud-bigquery-storage\n",
    "!pip install pandas-gbq\n",
    "\n",
    "# Reservation package needed to setup flex slots for flat-rate pricing\n",
    "!pip install google-cloud-bigquery-reservation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Automatically restart kernel after installs\n",
    "import IPython\n",
    "app = IPython.Application.instance()\n",
    "app.kernel.do_shutdown(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BF1j6f9HApxa"
   },
   "source": [
    "### Set up your Google Cloud Platform project\n",
    "\n",
    "_The following steps are required, regardless of your notebook environment._\n",
    "\n",
    "1. [Select or create a project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
    "\n",
    "1. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n",
    "\n",
    "1. [Enable the AI Platform APIs and Compute Engine APIs.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component)\n",
    "\n",
    "1. Enter your project ID and region in the cell below. Then run the  cell to make sure the\n",
    "Cloud SDK uses the right project for all the commands in this notebook.\n",
    "\n",
    "_Note_: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "W8SLpZgiyMSP"
   },
   "source": [
    "### Set project ID and authenticate\n",
    "\n",
    "Update your Project ID below.  The rest of the notebook will run using these credentials. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROJECT_ID = \"UPDATE TO YOUR PROJECT ID\" \n",
    "REGION = 'US'\n",
    "DATA_SET_ID = 'bqml_kmeans' # Ensure you first create a data set in BigQuery\n",
    "!gcloud config set project $PROJECT_ID\n",
    "# If you have not built the Data Set, the following command will build it for you\n",
    "# !bq mk --location=$REGION --dataset $PROJECT_ID:$DATA_SET_ID "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XoEqT2Y4DJmf"
   },
   "source": [
    "### Import libraries and define constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "bbnrwv-nyi82",
    "outputId": "d9b05979-e17b-411a-d910-8c91e8755501"
   },
   "outputs": [],
   "source": [
    "from google.cloud import bigquery\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_gbq\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x) # used to display float format\n",
    "client = bigquery.Client(project=PROJECT_ID)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ks4xTsQJk1Ia"
   },
   "source": [
    "# Data exploration and preparation\n",
    "\n",
    "Prior to building your models, you are typically expected to invest a significant amount of time cleaning, exploring, and aggregating your dataset in a meaningful way for modeling.  For the purpose of this demo, we aren't showing this step only to prioritize showcasing clustering with k-means in BigQuery ML.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "z3NG3XuNYKnO"
   },
   "source": [
    "## Building synthetic data\n",
    "\n",
    "Our goal is to use both online (GA360) and offline (CRM) data.  You can use your own CRM data, however, in this case since we don't have CRM data to showcase, we will instead generate synthetic data.  We will generate estimated House Hold Income, and Gender.  To do so, we will hash fullVisitorID and build simple rules based on the last digit of the hash. When you run this process with your own data, you can join CRM data with several dimensions, but this is just an example of what is possible.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We start with GA360 data, and will eventually build synthetic CRM as an example.  \n",
    "# This block is the first step, just working with GA360\n",
    "\n",
    "ga360_only_view = 'GA360_View'\n",
    "shared_dataset_ref = client.dataset(DATA_SET_ID)\n",
    "ga360_view_ref = shared_dataset_ref.table(ga360_only_view)\n",
    "ga360_view = bigquery.Table(ga360_view_ref)\n",
    "\n",
    "ga360_query = '''\n",
    "SELECT\n",
    "  fullVisitorID,\n",
    "  ABS(farm_fingerprint(fullVisitorID)) AS Hashed_fullVisitorID, # This will be used to generate random data.\n",
    "  MAX(device.operatingSystem) AS OS, # We can aggregate this because an OS is tied to a fullVisitorID.\n",
    "  SUM (CASE\n",
    "       WHEN REGEXP_EXTRACT (v2ProductCategory, \n",
    "                           r'^(?:(?:.*?)Home/)(.*?)/') \n",
    "                           = 'Apparel' THEN 1 ELSE 0 END) AS Apparel,\n",
    "  SUM (CASE  \n",
    "       WHEN REGEXP_EXTRACT (v2ProductCategory, \n",
    "                           r'^(?:(?:.*?)Home/)(.*?)/') \n",
    "                           = 'Office' THEN 1 ELSE 0 END) AS Office,\n",
    "  SUM (CASE\n",
    "       WHEN REGEXP_EXTRACT (v2ProductCategory, \n",
    "                           r'^(?:(?:.*?)Home/)(.*?)/') \n",
    "                           = 'Electronics' THEN 1 ELSE 0 END) AS Electronics,\n",
    "  SUM (CASE\n",
    "       WHEN REGEXP_EXTRACT (v2ProductCategory, \n",
    "                           r'^(?:(?:.*?)Home/)(.*?)/') \n",
    "                           = 'Limited Supply' THEN 1 ELSE 0 END) AS LimitedSupply,\n",
    "  SUM (CASE\n",
    "       WHEN REGEXP_EXTRACT (v2ProductCategory, \n",
    "                           r'^(?:(?:.*?)Home/)(.*?)/') \n",
    "                           = 'Accessories' THEN 1 ELSE 0 END) AS Accessories,\n",
    "  SUM (CASE\n",
    "       WHEN REGEXP_EXTRACT (v2ProductCategory, \n",
    "                           r'^(?:(?:.*?)Home/)(.*?)/') \n",
    "                           = 'Shop by Brand' THEN 1 ELSE 0 END) AS ShopByBrand,\n",
    "  SUM (CASE\n",
    "       WHEN REGEXP_EXTRACT (v2ProductCategory, \n",
    "                           r'^(?:(?:.*?)Home/)(.*?)/') \n",
    "                           = 'Bags' THEN 1 ELSE 0 END) AS Bags,\n",
    "  ROUND (SUM (productPrice/1000000),2) AS productPrice_USD\n",
    "FROM\n",
    "  `bigquery-public-data.google_analytics_sample.ga_sessions_*`,\n",
    "  UNNEST(hits) AS hits,\n",
    "  UNNEST(hits.product) AS hits_product\n",
    "WHERE\n",
    "  _TABLE_SUFFIX BETWEEN '20160801'\n",
    "  AND '20160831'\n",
    "  AND geoNetwork.country = 'United States'\n",
    "  AND type = 'EVENT'\n",
    "GROUP BY\n",
    "  1,\n",
    "  2\n",
    "'''\n",
    "\n",
    "\n",
    "ga360_view.view_query = ga360_query.format(PROJECT_ID)\n",
    "ga360_view = client.create_table(ga360_view)  # API request\n",
    "\n",
    "print(f\"Successfully created view at {ga360_view.full_table_id}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fullVisitorID</th>\n",
       "      <th>Hashed_fullVisitorID</th>\n",
       "      <th>OS</th>\n",
       "      <th>Apparel</th>\n",
       "      <th>Office</th>\n",
       "      <th>Electronics</th>\n",
       "      <th>LimitedSupply</th>\n",
       "      <th>Accessories</th>\n",
       "      <th>ShopByBrand</th>\n",
       "      <th>Bags</th>\n",
       "      <th>productPrice_USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0793220752145578759</td>\n",
       "      <td>4074807331962730552</td>\n",
       "      <td>Linux</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>148.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6626084732166116798</td>\n",
       "      <td>9209336555480734198</td>\n",
       "      <td>Windows</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6402635554390648387</td>\n",
       "      <td>6330846949202373940</td>\n",
       "      <td>Windows</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2247.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1774577907793414721</td>\n",
       "      <td>6826645565243937471</td>\n",
       "      <td>Chrome OS</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>289.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9875610913644487984</td>\n",
       "      <td>8099941684224314656</td>\n",
       "      <td>iOS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fullVisitorID  Hashed_fullVisitorID         OS  Apparel  Office  \\\n",
       "0  0793220752145578759   4074807331962730552      Linux        4       0   \n",
       "1  6626084732166116798   9209336555480734198    Windows        1       0   \n",
       "2  6402635554390648387   6330846949202373940    Windows      102       0   \n",
       "3  1774577907793414721   6826645565243937471  Chrome OS        7       0   \n",
       "4  9875610913644487984   8099941684224314656        iOS        0       0   \n",
       "\n",
       "   Electronics  LimitedSupply  Accessories  ShopByBrand  Bags  \\\n",
       "0            0              0            0            0     0   \n",
       "1            0              0            0            0     0   \n",
       "2            0              0            0            0     0   \n",
       "3            0              1            0            0     0   \n",
       "4            0              0            1            0     0   \n",
       "\n",
       "   productPrice_USD  \n",
       "0           148.960  \n",
       "1            28.990  \n",
       "2          2247.980  \n",
       "3           289.910  \n",
       "4            24.990  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show a sample of GA360 data\n",
    "\n",
    "ga360_query_df = f'''\n",
    "SELECT * FROM {ga360_view.full_table_id.replace(\":\", \".\")} LIMIT 5\n",
    "'''\n",
    "\n",
    "job_config = bigquery.QueryJobConfig()\n",
    "\n",
    "# Start the query\n",
    "query_job = client.query(ga360_query_df, job_config=job_config) #API Request\n",
    "df_ga360 = query_job.result()\n",
    "df_ga360 = df_ga360.to_dataframe()\n",
    "\n",
    "df_ga360"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create synthetic CRM data in SQL\n",
    "\n",
    "CRM_only_view = 'CRM_View'\n",
    "shared_dataset_ref = client.dataset(DATA_SET_ID)\n",
    "CRM_view_ref = shared_dataset_ref.table(CRM_only_view)\n",
    "CRM_view = bigquery.Table(CRM_view_ref)\n",
    "\n",
    "# Query below works by hashing the fullVisitorID, which creates a random distribution.  \n",
    "# We use modulo to artificially split gender and hhi distribution.\n",
    "CRM_query = '''\n",
    "SELECT\n",
    "  fullVisitorID,\n",
    "IF\n",
    "  (MOD(Hashed_fullVisitorID,2) = 0,\n",
    "    \"M\",\n",
    "    \"F\") AS gender,\n",
    "  CASE\n",
    "    WHEN MOD(Hashed_fullVisitorID,10) = 0 THEN 55000\n",
    "    WHEN MOD(Hashed_fullVisitorID,10) < 3 THEN 65000\n",
    "    WHEN MOD(Hashed_fullVisitorID,10) < 7 THEN 75000\n",
    "    WHEN MOD(Hashed_fullVisitorID,10) < 9 THEN 85000\n",
    "    WHEN MOD(Hashed_fullVisitorID,10) = 9 THEN 95000\n",
    "  ELSE\n",
    "  Hashed_fullVisitorID\n",
    "END\n",
    "  AS hhi\n",
    "FROM (\n",
    "  SELECT\n",
    "    fullVisitorID,\n",
    "    ABS(farm_fingerprint(fullVisitorID)) AS Hashed_fullVisitorID,\n",
    "  FROM\n",
    "    `bigquery-public-data.google_analytics_sample.ga_sessions_*`,\n",
    "    UNNEST(hits) AS hits,\n",
    "    UNNEST(hits.product) AS hits_product\n",
    "  WHERE\n",
    "    _TABLE_SUFFIX BETWEEN '20160801'\n",
    "    AND '20160831'\n",
    "    AND geoNetwork.country = 'United States'\n",
    "    AND type = 'EVENT'\n",
    "  GROUP BY\n",
    "    1,\n",
    "    2)\n",
    "'''\n",
    "\n",
    "CRM_view.view_query = CRM_query.format(PROJECT_ID)\n",
    "CRM_view = client.create_table(CRM_view)  # API request\n",
    "\n",
    "print(f\"Successfully created view at {CRM_view.full_table_id}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fullVisitorID</th>\n",
       "      <th>gender</th>\n",
       "      <th>hhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>297008845417084558</td>\n",
       "      <td>F</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8780554431432234301</td>\n",
       "      <td>F</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3912300160509220549</td>\n",
       "      <td>M</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0183860411504195373</td>\n",
       "      <td>M</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5824687589795910572</td>\n",
       "      <td>M</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fullVisitorID gender    hhi\n",
       "0   297008845417084558      F  85000\n",
       "1  8780554431432234301      F  65000\n",
       "2  3912300160509220549      M  85000\n",
       "3  0183860411504195373      M  55000\n",
       "4  5824687589795910572      M  75000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See an output of the synthetic CRM data\n",
    "\n",
    "CRM_query_df = f'''\n",
    "SELECT * FROM {CRM_view.full_table_id.replace(\":\", \".\")} LIMIT 5\n",
    "'''\n",
    "\n",
    "job_config = bigquery.QueryJobConfig()\n",
    "\n",
    "# Start the query\n",
    "query_job = client.query(CRM_query_df, job_config=job_config) #API Request\n",
    "df_CRM = query_job.result()\n",
    "df_CRM = df_CRM.to_dataframe()\n",
    "\n",
    "df_CRM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build a final view for to use as trainding data for clustering\n",
    "\n",
    "You may decide to change the view below based on your specific dataset.  This is fine, and is exactly why we're creating a view.  All steps subsequent to this will reference this view.  If you change the SQL below, you won't need to modify other parts of the notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build a final view, which joins GA360 data with CRM data\n",
    "\n",
    "final_data_view = 'Final_View'\n",
    "shared_dataset_ref = client.dataset(DATA_SET_ID)\n",
    "final_view_ref = shared_dataset_ref.table(final_data_view)\n",
    "final_view = bigquery.Table(final_view_ref)\n",
    "\n",
    "final_data_query = f'''\n",
    "SELECT\n",
    "    g.*,\n",
    "    c.* EXCEPT(fullVisitorId)\n",
    "FROM {ga360_view.full_table_id.replace(\":\", \".\")} g\n",
    "JOIN {CRM_view.full_table_id.replace(\":\", \".\")} c\n",
    "ON g.fullVisitorId = c.fullVisitorId\n",
    "'''\n",
    "\n",
    "final_view.view_query = final_data_query.format(PROJECT_ID)\n",
    "final_view = client.create_table(final_view)  # API request\n",
    "\n",
    "print(f\"Successfully created view at {final_view.full_table_id}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fullVisitorID</th>\n",
       "      <th>Hashed_fullVisitorID</th>\n",
       "      <th>OS</th>\n",
       "      <th>Apparel</th>\n",
       "      <th>Office</th>\n",
       "      <th>Electronics</th>\n",
       "      <th>LimitedSupply</th>\n",
       "      <th>Accessories</th>\n",
       "      <th>ShopByBrand</th>\n",
       "      <th>Bags</th>\n",
       "      <th>productPrice_USD</th>\n",
       "      <th>gender</th>\n",
       "      <th>hhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0314742824007569667</td>\n",
       "      <td>6946654416482415939</td>\n",
       "      <td>Android</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>106.940</td>\n",
       "      <td>F</td>\n",
       "      <td>95000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2242360300500476735</td>\n",
       "      <td>7127521692467899408</td>\n",
       "      <td>Macintosh</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.990</td>\n",
       "      <td>M</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6667898188086359119</td>\n",
       "      <td>928068574965520919</td>\n",
       "      <td>iOS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.990</td>\n",
       "      <td>F</td>\n",
       "      <td>95000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4757349846427056292</td>\n",
       "      <td>4102516958220717880</td>\n",
       "      <td>Chrome OS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.500</td>\n",
       "      <td>M</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7253626747991065846</td>\n",
       "      <td>2270834522148945194</td>\n",
       "      <td>Android</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92.990</td>\n",
       "      <td>M</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fullVisitorID  Hashed_fullVisitorID         OS  Apparel  Office  \\\n",
       "0  0314742824007569667   6946654416482415939    Android        6       0   \n",
       "1  2242360300500476735   7127521692467899408  Macintosh        0       0   \n",
       "2  6667898188086359119    928068574965520919        iOS        0       0   \n",
       "3  4757349846427056292   4102516958220717880  Chrome OS        0       0   \n",
       "4  7253626747991065846   2270834522148945194    Android        0       0   \n",
       "\n",
       "   Electronics  LimitedSupply  Accessories  ShopByBrand  Bags  \\\n",
       "0            0              0            0            0     0   \n",
       "1            0              0            1            0     0   \n",
       "2            0              0            0            0     0   \n",
       "3            1              0            0            0     0   \n",
       "4            0              0            0            0     0   \n",
       "\n",
       "   productPrice_USD gender    hhi  \n",
       "0           106.940      F  95000  \n",
       "1             1.990      M  85000  \n",
       "2             4.990      F  95000  \n",
       "3             1.500      M  55000  \n",
       "4            92.990      M  75000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show final data used prior to modeling\n",
    "\n",
    "sql_demo = f'''\n",
    "SELECT * FROM {final_view.full_table_id.replace(\":\", \".\")} LIMIT 5\n",
    "'''\n",
    "\n",
    "job_config = bigquery.QueryJobConfig()\n",
    "\n",
    "# Start the query\n",
    "query_job = client.query(sql_demo, job_config=job_config) #API Request\n",
    "df_demo = query_job.result()\n",
    "df_demo = df_demo.to_dataframe()\n",
    "\n",
    "df_demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wpGc5AggVOBN"
   },
   "source": [
    "# Create our initial model\n",
    "\n",
    "In this section, we will build our initial k-means model.  We won't focus on optimal k or other hyperparemeters just yet.\n",
    "\n",
    "Some additional points:  \n",
    "\n",
    "1. We remove fullVisitorId as an input, even though it is grouped at that level because we don't need fullVisitorID as a feature for clustering. fullVisitorID should never be used as feature.\n",
    "2. We have both categorical as well as numerical features\n",
    "3. We do not have to normalize any numerical features, as BigQuery ML will automatically do this for us. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DyOB3FePfiIe"
   },
   "source": [
    "## Build a function to build our model\n",
    "\n",
    "We will build a simple python function to build our model, rather than doing everything in SQL.  This approach means we can asynchronously start several models and let BQ run in parallel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeModel (n_Clusters, Model_Name):\n",
    "    sql =f'''\n",
    "    CREATE OR REPLACE MODEL `{PROJECT_ID}.{DATA_SET_ID}.{Model_Name}` \n",
    "    OPTIONS(model_type='kmeans',\n",
    "    kmeans_init_method = 'KMEANS++',\n",
    "    num_clusters={n_Clusters}) AS\n",
    "\n",
    "    SELECT * except(fullVisitorID, Hashed_fullVisitorID) FROM `{final_view.full_table_id.replace(\":\", \".\")}`\n",
    "    '''\n",
    "    job_config = bigquery.QueryJobConfig()\n",
    "    client.query(sql, job_config=job_config)  # Make an API request."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vDUIzlcYyMOt"
   },
   "outputs": [],
   "source": [
    "# Let's start with a simple test to ensure everything works. \n",
    "# After running makeModel(), allow a few minutes for training to complete.\n",
    "\n",
    "model_test_name = \"test\"\n",
    "makeModel(3, model_test_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# After training is completed, you can either check in the UI, or you can interact with it using list_models(). \n",
    "\n",
    "for model in client.list_models(DATA_SET_ID):\n",
    "    print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Y2mDX1-WZg8k"
   },
   "source": [
    "# Work towards creating a better model\n",
    "\n",
    "In this section, we want to determine the proper k value.  Determining the right value of k depends completely on the use case.  There are straight forward examples that will simply tell you how many clusters are needed.  Suppose you are pre-processing hand written digits - this tells us k should be 10. Or perhaps your business stakeholder only wants to deliver three different marketing campaigns and needs you to identify three clusters of customers, then setting k=3 might be meaningful. However, the use case is sometimes more open ended and you may want to explore different numbers of clusters to see how your datapoints group together with the minimal error within each cluster. To accomplish this process, we start by performing the 'Elbow Method', which simply charts loss vs k. Then, we'll also use the Davies-Bouldin score.\n",
    "(https://en.wikipedia.org/wiki/Davies%E2%80%93Bouldin_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ajkLrlFjlvPQ"
   },
   "source": [
    "Below we are going to create several models to perform both the Elbow Method and get the Davies-Bouldin score.  You may change parameters like low_k and high_k. Our process will create models between these two values.  There is an additional parameter called model_prefix_name.  We recommend you leave this as its current value.  It is used to generate a naming convention for our models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "WAuyizlkzzQU",
    "outputId": "68fb1c75-045b-4e8a-a7c3-2979ee4b3ed2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model started: kmeans_clusters_3\n",
      "Model started: kmeans_clusters_4\n",
      "Model started: kmeans_clusters_5\n",
      "Model started: kmeans_clusters_6\n",
      "Model started: kmeans_clusters_7\n",
      "Model started: kmeans_clusters_8\n",
      "Model started: kmeans_clusters_9\n",
      "Model started: kmeans_clusters_10\n",
      "Model started: kmeans_clusters_11\n",
      "Model started: kmeans_clusters_12\n",
      "Model started: kmeans_clusters_13\n",
      "Model started: kmeans_clusters_14\n",
      "Model started: kmeans_clusters_15\n"
     ]
    }
   ],
   "source": [
    "# Define upper and lower bound for k, then build individual models for each. \n",
    "# After running this loop, look at the UI to see several model objects that exist. \n",
    "\n",
    "low_k = 3\n",
    "high_k = 15\n",
    "model_prefix_name = 'kmeans_clusters_'\n",
    "\n",
    "lst = list(range (low_k, high_k+1)) #build list to iterate through k values\n",
    "\n",
    "for k in lst:\n",
    "    model_name = model_prefix_name + str(k)\n",
    "    makeModel(k, model_name)\n",
    "    print(f\"Model started: {model_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OVxjQFWmVVmH"
   },
   "source": [
    "## Select optimal k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "id": "tp7y6mksNY4D",
    "outputId": "07fbbd01-2387-46ad-b365-62f34d471a62"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bqml_kmeans.kmeans_clusters_10\n",
      "bqml_kmeans.kmeans_clusters_11\n",
      "bqml_kmeans.kmeans_clusters_12\n",
      "bqml_kmeans.kmeans_clusters_13\n",
      "bqml_kmeans.kmeans_clusters_14\n",
      "bqml_kmeans.kmeans_clusters_15\n",
      "bqml_kmeans.kmeans_clusters_3\n",
      "bqml_kmeans.kmeans_clusters_4\n",
      "bqml_kmeans.kmeans_clusters_5\n",
      "bqml_kmeans.kmeans_clusters_6\n",
      "bqml_kmeans.kmeans_clusters_7\n",
      "bqml_kmeans.kmeans_clusters_8\n",
      "bqml_kmeans.kmeans_clusters_9\n",
      "bqml_kmeans.test\n"
     ]
    }
   ],
   "source": [
    "# list all current models\n",
    "models = client.list_models(DATA_SET_ID)  # Make an API request.\n",
    "print(\"Listing current models:\")\n",
    "for model in models:\n",
    "    full_model_id = f\"{model.dataset_id}.{model.model_id}\"\n",
    "    print(full_model_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "AC8GAkKxhN9B",
    "outputId": "f2d30fb1-8fd8-40e2-9c71-0597231d6e1a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted model 'bqml_kmeans.test'.\n"
     ]
    }
   ],
   "source": [
    "# Remove our sample model from BigQuery, so we only have remaining models from our previous loop\n",
    "\n",
    "model_id = DATA_SET_ID+\".\"+model_test_name\n",
    "client.delete_model(model_id)  # Make an API request.\n",
    "print(f\"Deleted model '{model_id}'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SnGjnnrvH2Ej"
   },
   "outputs": [],
   "source": [
    "# This will create a dataframe with each model name, the Davies Bouldin Index, and Loss.  \n",
    "# It will be used for the elbow method and to help determine optimal K\n",
    "\n",
    "df = pd.DataFrame(columns=['davies_bouldin_index', 'mean_squared_distance'])\n",
    "models = client.list_models(DATA_SET_ID)  # Make an API request.\n",
    "for model in models:\n",
    "    full_model_id = f\"{model.dataset_id}.{model.model_id}\"\n",
    "    sql =f'''\n",
    "        SELECT \n",
    "            davies_bouldin_index,\n",
    "            mean_squared_distance \n",
    "        FROM ML.EVALUATE(MODEL `{full_model_id}`)\n",
    "    '''\n",
    "\n",
    "    job_config = bigquery.QueryJobConfig()\n",
    "\n",
    "    # Start the query, passing in the extra configuration.\n",
    "    query_job = client.query(sql, job_config=job_config)  # Make an API request.\n",
    "    df_temp = query_job.to_dataframe()  # Wait for the job to complete.\n",
    "    df_temp['model_name'] = model.model_id\n",
    "    df =  pd.concat([df, df_temp], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zLBMjSR-qqCq"
   },
   "source": [
    "The code below assumes we've used the naming convention originally created in this notebook, and the k value occurs after the 2nd underscore.  If you've changed the model_prefix_name variable, then this code might break. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "colab_type": "code",
    "id": "4DSIBlqVahZ7",
    "outputId": "a47da0cb-0430-479d-b979-f98a55b8f388"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>davies_bouldin_index</th>\n",
       "      <th>mean_squared_distance</th>\n",
       "      <th>model_name</th>\n",
       "      <th>n_clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.146</td>\n",
       "      <td>8.920</td>\n",
       "      <td>kmeans_clusters_3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.576</td>\n",
       "      <td>8.492</td>\n",
       "      <td>kmeans_clusters_4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.446</td>\n",
       "      <td>7.611</td>\n",
       "      <td>kmeans_clusters_5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.272</td>\n",
       "      <td>7.459</td>\n",
       "      <td>kmeans_clusters_6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.532</td>\n",
       "      <td>6.994</td>\n",
       "      <td>kmeans_clusters_7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.678</td>\n",
       "      <td>6.582</td>\n",
       "      <td>kmeans_clusters_8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.557</td>\n",
       "      <td>6.012</td>\n",
       "      <td>kmeans_clusters_9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.285</td>\n",
       "      <td>5.870</td>\n",
       "      <td>kmeans_clusters_10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.388</td>\n",
       "      <td>5.665</td>\n",
       "      <td>kmeans_clusters_11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.607</td>\n",
       "      <td>5.075</td>\n",
       "      <td>kmeans_clusters_12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.380</td>\n",
       "      <td>4.989</td>\n",
       "      <td>kmeans_clusters_13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.280</td>\n",
       "      <td>4.840</td>\n",
       "      <td>kmeans_clusters_14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.449</td>\n",
       "      <td>4.709</td>\n",
       "      <td>kmeans_clusters_15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   davies_bouldin_index  mean_squared_distance          model_name  n_clusters\n",
       "0                 2.146                  8.920   kmeans_clusters_3           3\n",
       "0                 1.576                  8.492   kmeans_clusters_4           4\n",
       "0                 1.446                  7.611   kmeans_clusters_5           5\n",
       "0                 2.272                  7.459   kmeans_clusters_6           6\n",
       "0                 1.532                  6.994   kmeans_clusters_7           7\n",
       "0                 1.678                  6.582   kmeans_clusters_8           8\n",
       "0                 1.557                  6.012   kmeans_clusters_9           9\n",
       "0                 1.285                  5.870  kmeans_clusters_10          10\n",
       "0                 1.388                  5.665  kmeans_clusters_11          11\n",
       "0                 1.607                  5.075  kmeans_clusters_12          12\n",
       "0                 1.380                  4.989  kmeans_clusters_13          13\n",
       "0                 1.280                  4.840  kmeans_clusters_14          14\n",
       "0                 1.449                  4.709  kmeans_clusters_15          15"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This will modify the dataframe above, produce a new field with 'n_clusters', and will sort for graphing\n",
    "\n",
    "df['n_clusters'] = df['model_name'].str.split('_').map(lambda x: x[2])\n",
    "df['n_clusters'] = df['n_clusters'].apply(pd.to_numeric)\n",
    "df = df.sort_values(by='n_clusters', ascending=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "id": "jLVVMKm8QIFv",
    "outputId": "df4ef800-3eb6-4018-ba2d-be751c313b5a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='n_clusters'>"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.line(x='n_clusters', y=['davies_bouldin_index', 'mean_squared_distance'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OKWIJMXBcatm"
   },
   "source": [
    "Note - when you run this notebook, you will get different results, due to random cluster initialization.  If you'd like to consistently return the same cluster for reach run, you may explicitly select your initialization through hyperparameter selection (https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create#kmeans_init_method). \n",
    "\n",
    "Making our k selection:  There is no perfect approach or process when determining the optimal k value. It can often be determined by business rules or requirements.  In this example, there isn't a simple requirement, so these considerations can also be followed:\n",
    "\n",
    "\n",
    "1.   We start with the 'elbow method', which is effectively charting loss vs k.  Sometimes, though not always, there's a natural 'elbow' where incremental clusters do not drastically reduce loss.  In this specific example, and as you often might find, unfortunately there isn't a natural 'elbow', so we must continue our process. \n",
    "2.  Next, we chart Davies-Bouldin vs k.  This score tells us how 'different' each cluster is, with the optimal score at zero.  With 5 clusters, we see a score of ~1.4, and only with k>9, do we see better values. \n",
    "3. Finally, we begin to try to interpret the difference of each model. You can review the evaluation module for various models to understand distributions of our features.  With our data, we can look for patterns by gender, house hold income, and shopping habits.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6aUBVjqsy3Lo"
   },
   "source": [
    "# Analyze our final cluster\n",
    "\n",
    "There are 2 options to understand the characteristics of your model.  You can either 1) look in the BigQuery UI, or you can 2) programmatically interact with your model object.  Below you’ll find a simple example for the latter option.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Uo6wjkebuOVB"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>centroid_id</th>\n",
       "      <th>feature</th>\n",
       "      <th>categorical_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>OS</td>\n",
       "      <td>[{'category': 'Linux', 'value': 0.035714285714...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>gender</td>\n",
       "      <td>[{'category': 'M', 'value': 0.4285714285714285...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>OS</td>\n",
       "      <td>[{'category': 'iOS', 'value': 0.04276315789473...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>gender</td>\n",
       "      <td>[{'category': 'F', 'value': 0.4967105263157895...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>OS</td>\n",
       "      <td>[{'category': 'iOS', 'value': 0.09637391424238...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   centroid_id feature                                  categorical_value\n",
       "0            1      OS  [{'category': 'Linux', 'value': 0.035714285714...\n",
       "1            1  gender  [{'category': 'M', 'value': 0.4285714285714285...\n",
       "2            2      OS  [{'category': 'iOS', 'value': 0.04276315789473...\n",
       "3            2  gender  [{'category': 'F', 'value': 0.4967105263157895...\n",
       "4            3      OS  [{'category': 'iOS', 'value': 0.09637391424238..."
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_to_use = 'kmeans_clusters_5' # User can edit this\n",
    "final_model = DATA_SET_ID+'.'+model_to_use\n",
    "\n",
    "sql_get_attributes = f'''\n",
    "SELECT\n",
    "  centroid_id,\n",
    "  feature,\n",
    "  categorical_value\n",
    "FROM\n",
    "  ML.CENTROIDS(MODEL {final_model})\n",
    "WHERE\n",
    "  feature IN ('OS','gender')\n",
    "'''\n",
    "\n",
    "job_config = bigquery.QueryJobConfig()\n",
    "\n",
    "# Start the query\n",
    "query_job = client.query(sql_get_attributes, job_config=job_config) #API Request\n",
    "df_attributes = query_job.result()\n",
    "df_attributes = df_attributes.to_dataframe()\n",
    "df_attributes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>centroid_id</th>\n",
       "      <th>Total_Users</th>\n",
       "      <th>Apparel</th>\n",
       "      <th>Office</th>\n",
       "      <th>Electronics</th>\n",
       "      <th>LimitedSupply</th>\n",
       "      <th>Accessories</th>\n",
       "      <th>ShopByBrand</th>\n",
       "      <th>Bags</th>\n",
       "      <th>Total_Purchases</th>\n",
       "      <th>productPrice_USD</th>\n",
       "      <th>hhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>90.800</td>\n",
       "      <td>11.200</td>\n",
       "      <td>9.300</td>\n",
       "      <td>4.000</td>\n",
       "      <td>5.900</td>\n",
       "      <td>1.600</td>\n",
       "      <td>1.300</td>\n",
       "      <td>158.900</td>\n",
       "      <td>3486.300</td>\n",
       "      <td>77857.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>344</td>\n",
       "      <td>14.600</td>\n",
       "      <td>5.100</td>\n",
       "      <td>5.700</td>\n",
       "      <td>2.400</td>\n",
       "      <td>0.800</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.100</td>\n",
       "      <td>44.700</td>\n",
       "      <td>1268.700</td>\n",
       "      <td>75296.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7198</td>\n",
       "      <td>1.900</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.200</td>\n",
       "      <td>5.500</td>\n",
       "      <td>153.600</td>\n",
       "      <td>74898.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>301</td>\n",
       "      <td>4.100</td>\n",
       "      <td>2.500</td>\n",
       "      <td>1.600</td>\n",
       "      <td>1.600</td>\n",
       "      <td>7.300</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>22.300</td>\n",
       "      <td>383.700</td>\n",
       "      <td>75415.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "      <td>340.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>354.000</td>\n",
       "      <td>2022.900</td>\n",
       "      <td>75000.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   centroid_id  Total_Users  Apparel  Office  Electronics  LimitedSupply  \\\n",
       "0            1           31   90.800  11.200        9.300          4.000   \n",
       "1            2          344   14.600   5.100        5.700          2.400   \n",
       "2            3         7198    1.900   0.600        0.500          0.400   \n",
       "3            4          301    4.100   2.500        1.600          1.600   \n",
       "4            5            1    0.000 340.000        0.000          0.000   \n",
       "\n",
       "   Accessories  ShopByBrand  Bags  Total_Purchases  productPrice_USD       hhi  \n",
       "0        5.900        1.600 1.300          158.900          3486.300 77857.100  \n",
       "1        0.800        3.000 3.100           44.700          1268.700 75296.100  \n",
       "2        0.200        0.200 0.200            5.500           153.600 74898.000  \n",
       "3        7.300        0.400 0.000           22.300           383.700 75415.200  \n",
       "4        0.000        0.000 0.000          354.000          2022.900 75000.000  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get numerical information about clusters\n",
    "\n",
    "sql_get_numerical_attributes = f'''\n",
    "WITH T AS (\n",
    "SELECT \n",
    "  centroid_id,\n",
    "  ARRAY_AGG(STRUCT(feature AS name, \n",
    "                   ROUND(numerical_value,1) AS value) \n",
    "                   ORDER BY centroid_id) \n",
    "                   AS cluster\n",
    "FROM ML.CENTROIDS(MODEL {final_model})\n",
    "GROUP BY centroid_id\n",
    "),\n",
    "\n",
    "Users AS(\n",
    "SELECT\n",
    "  centroid_id,\n",
    "  COUNT(*) AS Total_Users\n",
    "FROM(\n",
    "SELECT\n",
    "  * EXCEPT(nearest_centroids_distance)\n",
    "FROM\n",
    "  ML.PREDICT(MODEL {final_model},\n",
    "    (\n",
    "    SELECT\n",
    "      *\n",
    "    FROM\n",
    "      {final_view.full_table_id.replace(\":\", \".\")}\n",
    "      )))\n",
    "GROUP BY centroid_id\n",
    ")\n",
    "\n",
    "SELECT\n",
    "  centroid_id,\n",
    "  Total_Users,\n",
    "  (SELECT value from unnest(cluster) WHERE name = 'Apparel') AS Apparel,\n",
    "  (SELECT value from unnest(cluster) WHERE name = 'Office') AS Office,\n",
    "  (SELECT value from unnest(cluster) WHERE name = 'Electronics') AS Electronics,\n",
    "  (SELECT value from unnest(cluster) WHERE name = 'LimitedSupply') AS LimitedSupply,\n",
    "  (SELECT value from unnest(cluster) WHERE name = 'Accessories') AS Accessories,\n",
    "  (SELECT value from unnest(cluster) WHERE name = 'ShopByBrand') AS ShopByBrand,\n",
    "  (SELECT value from unnest(cluster) WHERE name = 'Bags') AS Bags,\n",
    "  (SELECT value from unnest(cluster) WHERE name = 'productPrice_USD') AS productPrice_USD,\n",
    "  (SELECT value from unnest(cluster) WHERE name = 'hhi') AS hhi\n",
    "\n",
    "FROM T LEFT JOIN Users USING(centroid_id)\n",
    "ORDER BY centroid_id ASC\n",
    "'''\n",
    "\n",
    "job_config = bigquery.QueryJobConfig()\n",
    "\n",
    "# Start the query\n",
    "query_job = client.query(sql_get_numerical_attributes, job_config=job_config) #API Request\n",
    "df_numerical_attributes = query_job.result()\n",
    "df_numerical_attributes = df_numerical_attributes.to_dataframe()\n",
    "df_numerical_attributes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t95SlHjlx3Qn"
   },
   "source": [
    "In addition to the output above, I'll note a few insights we get from our clusters. \n",
    "\n",
    "Cluster 1 - The apparel shopper, which also purchases more often than normal.  This (although synthetic data) segment skews female.\n",
    "\n",
    "Cluster 2 - Most likely to shop by brand, and interested in bags. This segment has fewer purchases on average than the first cluster, however, this is the highest value customer.\n",
    "\n",
    "Cluster 3 - The most populated cluster, this one has a small amount of purchases and spends less on average.  This segment is the one time buyer, rather than the brand loyalist.  \n",
    "\n",
    "Cluster 4 - Most interested in accessories, does not buy as often as cluster 1 and 2, however buys more than cluster 3.  \n",
    "\n",
    "Cluster 5 - This is an outlier as only 1 person belongs to this group.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Cv0hMz3rcb1s"
   },
   "source": [
    "# Use model to group new website behavior, and then push results to GA360 for marketing activation\n",
    "\n",
    "After we have a finalized model, we want to use it for inference.  The code below outlines how to score or assign users into clusters.  These are labeled as the CENTROID_ID.  Although this by itself is helpful, we also recommend a process to ingest these scores back into GA360. The easiest way to export your BigQuery ML predictions from a BigQuery table to Google Analytics 360 is to use the MoDeM (Model Deployment for Marketing, https://github.com/google/modem) reference implementation. MoDeM helps you load data into Google Analytics for eventual activation in Google Ads, Display & Video 360 and Search Ads 360."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-fAsssnWnv5C",
    "outputId": "a4fd7009-c808-4f42-972c-8fb9d1a34bde"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CENTROID_ID</th>\n",
       "      <th>fullVisitorID</th>\n",
       "      <th>Hashed_fullVisitorID</th>\n",
       "      <th>OS</th>\n",
       "      <th>Apparel</th>\n",
       "      <th>Office</th>\n",
       "      <th>Electronics</th>\n",
       "      <th>LimitedSupply</th>\n",
       "      <th>Accessories</th>\n",
       "      <th>ShopByBrand</th>\n",
       "      <th>Bags</th>\n",
       "      <th>productPrice_USD</th>\n",
       "      <th>gender</th>\n",
       "      <th>hhi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>3355435945434430291</td>\n",
       "      <td>2759439800079197041</td>\n",
       "      <td>Macintosh</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1079.880</td>\n",
       "      <td>F</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CENTROID_ID        fullVisitorID  Hashed_fullVisitorID         OS  Apparel  \\\n",
       "0            5  3355435945434430291   2759439800079197041  Macintosh        0   \n",
       "\n",
       "   Office  Electronics  LimitedSupply  Accessories  ShopByBrand  Bags  \\\n",
       "0       0            0              6            0            0     5   \n",
       "\n",
       "   productPrice_USD gender    hhi  \n",
       "0          1079.880      F  65000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql_score = f'''\n",
    "SELECT * EXCEPT(nearest_centroids_distance)\n",
    "FROM\n",
    "  ML.PREDICT(MODEL {final_model},\n",
    "    (\n",
    "    SELECT\n",
    "      *\n",
    "    FROM\n",
    "      {final_view.full_table_id.replace(\":\", \".\")}\n",
    "      LIMIT 1))\n",
    "'''\n",
    "\n",
    "job_config = bigquery.QueryJobConfig()\n",
    "\n",
    "# Start the query\n",
    "query_job = client.query(sql_score, job_config=job_config) #API Request\n",
    "df_score = query_job.result()\n",
    "df_score = df_score.to_dataframe()\n",
    "\n",
    "df_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lnHw5kQUYfkK"
   },
   "source": [
    "# Clean up: Delete all models and tables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "fJ8VwVcMYlW7",
    "outputId": "babbcc21-9ea3-4a4a-d107-6871aaa82bd9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted: bqml_kmeans.kmeans_clusters_10\n",
      "Deleted: bqml_kmeans.kmeans_clusters_11\n",
      "Deleted: bqml_kmeans.kmeans_clusters_12\n",
      "Deleted: bqml_kmeans.kmeans_clusters_13\n",
      "Deleted: bqml_kmeans.kmeans_clusters_14\n",
      "Deleted: bqml_kmeans.kmeans_clusters_15\n",
      "Deleted: bqml_kmeans.kmeans_clusters_3\n",
      "Deleted: bqml_kmeans.kmeans_clusters_4\n",
      "Deleted: bqml_kmeans.kmeans_clusters_5\n",
      "Deleted: bqml_kmeans.kmeans_clusters_6\n",
      "Deleted: bqml_kmeans.kmeans_clusters_7\n",
      "Deleted: bqml_kmeans.kmeans_clusters_8\n",
      "Deleted: bqml_kmeans.kmeans_clusters_9\n",
      "Deleted: bqml_kmeans.test\n"
     ]
    }
   ],
   "source": [
    "# Are you sure you want to do this? This is to delete all models\n",
    "\n",
    "models = client.list_models(DATA_SET_ID) # Make an API request.\n",
    "for model in models:\n",
    "    full_model_id = f\"{model.dataset_id}.{model.model_id}\"\n",
    "    client.delete_model(full_model_id)  # Make an API request.\n",
    "    print(f\"Deleted: {full_model_id}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "CAXrHpAJYwCI",
    "outputId": "969f2d34-a56c-4e7b-c4c3-230f7d8bbecc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted: bqml_kmeans.CRM_View\n",
      "Deleted: bqml_kmeans.Final_View\n",
      "Deleted: bqml_kmeans.GA360_View\n"
     ]
    }
   ],
   "source": [
    "# Are you sure you want to do this? This is to delete all tables and views\n",
    "\n",
    "tables = client.list_tables(DATA_SET_ID)  # Make an API request.\n",
    "for table in tables:\n",
    "    full_table_id = f\"{table.dataset_id}.{table.table_id}\"\n",
    "    client.delete_table(full_table_id)  # Make an API request.\n",
    "    print(f\"Deleted: {full_table_id}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LYucZEztyk2K"
   },
   "source": [
    "# Wrapping it all up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OfzOUkdiytXm"
   },
   "source": [
    "In this exercise, we’ve accomplished some cool things with k-means in BigQuery ML.  Most notably, we’re able to join online and offline user level information to gain more insight into a holistic view of our customers.  We’ve modeled user behavior, and detailed an approach to determine the optimal number of clusters.  We’re able to take this insight and apply to future behavior through inference.  Finally, we can import this inference score back into GA360 for future marketing campaigns. "
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "BQML_Scaled_Clustering_TC_Draft.ipynb",
   "provenance": [],
   "toc_visible": true
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  "environment": {
   "name": "common-cpu.m54",
   "type": "gcloud",
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  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}

================================================
FILE: retail/ltv/bqml/README.md
================================================
# Activate on LTV predictions.
This guide refactors the [final part][series_final] of an existing series about predicting Lifetime Value (LTV). The series uses Tensorflow and shows multiple approaches such as statistical models or deep neural networks to predict the monetary value of customers. The final part leverages [AutoML Tables][automl_tables]. 

This document shows an opiniated way to predict the monetary value of your customers for a specific time in the future using historical data.

This updated version differs in the following:
- Predicts future monetary value for a specific period of time.
- Minimizes development time by using AutoML directly from [BigQuery ML][bq_ml].
- Uses two new datasets for sales and customer data.
- Creates additional training examples by moving the date that separates input and target orders (more details in the notebook)
- Shows how to activate the LTV predictions to create similar audiences in marketing tools.

The end to end flow assumes that you start with a data dump stored into BigQuery and runs through the following steps:

1. Match your dataset to the sales dataset template.
1. Create features from a list of orders.
1. Train a model using monetary value as a label.
1. Predict future monetary value of customers.
1. Extract the emails of the top customers.

For more general  information about LTV, read the [first part][series_first] of the series.

[series_final]:https://cloud.google.com/solutions/machine-learning/clv-prediction-with-automl-tables
[automl_tables]:https://cloud.google.com/automl-tables
[bq_ml]:https://cloud.google.com/bigquery-ml/docs/bigqueryml-intro
[series_first]:https://cloud.google.com/solutions/machine-learning/clv-prediction-with-offline-training-intro

## Files

There are two main sets of files:

**[1. ./notebooks](./notebooks)**

You can use the notebook in this folder to manually run the flow using example datasets. You can also used your own data.

**[2. ./scripts](./scripts)**

The scripts in this folder facilitate automation through BigQuery scripting, BigQuery stored procedures and bash scripting. Scripts use statements from the notebook to:
1. Transform data.
1. Train and use model to predict LTV.
1. Extract emails of the top LTV customers.

*Note: For production use cases, you can reuse the SQL statements from the scripts folder in pipeline tools such as Kubeflow Pipelines or Cloud Composer.*

The scripts assume that you already have the sales and crm datasets stored in BigQuery.

## Recommended flow

1. Do research in the Notebook.
1. Extract important SQL.
1. Write SQL scripts.
1. Test end-to-end flow through bash scripts.
1. Integrate into a data pipeline.
1. Run as part of a CI/CD pipeline.

This code shows you the steps 1 to 4.

## Run code

After you went through the notebook, you can run through all the steps at once using the [run.sh script][run_script].

1. If you use your own sales table, update the [matching query][matching_query] to transform your table into a table with a schema that the script understands.
1. Make sure that you can run the run.sh script

    ```chmod +x run.sh```

1. Check how to set parameters

    ```./run.sh --help```

1. Run the script
    ```./run.sh --project-id [YOUR_PROJECT_ID] --dataset-id [YOUR_DATASET_ID]


## Questions? Feedback?
If you have any questions or feedback, please open up a [new issue](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/issues).


## Disclaimer
This is not an officially supported Google product.

All files in this folder are under the Apache License, Version 2.0 unless noted otherwise.

[run_script]:./scripts/run.sh
[matching_query]:./scripts/10_procedure_match.sql

================================================
FILE: retail/ltv/bqml/notebooks/bqml_automl_ltv_activate_lookalike.ipynb
================================================
{
  "nbformat": 4,
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    "colab": {
      "name": "bqml_automl_ltv_activate_lookalike.ipynb",
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      "metadata": {
        "id": "ur8xi4C7S06n",
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      },
      "source": [
        "# Copyright 2020 Google LLC\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JAPoU8Sm5E6e",
        "colab_type": "text"
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      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/ai-platform-samples/blob/master/notebooks/templates/ai_platform_notebooks_template_hybrid.ipynb\"\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/ai-platform-samples/blob/master/notebooks/templates/ai_platform_notebooks_template_hybrid.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
        "      View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tvgnzT1CKxrO",
        "colab_type": "text"
      },
      "source": [
        "## Overview"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a2VgGzVIlLo2",
        "colab_type": "text"
      },
      "source": [
        "### Objective\n",
        "\n",
        "Estimate how much an existing customer will spend in the future based on their historical orders to find similar new customers using lookalike features of advertising tools.\n",
        "\n",
        "In this tutorial, you will:\n",
        "- Define how far in the future you want to predict the monetary value of your customers (ex: 3 months)\n",
        "- Use a moving session concept to aggregate multiple inputs and targets per customer (For more details, see the *Create inputs and targets* section of this tutorial).\n",
        "- Use primarly inputs such as Recency, Frequency and Monetary which are common values to use in an LTV context, especially in statistical model due to their distribution patterns.\n",
        "- Accelerate model development by using AutoML from within BigQuery ML.\n",
        "- Predict the monetary value of all existing customers for a predefined period of time in the future.\n",
        "- Use first-party data to extract the most valuable customers email in order to run lookalike campaigns using the [Google Ads API][ads_api]. You can extend the concept to do the same using [Facebook API][fb_api].\n",
        "\n",
        "[ads_api]:https://developers.google.com/adwords/api/docs/samples/python/remarketing#create-and-populate-a-user-list\n",
        "[fb_api]:https://www.facebook.com/business/help/341425252616329?id=2469097953376494\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lAeYu9bBlLaZ",
        "colab_type": "text"
      },
      "source": [
        "### Sales dataset\n",
        "Sales data can be of different forms but generally look like a list of transactions where each record contains at a minimum the following:\n",
        "- a customer reference\n",
        "- a transaction date\n",
        "- a transaction reference\n",
        "- a monetary value\n",
        "\n",
        "Each record usually represents one of the following:\n",
        "- An entire order which contains aggregated values across products for that order. You can find the total order value in the record.\n",
        "- A part of a transaction which contains a unique product, some of its characteristics including SKU and unit price and the quantity ordered. \n",
        "\n",
        "This tutorial uses the latter.\n",
        "\n",
        "You can run this tutorial with your own dataset. The dataset that you provide must meet the following requirements:\n",
        "1. Each row represents a transaction related to a product item and linked to a transaction, a date and a customer. A row can be either a transaction (quantity is > 0) or a return (quantity is < 0)\n",
        "1. Columns must include the following:\n",
        "\n",
        "| Field name | Type | Description |\n",
        "| :-|:-|:-|\n",
        "| customer_id | STRING | First party identifier of the customer.\t |\n",
        "| order_id | STRING | First party identitfier of the order. |\n",
        "| order_date | DATE | Date of the order.  |\n",
        "| product_sku | STRING | First party identitifer of the product. |\n",
        "| qty | INTEGER | Quantity of the product either ordered or returned. |\n",
        "| unit_price | FLOAT | Unit price of the product. |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "__KXS_qcyNRK",
        "colab_type": "text"
      },
      "source": [
        "### Customer dataset\n",
        "Customer data often resides in a Customer Relationship Management (CRM). \n",
        "\n",
        "This first party data is key for companies that want to provide a certain level of customer service.\n",
        "\n",
        "This tutorial only uses two columns of the customer dataset:\n",
        "- customer_id to join with the sales data\n",
        "- email to create a marketing list.\n",
        "\n",
        "The public dataset contains other fields that are not relevant for this tutorial and your data might have other fields. This tutorial focuses on an activation based on email addresses."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LdyWWnIElL0C",
        "colab_type": "text"
      },
      "source": [
        "### Costs \n",
        "\n",
        "This tutorial uses billable components of Google Cloud Platform (GCP):\n",
        "\n",
        "* BigQuery\n",
        "* BigQuery ML\n",
        "* Cloud Storage\n",
        "\n",
        "To learn more about pricing:\n",
        "- Read [BigQuery pricing](https://cloud.google.com/bigquery/docs/pricing)\n",
        "- Read [BigQuery ML pricing](https://cloud.google.com/bigquery-ml/pricing)\n",
        "- Read [Cloud Storage pricing](https://cloud.google.com/storage/pricing)\n",
        "- Use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n",
        "to generate a cost estimate based on your projected usage."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UDn9SREOlXaN",
        "colab_type": "text"
      },
      "source": [
        "### Terminology\n",
        "- **'Input' transactions**: The set of transactions that the training task uses to create inputs values for the model.\n",
        "- **'Target' transactions**: The set of transactions that the training task uses to create the target value to predict. The target value is an aggregated monetary value per customer for a defined timeline.\n",
        "- **Threshold date**: Date that separates 'Input' transactions from 'Target' transactions per customer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fmvu_N0ovY8N",
        "colab_type": "text"
      },
      "source": [
        "## Setup\n",
        "This step sets up packages, variables, authentication, APIs clients and resources for Google Cloud and Adwords."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i7EUnXsZhAGF",
        "colab_type": "text"
      },
      "source": [
        "### Install packages and dependencies\n",
        "Installs libraries, packages and dependencies to run this tutorial"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wyy5Lbnzg5fi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5fa8b23-a707-4480-a170-061eaf260c01"
      },
      "source": [
        "# Install libraries. \n",
        "# The magic cells insures that those libraries can be part of a custom container\n",
        "# if moving the code somewhere else.\n",
        "%pip install -q googleads\n",
        "%pip install -q -U kfp matplotlib Faker --user\n",
        "\n",
        "# Automatically restart kernel after installs\n",
        "# import IPython\n",
        "# app = IPython.Application.instance()\n",
        "# app.kernel.do_shutdown(True)  "
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 51kB 2.5MB/s \n",
            "\u001b[K     |████████████████████████████████| 276kB 14.1MB/s \n",
            "\u001b[K     |████████████████████████████████| 102kB 8.7MB/s \n",
            "\u001b[K     |████████████████████████████████| 51kB 5.5MB/s \n",
            "\u001b[K     |████████████████████████████████| 61kB 6.5MB/s \n",
            "\u001b[?25h  Building wheel for googleads (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for PyYAML (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 122kB 4.7MB/s \n",
            "\u001b[K     |████████████████████████████████| 11.6MB 39.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.0MB 45.3MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.5MB 48.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 61kB 6.7MB/s \n",
            "\u001b[K     |████████████████████████████████| 61kB 6.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 204kB 45.9MB/s \n",
            "\u001b[?25h  Building wheel for kfp (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for kfp-server-api (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for strip-hints (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: nbclient 0.5.0 has requirement jupyter-client>=6.1.5, but you'll have jupyter-client 5.3.5 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "\u001b[33m  WARNING: The script jsonschema is installed in '/root/.local/bin' which is not on PATH.\n",
            "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
            "\u001b[33m  WARNING: The script strip-hints is installed in '/root/.local/bin' which is not on PATH.\n",
            "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
            "\u001b[33m  WARNING: The scripts dsl-compile and kfp are installed in '/root/.local/bin' which is not on PATH.\n",
            "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
            "\u001b[33m  WARNING: The script faker is installed in '/root/.local/bin' which is not on PATH.\n",
            "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vmSgVQ9x4aO0",
        "colab_type": "text"
      },
      "source": [
        "### Import packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouL1aMrvVgnL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9eac7e7-a422-4353-d06a-d8a69ec013a7"
      },
      "source": [
        "# Import\n",
        "from __future__ import absolute_import\n",
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "\n",
        "import os, json, random\n",
        "import hashlib, uuid\n",
        "import time, calendar, math\n",
        "import pandas as pd, numpy as np\n",
        "import matplotlib.pyplot as plt, seaborn as sns\n",
        "from datetime import datetime\n",
        "from google.cloud import bigquery\n",
        "\n",
        "from googleads import adwords"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BF1j6f9HApxa",
        "colab_type": "text"
      },
      "source": [
        "### Set up your GCP project\n",
        "\n",
        "**The following steps are required, regardless of your notebook environment.**\n",
        "\n",
        "1. [Select or create a GCP project.](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
        "\n",
        "2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n",
        "\n",
        "3. [Enable the AI Platform APIs and Compute Engine APIs.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component)\n",
        "\n",
        "4. If you are running this notebook locally, you will need to install [Google Cloud SDK](https://cloud.google.com/sdk).\n",
        "\n",
        "5. Enter your project ID in the cell below. Then run the  cell to make sure the\n",
        "Cloud SDK uses the right project for all the commands in this notebook."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oM1iC_MfAts1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd734b46-80c3-4149-e48d-1b2d26ad0d6b"
      },
      "source": [
        "PROJECT_ID = \"[YOUR-PROJECT]\" #@param {type:\"string\"}\n",
        "REGION = \"US\"\n",
        "! gcloud config set project $PROJECT_ID"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Updated property [core/project].\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dr--iN2kAylZ",
        "colab_type": "text"
      },
      "source": [
        "### Authenticate your GCP account\n",
        "If you are using AI Platform Notebooks, you are already authenticated so there is no need to run this step."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PyQmSRbKA8r-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sys\n",
        "\n",
        "if 'google.colab' in sys.modules:\n",
        "  from google.colab import auth as google_auth\n",
        "  google_auth.authenticate_user()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T4MuyozBoPv9",
        "colab_type": "text"
      },
      "source": [
        "### Create a working dataset\n",
        "This tutorial mostly uses BigQuery magic cells where the --params field does not support variables for datasets, tables and column names. \n",
        "\n",
        "This steps hardcode the dataset where all the steps of this tutorial happens."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sGRa9QxXoZMO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "! bq show $PROJECT_ID:ltv_ecommerce || bq mk $PROJECT_ID:ltv_ecommerce\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9i6ZRZgqCEPY",
        "colab_type": "text"
      },
      "source": [
        "### Load example tables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjc30TarCHSp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Loads CRM data\n",
        "!bq load \\\n",
        "  --project_id $PROJECT_ID \\\n",
        "  --skip_leading_rows 1 \\\n",
        "  --max_bad_records 100000 \\\n",
        "  --replace \\\n",
        "  --field_delimiter \",\" \\\n",
        "  --autodetect \\\n",
        "  ltv_ecommerce.00_crm \\\n",
        "  gs://solutions-public-assets/analytics-componentized-patterns/ltv/crm.csv"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fe4GT-fWEcUt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Loads Sales data\n",
        "!bq load \\\n",
        "  --project_id $PROJECT_ID \\\n",
        "  --skip_leading_rows 1 \\\n",
        "  --max_bad_records 100000 \\\n",
        "  --replace \\\n",
        "  --field_delimiter \",\" \\\n",
        "  --autodetect \\\n",
        "  ltv_ecommerce.10_orders \\\n",
        "  gs://solutions-public-assets/analytics-componentized-patterns/ltv/sales_*"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVNkkGV8jUN6",
        "colab_type": "text"
      },
      "source": [
        "### Create clients"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "amZzWHkjjWTi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# BigQuery client\n",
        "bq_client = bigquery.Client(project=PROJECT_ID)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TJB0PYUS7sYw",
        "colab_type": "text"
      },
      "source": [
        "## [Optional] Match your dataset to template\n",
        "If you use the example data, you can skip this step.\n",
        "\n",
        "This tutorial assumes that you have a dump of your sales data already available in BigQuery located at `[YOUR_PROJECT].[YOUR_DATASET].[YOUR_SOURCE_TABLE]`\n",
        "\n",
        "You are free to adapt the SQL query in the next cell to a SQL statement that transforms your data according to the template."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NK_wCQt78JjO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 32
        },
        "outputId": "b1f6d49d-3d26-46e0-d753-f5b1e55f1a65"
      },
      "source": [
        "%%bigquery --params $MATCH_FIELDS --project $PROJECT_ID\n",
        "\n",
        "CREATE OR REPLACE TABLE `ltv_ecommerce.10_orders` AS (\n",
        "SELECT\n",
        "  CAST(customer_id AS STRING)  AS customer_id,\n",
        "  order_id  AS order_id,\n",
        "  transaction_date AS transaction_date,\n",
        "  product_sku AS product_sku,\n",
        "  qty AS qty,\n",
        "  unit_price AS unit_price\n",
        "FROM\n",
        "  `[YOUR_PROJECT].[YOUR_DATASET].[YOUR_SOURCE_TABLE]`\n",
        ");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: []\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T9q6jKCM4-v8",
        "colab_type": "text"
      },
      "source": [
        "## Analyze dataset\n",
        "\n",
        "**Some charts might use a log scale.**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LEchqKz1HiOZ",
        "colab_type": "text"
      },
      "source": [
        "#### Quantity\n",
        "This sections shows how to use the BigQuery [ML BUCKETIZE][bucketize] preprocessing function to create buckets of data for quantity and display a log scaled distribution of the `qty` field.\n",
        "\n",
        "[bucketize]:https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-preprocessing-functions#bucketize"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "keis4kuj5A71",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%bigquery df_histo_qty --project $PROJECT_ID\n",
        "\n",
        "WITH\n",
        "  min_max AS (\n",
        "  SELECT\n",
        "    MIN(qty) min_qty,\n",
        "    MAX(qty) max_qty,\n",
        "    CEIL((MAX(qty) - MIN(qty)) / 100) step\n",
        "  FROM\n",
        "    `ltv_ecommerce.10_orders` \n",
        ")\n",
        "SELECT\n",
        "  COUNT(1) c,\n",
        "  bucket_same_size AS bucket\n",
        "FROM (\n",
        "  SELECT\n",
        "    -- Creates (1000-100)/100 + 1 buckets of data.\n",
        "    ML.BUCKETIZE(qty, GENERATE_ARRAY(min_qty, max_qty, step)) AS bucket_same_size,\n",
        "    -- Creates custom ranges.\n",
        "    ML.BUCKETIZE(qty, [-1, -1, -2, -3, -4, -5, 0, 1, 2, 3, 4, 5]) AS bucket_specific,\n",
        "  FROM\n",
        "    `ltv_ecommerce.10_orders`, min_max )\n",
        "  # WHERE bucket != \"bin_1\" and bucket != \"bin_2\"\n",
        "GROUP BY\n",
        "  bucket\n",
        "  -- Ohterwise, orders bin_10 before bin_2\n",
        "ORDER BY CAST(SPLIT(bucket, \"_\")[OFFSET(1)] AS INT64)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6JqHipXDPto",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "outputId": "33d8e932-c3f9-4b1f-d3df-fcf8355bcb42"
      },
      "source": [
        "# Uses a log scale for bucket_same_size.\n",
        "# Can remove the log scale when using bucket_specific.\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.title('Log scaled distribution for qty')\n",
        "hqty = sns.barplot( x='bucket', y='c', data=df_histo_qty)\n",
        "hqty.set_yscale(\"log\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAFOCAYAAABE5JExAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAeZElEQVR4nO3de5hkdX3n8fdHRjSKjijECzCOySAy8W4HTUwiMV7AdWS9ISNeMMjEC8k+a5KNCRpRQ9xo3M1FohldBG8goJud0UlYV0WUQMLgLcAscUJABlSQywjRqOB3/6gza9FO99Scruo6p+f9ep56puqcU7/61nequz99+nfOSVUhSZIkaffdbdoFSJIkSX1lmJYkSZJaMkxLkiRJLRmmJUmSpJYM05IkSVJLhmlJkiSpJcO0JC1QkuOSfGExnpvk6iRPa+7/QZL3tXndOca+PcnPNPdPT/JHYxz7PUneOK7xhsZNkvcnuSXJP457fEnaFcO0pE4ZDouaX1X9cVW9clfbJTk/yS63q6p9quqqhda1s18QqupVVfXWhY69E78EPB04sKoOm8D4d5Hk5CQfmvTrSOoPw7Qk7eGSLJt2DQvwUODqqvq33X1iz9+3pI4wTEvqhST3SPJnSa5vbn+W5B5D6/9Lkm80616ZpJKsmmOs45JcleS2JP+a5NihdSck2dKsuyLJ45vlr0/yL0PLnztPrY9I8qkkNye5MsnRQ+sekGRDku800xJ+dhfv+6VJrklyU5KTZq37/3tJk9wzyYea7W5NckmSByY5Bfhl4F3NNI53NdtXktcm+RrwtaFlwz3br3kftyX5XJKHNtutbLZdNlTL+U3fDwXeA/xC83q3NuvvMm2k6fPWpkcbkjxkaF0leVWSrzXv5dQk2UlvjgfeN/Rabx5x7Lu87131fMdfS5IcAfwB8KLm9b6S5IVJLp31/Ncl+V9z/JdKWmIM05L64iTgScBjgccAhwFvAGhCzuuApwGrgMPnGiTJvYG/AI6sqvsAvwh8uVn3QuBk4GXAfYHnADc1T/0XBqF0OfBm4ENJHjzH+J8CPgL8NHAM8FdJVjebnAr8O/Bg4Neb21y1rgbeDbwUeAjwAODAOTZ/eVPbQc12rwK+V1UnAZ8HTmymcZw49Jz/CDwRWD17sMaxwFuB/Rj06MNz1bpDVW1pXvui5vXut5P39VTgbcDRDPpwDXDWrM2eDfw88Ohmu2fu5LX+x6zXetOIY8/5vufreVX9HfDHwEeb13sMsAF4WPNLxA4vBT6w0wZJWnIM05L64ljgLVV1Q1XdyCDQvrRZdzTw/qq6vKq+yyAQz+dHwCOT/FRVfaOqLm+WvxJ4e1VdUgNbq+oagKo6p6qur6ofVdVHGezV3Nkc3WczmHbw/qq6o6q+BHwMeGGSvYDnA39YVf9WVZcBZ8xT5wuAT1TVBVX1feCNTe0780MGwW9VVd1ZVZdW1Xd20Ye3VdXNVfW9OdZ/cui1T2KwB/igXYw5imOB06rqi83Yv9+MvXJom/9aVbdW1deBzzL4JWpcY8/3vnen5zTbfBR4CUCSnwNWAp8YsV5JPWeYltQXD2Gwl3GHa5plO9ZdO7Ru+P5dNHNrX8Rgj+Y3knwyySOa1Qcx2AP9E5K8LMmXm2kHtwKPZLDHdraHAk/csV2z7bHAg4D9gWWz6rtmJ2PscJf31dR+0xzbfhA4Dzgrg6kub09y93nGhnn6NHt9Vd0O3MyPe74Qd/m/bMa+CThgaJtvDt3/LrDPGMee733vTs93OAN4cTMV5aXA2U3IlrQHMExL6ovrGQTVHVY0ywC+wV2nP8y797SqzquqpzOYBvB/gfc2q65lJ3OYm7nC7wVOBB7QTF24DPiJebzNGJ+rqvsN3fapqlcDNwJ3zKpvxTylfmN42yT3YrD3eWfv6YdV9eaqWs1g6sqzGUxXAag5xp9r+Q7Dr70PcH8GPd9xsN+9hrZ90G6Me5f/y2ZqzAOA63bxvFGMMvZ89e2q5z/x3Kq6GPgBg2lAL2bwi42kPYRhWlIX3b05oG7HbRlwJvCGJPsn2Q/4Q2DHKcrOBl6R5NAm/Mx5PuPmoLyjmpD1feB2fvxn/PcBv5PkCRlY1QTpezMIUTc2Y7yCwZ7pnfkE8PDmILa7N7efT3JoVd0JfBw4Ocm9mvm5L5+nD+cCz07yS0n2Bt7CHN+3k/xqkkc1U0m+w2Dax4739S3gZ+Z5nbk8a+i13wpcXFXXNtNsrgNekmSvJL/OXX8J+RZwYPO8nTmTwf/XYzM4iPSPgX+oqqtb1DjusXfV828BK5PM/n/4APAu4IdV1eqc45L6yTAtqYs2Ad8bup0M/BGwGfgq8E/AF5tlVNXfMjio8LPAVuDiZpyd/an9bgwOVryewbSFpwCvbsY5BziFwcGDtwF/A9y/qq4A3glcxCBMPQq4cGeFV9VtwDMYHHh4PYPpCn8C7DjzyIkMpix8EzgdeP9cTWjmcr+2qecbwC3Atjk2fxCDIPgdYAvwOX68h/TPgRdkcGGTv5jr9XbiI8CbGPTpCTTzghsnAL/LYArEzwF/P7TuM8DlwDeTfHsn7+v/MPiF52PN+/pZBv1asIWOPULPz2n+vSnJF4eWf5DBL1ieg1raw6RqV3+Nk6R+ac6scBlwj6q6Y9r1qN+SXA28sgnqc23zU8ANwOOraqen3JO0NLlnWtKSkOS5GZyLel8Ge4I3GqS1iF4NXGKQlvY8Xv1J0lLxGwymTdzJYIrDa6ZajfYYzZ7rMDh/taQ9jNM8JEmSpJac5iFJkiS1ZJiWJEmSWur1nOn99tuvVq5cOe0yJEmStMRdeuml366q/Wcv73WYXrlyJZs3b552GZIkSVriklyzs+W9nOaRZE2S9du3b592KZIkSdqD9TJMV9XGqlq3fPnyaZciSZKkPVgvw7QkSZLUBYZpSZIkqSXDtCRJktSSYVqSJElqyTAtSZIktWSYliRJkloyTEuSJEkt9TJMe9EWSZIkdUEvLydeVRuBjTMzMydMuxZJautdv71x2iUsqhPfuWbaJUjS2PVyz7QkSZLUBYZpSZIkqSXDtCRJktSSYVqSJElqyTAtSZIktWSYliRJkloyTEuSJEktGaYlSZKklgzTkiRJUkuduQJikl8GjmVQ0+qq+sUplyRJkiTNa6J7ppOcluSGJJfNWn5EkiuTbE3yeoCq+nxVvQr4BHDGJOuSJEmSxmHS0zxOB44YXpBkL+BU4EhgNbA2yeqhTV4MfGTCdUmSJEkLNtEwXVUXADfPWnwYsLWqrqqqHwBnAUcBJFkBbK+q2yZZlyRJkjQO0zgA8QDg2qHH25plAMcD75/vyUnWJdmcZPONN944oRIlSZKkXevU2Tyq6k1V9fe72GZ9Vc1U1cz++++/WKVJkiRJP2EaYfo64KChxwc2y0aWZE2S9du3bx9rYZIkSdLumEaYvgQ4OMnDkuwNHANs2J0BqmpjVa1bvnz5RAqUJEmSRjHpU+OdCVwEHJJkW5Ljq+oO4ETgPGALcHZVXT7JOiRJkqRJmOhFW6pq7RzLNwGb2o6bZA2wZtWqVW2HkCRJkhasUwcgjsppHpIkSeqCXoZpSZIkqQt6GaY9m4ckSZK6oJdh2mkekiRJ6oJehmlJkiSpC3oZpp3mIUmSpC7oZZh2mockSZK6oJdhWpIkSeoCw7QkSZLUUi/DtHOmJUmS1AW9DNPOmZYkSVIX9DJMS5IkSV1gmJYkSZJaMkxLkiRJLfUyTHsAoiRJkrqgl2HaAxAlSZLUBb0M05IkSVIXGKYlSZKklgzTkiRJUkuGaUmSJKmlXoZpz+YhSZKkLuhlmPZsHpIkSeqCXoZpSZIkqQsM05IkSVJLhmlJkiSpJcO0JEmS1JJhWpIkSWrJMC1JkiS1ZJiWJEmSWuplmPaiLZIkSeqCXoZpL9oiSZKkLlg27QIkLT2f+5WnTLuERfOUCz437RIkSVPUyz3TkiRJUhcYpiVJkqSWDNOSJElSS4ZpSZIkqSXDtCRJktSSYVqSJElqyTAtSZIktWSYliRJklrqzEVbktwNeCtwX2BzVZ0x5ZIkSZKkeU10z3SS05LckOSyWcuPSHJlkq1JXt8sPgo4EPghsG2SdUmSJEnjMOlpHqcDRwwvSLIXcCpwJLAaWJtkNXAI8PdV9Trg1ROuS5IkSVqwiYbpqroAuHnW4sOArVV1VVX9ADiLwV7pbcAtzTZ3TrIuSZIkaRymcQDiAcC1Q4+3Ncs+DjwzyV8CF8z15CTrkmxOsvnGG2+cbKWSJEnSPDpzAGJVfRc4foTt1gPrAWZmZmrSdUmSJElzmcae6euAg4YeH9gskyRJknplGmH6EuDgJA9LsjdwDLBhdwZIsibJ+u3bt0+kQEmSJGkUkz413pnARcAhSbYlOb6q7gBOBM4DtgBnV9XluzNuVW2sqnXLly8ff9GSJEnSiCY6Z7qq1s6xfBOwqe24SdYAa1atWtV2CEmSJGnBenk5cfdMS5IkqQt6GaYlSZKkLuhlmPYAREmSJHVBL8O00zwkSZLUBb0M05IkSVIX9DJMO81DkiRJXdDLMO00D0mSJHVBL8O0JEmS1AWGaUmSJKmlXoZp50xLkiSpC3oZpp0zLUmSpC7oZZiWJEmSusAwLUmSJLVkmJYkSZJa6mWY9gBESZIkdUEvw7QHIEqSJKkLehmmJUmSpC4wTEuSJEktGaYlSZKklgzTkiRJUku9DNOezUOSJEld0Msw7dk8JEmS1AW9DNOSJElSFximJUmSpJYM05IkSVJLhmlJkiSpJcO0JEmS1JJhWpIkSWrJMC1JkiS11Msw7UVbJEmS1AW9DNNetEWSJEld0MswLUmSJHWBYVqSJElqyTAtSZIktWSYliRJkloyTEuSJEktGaYlSZKklgzTkiRJUkuGaUmSJKklw7QkSZLUUmfCdJLDk3w+yXuSHD7teiRJkqRdmWiYTnJakhuSXDZr+RFJrkyyNcnrm8UF3A7cE9g2ybokSZKkcZj0nunTgSOGFyTZCzgVOBJYDaxNshr4fFUdCfwe8OYJ1yVJkiQt2ETDdFVdANw8a/FhwNaquqqqfgCcBRxVVT9q1t8C3GOSdUmSJEnjsGwKr3kAcO3Q423AE5M8D3gmcD/gXXM9Ock6YB3AihUrJlimJEmSNL9phOmdqqqPAx8fYbv1wHqAmZmZmnRdkiRJ0lymcTaP64CDhh4f2CyTJEmSemUaYfoS4OAkD0uyN3AMsGF3BkiyJsn67du3T6RASZIkaRSTPjXemcBFwCFJtiU5vqruAE4EzgO2AGdX1eW7M25VbayqdcuXLx9/0ZIkSdKIJjpnuqrWzrF8E7Cp7bhJ1gBrVq1a1XYISZIkacE6cwXE3eGeaUmSJHVBL8O0JEmS1AW9DNMegChJkqQuGClMJzkjyf2GHu+b5LTJlTU/p3lIkiSpC0bdM/3oqrp1x4OqugV43GRKkiRJkvph1DB9tyT77niQ5P506OqJkiRJ0jSMGojfCVyU5Jzm8QuBUyZT0q55ajxJkiR1wUh7pqvqA8DzgG81t+dV1QcnWdgu6nHOtCRJkqZu5KkaVXUFcMUEa5EkSZJ6pZenxpMkSZK6oJdh2vNMS5IkqQt6GaadMy1JkqQu6GWYliRJkrrAMC1JkiS11Msw7ZxpSZIkdUEvw7RzpiVJktQFvQzTkiRJUhcYpiVJkqSWDNOSJElSS4ZpSZIkqSXDtCRJktRSL8O0p8aTJElSF/QyTHtqPEmSJHVBL8O0JEmS1AWGaUmSJKklw7QkSZLU0rJpFyBJ0nxOeckLpl3CojrpQ+dOuwRJu8E905IkSVJLhmlJkiSpJcO0JEmS1FIvw7QXbZEkSVIX9DJMe9EWSZIkdUEvw7QkSZLUBYZpSZIkqSXDtCRJktSSYVqSJElqyTAtSZIktWSYliRJkloyTEuSJEktGaYlSZKklgzTkiRJUkudCtNJ7p1kc5JnT7sWSZIkaVcmGqaTnJbkhiSXzVp+RJIrk2xN8vqhVb8HnD3JmiRJkqRxmfSe6dOBI4YXJNkLOBU4ElgNrE2yOsnTgSuAGyZckyRJkjQWyyY5eFVdkGTlrMWHAVur6iqAJGcBRwH7APdmELC/l2RTVf1okvVJkrSUbDnlM9MuYdEcetJTp12CBEw4TM/hAODaocfbgCdW1YkASY4Dvj1XkE6yDlgHsGLFislWKkmSJM2jUwcgAlTV6VX1iXnWr6+qmaqa2X///RezNEmSJOkuphGmrwMOGnp8YLNsZEnWJFm/ffv2sRYmSZIk7Y5pTPO4BDg4ycMYhOhjgBfvzgBVtRHYODMzc8IE6pMkSRJw9jmHTbuERXP0C/+x1fMmfWq8M4GLgEOSbEtyfFXdAZwInAdsAc6uqssnWYckSZI0CZM+m8faOZZvAja1HTfJGmDNqlWr2g4hSZIkLVjnDkAcRVVtrKp1y5cvn3YpkiRJ2oP1MkxLkiRJXTCNAxAXzGkekiSprZNPPnnaJSyqPe39LrZe7pl2mockSZK6oJdhWpIkSeqCXoZpL9oiSZKkLuhlmHaahyRJkrqgl2FakiRJ6gLDtCRJktRSL8O0c6YlSZLUBb0M086ZliRJUhf0MkxLkiRJXWCYliRJkloyTEuSJEkt9TJMewCiJEmSuqCXYdoDECVJktQFvQzTkiRJUhcYpiVJkqSWDNOSJElSS4ZpSZIkqaVehmnP5iFJkqQu6GWY9mwekiRJ6oJehmlJkiSpCwzTkiRJUkuGaUmSJKklw7QkSZLUkmFakiRJaskwLUmSJLVkmJYkSZJa6mWY9qItkiRJ6oJehmkv2iJJkqQu6GWYliRJkrrAMC1JkiS1ZJiWJEmSWjJMS5IkSS0ZpiVJkqSWDNOSJElSS4ZpSZIkqSXDtCRJktSSYVqSJElqqTNhOsmhSd6T5Nwkr552PZIkSdKuTDRMJzktyQ1JLpu1/IgkVybZmuT1AFW1papeBRwNPHmSdUmSJEnjMOk906cDRwwvSLIXcCpwJLAaWJtkdbPuOcAngU0TrkuSJElasImG6aq6ALh51uLDgK1VdVVV/QA4Cziq2X5DVR0JHDvJuiRJkqRxWDaF1zwAuHbo8TbgiUkOB54H3IN59kwnWQesA1ixYsXkqpQkSZJ2YRpheqeq6nzg/BG2Ww+sB5iZmanJViVJkiTNbRpn87gOOGjo8YHNspElWZNk/fbt28damCRJkrQ7phGmLwEOTvKwJHsDxwAbdmeAqtpYVeuWL18+kQIlSZKkUUz61HhnAhcBhyTZluT4qroDOBE4D9gCnF1Vl0+yDkmSJGkSJjpnuqrWzrF8Ews4/V2SNcCaVatWtR1CkiRJWrDOHIC4O6pqI7BxZmbmhLm2ecLvfmARK5q+S9/xsmmXIEmStMfpZZiWpuHJf7lnXZjzwt+8cNolSJLUedM4AHHBPJuHJEmSuqCXYdqzeUiSJKkLehmmJUmSpC7oZZh2mockSZK6oJdh2mkekiRJ6oJehmlJkiSpCwzTkiRJUku9DNPOmZYkSVIX9DJMO2dakiRJXdDLMC1JkiR1gWFakiRJaskwLUmSJLXUyzDtAYiSJEnqgl6GaQ9AlCRJUhf0MkxLkiRJXWCYliRJkloyTEuSJEktGaYlSZKklnoZpj2bhyRJkrqgl2Has3lIkiSpC3oZpiVJkqQuMExLkiRJLRmmJUmSpJYM05IkSVJLhmlJkiSppWXTLkDT9/W3PGraJSyaFX/4T9MuQZIkLSHumZYkSZJa6mWY9qItkiRJ6oJehmkv2iJJkqQu6GWYliRJkrrAMC1JkiS1ZJiWJEmSWjJMS5IkSS0ZpiVJkqSWDNOSJElSS4ZpSZIkqSXDtCRJktRSqmraNbSW5EbgmmnXMct+wLenXURP2KvR2KfR2KfR2avR2KfR2avR2KfRdLVPD62q/Wcv7HWY7qIkm6tqZtp19IG9Go19Go19Gp29Go19Gp29Go19Gk3f+uQ0D0mSJKklw7QkSZLUkmF6/NZPu4AesVejsU+jsU+js1ejsU+js1ejsU+j6VWfnDMtSZIkteSeaUmSJKklw7QkSZLUkmF6F5KsTHLZTpa/L8nqFuO9LskVSb6a5NNJHjqeSqdr3H0aev7zk1SS3pwiZz4T+Dwdl+TGJF9ubq8cT6XTN4nPVJKjm6+/y5N8ZOFVTt8EPlMrknw2yZea71PPGk+l0zeBXj20+T7+1STnJzlwPJUuvgn05leSfDHJHUleMGvdy5N8rbm9fCF1L7ZF7tPfJbk1yScWUvO0LFavkjw2yUXN9/WvJnnRQmvfXcsW+wWXiqpqG1q+BMxU1XeTvBp4O7Do//GLZQF9Isl9gP8E/MP4KuqmhfQJ+GhVnTi2Yjquba+SHAz8PvDkqrolyU+Pt7JuWcBn6g3A2VX17uYH3iZg5dgK66AF9OpPgQ9U1RlJngq8DXjp+CqbvgX05uvAccDvDC9Mcn/gTcAMUMClSTZU1S0LqXPaxt2nxjuAewG/0XLsTppAr74LvKyqvpbkIQw+U+dV1a0LKHO3uGd6NMuSfDjJliTnJrlXsxdiBiDJ7UlOSfKVJBcneeBcA1XVZ6vqu83Di4He7snYibH1qfFW4E+Af5945Ytr3H1aysbZqxOAU3f80K6qGxbjDSyScfapgPs295cD10+6+EU2zl6tBj7T3P8scNSki5+wcf6su7qqvgr8aNaqZwKfqqqbm6/FTwFHTOwdTcZi9Imq+jRw2+TexqKYeK+q6p+r6mvN/euBG4CfuErhJBmmR3MI8FdVdSjwHeA1s9bfG7i4qh4DXMDgh/Yojgf+dmxVTt/Y+pTk8cBBVfXJSRU7ReP+PD2/+dPWuUkOGn+5UzXOXj0ceHiSC5tv2n37AT6fcfbpZOAlSbYx2Cv9m+Mvd6rG2auvAM9r7j8XuE+SB4y53sU0qZ91ww4Arh16vK1Z1ieL0aelYlF7leQwYG/gXxYyzu4yTI/m2qq6sLn/IeCXZq3/AbBjTtOljPAn0SQvYfBnrneMqcYuGEufktwN+G/Ab0+gxi4Y5+dpI7Cyqh7NYA/PGWOsswvG2atlwMHA4cBa4L1J7je2SqdrnH1aC5xeVQcCzwI+2HxNLhXj7NXvAE9J8iXgKcB1wJ3jK3XRjf1n3RJln0a3aL1K8mDgg8Arquon9vRP0lL6BjlJs0/GPfvxD+vHJ+y+k13MRU/yNOAk4DlV9f3xlNgJ4+rTfYBHAucnuRp4ErAhS+QgRMb4eaqqm4Y+Q+8DnjCeEjtjnF9724ANVfXDqvpX4J8ZhOulYJx9Oh44G6CqLgLuCew3jiI7Ypxff9dX1fOq6nEMvqezmPM0J2CsP+vmcB0w/Be0A5tlfbIYfVoqFqVXSe4LfBI4qaoubjPGQhimR7MiyS80918MfKHtQEkeB/w1gyC9lOZswpj6VFXbq2q/qlpZVSsZzC1/TlVtHlOd0zbOz9ODhx4+B9iykMI6aGy9Av6GwV5pkuzHYNrHVQuqrjvG2aevA78GkORQBmH6xoWV1ynj/Prbb2iv/e8Dpy20uCkb5+doLucBz0iyb5J9gWc0y/pkMfq0VEy8V0n2Bv4ng4OBzx33+KMwTI/mSuC1SbYA+wLvXsBY7wD2Ac7J4FRmG8ZRYEeMs09L2Tj79FsZnA7oK8BvMTjSeSkZZ6/OA25KcgWDg8V+t6puGkONXTDOPv02cELzmToTOG5oz9FSMM5eHQ5cmeSfgQcCpyy8vKkaW2+S/Hwz7/6FwF8nuRygqm5mcHD5Jc3tLc2yPpl4n5p1nwfOAX4tybYkz1xg3dOwGL06GvgV4Lj8+DSxj11o4btV29L6HilJkiQtHvdMS5IkSS3tyZPiJyrJSQz+FDHsnKrq+58Bx8o+jcY+jc5ejcY+jc5ezc3ejMY+ja6PvXKahyRJktSS0zwkSZKklgzTkiRJUkuGaUnqmSQrk1w2hnFu341tD0/yiwt9TUlaagzTkqRRHA4YpiVpFsO0JPXTsiQfTrIlyblJ7pXk6ubqjiSZSXJ+c3+fJO9P8k9Jvprk+cMDNVfyuyjJf0iyf5KPJbmkuT05yUrgVcB/bi6I8MuL/F4lqbM8NZ4k9dMhwPFVdWGS04DXzLPtG4HtVfUogOYyzjT3HwhsAN5QVZ9K8hHgv1fVF5KsAM6rqkOTvAe4var+dGLvSJJ6yDAtSf10bVVd2Nz/EIPLyc/lacAxOx5U1S3N3bsDnwZeW1WfG9p2dZIdm983yT5jq1qSlhjDtCT10+yLBBRwBz+evnfPEca4A7gUeCawI0zfDXhSVf378IZD4VqSNMQ505LUTyuS/EJz/8XAF4CrgSc0y4bnRX8KeO2OB0PTPAr4deARSX6vWfa/gd8c2vaxzd3bgPuMsX5JWhIM05LUT1cCr02yBdgXeDfwZuDPk2wG7hza9o+AfZNcluQrwK/uWFFVdwJrgacmeQ2D6SIzzYGKVzA48BBgI/BcD0CUpLvycuKSJElSS+6ZliRJkloyTEuSJEktGaYlSZKklgzTkiRJUkuGaUmSJKklw7QkSZLUkmFakiRJaskwLUmSJLX0/wBz6u/yybN13wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5v8wFktnHlA_",
        "colab_type": "text"
      },
      "source": [
        "#### Unit price"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OFYqRpzLHnuE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%bigquery df_histo_unit_price --project $PROJECT_ID\n",
        "\n",
        "WITH\n",
        "  min_max AS (\n",
        "  SELECT\n",
        "    MIN(unit_price) min_unit_price,\n",
        "    MAX(unit_price) max_unit_price,\n",
        "    CEIL((MAX(unit_price) - MIN(unit_price)) / 10) step\n",
        "  FROM\n",
        "    `ltv_ecommerce.10_orders` \n",
        ")\n",
        "\n",
        "SELECT\n",
        "  COUNT(1) c,\n",
        "  bucket_same_size AS bucket\n",
        "FROM (\n",
        "  SELECT\n",
        "    -- Creates (1000-100)/100 + 1 buckets of data.\n",
        "    ML.BUCKETIZE(unit_price, GENERATE_ARRAY(min_unit_price, max_unit_price, step)) AS bucket_same_size,\n",
        "    -- Creates custom ranges.\n",
        "    ML.BUCKETIZE(unit_price, [10, 20, 30, 40, 50, 100, 200, 300, 400, 500, 1000]) AS bucket_specific,\n",
        "  FROM\n",
        "    `ltv_ecommerce.10_orders`, min_max )\n",
        "  # WHERE bucket != \"bin_1\" and bucket != \"bin_2\"\n",
        "GROUP BY\n",
        "  bucket\n",
        "  -- Ohterwise, orders bin_10 before bin_2\n",
        "ORDER BY CAST(SPLIT(bucket, \"_\")[OFFSET(1)] AS INT64)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "02bF2qA7HuiU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "outputId": "053433c5-b4f9-4ee9-b4f4-d1793d729900"
      },
      "source": [
        "# Uses a log scale for bucket_same_size.\n",
        "# Can remove the log scale when using bucket_specific.\n",
        "plt.figure(figsize=(12,5))\n",
        "q = sns.barplot( x='bucket', y='c', data=df_histo_unit_price)\n",
        "q.set_yscale(\"log\")\n",
        "plt.title('Log scaled distribution for unit_price')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Log scaled distribution for unit_price')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K6Ih3v9D6ZGE",
        "colab_type": "text"
      },
      "source": [
        "## Set parameters for LTV\n",
        "Some parameters useful to run some of the queries in this tutorial:\n",
        "\n",
        "1. **WINDOW_STEP**: How many days between threshold dates.\n",
        "1. **WINDOW_STEP_INITIAL**: How many days between the first order and the first threshold date. A threshold date is when BigQuery computes inputs and targets.\n",
        "1. **WINDOW_LENGTH**: How many days back to use for input transactions. The default value is 0 which means that this tutorial takes all transactions before the threshold date.\n",
        "1. **LENGTH_FUTURE**: How far in the future to predict the monetary value. At every threshold date, BigQuery calculate the target value for all orders that happen LENGTH_FUTURE after the threshold date.\n",
        "1. **MAX_STDV_MONETARY**: Standard deviation of the monetary value per customer. Removes orders per customer that have order values with a greater standard deviation.\n",
        "1. **MAX_STDV_QTY**: Standard deviation of the quantity of products per customer. Removes orders per customer that have product quantity with a greater standard deviation.\n",
        "1. **TOP_LTV_RATIO**: Percentage of top customers that you want to keep for your lookalike activation.\n",
        "\n",
        "You can change those parameters to see how they impact the model especially the parameters related to the window. There is no obvious rule to set values as they depend on how data looks.\n",
        "\n",
        "For example:\n",
        "- If your customers buy multiple times a week, you could try to predict monetary value on a weekly basis.\n",
        "- If you have a lot of data, you can create more windows by decreasing their sizes and possibly the number of days between threshold dates.\n",
        "\n",
        "After multiple trials, this tutorial chose values that provides a decent result for the example dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wZGi-Sm5suzM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00034056-2fca-4c73-a40d-1a050994810b"
      },
      "source": [
        "LTV_PARAMS = {\n",
        "    'WINDOW_LENGTH': 0,\n",
        "    'WINDOW_STEP': 30,\n",
        "    'WINDOW_STEP_INITIAL': 90,\n",
        "    'LENGTH_FUTURE': 30,\n",
        "    'MAX_STDV_MONETARY': 500,\n",
        "    'MAX_STDV_QTY': 100, \n",
        "    'TOP_LTV_RATIO': 0.2\n",
        "}\n",
        "LTV_PARAMS"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'LENGTH_FUTURE': 30,\n",
              " 'MAX_STDV_MONETARY': 500,\n",
              " 'MAX_STDV_QTY': 100,\n",
              " 'TOP_LTV_RATIO': 0.2,\n",
              " 'WINDOW_LENGTH': 0,\n",
              " 'WINDOW_STEP': 30,\n",
              " 'WINDOW_STEP_INITIAL': 90}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mhOH4XM52otf",
        "colab_type": "text"
      },
      "source": [
        "## Aggregate per day per customer\n",
        "This query aggregates all orders per day per customer. This is useful if:\n",
        "- Your database has multiple records for one order, for example if each record represent a product in a transaction, which is the case in this tutorial.\n",
        "- Customers bought multiple times during one day. This guide is based on LTV per day so no need to keep hourly records (unless you decided to use that data as a feature).\n",
        "\n",
        "This query also\n",
        "1. Creates inputs related to returns.\n",
        "1. Removes orders with outliers per customer. This means that high spending customers remain in the dataset but orders that seems unusual for a unique customers are filtered out."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0sb0KFeik5M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "c085dc6d-5dba-4641-cea9-d5980ccbc497"
      },
      "source": [
        "%%bigquery --params $LTV_PARAMS --project $PROJECT_ID\n",
        "\n",
        "DECLARE MAX_STDV_MONETARY INT64 DEFAULT @MAX_STDV_MONETARY;\n",
        "DECLARE MAX_STDV_QTY INT64 DEFAULT @MAX_STDV_QTY;\n",
        "\n",
        "CREATE OR REPLACE TABLE `ltv_ecommerce.20_aggred` AS\n",
        "SELECT\n",
        "  customer_id,\n",
        "  order_day,\n",
        "  ROUND(day_value_after_returns, 2) AS value,\n",
        "  day_qty_after_returns as qty_articles,\n",
        "  day_num_returns AS num_returns,\n",
        "  CEIL(avg_time_to_return) AS time_to_return\n",
        "FROM (\n",
        "  SELECT\n",
        "    customer_id,\n",
        "    order_day,\n",
        "    SUM(order_value_after_returns) AS day_value_after_returns,\n",
        "    STDDEV(SUM(order_value_after_returns)) OVER(PARTITION BY customer_id ORDER BY SUM(order_value_after_returns)) AS stdv_value,\n",
        "    SUM(order_qty_after_returns) AS day_qty_after_returns,\n",
        "    STDDEV(SUM(order_qty_after_returns)) OVER(PARTITION BY customer_id ORDER BY SUM(order_qty_after_returns)) AS stdv_qty,\n",
        "    CASE\n",
        "      WHEN MIN(order_min_qty) < 0 THEN count(1)\n",
        "      ELSE 0\n",
        "    END AS day_num_returns,\n",
        "    CASE\n",
        "      WHEN MIN(order_min_qty) < 0 THEN AVG(time_to_return)\n",
        "      ELSE NULL\n",
        "    END AS avg_time_to_return\n",
        "  FROM (\n",
        "    SELECT \n",
        "      customer_id,\n",
        "      order_id,\n",
        "      -- Gives the order date vs return(s) dates.\n",
        "      MIN(transaction_date) AS order_day,\n",
        "      MAX(transaction_date) AS return_final_day,\n",
        "      DATE_DIFF(MAX(transaction_date), MIN(transaction_date), DAY) AS time_to_return,\n",
        "      -- Aggregates all products in the order \n",
        "      -- and all products returned later.\n",
        "      SUM(qty * unit_price) AS order_value_after_returns,\n",
        "      SUM(qty) AS order_qty_after_returns,\n",
        "      -- If negative, order has qty return(s).\n",
        "      MIN(qty) order_min_qty\n",
        "    FROM \n",
        "      `ltv_ecommerce.10_orders`\n",
        "    GROUP BY\n",
        "      customer_id,\n",
        "      order_id)\n",
        "  GROUP BY\n",
        "    customer_id,\n",
        "    order_day)\n",
        "WHERE\n",
        "  -- [Optional] Remove dates with outliers per a customer.\n",
        "  (stdv_value < MAX_STDV_MONETARY\n",
        "    OR stdv_value IS NULL) AND\n",
        "  (stdv_qty < MAX_STDV_QTY\n",
        "    OR stdv_qty IS NULL);\n",
        "\n",
        "\n",
        "SELECT * FROM `ltv_ecommerce.20_aggred` LIMIT 5;"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_id</th>\n",
              "      <th>order_day</th>\n",
              "      <th>value</th>\n",
              "      <th>qty_articles</th>\n",
              "      <th>num_returns</th>\n",
              "      <th>time_to_return</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8</td>\n",
              "      <td>2017-03-15</td>\n",
              "      <td>141.88</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11</td>\n",
              "      <td>2017-03-22</td>\n",
              "      <td>914.77</td>\n",
              "      <td>25</td>\n",
              "      <td>24</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>22</td>\n",
              "      <td>2017-01-10</td>\n",
              "      <td>46.98</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26</td>\n",
              "      <td>2017-02-16</td>\n",
              "      <td>374.92</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>34</td>\n",
              "      <td>2017-05-13</td>\n",
              "      <td>651.70</td>\n",
              "      <td>32</td>\n",
              "      <td>34</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   customer_id   order_day   value  qty_articles  num_returns  time_to_return\n",
              "0            8  2017-03-15  141.88            12            0             NaN\n",
              "1           11  2017-03-22  914.77            25           24             2.0\n",
              "2           22  2017-01-10   46.98             2            0             NaN\n",
              "3           26  2017-02-16  374.92             8           12             3.0\n",
              "4           34  2017-05-13  651.70            32           34             3.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bq0WFrHqGgjE",
        "colab_type": "text"
      },
      "source": [
        "## Check distributions\n",
        "This tutorial does minimum data cleansing and focuses mostly on transforming a list of transactions into workable inputs for the model.\n",
        "\n",
        "This section checks that data is generally usable."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_cYheRxJIYjs",
        "colab_type": "text"
      },
      "source": [
        "### Per date"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ulyzgY2RGg0N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%bigquery df_dist_dates --project $PROJECT_ID\n",
        "\n",
        "SELECT count(1) c, SUBSTR(CAST(order_day AS STRING), 0, 7) as yyyy_mm\n",
        "FROM `ltv_ecommerce.20_aggred`\n",
        "WHERE qty_articles > 0\n",
        "GROUP BY yyyy_mm \n",
        "ORDER BY yyyy_mm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sgSVTR0pIxpV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "8d014db8-e565-4d15-ce16-3b7b626260ba"
      },
      "source": [
        "plt.figure(figsize=(12,5))\n",
        "sns.barplot( x='yyyy_mm', y='c', data=df_dist_dates)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbf54bbf160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_85oPxL4zqQF",
        "colab_type": "text"
      },
      "source": [
        "Orders are quite well distributed across the year despite a lower number in the early days of the dataset. You can keep this in mind when choosing a value for `WINDOW_STEP_INITIAL`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "opxsPUhZJQtY",
        "colab_type": "text"
      },
      "source": [
        "### Per customer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "giPBoy4dJQ4O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%bigquery df_dist_customers --params $LTV_PARAMS --project $PROJECT_ID\n",
        "\n",
        "SELECT customer_id, count(1) c\n",
        "FROM `ltv_ecommerce.20_aggred`\n",
        "GROUP BY customer_id"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6jnDQRtiJQ7r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "3ed301a2-ff33-42ea-e3ce-c228a3626d72"
      },
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "sns.distplot(df_dist_customers['c'], hist_kws=dict(ec=\"k\"), kde=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0b37202518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hzvPvPvl0WHD",
        "colab_type": "text"
      },
      "source": [
        "The number of transactions per customer is distributed across a few discrete values with no clear outliers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zEkWNb1Z11GE",
        "colab_type": "text"
      },
      "source": [
        "### Per quantity\n",
        "This section looks at the general distribution of the number of articles per orders and check if there are some outliers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Ak9z1us2Da5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%bigquery df_dist_qty --params $LTV_PARAMS --project $PROJECT_ID\n",
        "\n",
        "SELECT qty_articles, count(1) c\n",
        "FROM `ltv_ecommerce.20_aggred`\n",
        "GROUP BY qty_articles"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MlF-3blV2M_N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "e7a62f01-2b5e-4260-fc31-dacad3cabaed"
      },
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "sns.distplot(df_dist_qty['qty_articles'], hist_kws=dict(ec=\"k\"), kde=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0b3703cc50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xo9ya0+nUxIkT1bdvX6WlpWnBggV67bXXtG3bNq83CwAAANSUKgXj3/3udxo6dKjatWtXbm379u3y9/fXlClT1LhxY0VFRSk+Pl4pKSlebxYAAACoKVf8OsZZWVkKCwuTv//PGTs8PFx2u73ajxUUZLnSdi6L1dLEtFoNAxqYVs/MWnWxnjd7qWv3Vp/rNWvWSMHB1su69nKvQ93GXH0PM/VNV8NcrzgYO51OWa2enyibzSan01ntxzpzximXy7jSlqrN4Sw0rVZJaZlp9cysVdfqWS1NvNpLXbq3+l4vP79YubmOal8XHGy9rOtQtzFX38NMfZOvzNXf3++ST8Re8atSWCyWciE4Ly9PFkvtPPsLAAAAXI4rDsahoaHKyMiQy+VyH0tPT1doaOiVPjQAAABgmioF47KyMhUVFamkpESSVFRUpKKiIrlcLsXGxqqsrExJSUkqLi7W4cOHZbfbNXr06BptHAAAAPCmKgXjjRs3KiIiQg888IAkKSIiQhEREfrkk09ksVi0dOlS7d69Wz179tQjjzyiqVOnatCgQTXaOAAAAOBNVfrhu7i4OMXFxV10PSwsTGvWrPFaUwAAAIDZeEtoAAAAQARjAAAAQBLBGAAAAJBEMAYAAAAkEYwBAAAASQRjAAAAQBLBGAAAAJBEMAYAAAAkEYwBAAAASQRjAAAAQBLBGAAAAJBEMAYAAAAkEYwBAAAASQRjAAAAQBLBGAAAAJAkBdR2AwCuLpmZGVqyfEW1r2vWrJHy84urfV1gS5vi44ZX+zoAwNWHYAzAVIXFpeoSM6Da11ktTeRwFlb7uuwDO6p9DQDg6sRWCgAAAEAEYwAAAEASwRgAAACQRDAGAAAAJBGMAQAAAEkEYwAAAEASwRgAAACQRDAGAAAAJBGMAQAAAEkEYwAAAEASwRgAAACQJAXUdgMAUJMyMzO0ZPkK0+oFtrQpPm64KbXs/7deZ8/nmVJLMvfeUH8lr1qj4ydzTavH1yW8iWAMwKcVFpeqS8wA0+plH9hhWq2z5/N89t5Qf31/9gJfl6i32EoBAAAAiGAMAAAASGIrBQB4lZl7mjOzsk39J2tf5uv7tc28v6//95Xahd5mSi1fZ/bX5bFjR9W+fccK15o1a6T8/GKv1quL+8O9FoxdLpcWLFigdevWqaCgQDExMZo9e7batGnjrRIAUOeZuaf54KHDptS5Gvj6fm0z7+9Ieropda4GZn9dHjz0D8UOr7ie1dJEDmehV+vVxf3hXttKsXTpUm3evFkrV67Uhx9+qNatW+uhhx6Sy+XyVgkAAACgxnjtGePVq1dr4sSJuuGGGyRJf/nLX9SnTx99+umn6tWrV5Uew9/fz1vtVJnNalGzJg1Nqxd8TZBp9cysVdfqNW0coLJS7/VSl+6tvte73FqXO1M+l97TwmatkT+na+PP/l9rYbP6xOfyUvXM+7oM9OnPpZnM/ro08/uqVDuzq6yen2EYxpUWcTgc6tmzp+x2uyIiItzHhwwZolGjRmncuHFXWgIAAACoUV7ZSuF0OiVJNpvN47jVanWvAQAAAHWZV4KxxWKR9OMzx7/kcDjcawAAAEBd5pVgbLVa1aZNGx05csR9zOFw6Pjx4+ratas3SgAAAAA1ymuvSpGQkKBly5bp6NGjys/P19y5c9WhQwf16NHDWyUAAACAGuO1V6WYOHGiHA6HxowZo4KCAvXo0UNJSUny9+fN9QAAAFD3eeVVKQAAAID6jqdzAQAAABGMAQAAAEkEYwAAAEASwRgAAACQdBUHY5fLpX/84x/q06ePoqOj9cADDygnJ6e228JFzJ07V0OGDFFMTIz69u2rGTNm6Ny5cx7nZGRkKCEhQZGRkerXr5+Sk5M91gsLC/Xcc8/ppptuUkxMjB577DGdP3/ezNvAJUydOlVdunTRvn373Mf27NmjYcOGKTIyUgMHDtTWrVs9rjl37pwee+wxxcTE6KabbtJzzz2n4uJis1tHBdLS0jRmzBhFR0frpptu0uTJk91rzLX++f777zV9+nTdcsst6tmzpxISEvTJJ5+415lp3bdlyxaNGTNGMTEx6tKlS7l1b3wP3bx5s2JjYxUREaFhw4bp448/rtF7qhHGVWrJkiVG//79ja+//tpwOp3G3/72N2Po0KFGWVlZbbeGCsyfP99IT083iouLje+//94YP3688ec//9m97nA4jFtuucVYtGiRUVhYaBw8eNDo1auX8e6777rPmTlzpjF8+HDj1KlTxvnz541JkyYZDz74YG3cDn5l/fr1xoQJE4yQkBBj7969hmEYxokTJ4yIiAhj7dq1RlFRkbFz504jIiLC+Oyzz9zXTZgwwZg0aZJx/vx549SpU8bw4cONxMTE2roN/H9paWlGTEyMsXHjRqOgoMAoKioyDh06ZBgGc62vHn74YeMPf/iDcebMGaO0tNRYtmyZERUVZVy4cIGZ1hO7d+82Nm3aZNjtdiMkJMRjzRvfQz/99FOje/fuxs6dO42ioiJj7dq1RmRkpJGTk2PaPXrDVRuM+/fvb6xatcr98YULF4xu3boZaWlptdgVqmrnzp1GdHS0++PU1FTj1ltv9fiLzSuvvGKMHTvWMAzDKCgoMLp3727s2rXLvf7VV18ZISEh9e43ra/57rvvjNtvv93IycnxCMYLFy40RowY4XHuo48+ajz99NOGYfwYsEJCQoyvvvrKvb5r1y4jMjLSKCwsNO8GUM6oUaOMv//97xWuMdf66Z577jFWrlzp/tjpdBohISHG559/zkzrmb1795YLxt74HvrUU08Zjz76qMfjjhgxwli0aFFN3UqNuCq3UjgcDuXk5Cg8PNx9zGazqX379srMzKzFzlBVH3/8sUJDQ90fZ2VlKSwszOMNZcLDw5WVlSVJ+uabb1RUVKTu3bu71zt16qSmTZsy81pkGIZmzJihyZMnq3Xr1h5rWVlZHr9HJc+ZZmVlqWnTpurUqZN7vXv37iooKNDRo0drvnlUKD8/X4cOHZIkxcXF6eabb9aoUaPc/6TKXOunSZMmafv27crNzVVJSYlWrVqlDh06KCQkhJn6AG98D63s66C+8No739UnTqdT0o9h+JesVqt7DXXX1q1bZbfbtXLlSvcxp9Mpq9XqcZ7NZnPP86f//vocZl67UlJSZBiGRo0aVW7N6XTqxhtv9Dj265lWNM+f1lA78vLy5HK5tGnTJv3rX/9S586dtX79ej300EPavHkzc62noqOjtWHDBvXt21cNGjRQy5Yt9frrr6tRo0bM1Ad443uo0+ksl6tsNlu9+8vPVfmMscVikfTjM8e/5HA43Guom7Zs2aJZs2YpKSlJ3bp1cx+3WCzl/oDNy8tzz5OZ1z3Hjx9XUlKSXnjhhQrXLRZLuXn9eqa/nvlP5zPT2tO8eXNJ0n333aewsDA1bNhQI0eOVNu2bfXBBx8w13rI5XLpT3/6k6699lqlpaXp8OHDmj17tiZNmqTs7Gxm6gO88T20sq+D+uKqDMZWq1Vt2rTRkSNH3MccDoeOHz+url271mJnuBS73a7ExEQtXrxYvXv39lgLDQ1VRkaGXC6X+1h6erp7u0WHDh3UuHFjj5l//fXXKigo8NiSAfPs379f58+fd/9z+8033yxJmjJlimbNmqXQ0FCPeUmeMw0NDVV+fr6+/vpr9/qRI0fUpEkTdezY0bwbgQer1ap27dqVO+7n5ydJzLUeunDhgk6cOKFx48apRYsWCggI0IABA9SuXTt99NFHzNQHeON7aGVfB/XFVRmMJSkhIUHLli3T0aNHlZ+fr7lz56pDhw7q0aNHbbeGCiQnJ2vevHlatmxZhTOKjY1VWVmZkpKSVFxcrMOHD8tut2v06NGSpCZNmujee+/VwoULdfr0aV24cEFz587V7bffrjZt2ph9O5A0aNAg7dixQxs3bnT/kqQXXnhBTzzxhO69915lZ2crNTVVJSUlev/99/Xee+8pISFBktS2bVv17dtXc+fO1YULF3T69GktXLhQcXFxaty4cW3e2lXv/vvvV2pqqrKzs1VWVqbU1FTl5OTotttuY671UKtWrdSpUyetWrVKTqdTLpdL//3vf/Xll1+qW7duzLSeKCsrU1FRkUpKSiRJRUVFKioqksvl8sr30JEjR2rnzp16//33VVJSotTUVH3xxRcaPnx4rd3zZantn/6rLWVlZca8efOM3r17G5GRkcaECROMEydO1HZbuIiQkBAjLCzMiIqK8vj1y1eUSE9PN0aOHGl0797duO2224wVK1Z4PEZBQYHxt7/9zejZs6cRHR1tTJs2zTh37pzZt4JL+OWrUhiGYXz00UfG0KFDje7duxt33XWXsWXLFo/zz5w5Y0ybNs2Ijo42evbsacycOZOfcq8DXC6XsWjRIuPWW281oqOjjZEjRxr79u1zrzPX+ufo0aPG5MmTjd69exvR0dHG0KFDjbVr17rXmWndl5qaaoSEhJT79dOfud74Hrpp0ybjrrvuMrp3724MHTrU2LNnj2n35y1+hmEYtR3OAQAAgNp21W6lAAAAAH6JYAwAAACIYAwAAABIIhgDAAAAkgjGAAAAgCSCMQAAACCJYAwAAABIIhgDgM+744479MYbb9TY+QDgKwJquwEAqK+effZZHT9+XG+99VZttyLp4v2sW7dOTZo0qaWuAKD+IBgDQD3ncrl0qTcxDQwMNLEbAKi/CMYAcBFFRUWaM2eONm/eLH9/fw0ePFg2m03btm3TsGHDtG7dOklSly5dJEkvvfSS0tLSdPr0aS1fvtzjscaNG6e2bdtqzpw5l6xpGIZmzpypvXv36vTp0woODtaQIUP08MMPq1GjRpKkRYsW6Z133tHjjz+uhQsX6vjx44qNjdW7775brp+4uDjdcccdGjFihKZMmSJJKi0t1eLFi7VhwwadOnVKrVq1UmxsrGbOnFlhTyUlJVq8eLHWr1+v3NxcXX/99Ro7dqwSEhLc59jtdi1fvlwnT55U06ZN1blzZ82fP1/XXnttdT/tAFBrCMYAcBHz58/X9u3b9fLLL6tjx45at26dVq1apaCgIE2YMEHffPONcnJytGjRIkmS1WpVx44dNXr0aJ04cULt2rWTJB07dkxpaWmaPn16pTUNw1BQUJDmz5+voKAgZWdna9asWQoICNC0adPc550+fVopKSl6+eWXZbPZFBwcrAYNGpTrpyLPPvusdu/eraeeekoxMTE6e/asPvvss4v2NHPmTKWnp2v27Nlq3769Pv/8cz333HNq0KCB4uPjdeTIEc2aNUtz5sxRr1695HQ6dfjw4Sp/ngGgriAYA0AF8vPz9fbbb2vmzJkaMGCAJOmpp57Svn375HA41Lx5czVp0kQNGzZUcHCw+7ro6Gh17txZ69at0+OPPy7pxz2+ISEhioyMrLSuv7+/+zpJatu2rU6cOKGUlBSPYFxUVKRXXnlFrVu3dh+rqJ9fO3bsmDZs2KBXX31Vd999tyTp+uuvV1RUVIXnnzhxQhs2bNCWLVvUqVMnSVK7du30v//9TytXrlR8fLy+++47NW3aVAMGDJDFYpH087PWAFCfEIwBoALHjx9XcXGxoqOjPY736NFDu3btuuS1CQkJWrx4saZNmybDMLR+/Xo99NBDVa69du1a2e125eTkqKCgQKWlpeX2EF9zzTUeobiq0tPTJUl9+/at0vlHjhyRYRgaMWKEx/HS0lI1aNBAktSnTx+1a9dOd955p/r06aPevXvrrrvuYm8zgHqHYAwAXvb73/9e8+bN065du2QYhhwOh4YNG1ala999913Nnj1b06dPV69evWSxWLRt2zb985//9DivadOmNdF6OT8F8rfffrtcTT8/P0lS8+bNlZqaqgMHDmjPnj1avXq15s6dqzfffFPh4eGm9AkA3kAwBoAKXH/99WrYsKEOHjyozp07u48fOHDA/f8NGzZUWVlZuWstFosGDx4su90ul8ulu+++WzabrUp19+/fr65du2r8+PHuYzk5OVW69mL9/FK3bt0kSR9++KF7K0VVzv/uu+/Uv3//i57XoEED9erVS7169dK0adM0ePBgbd68mWAMoF4hGANABZo1a6aEhAQtWLBAQUFB7h++O3r0qIKCgiT9uAkNqNoAAAIASURBVP9327Zt+vLLLxUUFCSLxeJ+5YhRo0a5X7WhOq9z/FOdHTt2KCQkRO+99562b99epWsv1c9P2rdvr3vuuUeJiYkqKipSdHS0zp8/r4MHD+qPf/xjucds37697rvvPs2cOVNPPvmkoqOjVVBQoCNHjujs2bN68MEHtWPHDp08eVI9e/ZUYGCg0tPTderUKfeeZACoLwjGAHARTz75pIqLi/XXv/5VkjR48GDdf//92rZtmyRpxIgR2rdvnxISEuR0Ot0vjyZJERERCgkJUUlJiXr06FHlmqNGjdIXX3yhGTNmqLS0VP3799cjjzyi559/vtJrL9XPL7300kt6/fXX9eqrr+r06dMKDAzUwIEDL/q4zz//vJYvX67Fixfr5MmTat68uTp37qz7779fktSiRQslJydr8eLF+uGHH3Tddddp8uTJio+Pr/J9A0Bd4Gdc6lXhAQAefnoN4f/85z+XPK+kpER33HGHJk6cWOEzsQCAuodnjAHAi1wul86dO6fVq1eroKBA9913X223BACoIoIxAHjRt99+qzvvvFPBwcGaM2eO+3V9fzJkyBB9++23FV57zz33aPbs2Wa0CQCoAFspAMBEOTk5Ki0trXDNYrG4f7APAGA+gjEAAAAgyb+2GwAAAADqAoIxAAAAIIIxAAAAIIlgDAAAAEiS/h9nRvWwvgLo9AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dK-0HMq63XZD",
        "colab_type": "text"
      },
      "source": [
        "A few customers seems to have quite large quantities in their orders but the distribution is generally healthy. \n",
        "\n",
        "They could be weird behavior for the same customers but the Standard Deviation filters should minimize the risk. \n",
        "\n",
        "Analyzing those few outliers can be part of additional data preparation work that can help improve your model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1pFm1_oKJkVZ",
        "colab_type": "text"
      },
      "source": [
        "### Per value\n",
        "This section shows that there are quite a few outliers 'outliers' that spend way more than others. We want to ignore them when creating the RFM values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xnMBqS7uhooL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%bigquery df_dist_values --params $LTV_PARAMS --project $PROJECT_ID\n",
        "\n",
        "SELECT value\n",
        "FROM `ltv_ecommerce.20_aggred`"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6rO6XsZkd-bh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "18a93e73-ee34-4cec-d1e6-bdb38c8ed2ce"
      },
      "source": [
        "axv = sns.violinplot(x=df_dist_values[\"value\"])\n",
        "axv.set_xlim(-200, 3500)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-200.0, 3500.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2uY8l0Os51xW",
        "colab_type": "text"
      },
      "source": [
        "The distribution shows a few outliers that you could investigate to improve the base model that you create in this tutorial."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UxrNfMNMUifK",
        "colab_type": "text"
      },
      "source": [
        "## Create inputs and targets for the ML model\n",
        "The goal of this tutorial is to predict *How much each customer is going to spend in the next N days knowing their transactions history.*\n",
        "\n",
        "Because all customers have different buying behavior over a specific period of time, this tutorial create multiple records per customer by:\n",
        "\n",
        "1. Moving a threshold date by `WINDOW_STEP` over the dataset to create input and targets.\n",
        "1. Aggregates input data per customer using transactions between `WINDOW_START` and `THRESHOLD_DATE` threshold date.\n",
        "1. Aggregates the monetary value of the target transactions whose dates are between `THRESHOLD_DATE` and `THRESHOLD_DATE + LENGTH_FUTURE`.\n",
        "\n",
        "Based on the size of your dataset and what you are trying to predict, you can update those values as needed in the *Set parameters for LTV* section.\n",
        "\n",
        "This tutorial uses all historical data to predict the monetary value of its customers in the next 30 (`LENGTH_FUTURE`) days with a dataset that contains inputs/target value calculated every 30 days (`WINDOW_STEP`)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nFjSNi_lFgPc",
        "colab_type": "text"
      },
      "source": [
        "### Query"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tY96qAzgXjh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 32
        },
        "outputId": "5d0da276-abf1-47d0-ae0e-48ea4c58d22f"
      },
      "source": [
        "%%bigquery --params $LTV_PARAMS --project $PROJECT_ID\n",
        "\n",
        "-- Lenght are number of days\n",
        "--\n",
        "-- Date of the first order in the dataset.\n",
        "DECLARE MIN_DATE DATE;                                          \n",
        "-- Date of the final order in the dataset.\n",
        "DECLARE MAX_DATE DATE;                                          \n",
        "-- Date that separates inputs orders from target transactions.\n",
        "DECLARE THRESHOLD_DATE DATE;                                    \n",
        "-- How many days back for inputs transactions. 0 means from the start.\n",
        "DECLARE WINDOW_LENGTH INT64 DEFAULT @WINDOW_LENGTH;\n",
        "-- Date at which an input transactions window starts.\n",
        "DECLARE WINDOW_START DATE;\n",
        "-- How many days between thresholds.\n",
        "DECLARE WINDOW_STEP INT64 DEFAULT @WINDOW_STEP;                 \n",
        "-- How many days for the first window.\n",
        "DECLARE WINDOW_STEP_INITIAL INT64 DEFAULT @WINDOW_STEP_INITIAL; \n",
        "-- Index of the window being run.\n",
        "DECLARE STEP INT64 DEFAULT 1;                                   \n",
        "-- How many days to predict for.\n",
        "DECLARE LENGTH_FUTURE INT64 DEFAULT @LENGTH_FUTURE;                            \n",
        "\n",
        "SET (MIN_DATE, MAX_DATE) = (\n",
        "  SELECT AS STRUCT \n",
        "    MIN(order_day) AS min_days,\n",
        "    MAX(order_day) AS max_days\n",
        "  FROM\n",
        "    `ltv_ecommerce.20_aggred`\n",
        ");\n",
        "\n",
        "SET THRESHOLD_DATE = MIN_DATE;\n",
        "\n",
        "-- For more information about the features of this table,\n",
        "-- see https://github.com/CamDavidsonPilon/lifetimes/blob/master/lifetimes/utils.py#L246\n",
        "-- and https://cloud.google.com/solutions/machine-learning/clv-prediction-with-offline-training-train#aggregating_data\n",
        "CREATE OR REPLACE TABLE ltv_ecommerce.30_featured\n",
        "(\n",
        "  -- dataset STRING,\n",
        "  customer_id STRING,\n",
        "  monetary FLOAT64,\n",
        "  frequency INT64,\n",
        "  recency INT64,\n",
        "  T INT64,\n",
        "  time_between FLOAT64,\n",
        "  avg_basket_value FLOAT64,\n",
        "  avg_basket_size FLOAT64,\n",
        "  has_returns STRING,\n",
        "  avg_time_to_return FLOAT64,\n",
        "  num_returns INT64,\n",
        "  -- threshold DATE,\n",
        "  -- step INT64,\n",
        "  target_monetary FLOAT64,\n",
        ");\n",
        "\n",
        "LOOP\n",
        "  -- Can choose a longer original window in case \n",
        "  -- there were not many orders in the early days.\n",
        "  IF STEP = 1 THEN\n",
        "    SET THRESHOLD_DATE = DATE_ADD(THRESHOLD_DATE, INTERVAL WINDOW_STEP_INITIAL DAY); \n",
        "  ELSE\n",
        "    SET THRESHOLD_DATE = DATE_ADD(THRESHOLD_DATE, INTERVAL WINDOW_STEP DAY);\n",
        "  END IF;\n",
        "  SET STEP = STEP + 1;\n",
        "\n",
        "  IF THRESHOLD_DATE >= DATE_SUB(MAX_DATE, INTERVAL (WINDOW_STEP) DAY) THEN\n",
        "    LEAVE;\n",
        "  END IF;\n",
        "\n",
        "  -- Takes all transactions before the threshold date unless you decide\n",
        "  -- to use a different window lenght to test model performance.\n",
        "  IF WINDOW_LENGTH != 0 THEN\n",
        "    SET WINDOW_START = DATE_SUB(THRESHOLD_DATE, INTERVAL WINDOW_LENGTH DAY);\n",
        "  ELSE\n",
        "    SET WINDOW_START = MIN_DATE;\n",
        "  END IF;\n",
        "\n",
        "  INSERT ltv_ecommerce.30_featured\n",
        "  SELECT\n",
        "    -- CASE\n",
        "    --   WHEN THRESHOLD_DATE <= DATE_SUB(MAX_DATE, INTERVAL LENGTH_FUTURE DAY) THEN 'UNASSIGNED'\n",
        "    --   ELSE 'TEST'\n",
        "    -- END AS dataset,\n",
        "    CAST(tf.customer_id AS STRING),\n",
        "    ROUND(tf.monetary_orders, 2) AS monetary,\n",
        "    tf.cnt_orders AS frequency,\n",
        "    tf.recency,\n",
        "    tf.T,\n",
        "    ROUND(tf.recency/cnt_orders, 2) AS time_between,\n",
        "    ROUND(tf.avg_basket_value, 2) AS avg_basket_value,\n",
        "    ROUND(tf.avg_basket_size, 2) AS avg_basket_size,\n",
        "    has_returns,\n",
        "    CEIL(avg_time_to_return) AS avg_time_to_return,\n",
        "    num_returns,\n",
        "    -- THRESHOLD_DATE AS threshold,\n",
        "    -- STEP - 1 AS step,\n",
        "    ROUND(tt.target_monetary, 2) AS target_monetary,\n",
        "  FROM (\n",
        "      -- This SELECT uses only data before THRESHOLD_DATE to make features.\n",
        "      SELECT\n",
        "        customer_id,\n",
        "        SUM(value) AS monetary_orders,\n",
        "        DATE_DIFF(MAX(order_day), MIN(order_day), DAY) AS recency,\n",
        "        DATE_DIFF(THRESHOLD_DATE, MIN(order_day), DAY) AS T,\n",
        "        COUNT(DISTINCT order_day) AS cnt_orders,\n",
        "        AVG(qty_articles) avg_basket_size,\n",
        "        AVG(value) avg_basket_value,\n",
        "        CASE\n",
        "          WHEN SUM(num_returns) > 0 THEN 'y'\n",
        "          ELSE 'n'\n",
        "        END AS has_returns,\n",
        "        AVG(time_to_return) avg_time_to_return,\n",
        "        THRESHOLD_DATE AS threshold,\n",
        "        SUM(num_returns) num_returns,\n",
        "      FROM\n",
        "        `ltv_ecommerce.20_aggred`\n",
        "      WHERE\n",
        "        order_day <= THRESHOLD_DATE AND\n",
        "        order_day >= WINDOW_START\n",
        "      GROUP BY\n",
        "        customer_id\n",
        "    ) tf\n",
        "  INNER JOIN (\n",
        "    -- This SELECT uses all data after threshold as target.\n",
        "    SELECT\n",
        "      customer_id,\n",
        "      SUM(value) target_monetary\n",
        "    FROM\n",
        "      `ltv_ecommerce.20_aggred`\n",
        "    WHERE\n",
        "      order_day <= DATE_ADD(THRESHOLD_DATE, INTERVAL LENGTH_FUTURE DAY)\n",
        "      -- Overall value is similar to predicting only what's after threshold.\n",
        "      -- and the prediction performs better. We can substract later.\n",
        "      -- AND order_day > THRESHOLD_DATE\n",
        "    GROUP BY\n",
        "      customer_id) tt\n",
        "  ON\n",
        "    tf.customer_id = tt.customer_id;\n",
        "\n",
        "END LOOP;"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: []\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JPwha05IFivK",
        "colab_type": "text"
      },
      "source": [
        "### Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1kNnLuWt-HBb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "95530fe0-f56f-4929-ee07-fe44461d45f1"
      },
      "source": [
        "%%bigquery --project $PROJECT_ID\n",
        "\n",
        "-- Shows all data for a specific customer and some other random records.\n",
        "SELECT * FROM `ltv_ecommerce.30_featured` WHERE customer_id = \"10\"\n",
        "UNION ALL\n",
        "(SELECT * FROM `ltv_ecommerce.30_featured` LIMIT 5)\n",
        "ORDER BY customer_id, frequency, T"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_id</th>\n",
              "      <th>monetary</th>\n",
              "      <th>frequency</th>\n",
              "      <th>recency</th>\n",
              "      <th>T</th>\n",
              "      <th>time_between</th>\n",
              "      <th>avg_basket_value</th>\n",
              "      <th>avg_basket_size</th>\n",
              "      <th>has_returns</th>\n",
              "      <th>avg_time_to_return</th>\n",
              "      <th>num_returns</th>\n",
              "      <th>target_monetary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10</td>\n",
              "      <td>557.93</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>557.93</td>\n",
              "      <td>7.00</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1907.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10</td>\n",
              "      <td>1907.18</td>\n",
              "      <td>2</td>\n",
              "      <td>16</td>\n",
              "      <td>35</td>\n",
              "      <td>8.00</td>\n",
              "      <td>953.59</td>\n",
              "      <td>25.50</td>\n",
              "      <td>y</td>\n",
              "      <td>3.0</td>\n",
              "      <td>51</td>\n",
              "      <td>3126.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10</td>\n",
              "      <td>3126.09</td>\n",
              "      <td>4</td>\n",
              "      <td>52</td>\n",
              "      <td>65</td>\n",
              "      <td>13.00</td>\n",
              "      <td>781.52</td>\n",
              "      <td>22.50</td>\n",
              "      <td>y</td>\n",
              "      <td>2.0</td>\n",
              "      <td>93</td>\n",
              "      <td>3227.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10</td>\n",
              "      <td>3227.99</td>\n",
              "      <td>5</td>\n",
              "      <td>75</td>\n",
              "      <td>95</td>\n",
              "      <td>15.00</td>\n",
              "      <td>645.60</td>\n",
              "      <td>20.00</td>\n",
              "      <td>y</td>\n",
              "      <td>2.0</td>\n",
              "      <td>105</td>\n",
              "      <td>3227.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10</td>\n",
              "      <td>3227.99</td>\n",
              "      <td>5</td>\n",
              "      <td>75</td>\n",
              "      <td>125</td>\n",
              "      <td>15.00</td>\n",
              "      <td>645.60</td>\n",
              "      <td>20.00</td>\n",
              "      <td>y</td>\n",
              "      <td>2.0</td>\n",
              "      <td>105</td>\n",
              "      <td>4216.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>10</td>\n",
              "      <td>4216.18</td>\n",
              "      <td>6</td>\n",
              "      <td>141</td>\n",
              "      <td>155</td>\n",
              "      <td>23.50</td>\n",
              "      <td>702.70</td>\n",
              "      <td>22.00</td>\n",
              "      <td>y</td>\n",
              "      <td>2.0</td>\n",
              "      <td>140</td>\n",
              "      <td>4216.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>10</td>\n",
              "      <td>4216.18</td>\n",
              "      <td>6</td>\n",
              "      <td>141</td>\n",
              "      <td>185</td>\n",
              "      <td>23.50</td>\n",
              "      <td>702.70</td>\n",
              "      <td>22.00</td>\n",
              "      <td>y</td>\n",
              "      <td>2.0</td>\n",
              "      <td>140</td>\n",
              "      <td>4354.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10</td>\n",
              "      <td>4354.60</td>\n",
              "      <td>7</td>\n",
              "      <td>193</td>\n",
              "      <td>215</td>\n",
              "      <td>27.57</td>\n",
              "      <td>622.09</td>\n",
              "      <td>19.14</td>\n",
              "      <td>y</td>\n",
              "      <td>2.0</td>\n",
              "      <td>140</td>\n",
              "      <td>5215.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>10</td>\n",
              "      <td>5215.47</td>\n",
              "      <td>8</td>\n",
              "      <td>231</td>\n",
              "      <td>245</td>\n",
              "      <td>28.88</td>\n",
              "      <td>651.93</td>\n",
              "      <td>21.25</td>\n",
              "      <td>y</td>\n",
              "      <td>2.0</td>\n",
              "      <td>165</td>\n",
              "      <td>5694.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>11904</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>82</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>y</td>\n",
              "      <td>29.0</td>\n",
              "      <td>1</td>\n",
              "      <td>243.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>31570</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>56</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>y</td>\n",
              "      <td>19.0</td>\n",
              "      <td>3</td>\n",
              "      <td>301.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>63396</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>75</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>y</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>64397</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>72</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>y</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>241.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>68429</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>66</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>y</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   customer_id  monetary  ...  num_returns  target_monetary\n",
              "0           10    557.93  ...            0          1907.18\n",
              "1           10   1907.18  ...           51          3126.09\n",
              "2           10   3126.09  ...           93          3227.99\n",
              "3           10   3227.99  ...          105          3227.99\n",
              "4           10   3227.99  ...          105          4216.18\n",
              "5           10   4216.18  ...          140          4216.18\n",
              "6           10   4216.18  ...          140          4354.60\n",
              "7           10   4354.60  ...          140          5215.47\n",
              "8           10   5215.47  ...          165          5694.20\n",
              "9        11904      0.00  ...            1           243.88\n",
              "10       31570      0.00  ...            3           301.88\n",
              "11       63396      0.00  ...            1             0.00\n",
              "12       64397      0.00  ...            1           241.89\n",
              "13       68429      0.00  ...            1             0.00\n",
              "\n",
              "[14 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4qKPX7HV2VoP",
        "colab": {}
      },
      "source": [
        "%%bigquery df_featured --project $PROJECT_ID\n",
        "\n",
        "ltv_ecommerce.30_featured"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XuG-eO8qMJdo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "64f8a981-6956-4ae6-cf31-c92b215e4c78"
      },
      "source": [
        "df_featured.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>monetary</th>\n",
              "      <th>frequency</th>\n",
              "      <th>recency</th>\n",
              "      <th>T</th>\n",
              "      <th>time_between</th>\n",
              "      <th>avg_basket_value</th>\n",
              "      <th>avg_basket_size</th>\n",
              "      <th>avg_time_to_return</th>\n",
              "      <th>num_returns</th>\n",
              "      <th>target_monetary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>711486.000000</td>\n",
              "      <td>711486.000000</td>\n",
              "      <td>711486.000000</td>\n",
              "      <td>711486.000000</td>\n",
              "      <td>711486.000000</td>\n",
              "      <td>711486.000000</td>\n",
              "      <td>711486.000000</td>\n",
              "      <td>632923.000000</td>\n",
              "      <td>711486.000000</td>\n",
              "      <td>711486.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1083.651997</td>\n",
              "      <td>2.340872</td>\n",
              "      <td>72.325732</td>\n",
              "      <td>138.014234</td>\n",
              "      <td>23.380073</td>\n",
              "      <td>475.360193</td>\n",
              "      <td>20.357123</td>\n",
              "      <td>2.488856</td>\n",
              "      <td>37.084156</td>\n",
              "      <td>1209.046128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>948.881772</td>\n",
              "      <td>1.451241</td>\n",
              "      <td>80.694212</td>\n",
              "      <td>81.896254</td>\n",
              "      <td>25.485559</td>\n",
              "      <td>479.298791</td>\n",
              "      <td>18.767243</td>\n",
              "      <td>1.320465</td>\n",
              "      <td>33.045139</td>\n",
              "      <td>1003.381286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>405.310000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>243.880000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>485.780000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>838.355000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>405.550000</td>\n",
              "      <td>16.670000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>960.890000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1493.560000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>127.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>39.500000</td>\n",
              "      <td>596.900000</td>\n",
              "      <td>24.750000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>1670.230000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>31088.880000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>164.500000</td>\n",
              "      <td>31088.880000</td>\n",
              "      <td>1036.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>351.000000</td>\n",
              "      <td>31088.880000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            monetary      frequency  ...    num_returns  target_monetary\n",
              "count  711486.000000  711486.000000  ...  711486.000000    711486.000000\n",
              "mean     1083.651997       2.340872  ...      37.084156      1209.046128\n",
              "std       948.881772       1.451241  ...      33.045139      1003.381286\n",
              "min         0.000000       1.000000  ...       0.000000         0.000000\n",
              "25%       405.310000       1.000000  ...      12.000000       485.780000\n",
              "50%       838.355000       2.000000  ...      29.000000       960.890000\n",
              "75%      1493.560000       3.000000  ...      52.000000      1670.230000\n",
              "max     31088.880000      15.000000  ...     351.000000     31088.880000\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "TBr78kKG2j-3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "outputId": "2dfb9dda-0e2c-40ee-88cb-f9b2890f855b"
      },
      "source": [
        "# Display distribution for all columns that are numerical (but will still ignore the categorical ones like day of the week)\n",
        "valid_column_names = [key for key in dict(df_featured.dtypes) if dict(df_featured.dtypes)[key] in ['float64', 'int64']]\n",
        "NUM_COLS = 5\n",
        "NUM_ROWS = math.ceil(int(len(valid_column_names)) / NUM_COLS)\n",
        "\n",
        "fig, axs = plt.subplots(nrows=NUM_ROWS, ncols=NUM_COLS, figsize=(25, 7))\n",
        "\n",
        "for idx, cname in enumerate(valid_column_names):\n",
        "  x = int(idx/NUM_COLS)\n",
        "  y = idx % NUM_COLS\n",
        "  sns.violinplot(df_featured[cname], ax=axs[x, y], label=cname)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x504 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w-vSwvNNBKgZ",
        "colab_type": "text"
      },
      "source": [
        "Seems like for most values, there is a long tail of records. This is something that might required additional feature preparation even if AutoML already provides some automatic engineering. You can investigate this if you want to improve the base model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6g8ZoZcJfxoJ",
        "colab_type": "text"
      },
      "source": [
        "## Train the model\n",
        "This tutorial uses an AutoML regressor to predict the continuous value of target_monetary.\n",
        "\n",
        "With a non-AutoML model, you would generally need to:\n",
        "1. Apply common ML patterns such as normalization or clipping.\n",
        "1. Split data in two to three datasets for training, evaluating and testing.\n",
        "\n",
        "AutoML lets you split your data:\n",
        "- Manually using a column with a name for each split.\n",
        "- Manually using a column that defines a time\n",
        "- Automatically\n",
        "\n",
        "This tutorial uses the latter option where AutoML automatically assigns each row to a split."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VyjyGOicfx-L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "38e4dc66-0234-4d34-898b-9c6ed82c0d57"
      },
      "source": [
        "# You can run this query using the magic cell but the cell would run for hours.\n",
        "# Although stopping the cell would not stop the query, using the Python client\n",
        "# also enables you to add a custom parameter for the model name.\n",
        "suffix_now = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
        "train_model_jobid = f'train_model_{suffix_now}'\n",
        "\n",
        "train_model_sql = f'''\n",
        "CREATE OR REPLACE MODEL `ltv_ecommerce.model_tutorial_{suffix_now}`\n",
        "       OPTIONS(MODEL_TYPE=\"AUTOML_REGRESSOR\",\n",
        "               INPUT_LABEL_COLS=[\"target_monetary\"],\n",
        "               OPTIMIZATION_OBJECTIVE=\"MINIMIZE_MAE\")\n",
        "AS SELECT \n",
        "  * EXCEPT(customer_id)\n",
        "FROM \n",
        "  `ltv_ecommerce.30_featured`\n",
        "'''\n",
        "\n",
        "bq_client.query(train_model_sql, job_id=train_model_jobid)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<google.cloud.bigquery.job.QueryJob at 0x7fbf053b8240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1rW-h3wQW80",
        "colab_type": "text"
      },
      "source": [
        "This is an example of a model evaluation\n",
        "\n",
        "![Model Evaluation](https://storage.googleapis.com/solutions-public-assets/analytics-componentized-patterns/ltv/model_evaluation.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IJnwrWjT_4W7",
        "colab_type": "text"
      },
      "source": [
        "## Predict LTV\n",
        "Predicts LTV for all customers. It uses the overall monetary value for each customer to predict a future one."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6_nmt4DuIVzL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%bigquery --params $LTV_PARAMS --project $PROJECT_ID\n",
        "\n",
        "-- TODO(developer): \n",
        "-- 1. Update the model name to the one you want to use.\n",
        "-- 2. Update the table where to output predictions.\n",
        "\n",
        "-- How many days back for inputs transactions. 0 means from the start.\n",
        "DECLARE WINDOW_LENGTH INT64 DEFAULT @WINDOW_LENGTH;\n",
        "-- Date at which an input transactions window starts.\n",
        "DECLARE WINDOW_START DATE;\n",
        "\n",
        "-- Date of the first transaction in the dataset.\n",
        "DECLARE MIN_DATE DATE;                                          \n",
        "-- Date of the final transaction in the dataset.\n",
        "DECLARE MAX_DATE DATE; \n",
        "-- Date from which you want to predict.\n",
        "DECLARE PREDICT_FROM_DATE DATE;\n",
        "\n",
        "SET (MIN_DATE, MAX_DATE) = (\n",
        "  SELECT AS STRUCT \n",
        "    MIN(order_day) AS min_days,\n",
        "    MAX(order_day) AS max_days\n",
        "  FROM\n",
        "    `ltv_ecommerce.20_aggred`\n",
        ");\n",
        "\n",
        "-- You can set any date here. In production, it is generally today.\n",
        "SET PREDICT_FROM_DATE = MAX_DATE;\n",
        "\n",
        "IF WINDOW_LENGTH != 0 THEN\n",
        "  SET WINDOW_START = DATE_SUB(PREDICT_FROM_DATE, INTERVAL WINDOW_LENGTH DAY);\n",
        "ELSE\n",
        "  SET WINDOW_START = MIN_DATE;\n",
        "END IF;\n",
        "\n",
        "CREATE OR REPLACE TABLE `ltv_ecommerce.predictions_tutorial`\n",
        "AS (\n",
        "SELECT\n",
        "  customer_id,\n",
        "  monetary AS monetary_so_far,\n",
        "  ROUND(predicted_target_monetary, 2) AS monetary_predicted,\n",
        "  ROUND(predicted_target_monetary - monetary, 2) AS monetary_future\n",
        "FROM\n",
        "  ML.PREDICT(\n",
        "    -- /!\\ Set your model name here.\n",
        "    MODEL ltv_ecommerce.model_tutorial_YYYYMMDD,\n",
        "    (\n",
        "      SELECT\n",
        "        customer_id,\n",
        "        ROUND(monetary_orders, 2) AS monetary,\n",
        "        cnt_orders AS frequency,\n",
        "        recency,\n",
        "        T,\n",
        "        ROUND(recency/cnt_orders, 2) AS time_between,\n",
        "        ROUND(avg_basket_value, 2) AS avg_basket_value,\n",
        "        ROUND(avg_basket_size, 2) AS avg_basket_size,\n",
        "        has_returns,\n",
        "        CEIL(avg_time_to_return) AS avg_time_to_return,\n",
        "        num_returns\n",
        "      FROM (\n",
        "        SELECT\n",
        "          customer_id,\n",
        "          SUM(value) AS monetary_orders,\n",
        "          DATE_DIFF(MAX(order_day), MIN(order_day), DAY) AS recency,\n",
        "          DATE_DIFF(PREDICT_FROM_DATE, MIN(order_day), DAY) AS T,\n",
        "          COUNT(DISTINCT order_day) AS cnt_orders,\n",
        "          AVG(qty_articles) avg_basket_size,\n",
        "          AVG(value) avg_basket_value,\n",
        "          CASE\n",
        "            WHEN SUM(num_returns) > 0 THEN 'y'\n",
        "            ELSE 'n'\n",
        "          END AS has_returns,\n",
        "          AVG(time_to_return) avg_time_to_return,\n",
        "          SUM(num_returns) num_returns,\n",
        "        FROM\n",
        "          `ltv_ecommerce.20_aggred`\n",
        "        WHERE\n",
        "          order_day <= PREDICT_FROM_DATE AND\n",
        "          order_day >= WINDOW_START\n",
        "        GROUP BY\n",
        "          customer_id\n",
        "      )\n",
        "    )\n",
        "  )\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQVtTFK0Q7fF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%bigquery df_predictions --project $PROJECT_ID\n",
        "\n",
        "ltv_ecommerce.predictions_windowed"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPSdMaTdRCzz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "22a6b85b-241c-4bed-aa00-8d330adec418"
      },
      "source": [
        "df_predictions.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>monetary_so_far</th>\n",
              "      <th>monetary_predicted</th>\n",
              "      <th>monetary_future</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>99070.000000</td>\n",
              "      <td>99070.000000</td>\n",
              "      <td>99070.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1524.427797</td>\n",
              "      <td>1526.421037</td>\n",
              "      <td>1.993238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1158.682823</td>\n",
              "      <td>1159.478577</td>\n",
              "      <td>1.632896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>-31.340000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>652.230000</td>\n",
              "      <td>653.422500</td>\n",
              "      <td>0.950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1279.300000</td>\n",
              "      <td>1281.170000</td>\n",
              "      <td>1.610000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2131.797500</td>\n",
              "      <td>2134.117500</td>\n",
              "      <td>2.770000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>31088.880000</td>\n",
              "      <td>31057.540000</td>\n",
              "      <td>24.630000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       monetary_so_far  monetary_predicted  monetary_future\n",
              "count     99070.000000        99070.000000     99070.000000\n",
              "mean       1524.427797         1526.421037         1.993238\n",
              "std        1158.682823         1159.478577         1.632896\n",
              "min           0.000000            0.100000       -31.340000\n",
              "25%         652.230000          653.422500         0.950000\n",
              "50%        1279.300000         1281.170000         1.610000\n",
              "75%        2131.797500         2134.117500         2.770000\n",
              "max       31088.880000        31057.540000        24.630000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rhy9Sb2Hwmox",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "5af70098-494b-4577-ed89-6a11d3156258"
      },
      "source": [
        "from matplotlib.gridspec import GridSpec\n",
        "\n",
        "fig = plt.figure(constrained_layout=True, figsize=(15, 5))\n",
        "gs = GridSpec(2, 2, figure=fig)\n",
        "\n",
        "sns.set(font_scale = 1)\n",
        "plt.tick_params(axis='x', labelsize=14)\n",
        "\n",
        "ax0 = plt.subplot(gs.new_subplotspec((0, 0), colspan=1))\n",
        "ax1 = plt.subplot(gs.new_subplotspec((0, 1), colspan=1))\n",
        "ax2 = plt.subplot(gs.new_subplotspec((1, 0), colspan=2))\n",
        "\n",
        "sns.violinplot(df_predictions['monetary_so_far'], ax=ax0, label='monetary_so_far')\n",
        "sns.violinplot(df_predictions['monetary_predicted'], ax=ax1, label='monetary_predicted')\n",
        "sns.violinplot(df_predictions['monetary_future'], ax=ax2, label='monetary_future')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0b36c14b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l6YQTMvX5Q08",
        "colab_type": "text"
      },
      "source": [
        "The monetary distribution analysis shows small monetary amounts for the next month compare to the overall historical value. The difference is about 3 to 4 orders of magnitude.\n",
        "\n",
        "One reason is that the model is trained to predict the value for the next month (LENGTH_FUTURE = 30). \n",
        "\n",
        "You can play around with that value to train and predict for the next quarter for example (LENGTH_FUTURE = 90)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BD8paLvr7Kvb",
        "colab_type": "text"
      },
      "source": [
        "## Activation\n",
        "This part shows how to activate on [Google Ads][lookalike_adwords] using similar audience.\n",
        "\n",
        "You can follow a similar process for [Facebook][lookalike_facebook] for example\n",
        "\n",
        "[lookalike_adwords]: https://developers.google.com/adwords/api/docs/guides/remarketing#customer_match_with_email_address_address_or_user_id\n",
        "[lookalike_facebook]: https://www.facebook.com/business/help/170456843145568?id=2469097953376494"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tmK61nL6_KbC",
        "colab_type": "text"
      },
      "source": [
        "### Extract top customers\n",
        "\n",
        "This step extracts the top 20% customers with the highest **future** monetary value and join with a CRM table to get their email. \n",
        "\n",
        "The prediction used the overall monetary value but in this use case, we are looking at the most valuable in the future. You can modify the PERCENT_RANK to use another KPI."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r4QBQflQ_1NU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%bigquery df_top_ltv --params $LTV_PARAMS --project $PROJECT_ID\n",
        "\n",
        "DECLARE TOP_LTV_RATIO FLOAT64 DEFAULT @TOP_LTV_RATIO;\n",
        "\n",
        "SELECT\n",
        "  p.customer_id,\n",
        "  monetary_future,\n",
        "  c.email AS email\n",
        "FROM (\n",
        "  SELECT\n",
        "    customer_id,\n",
        "    monetary_future,\n",
        "    PERCENT_RANK() OVER (ORDER BY monetary_future DESC) AS percent_rank_monetary\n",
        "  FROM\n",
        "    `ltv_ecommerce.predictions_windowed` ) p\n",
        "-- This creates fake emails. You need to join with your own CRM table.\n",
        "INNER JOIN (\n",
        "  SELECT\n",
        "    customer_id,\n",
        "    email\n",
        "  FROM\n",
        "    `ltv_ecommerce.00_crm` ) c\n",
        "ON\n",
        "  p.customer_id = CAST(c.customer_id AS STRING)\n",
        "WHERE\n",
        "  -- Decides the size of your list of emails. For similar-audience use cases \n",
        "  -- where you need to find a minimum of matching emails, 20% should provide\n",
        "  -- enough potential emails.\n",
        "  percent_rank_monetary <= TOP_LTV_RATIO\n",
        "ORDER BY monetary_future DESC"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOlGSv1eHFGh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "57a37ed8-de3e-42de-ce5b-7abe5aa82d40"
      },
      "source": [
        "df_top_ltv.head(5)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_id</th>\n",
              "      <th>monetary_future</th>\n",
              "      <th>email</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11494</td>\n",
              "      <td>24.63</td>\n",
              "      <td>rmuir8v9@admin-example.ch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>34304</td>\n",
              "      <td>19.11</td>\n",
              "      <td>ltomasuttiqgv@yellowpages-example.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>87023</td>\n",
              "      <td>16.41</td>\n",
              "      <td>rruhben1v5a@sakura.ne-example.jp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>38601</td>\n",
              "      <td>15.93</td>\n",
              "      <td>dchelamts8@va-example.gov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>42317</td>\n",
              "      <td>15.26</td>\n",
              "      <td>wwaudwng@dagondesign-example.com</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  customer_id  monetary_future                                  email\n",
              "0       11494            24.63              rmuir8v9@admin-example.ch\n",
              "1       34304            19.11  ltomasuttiqgv@yellowpages-example.com\n",
              "2       87023            16.41       rruhben1v5a@sakura.ne-example.jp\n",
              "3       38601            15.93              dchelamts8@va-example.gov\n",
              "4       42317            15.26       wwaudwng@dagondesign-example.com"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0KtOjoRTkCjF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "860cce9e-0d11-43f4-8669-a233ed8d1b71"
      },
      "source": [
        "# Shows distribution of the predicted monetary value for the top LTV customers.\n",
        "print(df_top_ltv.describe())\n",
        "\n",
        "fig, axs = plt.subplots()\n",
        "sns.set(font_scale = 1.2)\n",
        "sns.distplot(df_top_ltv['monetary_future'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       monetary_future\n",
            "count     19880.000000\n",
            "mean          4.451095\n",
            "std           1.163329\n",
            "min           3.100000\n",
            "25%           3.520000\n",
            "50%           4.130000\n",
            "75%           5.120000\n",
            "max          24.630000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0b3eb7fbe0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y7yvUDKWgZ5_",
        "colab_type": "text"
      },
      "source": [
        "### Setup Adwords client\n",
        "Creates the configuration YAML file for the Google Ads client. You need to:\n",
        "1. Create Client ID and Secret using the Cloud Console\n",
        "2. Follow [these steps](https://developers.google.com/google-ads/api/docs/client-libs/python/oauth-installed)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVDjGJ87hhQG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Sets your variables.\n",
        "if 'google.colab' in sys.modules:\n",
        "  from google.colab import files\n",
        "\n",
        "ADWORDS_FILE = \"/tmp/adwords.yaml\"\n",
        "\n",
        "DEVELOPER_TOKEN = \"[YOUR_DEVELOPER_TOKEN]\"\n",
        "OAUTH_2_CLIENT_ID = \"[YOUR_OAUTH_2_CLIENT_ID]\"\n",
        "CLIENT_SECRET = \"[YOUR_CLIENT_SECRET]\"\n",
        "REFRESH_TOKEN = \"[YOUR_REFRESH_TOKEN]\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XIm9PUspgco4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Creates a local YAML file\n",
        "adwords_content = f\"\"\"\n",
        "# AdWordsClient configurations\n",
        "adwords:\n",
        "  #############################################################################\n",
        "  # Required Fields                                                           #\n",
        "  #############################################################################\n",
        "  developer_token: {DEVELOPER_TOKEN}\n",
        "  #############################################################################\n",
        "  # Optional Fields                                                           #\n",
        "  #############################################################################\n",
        "  # client_customer_id: INSERT_CLIENT_CUSTOMER_ID_HERE\n",
        "  # user_agent: INSERT_USER_AGENT_HERE\n",
        "  # partial_failure: True\n",
        "  # validate_only: True\n",
        "  #############################################################################\n",
        "  # OAuth2 Configuration                                                      #\n",
        "  # Below you may provide credentials for either the installed application or #\n",
        "  # service account flows. Remove or comment the lines for the flow you're    #\n",
        "  # not using.                                                                #\n",
        "  #############################################################################\n",
        "  # The following values configure the client for the installed application\n",
        "  # flow.\n",
        "  client_id: {OAUTH_2_CLIENT_ID}\n",
        "  client_secret: {CLIENT_SECRET}\n",
        "  refresh_token: {REFRESH_TOKEN}\n",
        "  # The following values configure the client for the service account flow.\n",
        "  # path_to_private_key_file: INSERT_PATH_TO_JSON_KEY_FILE_HERE\n",
        "  # delegated_account: INSERT_DOMAIN_WIDE_DELEGATION_ACCOUNT\n",
        "  #############################################################################\n",
        "  # ReportDownloader Headers                                                  #\n",
        "  # Below you may specify boolean values for optional headers that will be    #\n",
        "  # applied to all requests made by the ReportDownloader utility by default.  #\n",
        "  #############################################################################\n",
        "  # report_downloader_headers:\n",
        "    # skip_report_header: False\n",
        "    # skip_column_header: False\n",
        "    # skip_report_summary: False\n",
        "    # use_raw_enum_values: False\n",
        "\n",
        "\n",
        "# AdManagerClient configurations\n",
        "ad_manager:\n",
        "  #############################################################################\n",
        "  # Required Fields                                                           #\n",
        "  #############################################################################\n",
        "  application_name: INSERT_APPLICATION_NAME_HERE\n",
        "  #############################################################################\n",
        "  # Optional Fields                                                           #\n",
        "  #############################################################################\n",
        "  # The network_code is required for all services except NetworkService:\n",
        "  # network_code: INSERT_NETWORK_CODE_HERE\n",
        "  # delegated_account: INSERT_DOMAIN_WIDE_DELEGATION_ACCOUNT\n",
        "  #############################################################################\n",
        "  # OAuth2 Configuration                                                      #\n",
        "  # Below you may provide credentials for either the installed application or #\n",
        "  # service account (recommended) flows. Remove or comment the lines for the  #\n",
        "  # flow you're not using.                                                    #\n",
        "  #############################################################################\n",
        "  # The following values configure the client for the service account flow.\n",
        "  path_to_private_key_file: INSERT_PATH_TO_JSON_KEY_FILE_HERE\n",
        "  # delegated_account: INSERT_DOMAIN_WIDE_DELEGATION_ACCOUNT\n",
        "  # The following values configure the client for the installed application\n",
        "  # flow.\n",
        "  # client_id: INSERT_OAUTH_2_CLIENT_ID_HERE\n",
        "  # client_secret: INSERT_CLIENT_SECRET_HERE\n",
        "  # refresh_token: INSERT_REFRESH_TOKEN_HERE\n",
        "\n",
        "\n",
        "# Common configurations:\n",
        "###############################################################################\n",
        "# Compression (optional)                                                      #\n",
        "# Below you may specify whether to accept and automatically decompress gzip   #\n",
        "# encoded SOAP requests. By default, gzip compression is not enabled.         #\n",
        "###############################################################################\n",
        "# enable_compression: False\n",
        "###############################################################################\n",
        "# Logging configuration (optional)                                            #\n",
        "# Below you may specify the logging configuration. This will be provided as   #\n",
        "# an input to logging.config.dictConfig.                                      #\n",
        "###############################################################################\n",
        "# logging:\n",
        "  # version: 1\n",
        "  # disable_existing_loggers: False\n",
        "  # formatters:\n",
        "    # default_fmt:\n",
        "      # format: ext://googleads.util.LOGGER_FORMAT\n",
        "  # handlers:\n",
        "    # default_handler:\n",
        "      # class: logging.StreamHandler\n",
        "      # formatter: default_fmt\n",
        "      # level: INFO\n",
        "  # loggers:\n",
        "    # Configure root logger\n",
        "    # \"\":\n",
        "      # handlers: [default_handler]\n",
        "      # level: INFO\n",
        "###############################################################################\n",
        "# Proxy configurations (optional)                                             #\n",
        "# Below you may specify an HTTP or HTTPS Proxy to be used when making API     #\n",
        "# requests. Note: You must specify the scheme used for the proxy endpoint.    #\n",
        "#                                                                             #\n",
        "# For additional information on configuring these values, see:                #\n",
        "# http://docs.python-requests.org/en/master/user/advanced/#proxies            #\n",
        "###############################################################################\n",
        "# proxy_config:\n",
        "  # http: INSERT_HTTP_PROXY_URI_HERE\n",
        "  # https: INSERT_HTTPS_PROXY_URI_HERE\n",
        "  # If specified, the given cafile will only be used if certificate validation\n",
        "  # is not disabled.\n",
        "  # cafile: INSERT_PATH_HERE\n",
        "  # disable_certificate_validation: False\n",
        "################################################################################\n",
        "# Utilities Included (optional)                                                #\n",
        "# Below you may specify whether the library will include utilities used in the #\n",
        "# user agent. By default, the library will include utilities used in the user  #\n",
        "# agent.                                                                       #\n",
        "################################################################################\n",
        "# include_utilities_in_user_agent: True\n",
        "################################################################################\n",
        "# Custom HTTP headers (optional)                                               #\n",
        "# Specify one or more custom headers to pass along with all requests to        #\n",
        "# the API.                                                                     #\n",
        "################################################################################\n",
        "# custom_http_headers:\n",
        "#   X-My-Header: 'content'\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dX99FqKHh4gb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open(ADWORDS_FILE, \"w\") as adwords_file:\n",
        "    print(adwords_content, file=adwords_file)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sDJ3_QYYS1Ua",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Google Ads client\n",
        "# adwords_client = adwords.AdWordsClient.LoadFromStorage(ADWORDS_FILE)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ObzXCrly_gZe",
        "colab_type": "text"
      },
      "source": [
        "### Create an AdWords user list\n",
        "Using emails of the top LTV customers, you create an AdWords list. If more than 5000 of the users are matched with AdWords email, a similar audience list will be created.\n",
        "\n",
        "> Note that this guide uses fake emails so running these steps is not going to work but you can leverage this code with emails coming from your CRM."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BfBQWgj8HhQQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ltv_emails = list(set(df_top_ltv['email']))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "evaDxOKy7aos",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# https://developers.google.com/adwords/api/docs/samples/python/remarketing#create-and-populate-a-user-list\n",
        "# https://github.com/googleads/googleads-python-lib/blob/7c41584c65759b6860572a13bde65d7395c5b2d8/examples/adwords/v201809/remarketing/add_crm_based_user_list.py\n",
        "\n",
        "# \"\"\"Adds a user list and populates it with hashed email addresses.\n",
        "\n",
        "# Note: It may take several hours for the list to be populated with members. Email\n",
        "# addresses must be associated with a Google account. For privacy purposes, the\n",
        "# user list size will show as zero until the list has at least 1000 members. After\n",
        "# that, the size will be rounded to the two most significant digits.\n",
        "# \"\"\"\n",
        "# def normalize_and_SHA256(s):\n",
        "#   \"\"\"Normalizes (lowercase, remove whitespace) and hashes a string with SHA-256.\n",
        "\n",
        "#   Args:\n",
        "#     s: The string to perform this operation on.\n",
        "\n",
        "#   Returns:\n",
        "#     A normalized and SHA-256 hashed string.\n",
        "#   \"\"\"\n",
        "#   return hashlib.sha256(s.strip().lower()).hexdigest()\n",
        "  \n",
        "# def create_user_list(client):\n",
        "#   # Initialize appropriate services.\n",
        "#   user_list_service = client.GetService('AdwordsUserListService', 'v201809')\n",
        "\n",
        "#   user_list = {\n",
        "#       'xsi_type': 'CrmBasedUserList',\n",
        "#       'name': f'Customer relationship management list #{uuid.uuid4()}',\n",
        "#       'description': 'A list of customers that originated from email addresses',\n",
        "#       # CRM-based user lists can use a membershipLifeSpan of 10000 to indicate\n",
        "#       # unlimited; otherwise normal values apply.\n",
        "#       'membershipLifeSpan': 30,\n",
        "#       'uploadKeyType': 'CONTACT_INFO'\n",
        "#   }\n",
        "\n",
        "#   # Create an operation to add the user list.\n",
        "#   operations = [{\n",
        "#       'operator': 'ADD',\n",
        "#       'operand': user_list\n",
        "#   }]\n",
        "\n",
        "#   result = user_list_service.mutate(operations)\n",
        "#   user_list_id = result['value'][0]['id']\n",
        "\n",
        "#   emails = ltv_emails\n",
        "#   members = [{'hashedEmail': normalize_and_SHA256(email)} for email in emails]\n",
        "\n",
        "#   mutate_members_operation = {\n",
        "#       'operand': {\n",
        "#           'userListId': user_list_id,\n",
        "#           'membersList': members\n",
        "#       },\n",
        "#       'operator': 'ADD'\n",
        "#   }\n",
        "\n",
        "#   response = user_list_service.mutateMembers([mutate_members_operation])\n",
        "\n",
        "#   if 'userLists' in response:\n",
        "#     for user_list in response['userLists']:\n",
        "#       print('User list with name \"%s\" and ID \"%d\" was added.'\n",
        "#             % (user_list['name'], user_list['id']))\n",
        "\n",
        "# create_user_list(adwords_client)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xcw1uVqmgdrG",
        "colab_type": "text"
      },
      "source": [
        "## Thoughts\n",
        "As mentioned a few time, the goal of this tutorial is not to provide a state of the art model for predicting LTV but show a possible approach to create a base model for predicting future monetary value of your customers.\n",
        "\n",
        "If you decided to investigate more the approach, you could:\n",
        "- Do additional data engineering or find additional example when you see outliers.\n",
        "- Try a different approach to create inputs. For example, you could:\n",
        "  - Use a ratio of transactions as *input transactions* and the rest as *target transactions*. An earlier version of this tutorial tried that but the current method led to better results for the problem at hand (predict value in the next X days).\n",
        "  - Customize the time range for input transactions. This tutorial uses all orders that happened before a threshold but you could image to use 6 months before to predict one month after.\n",
        "  - The data preparation creates several examples from a unique customer by using multiple threshold dates. The model can link those example with their original customer. A more advanced approach could try to treat those example as timeseries examples and use how the customer behavior evolve over time. Model might perform better...or not. As you noticed, the `T` value kind of contains that information already across examples that might seem random to the model. Let us know if you decide to try and find some great results.\n",
        "- When comparing models, make sure that you keep a unique test set to fairly compare the model's performance.\n",
        "- This tutorial prepares the data knowing that you want to predict the orders for the next 30 days. If you want a model to predict for the next quarter, you need to update `LENGTH_FUTURE` and prepare a new training dataset.\n",
        "- The original dataset uses a limited number of fields. If you have other dimensions such as product categories, regions or demographics, try to create additional inputs for your model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TpV-iwP9qw9c",
        "colab_type": "text"
      },
      "source": [
        "# Cleaning up\n",
        "\n",
        "To clean up all GCP resources used in this project, you can [delete the GCP\n",
        "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial."
      ]
    }
  ]
}

================================================
FILE: retail/ltv/bqml/scripts/00_procedure_persist.sql
================================================
-- Copyright 2020 Google LLC
--
-- Licensed under the Apache License, Version 2.0 (the "License");
-- you may not use this file except in compliance with the License.
-- You may obtain a copy of the License at
--
--     https://www.apache.org/licenses/LICENSE-2.0
--
-- Unless required by applicable law or agreed to in writing, software
-- distributed under the License is distributed on an "AS IS" BASIS,
-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-- See the License for the specific language governing permissions and
-- limitations under the License.
--
-- Persists the data from a temporary table to a materialized table.
CREATE OR REPLACE PROCEDURE PersistData(
  TEMP_TABLE STRING,
  DEST_TABLE STRING)

BEGIN
  EXECUTE IMMEDIATE """
  CREATE OR REPLACE TABLE """|| DEST_TABLE || """ AS (
  SELECT * FROM """ || TEMP_TABLE || """)""";
END

================================================
FILE: retail/ltv/bqml/scripts/10_procedure_match.sql
================================================
-- Copyright 2020 Google LLC
--
-- Licensed under the Apache License, Version 2.0 (the "License");
-- you may not use this file except in compliance with the License.
-- You may obtain a copy of the License at
--
--     https://www.apache.org/licenses/LICENSE-2.0
--
-- Unless required by applicable law or agreed to in writing, software
-- distributed under the License is distributed on an "AS IS" BASIS,
-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-- See the License for the specific language governing permissions and
-- limitations under the License.
--
-- Transforms raw data to match fields of the template

CREATE OR REPLACE PROCEDURE MatchFields(
  SOURCE_TABLE STRING)

BEGIN

-- || because dynamic table names are not supported.
EXECUTE IMMEDIATE """
CREATE OR REPLACE TEMP TABLE Orders AS 
SELECT 
  CAST(customer_id AS STRING) AS customer_id,
  order_id AS order_id,
  transaction_date AS transaction_date,
  product_sku AS product_sku,
  qty AS qty,
  unit_price AS unit_price 
FROM """ ||
  SOURCE_TABLE;

END

================================================
FILE: retail/ltv/bqml/scripts/20_procedure_prepare.sql
================================================
-- Copyright 2020 Google LLC
--
-- Licensed under the Apache License, Version 2.0 (the "License");
-- you may not use this file except in compliance with the License.
-- You may obtain a copy of the License at
--
--     https://www.apache.org/licenses/LICENSE-2.0
--
-- Unless required by applicable law or agreed to in writing, software
-- distributed under the License is distributed on an "AS IS" BASIS,
-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-- See the License for the specific language governing permissions and
-- limitations under the License.

CREATE OR REPLACE PROCEDURE PrepareForML(
  MAX_STDV_MONETARY INT64,
  MAX_STDV_QTY INT64,
  WINDOW_LENGTH INT64,         -- How many days back for inputs transactions.
  WINDOW_STEP INT64,          -- How many days between thresholds.
  WINDOW_STEP_INITIAL INT64,  -- How many days for the first window.        
  LENGTH_FUTURE INT64,        -- How many days to predict for.  
  TABLE_FOR_PREDICTING STRING,
  TABLE_FOR_TRAINING STRING)

BEGIN

DECLARE MIN_DATE DATE;        -- Date of the first order in the dataset.                                     
DECLARE MAX_DATE DATE;        -- Date of the final order in the dataset.
DECLARE THRESHOLD_DATE DATE;  -- Date that separates inputs orders from target orders.  
DECLARE WINDOW_START DATE;    -- Date at which an input transactions window starts.
DECLARE STEP INT64 DEFAULT 1; -- Index of the window being run.

-- Aggregates per date per customers.
CREATE OR REPLACE TEMP TABLE Aggred AS
SELECT
  customer_id,
  order_day,
  ROUND(day_value_after_returns, 2) AS value,
  day_qty_after_returns as qty_articles,
  day_num_returns AS num_returns,
  CEIL(avg_time_to_return) AS time_to_return
FROM (
  SELECT
    customer_id,
    order_day,
    SUM(order_value_after_returns) AS day_value_after_returns,
    STDDEV(SUM(order_value_after_returns)) OVER(PARTITION BY customer_id ORDER BY SUM(order_value_after_returns)) AS stdv_value,
    SUM(order_qty_after_returns) AS day_qty_after_returns,
    STDDEV(SUM(order_qty_after_returns)) OVER(PARTITION BY customer_id ORDER BY SUM(order_qty_after_returns)) AS stdv_qty,
    CASE
      WHEN MIN(order_min_qty) < 0 THEN count(1)
      ELSE 0
    END AS day_num_returns,
    CASE
      WHEN MIN(order_min_qty) < 0 THEN AVG(time_to_return)
      ELSE NULL
    END AS avg_time_to_return
  FROM (
    SELECT 
      customer_id,
      order_id,
      -- Gives the order date vs return(s) dates.
      MIN(transaction_date) AS order_day,
      MAX(transaction_date) AS return_final_day,
      DATE_DIFF(MAX(transaction_date), MIN(transaction_date), DAY) AS time_to_return,
      -- Aggregates all products in the order 
      -- and all products returned later.
      SUM(qty * unit_price) AS order_value_after_returns,
      SUM(qty) AS order_qty_after_returns,
      -- If negative, order has qty return(s).
      MIN(qty) order_min_qty
    FROM 
      Orders
    GROUP BY
      customer_id,
      order_id)
  GROUP BY
    customer_id,
    order_day)
WHERE
  -- [Optional] Remove dates with outliers per a customer.
  (stdv_value < MAX_STDV_MONETARY
    OR stdv_value IS NULL) AND
  (stdv_qty < MAX_STDV_QTY
    OR stdv_qty IS NULL);

-- Creates the inputs and targets accross multiple threshold dates.
SET (MIN_DATE, MAX_DATE) = (
  SELECT AS STRUCT 
    MIN(order_day) AS min_days,
    MAX(order_day) AS max_days
  FROM
    Aggred
);

SET THRESHOLD_DATE = MIN_DATE;

CREATE OR REPLACE TEMP TABLE Featured
(
  -- dataset STRING,
  customer_id STRING,
  monetary FLOAT64,
  frequency INT64,
  recency INT64,
  T INT64,
  time_between FLOAT64,
  avg_basket_value FLOAT64,
  avg_basket_size FLOAT64,
  has_returns STRING,
  avg_time_to_return FLOAT64,
  num_returns INT64,
  -- threshold DATE,
  -- step INT64,
  target_monetary FLOAT64,
);

LOOP
  -- Can choose a longer original window in case 
  -- there were not many orders in the early days.
  IF STEP = 1 THEN
    SET THRESHOLD_DATE = DATE_ADD(THRESHOLD_DATE, INTERVAL WINDOW_STEP_INITIAL DAY); 
  ELSE
    SET THRESHOLD_DATE = DATE_ADD(THRESHOLD_DATE, INTERVAL WINDOW_STEP DAY);
  END IF;
  SET STEP = STEP + 1;

  IF THRESHOLD_DATE >= DATE_SUB(MAX_DATE, INTERVAL (WINDOW_STEP) DAY) THEN
    LEAVE;
  END IF;

  -- Takes all transactions before the threshold date unless you decide
  -- to use a different window lenght to test model performance.
  IF WINDOW_LENGTH != 0 THEN
    SET WINDOW_START = DATE_SUB(THRESHOLD_DATE, INTERVAL WINDOW_LENGTH DAY);
  ELSE
    SET WINDOW_START = MIN_DATE;
  END IF;

  INSERT Featured
  SELECT
    -- CASE
    --   WHEN THRESHOLD_DATE <= DATE_SUB(MAX_DATE, INTERVAL LENGTH_FUTURE DAY) THEN 'UNASSIGNED'
    --   ELSE 'TEST'
    -- END AS dataset,
    tf.customer_id,
    ROUND(tf.monetary_orders, 2) AS monetary,
    tf.cnt_orders AS frequency,
    tf.recency,
    tf.T,
    ROUND(tf.recency/cnt_orders, 2) AS time_between,
    ROUND(tf.avg_basket_value, 2) AS avg_basket_value,
    ROUND(tf.avg_basket_size, 2) AS avg_basket_size,
    has_returns,
    CEIL(avg_time_to_return) AS avg_time_to_return,
    num_returns,
    -- THRESHOLD_DATE AS threshold,
    -- STEP - 1 AS step,
    ROUND(tt.target_monetary, 2) AS target_monetary,
  FROM (
      -- This SELECT uses only data before THRESHOLD_DATE to make features.
      SELECT
        customer_id,
        SUM(value) AS monetary_orders,
        DATE_DIFF(MAX(order_day), MIN(order_day), DAY) AS recency,
        DATE_DIFF(THRESHOLD_DATE, MIN(order_day), DAY) AS T,
        COUNT(DISTINCT order_day) AS cnt_orders,
        AVG(qty_articles) avg_basket_size,
        AVG(value) avg_basket_value,
        CASE
          WHEN SUM(num_returns) > 0 THEN 'y'
          ELSE 'n'
        END AS has_returns,
        AVG(time_to_return) avg_time_to_return,
        THRESHOLD_DATE AS threshold,
        SUM(num_returns) num_returns,
      FROM
        Aggred
      WHERE
        order_day <= THRESHOLD_DATE AND
        order_day >= WINDOW_START
      GROUP BY
        customer_id
    ) tf
  INNER JOIN (
    -- This SELECT uses all orders that happened between threshold and 
    -- threshold + LENGTH_FUTURE to calculte the target monetary.
    SELECT
      customer_id,
      SUM(value) target_monetary
    FROM
      Aggred
    WHERE
      order_day <= DATE_ADD(THRESHOLD_DATE, INTERVAL LENGTH_FUTURE DAY)
      -- Overall value is similar to predicting only what's after threshold.
      -- and the prediction performs better. We can substract later.
      -- AND order_day > THRESHOLD_DATE
    GROUP BY
      customer_id) tt
  ON
    tf.customer_id = tt.customer_id;

END LOOP;

-- Persists the temporary ml table. Could do it directly from the above query
-- but this tutorial tries to limit String SQL statements as much as possible
-- and CREATE OR REPLACE TABLE without specifying a dataset is not supported.
-- The TrainLTV needs the table to be persisted.
EXECUTE IMMEDIATE """
CREATE OR REPLACE TABLE """|| TABLE_FOR_PREDICTING || """ AS (
SELECT * FROM Aggred )""";

EXECUTE IMMEDIATE """
CREATE OR REPLACE TABLE """|| TABLE_FOR_TRAINING || """ AS (
SELECT * FROM Featured )""";

-- CALL PersistData(TABLE_FOR_PREDICTING, "Aggred");
-- CALL PersistData(TABLE_FOR_TRAINING, "Featured");

END

================================================
FILE: retail/ltv/bqml/scripts/30_procedure_train.sql
================================================
-- Copyright 2020 Google LLC
--
-- Licensed under the Apache License, Version 2.0 (the "License");
-- you may not use this file except in compliance with the License.
-- You may obtain a copy of the License at
--
--     https://www.apache.org/licenses/LICENSE-2.0
--
-- Unless required by applicable law or agreed to in writing, software
-- distributed under the License is distributed on an "AS IS" BASIS,
-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-- See the License for the specific language governing permissions and
-- limitations under the License.

CREATE OR REPLACE PROCEDURE TrainLTV(
  MODEL_NAME STRING,
  DATA_TABLE STRING)

BEGIN

#[START TRAIN_MODEL]
EXECUTE IMMEDIATE """
CREATE OR REPLACE MODEL """ || MODEL_NAME || """
  OPTIONS(MODEL_TYPE='AUTOML_REGRESSOR',
          INPUT_LABEL_COLS=['target_monetary'],
          OPTIMIZATION_OBJECTIVE='MINIMIZE_MAE')
AS 
SELECT 
  * EXCEPT(customer_id)
FROM """ || DATA_TABLE;
#[END TRAIN_MODEL]

END

================================================
FILE: retail/ltv/bqml/scripts/40_procedure_predict.sql
================================================
-- Copyright 2020 Google LLC
--
-- Licensed under the Apache License, Version 2.0 (the "License");
-- you may not use this file except in compliance with the License.
-- You may obtain a copy of the License at
--
--     https://www.apache.org/licenses/LICENSE-2.0
--
-- Unless required by applicable law or agreed to in writing, software
-- distributed under the License is distributed on an "AS IS" BASIS,
-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-- See the License for the specific language governing permissions and
-- limitations under the License.

CREATE OR REPLACE PROCEDURE PredictLTV(
  MODEL_NAME STRING,
  TABLE_DATA STRING,
  PREDICT_FROM_DATE STRING,
  WINDOW_LENGTH INT64,
  TABLE_PREDICTION STRING)

BEGIN

-- Date at which an input transactions window starts.
DECLARE WINDOW_START STRING;

IF WINDOW_LENGTH != 0 THEN
  SET WINDOW_START = (SELECT CAST(DATE_SUB(PREDICT_FROM_DATE, INTERVAL WINDOW_LENGTH DAY) AS STRING));
ELSE
  SET WINDOW_START = "1900-01-01";
END IF;

IF PREDICT_FROM_DATE = 'NULL' THEN
  SET PREDICT_FROM_DATE = (SELECT CAST(CURRENT_DATE() AS STRING));
END IF;

EXECUTE IMMEDIATE """
CREATE OR REPLACE TABLE """ || TABLE_PREDICTION || """ AS
SELECT
  customer_id,
  monetary AS monetary_so_far,
  ROUND(predicted_target_monetary, 2) AS monetary_predicted,
  ROUND(predicted_target_monetary - monetary, 2) AS monetary_future
FROM
  ML.PREDICT(
    MODEL """ || MODEL_NAME || """,
    (
      SELECT
        customer_id,
        ROUND(monetary_orders, 2) AS monetary,
        cnt_orders AS frequency,
        recency,
        T,
        ROUND(recency/cnt_orders, 2) AS time_between,
        ROUND(avg_basket_value, 2) AS avg_basket_value,
        ROUND(avg_basket_size, 2) AS avg_basket_size,
        has_returns,
        CEIL(avg_time_to_return) AS avg_time_to_return,
        num_returns
      FROM (
        SELECT
          customer_id,
          SUM(value) AS monetary_orders,
          DATE_DIFF(MAX(order_day), MIN(order_day), DAY) AS recency,
          DATE_DIFF(PARSE_DATE('%Y-%m-%d', '""" || PREDICT_FROM_DATE || """'), MIN(order_day), DAY) AS T,
          COUNT(DISTINCT order_day) AS cnt_orders,
          AVG(qty_articles) avg_basket_size,
          AVG(value) avg_basket_value,
          CASE
            WHEN SUM(num_returns) > 0 THEN 'y'
            ELSE 'n'
          END AS has_returns,
          AVG(time_to_return) avg_time_to_return,
          SUM(num_returns) num_returns,
        FROM
          """ || TABLE_DATA || """
        WHERE
          order_day <= PARSE_DATE('%Y-%m-%d', '""" || PREDICT_FROM_DATE || """') AND
          order_day >= PARSE_DATE('%Y-%m-%d', '""" || WINDOW_START || """')
        GROUP BY
          customer_id
      )
    )
  )""";

END

================================================
FILE: retail/ltv/bqml/scripts/50_procedure_top.sql
================================================
-- Copyright 2020 Google LLC
--
-- Licensed under the Apache License, Version 2.0 (the "License");
-- you may not use this file except in compliance with the License.
-- You may obtain a copy of the License at
--
--     https://www.apache.org/licenses/LICENSE-2.0
--
-- Unless required by applicable law or agreed to in writing, software
-- distributed under the License is distributed on an "AS IS" BASIS,
-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-- See the License for the specific language governing permissions and
-- limitations under the License.
--
-- Extracts top LTV customers.
CREATE OR REPLACE PROCEDURE ExtractTopEmails(
  TOP_LTV_RATIO FLOAT64,
  TABLE_SOURCE STRING,
  TABLE_CRM STRING,
  TABLE_OUTPUT STRING)

BEGIN

EXECUTE IMMEDIATE """
CREATE OR REPLACE TABLE """|| TABLE_OUTPUT || """ AS (
  SELECT
  p.customer_id,
  monetary_future,
  c.email AS email
FROM (
  SELECT
    customer_id,
    monetary_future,
    PERCENT_RANK() OVER (ORDER BY monetary_future DESC) AS percent_rank_monetary
  FROM
      """ || TABLE_SOURCE || """ ) p
  -- This creates fake emails. You need to join with your own CRM table.
  INNER JOIN (
  SELECT
    customer_id,
    email
  FROM
    """ || TABLE_CRM || """ ) c
ON
  p.customer_id = CAST(c.customer_id AS STRING)
WHERE
  -- Decides the size of your list of emails. For similar-audience use cases 
  -- where you need to find a minimum of matching emails, 20% should provide
  -- enough potential emails.
  percent_rank_monetary <= """ || TOP_LTV_RATIO || """
ORDER BY 
  monetary_future DESC
)""";

END

================================================
FILE: retail/ltv/bqml/scripts/run.sh
================================================
#!/bin/bash
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Reads command line arguments.
while [[ $# -gt 0 ]]
do
  case ${1} in
  -p|--project-id)
      shift
      PROJECT_ID="${1}"
      ;;
  -d|--dataset-id)
      shift
      DATASET_ID="${1}"
      ;;
  -o|table-sales)
      shift
      TABLE_SALES="${1}"
      ;;
  -c|table-crm)
      shift
      TABLE_CRM="${1}"
      ;;
  -t|--train-model-id)
      shift
      TRAIN_MODEL_ID="${1}"
      ;;
  -u|--use-model-id)
      shift
      USE_MODEL_ID="${1}"
      ;;
  -w|--window-length)
      shift
      WINDOW_LENGTH="${1}"
      ;;
  -s|--window-step)
      shift
      WINDOW_STEP="${1}"
      ;;
  -i|--window-step-initial)
      shift
      WINDOW_STEP_INITIAL="${1}"
      ;;
  -l|--length-future)
      shift
      LENGTH_FUTURE="${1}"
      ;;
  -m|--max-stdv-monetary)
      shift
      MAX_STDV_MONETARY="${1}"
      ;;
  -q|--max-stdv-qty)
      shift
      MAX_STDV_QTY="${1}"
      ;;
  -r|--top-ltv-ratio)
      shift
      TOP_LTV_RATIO="${1}"
      ;;
  -h|--help)
      echo "FLAGS"
      echo -e "  --project-id=PROJECT_ID, -p"
      echo -e "     [Required] Project ID."
      echo -e "  --dataset-id=DATASET_ID, -d"
      echo -e "     [Required] Dataset ID."
      echo -e "  --train-model-id=TRAIN_MODEL_ID, -t"
      echo -e "     Name of the trained model. Set to *null* if you do not want to train a model."
      echo -e "  --use-model-id=USE_MODEL_ID, -u"
      echo -e "     Name of the model to use for predictions. Must include dataset: [DATASET].[MODEL]"
      echo -e "  --window-length=WINDOW_LENGTH, -w"
      echo -e "     Time range in days for input transactions."
      echo -e "  --window-step=WINDOW_STEP, -s"
      echo -e "     Time in days between two windows. Equivalent to the time between two threshold dates."
      echo -e "  --window-step-initial=WINDOW_STEP_INITIAL, -i"
      echo -e "     Initial time in days before setting the first threshold date."
      echo -e "  --length-future=LENGTH_FUTURE, -l"
      echo -e "     Time in days for which to make a prediction."
      echo -e "  --max-stdv-monetary=MAX_STDV_MONETARY, -m"
      echo -e "     Standard deviation of the monetary value per customer above which the script removes transactions."
      echo -e "  --max-stdv-qty=MAX_STDV_QTY, -q"
      echo -e "     Standard deviation of the quantity value per customer above which the script removes transactions."
      echo -e "  --top-ltv-ratio=TOP_LTV_RATIO, -r"
      echo -e "     Percentage of the top customers to extract."
      exit
      ;;
  *)
      echo "Flag not supported. See ./run.sh --help"
      exit 1
      shift
      ;;
  esac
  shift
done

# Sets default values if missing from the command line.
NOW=$(date +"%Y%m%d%H%M%S")
TRAIN_MODEL_ID="${TRAIN_MODEL_ID:-$DATASET_ID.model_$NOW}"
USE_MODEL_ID="${USE_MODEL_ID:-$DATASET_ID.model_$NOW}"
WINDOW_LENGTH=${WINDOW_LENGTH:-0}
WINDOW_STEP=${WINDOW_STEP:-30}
WINDOW_STEP_INITIAL=${WINDOW_STEP_INITIAL:-90}
LENGTH_FUTURE=${LENGTH_FUTURE:-30}
MAX_STDV_MONETARY=${MAX_STDV_MONETARY:-500}
MAX_STDV_QTY=${MAX_STDV_QTY:-100}
TOP_LTV_RATIO=${TOP_LTV_RATIO:-0.2}

SOURCE_TABLE="${TABLE_SALES:-$DATASET_ID.sales}"  # Original data dump.
TABLE_CRM="${TABLE_CRM:-$DATASET_ID.crm}"         # Table with user information.
TABLE_AGGRED="${DATASET_ID}.aggred"               # Used to create training dataset and to run predictions.
TABLE_ML="${DATASET_ID}.ml"                       # Used to train the model
TABLE_PREDICTIONS="${DATASET_ID}.predictions"     # Table where to output predictions.
TABLE_EMAILS="${DATASET_ID}.top_emails"           # Table where to output top emails.

# Project and Dataset IDs are required.
if [ -z "$PROJECT_ID" ]; then
  echo "Please specify a project id. See ./run.sh --help"
  exit 1
fi

if [ -z "$DATASET_ID" ]; then
  echo "Please specify a dataset id. See ./run.sh --help"
  exit 1
fi

echo "---------------------------------------"
echo "Runs script with the follow parameters."
echo "PROJECT_ID: ${PROJECT_ID}"
echo "DATASET_ID: ${DATASET_ID}"
echo "WINDOW_STEP: ${WINDOW_STEP}"
echo "WINDOW_STEP_INITIAL: ${WINDOW_STEP_INITIAL}"
echo "LENGTH_FUTURE: ${LENGTH_FUTURE}"
echo "MAX_STDV_MONETARY: ${MAX_STDV_MONETARY}"
echo "MAX_STDV_QTY: ${MAX_STDV_QTY}"
echo "TOP_LTV_RATIO: ${TOP_LTV_RATIO}"
echo "Source table for transactions is: ${SOURCE_TABLE}"
echo "Source table for CRM is: ${TABLE_CRM}"
echo "Will train a model named: ${TRAIN_MODEL_ID}"
echo "Will run predictions using model: ${USE_MODEL_ID}"
echo "--------------------------------------"

gcloud config set project $PROJECT_ID

bq show ${PROJECT_ID}:${DATASET_ID} || bq mk ${PROJECT_ID}:${DATASET_ID}

# Load example datasets from public GCS to BQ if they don't exist.
bq show ${TABLE_CRM} || \
bq load \
  --project_id $PROJECT_ID \
  --skip_leading_rows 1 \
  --max_bad_records 100000 \
  --replace \
  --field_delimiter "," \
  --autodetect \
  ${TABLE_CRM} \
  gs://solutions-public-assets/analytics-componentized-patterns/ltv/crm.csv

bq show ${SOURCE_TABLE} || \
bq load \
  --project_id $PROJECT_ID \
  --skip_leading_rows 1 \
  --max_bad_records 100000 \
  --replace \
  --field_delimiter "," \
  --autodetect \
  ${SOURCE_TABLE} \
  gs://solutions-public-assets/analytics-componentized-patterns/ltv/sales_*


function store_procedure() {
  echo ""
  echo "------------------------------------------------------"
  echo "--- Deploys procedure ${1}. ---"
  echo ""
  bq query \
  --project_id ${PROJECT_ID} \
  --dataset_id ${DATASET_ID} \
  --use_legacy_sql=false \
  < ${1}
}

function run_action() {
  echo ""
  echo "--------------------------------------------"
  echo " Run the following procedure:"
  echo "$@"
  echo ""
  bq query \
  --project_id ${PROJECT_ID} \
  --dataset_id ${DATASET_ID} \
  --use_legacy_sql=false \
  "$@"
}

store_procedure 00_procedure_persist.sql
store_procedure 10_procedure_match.sql
store_procedure 20_procedure_prepare.sql

run_action """
  CALL MatchFields('${SOURCE_TABLE}');
  CALL PrepareForML(
    CAST('${MAX_STDV_MONETARY}' AS INT64),
    CAST('${MAX_STDV_QTY}' AS INT64),
    CAST('${WINDOW_LENGTH}' AS INT64),
    CAST('${WINDOW_STEP}' AS INT64),
    CAST('${WINDOW_STEP_INITIAL}' AS INT64),
    CAST('${LENGTH_FUTURE}' AS INT64),
    '${TABLE_AGGRED}',
    '${TABLE_ML}');"""

store_procedure 30_procedure_train.sql
if [[ $TRAIN_MODEL_ID != "null" ]] ;then
  run_action """
    CALL TrainLTV(
      '${TRAIN_MODEL_ID}',
      '${TABLE_ML}');"""
fi

store_procedure 40_procedure_predict.sql
if [[ $USE_MODEL_ID != "null" ]] ;then
  run_action """
    CALL PredictLTV(
      '${USE_MODEL_ID}',
      '${TABLE_AGGRED}',
      'NULL',
      CAST('${WINDOW_LENGTH}' AS INT64),
      '${TABLE_PREDICTIONS}');"""
fi

store_procedure 50_procedure_top.sql
run_action """
  DECLARE TOP_EMAILS ARRAY<STRING>;

  CALL ExtractTopEmails(
      CAST('${TOP_LTV_RATIO}' AS FLOAT64),
      '${TABLE_PREDICTIONS}',
      '${TABLE_CRM}',
      '${TABLE_EMAILS}');"""

================================================
FILE: retail/propensity-model/bqml/README.md
================================================
## License
```
Copyright 2020 Google LLC

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

     https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
```
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)

# How to build an end-to-end propensity to purchase solution using BigQuery ML and Kubeflow Pipelines
You’ll learn how to build a Propensity a model (if a Customer is going to buy) and how to orchestrate an ML Pipeline for doing so. You can use the code as a reference guide. Amend, replace, or add new AI Pipeline Components according to your use case. Please refer to the [Notebook](bqml_kfp_retail_propensity_to_purchase.ipynb), which contains further documentation and detailed instruction.

To see a step-by-step tutorial that walks you through implementing this solution, see [Predicting customer propensity to buy by using BigQuery ML and AI Platform](https://cloud.google.com/solutions/predicting-customer-propensity-to-buy).   

### The Notebook does the followings:
<ul>
    <li>Environment Setup</li>
    <ul>
        <li>Setup Cloud AI Platform Pipelines (using the CloudConsole)</li>
        <li>Install KFP client</li>
        <li>Install Python packages for Google Cloud Services</li>
    </ul>
    <li>Kubeflow Pipelines (KFP) Setup</li>
    <ul>
        <li>Prepare Data for the training</li>
        <ul>
            <li>Create/Validate a Google Cloud Storage Bucket/Folder</li>
            <li>Create the input table in BigQuery</li>
        </ul>
        <li>Train the model</li>
        <li>Evaluate the model</li>
        <li>Prepare the Model for batch prediction</li>
        <ul>
            <li>Prepare a test dataset (a table)</li>
            <li>Predict the model in BigQuery</li>
        </ul>
        <li>Prepare the Model for online prediction</li>
        <ul>
            <li>Create a new revision <i>(Model revision management)</i></li>
            <li>Export the BigQuery Model</li>
            <ul>
                <li>Export the Model from BigQuery to Google Cloud Storage</li>
                <li>Export the Training Stats to Google Cloud Storage</li>
                <li>Export the Eval Metrics to Google Cloud Storage</li>
            </ul>
            <li>Deploy to Cloud AI Platform Prediction</li>
            <li>Predict the model in Cloud AI Platform Prediction</li>
        </ul>
    </ul>
    <li>Data Exploration using BigQuery, Pandas, matplotlib</li>
    <li>SDLC methodologies Adherence (opinionated)</li>
        <ul>
            <li>Variables naming conventions</li>
            <ul>
                <li>Upper case Names for immutable variables</li>
                <li>Lower case Names for mutable variables</li>
                <li>Naming prefixes with <i>rpm_</i> or <i>RPM_</i></li>
            </ul>
            <li>Unit Tests</li>
            <li>Cleanup/Reset utility functions</li>
        </ul>
    <li>KFP knowledge share (demonstration)</li>
        <ul>
            <li>Pass inputs params through function args</li>
            <li>Pass params through pipeline args</li>
            <li>Pass Output from one Component as input of another</li>
            <li>Create an external Shared Volume available to all the Comp</li>
            <li>Use built in Operators</li>
            <li>Built light weight Component</li>
            <li>Set Component not to cache</li>
        </ul>
</ul>

### Architecture of the pipeline
![Pipeline components](images/MLOPs-Pipeline-Architecture.png?raw=true "Architecture of the Pipeline")

### Data Exploration and Visualization in the notebook

![Data Exploration](images/DataExploration.png?raw=true "Data Exploration")

![Data Visualization](images/DataVisualization.png?raw=true "Data Visualization")

## Running the Unit tests

Create a <b>local context</b> and use it to unit test the KFP Pipeline component locally. Below is an example of testing your component locally:
```python
# test locally create_gcs_bucket_folder
local_context = get_local_context()
import json
update_local_context (create_gcs_bucket_folder(
    json.dumps(local_context),
    local_context['RPM_GCP_STORAGE_BUCKET'],
    local_context['RPM_GCP_PROJECT'],
    local_context['RPM_DEFAULT_BUCKET_EXT'],
    local_context['RPM_GCP_STORAGE_BUCKET_FOLDER'],
    local_context['RPM_DEFAULT_BUCKET_FOLDER_NAME']
))
```

### Utility functions

Below is an utility function which purges GCS artifacts while unit/integration testing:

```python
#delete BQ Table if not needed...!!!BE CAREFUL!!!
def delete_table(table_id):
    from google.cloud import bigquery
    # Construct a BigQuery client object.
    client = bigquery.Client()
    # client.delete_table(table_id, not_found_ok=True)  # Make an API request.
    client.delete_table(table_id)  # Make an API request.
    print("Deleted table '{}'.".format(table_id))
#delete the table in the bigquery
delete_table(get_local_context()['rpm_table_id'])
```

## Mandatory Variables

You must set values of these parameters, please refer to the instructions in the Notebook for details:
```python
RPM_GCP_PROJECT:'<Your GCP Project>'
RPM_GCP_KFP_HOST='<Your KFP pipeline host>'
RPM_GCP_APPLICATION_CREDENTIALS="<Full path with the file name to the above downloaded json file>"
```

## A screen grab of the Output of the KFP pipeline
![KFP Graph](images/KFP-Graph.png?raw=true "KFP Graph")

## KFP Knowledge Share

The below code snippets demonstrates various KFP syntaxes. It shows various ways to pass parameters. You could use whatever works for you.

### Pass inputs params through function args example:
```python
# create BQ DS only if it doesn't exist
from typing import NamedTuple
def create_bq_ds (ctx:str, 
    RPM_GCP_PROJECT: str,
    RPM_BQ_DATASET_NAME: str, 
    RPM_LOCATION: str
    ) -> NamedTuple('Outputs', [
    ('rpm_context', str), 
    ('rpm_bq_ds_name', str), 
    ]):
```
![Input Output](images/KFP-Function_Params.png?raw=true "Input Output")

### Pass params through pipeline args example:
```python
def bq_googlestr_dataset_to_bq_to_caip_pipeline(
    data_path = all_vars['RPM_PVC_NAME'] #you can pass input variables
):
```

### Pass Output from one Kubeflow Pipelines Component as Input of another Kubeflow Pipelines Component example:
Output 'rpm_table_id' from load_bq_ds_op component passed as input to create_bq_ml_op comp
```python
    create_bq_ml_op = create_kfp_comp(create_bq_ml)(
        load_bq_ds_op.outputs['rpm_context'],
        all_vars['RPM_GCP_PROJECT'],
        all_vars['RPM_MODEL_NAME'],
        all_vars['RPM_DEFAULT_MODEL_NAME'],
        create_bq_ds_op.outputs['rpm_bq_ds_name'],
        load_bq_ds_op.outputs['rpm_table_id']
        )
```

### Create an external Shared Volume available to all the Kubeflow Pipelines Component example:
``` python
    #create a volume where the dataset will be temporarily stored.
    pvc_op = VolumeOp(
        name=all_vars['RPM_PVC_NAME'],
        resource_name=all_vars['RPM_PVC_NAME'],
        size="20Gi",
        modes=dsl.VOLUME_MODE_RWO
    )
```

### Use built in Ops example:
``` python
    #create a volume where the dataset will be temporarily stored.
    pvc_op = VolumeOp(
        name=all_vars['RPM_PVC_NAME'],
        resource_name=all_vars['RPM_PVC_NAME'],
        size="20Gi",
        modes=dsl.VOLUME_MODE_RWO
    )
```

### Built light weight Kubeflow Pipelines Component example:
``` python
# converting functions to container operations
import kfp.components as comp
def create_kfp_comp(rpm_comp):
    return comp.func_to_container_op(
        func=rpm_comp, 
        base_image="google/cloud-sdk:latest")
```

### Set Kubeflow Pipelines Component not to cache example:
```python
    get_versioned_bqml_model_export_path_op.execution_options.caching_strategy.max_cache_staleness = "P0D"
```

## Questions? Feedback?
If you have any questions or feedback, please open up a [new issue](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/issues).

================================================
FILE: retail/propensity-model/bqml/bqml_kfp_retail_propensity_to_purchase.ipynb
================================================
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      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vTuyPqeywaoQ",
        "colab_type": "text"
      },
      "source": [
        "# Overview\n",
        "Propensity to purchase use cases are widely applicable across many industry verticals such as Retail, Finance and more. In this article, we will show you how to build an end to end solution using [BigQuery ML](https://cloud.google.com/bigquery-ml/docs) and [Kubeflow Pipelines (KFP)](https://www.kubeflow.org/docs/pipelines/overview/pipelines-overview/) using a [Google Analytics dataset](https://console.cloud.google.com/marketplace/details/obfuscated-ga360-data/obfuscated-ga360-data?filter=solution-type:dataset) to determine which customers have the propensity to purchase. You could use the solution to reach out to your targeted customers in an offline campaign via email or postal channels. You could also use it in an online campaign via on the spot decision, when the customer is browsing your products in your website, to recommend some products or trigger a personalized email for the customer.\n",
        "\n",
        "Propensity to purchase use case is a subset of personalization use case. It is a key driver of how many organizations do marketing today. In today's changing times, you need to ensure that you are targeting the right messages to the right customers at the right time. “Personalization at scale has the potential to create $1.7 trillion to $3 trillion in new value” \\([McKinsey study](https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/a-technology-blueprint-for-personalization-at-scale)\\). Propensity modeling helps companies to identify these \"right\" customers and prospects that have a high likelihood to purchase a particular product or service.\n",
        "\n",
        "Propensity models are important as it is a mechanism for targeting sales outreach with personalized messages as they are keys to the success of getting attention of the customers. By using a propensity to purchase model, you can more effectively target customers who are most likely to purchase certain products.\n",
        "\n",
        "# What is BigQuery ML?\n",
        "[BigQuery ML](https://cloud.google.com/bigquery-ml/docs/bigqueryml-intro) enables users to create and execute machine learning models in BigQuery by using standard SQL queries. This means, if your data is already in BigQuery, you don’t need to export your data to train and deploy machine learning models — by training, you’re also deploying in the same step. Combined with BigQuery’s auto-scaling of compute resources, you won’t have to worry about spinning up a cluster or building a model training and deployment pipeline. This means you’ll be saving time building your machine learning pipeline, enabling your business to focus more on the value of machine learning instead of spending time setting up the infrastructure.\n",
        "\n",
        "## Scope of this notebook\n",
        "### Dataset\n",
        "The Google Analytics dataset is hosted publicly on BigQuery and is a dataset that provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the [Google Merchandise Store](https://www.googlemerchandisestore.com/), a real e-commerce store that sells Google-branded merchandise.\n",
        "\n",
        "### Objective\n",
        " To help you be conversant on the following:\n",
        "1. Environment Setup\n",
        "1. KFP Setup\n",
        "1. Data Exploration using BigQuery, Pandas, matplotlib\n",
        "1. SDLC methodologies Adherence (opinionated)\n",
        "1. KFP knowledge share (demonstration)\n",
        "\n",
        "### Costs\n",
        "This tutorial uses billable components of Google Cloud:\n",
        "* [BigQuery](https://cloud.google.com/bigquery)\n",
        "* [BigQuery ML](https://cloud.google.com/bigquery-ml/docs)\n",
        "* [Cloud Storage](https://cloud.google.com/storage)\n",
        "* [Cloud AI Platform Pipelines](https://cloud.google.com/ai-platform/pipelines/docs/) \\(uses [Google Cloud Kubernetes Engine (GKE)](https://cloud.google.com/kubernetes-engine)\\)\n",
        "* [Cloud AI Prediction](https://cloud.google.com/ai-platform/prediction/docs)\n",
        "\n",
        "Use the [Pricing Calculator[(https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage.\n",
        "\n",
        "## Before you begin\n",
        "For this reference guide, you need a [Google Cloud project](https://console.cloud.google.com/cloud-resource-manager).\n",
        "\n",
        "You can create a new one, or select a project you already created.\n",
        "The following steps are required, regardless where you are running your notebook (local or in Cloud AI Platform Notebook).\n",
        "* [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
        "* [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project). \n",
        "* (When using non-Google Cloud local envirionments)Install Google Cloud SDK [Google Cloud SDK](https://cloud.google.com/sdk/)\n",
        "\n",
        "### Mandatory variables\n",
        "You must set the below variables:\n",
        "* RPM_GCP_PROJECT to [Your Google Cloud Project]\n",
        "* RPM_GCP_KFP_HOST to [Your KFP pipeline host]: See the instruction later to find out how to get that\n",
        "* RPM_GCP_APPLICATION_CREDENTIALS to [Full path with the file name to the Service Account json file]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rn0zRs76waoQ",
        "colab_type": "text"
      },
      "source": [
        "# Setup local environment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n0QAU9KKwaoz",
        "colab_type": "text"
      },
      "source": [
        "## PIP install appropriate packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "Vidii1fJwao0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install google-cloud-storage #for Storage Account\n",
        "!pip install google-cloud #for cloud sdk\n",
        "!pip install google-cloud-bigquery #for BigQuery\n",
        "!pip install google-cloud-bigquery-storage #for BigQuery Storage client\n",
        "!pip install kfp # Install the KFP AI SDK"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "98mkp-d-GWtn",
        "colab_type": "text"
      },
      "source": [
        "## Setup KFP Host\n",
        "Prepare the Cloud AI Platform Pipelines in Google Cloud:\n",
        "* [Setting up AI Platform Pipelines](https://cloud.google.com/ai-platform/pipelines/docs/getting-started#set_up_your_instance).\n",
        "* [Optional] Read the 'Introducing Cloud AI Platform Pipelines' [blogpost](https://cloud.google.com/blog/products/ai-machine-learning/introducing-cloud-ai-platform-pipelines)\n",
        "* Follow the [instructions](https://cloud.google.com/ai-platform/pipelines/docs/connecting-with-sdk#using_the_to_connect_to_an_cluster) to collect the Kubeflow Pipelines host:\n",
        "1. Copy from the code snippet from the popup dialogbox.\n",
        "1. Find the hostname in the URL of the Kubeflow Pipelines dashboard. The hostname is the portion of the URL between https:// and /#/start, and should match the pattern *.pipelines.googleusercontent.com.\n",
        "\n",
        "\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w2q-UGy_waoR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "RPM_GCP_KFP_HOST = \"<Your KFP pipeline host>\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3647-gHRwaoU",
        "colab_type": "text"
      },
      "source": [
        "## Setup Google Cloud Project"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-aDrzxH5waoV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# set the Google Cloud project id\n",
        "RPM_GCP_PROJECT = \"<Your Google Cloud project>\" #for local !bash"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "poJmkPFlwaoY",
        "colab_type": "text"
      },
      "source": [
        "## Setup Google Cloud Credentials"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3wrGKHIqwaoY",
        "colab_type": "text"
      },
      "source": [
        "### Specify the location of the ServiceAccount Key file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OJ8peThgwaoZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# download the ServiceAccount key and provide the path to the file below\n",
        "RPM_GCP_APPLICATION_CREDENTIALS = \"<Full path with the file name to the above downloaded json file>\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_tY9FxElwaoc",
        "colab_type": "text"
      },
      "source": [
        "### Upload your service account file (colab specific code block)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_sLTsZcwwaoc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# uncomment the below code in codelab environment\n",
        "# from google.colab import files\n",
        "# # Upload service account key\n",
        "# keyfile_upload = files.upload()\n",
        "# RPM_GCP_APPLICATION_CREDENTIALS = list(keyfile_upload.keys())[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "usviVg5dwaof",
        "colab_type": "text"
      },
      "source": [
        "## Setup your local runtime enviorment\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3tB93tQjwaof",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!export RPM_GCP_PROJECT\n",
        "!echo $RPM_GCP_PROJECT"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "KNexaYJcwaoi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# et the desired Google Cloud project\n",
        "!gcloud config set project $RPM_GCP_PROJECT"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "PGlZ6hOiwaol",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# alidate that the Google Cloud project has been set properly.\n",
        "!gcloud info --format='value(config.project)'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZ5pwiIrwaon",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = RPM_GCP_APPLICATION_CREDENTIALS"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "NocjwMIYwaop",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# set the account\n",
        "!gcloud auth activate-service-account --key-file=$RPM_GCP_APPLICATION_CREDENTIALS"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wjgaMOYhwaos",
        "colab_type": "text"
      },
      "source": [
        "# Enable the below Google Cloud Services for the solution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "azRj5bbjwaov",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# set the proper Permission for the required Google Cloud Services\n",
        "!gcloud services enable \\\n",
        "    storage-component.googleapis.com \\\n",
        "    bigquery.googleapis.com \\\n",
        "    ml.googleapis.com \\\n",
        "    notebooks.googleapis.com"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "kQ97mGj_waox",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# validate that all desired Permission have been set properl.\n",
        "!gcloud services list | grep 'storage-component.googleapis.com\\|bigquery.googleapis.com\\|ml.googleapis.com\\|notebooks.googleapis.com'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K-UMCR1-wao5",
        "colab_type": "text"
      },
      "source": [
        "# Adjust other varables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Asjw1Qc4wao5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# load_params\n",
        "import json\n",
        "def load_params():\n",
        "  \"\"\"The variables are used in the pipeline.\n",
        "\n",
        "    Provide appropriate variables for your environments\n",
        "    Set apppriate variables with the pattern RPM_*\n",
        "      (these are IMMUTABLE variables those acts as default)\n",
        "    You could print all the variables used in your environment\n",
        "      (e.g. local environment) which starts with RPM_* or rpm_*\n",
        "        (comes handy while troubleshooting)\n",
        "\n",
        "  Returns:\n",
        "    dict: all python variables used in the pipeline\n",
        "  \"\"\"\n",
        "  return {\n",
        "      'RPM_GCP_PROJECT': RPM_GCP_PROJECT,\n",
        "      'RPM_LOCATION': 'us-central1-b',  # KFP/K8s cluster zone\n",
        "      'RPM_PVC_DATASET_FOLDER_NAME': 'rpm_ds',\n",
        "      'RPM_PVC_NAME': 'rpm-vol',\n",
        "      # create the bucket if it don't exists\n",
        "      'RPM_GCP_STORAGE_BUCKET': '',\n",
        "      # create the folder if it don't exists\n",
        "      'RPM_GCP_STORAGE_BUCKET_FOLDER': '',\n",
        "      'RPM_DEFAULT_BUCKET_EXT': '_retail_propensity_model_assets',\n",
        "      'RPM_DEFAULT_BUCKET_FOLDER_NAME': 'rpm_data_set',\n",
        "      'RPM_BQ_DATASET_NAME': '',  # create the dataset if it don't exists\n",
        "      'RPM_BQ_TABLE_NAME': '',\n",
        "      'RPM_DEFAULT_BQ_TABLE_NAME_EXT': '_tbl',\n",
        "      'RPM_DEFAULT_DATA_SET_NAME_EXT': '_rpm_data_set',\n",
        "      'RPM_MODEL_NAME': '',\n",
        "      'RPM_DEFAULT_MODEL_NAME': 'rpm_bqml_model',\n",
        "      'RPM_MODEL_EXPORT_PATH': '',\n",
        "      'RPM_DEFAULT_MODEL_EXPORT_PATH': 'bqml/model/export/',\n",
        "      'RPM_MODEL_VER_PREFIX': 'V_',\n",
        "      'RPM_RUNTIME_VERSION': '1.15',  # do not change\n",
        "      'RPM_PYTHON_VERSION': '3.7',  # do not change\n",
        "      'RPM_CLUSTER_NAME': 'cluster-1',  # KFP/K8s cluster name\n",
        "      # variables created by the program\n",
        "      # from user supplied set or from the program defaults\n",
        "      'rpm_bq_ds_name': '',\n",
        "      'rpm_gcs_rpm_ds_url': '',\n",
        "      'rpm_file_name': '',\n",
        "      'rpm_table_id': '',\n",
        "      'rpm_bqml_model': '',\n",
        "      'rpm_bqml_model_export_path': '',\n",
        "      'rpm_model_version': '',\n",
        "      'rpm_model_uri': '',\n",
        "      'rpm_pred_table_id': '',\n",
        "  }\n",
        "all_vars = load_params()\n",
        "RPM_DS_DOWNLOAD_EXPERIMENT_NAME = 'GoogleStore Retail Pipeline'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qZnuhxRawao7",
        "colab_type": "text"
      },
      "source": [
        "# Reset the local context for local development if needed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GkAns6Jtwao8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# reset_local_context\n",
        "def reset_local_context():\n",
        "  \"\"\"Resets all the variables used in the local environment.\n",
        "\n",
        "    Comes handy while deveoping and testing the code locally.\n",
        "  \"\"\"\n",
        "  try:\n",
        "    del globals()['local_context']\n",
        "  except KeyError as e:\n",
        "    print('local_context not found!!!')\n",
        "    print(e)\n",
        "  globals().get('local_context')  # validate that the local variable is removed\n",
        "# reset_local_context()  # use before testing a component locally if needed"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4v6uuCI-wao_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# get_local_context\n",
        "def get_local_context(init_var=None):\n",
        "  \"\"\"Define local rpm_context object\n",
        "\n",
        "      The funtion sets the appropriate variables to\n",
        "        execute the code in a local environment.\n",
        "      local_context contains all the variables used in the local envrionmnet.\n",
        "      You could check the variable before and after the call to find out the\n",
        "        desired result (comes handy while developing\n",
        "        and testing the code locally)\n",
        "    Args:\n",
        "        init_var (:obj:`dict`, optional): The dict object overrides the existing\n",
        "          local context (use sparingly only, when needed)\n",
        "    Returns:\n",
        "      dict: all python variables used in the pipeline\n",
        "  \"\"\"\n",
        "  global local_context\n",
        "  local_context = globals().get('local_context')\n",
        "  if not local_context:\n",
        "      local_context = load_params()\n",
        "  if init_var:\n",
        "      local_context = init_var\n",
        "  local_context[\"RPM_PVC_NAME\"] = os.environ[\"HOME\"]\n",
        "  if not local_context.get(\"rpm_bq_ds_name\"):\n",
        "      local_context[\"rpm_bq_ds_name\"] = f\"{all_vars['RPM_GCP_PROJECT'].replace('-','_')}_rpm_data_set\"\n",
        "  if not local_context.get(\"rpm_gcs_rpm_ds_url\"):\n",
        "      local_context[\"rpm_gcs_rpm_ds_url\"] = f\"gs://{all_vars['RPM_GCP_PROJECT']}_retail_propensity_model_assets/rpm_data_set/\"\n",
        "  if not local_context.get(\"rpm_table_id\"):\n",
        "      local_context[\"rpm_table_id\"] = f\"{all_vars['RPM_GCP_PROJECT']}.{all_vars['RPM_GCP_PROJECT'].replace('-','_')}_rpm_data_set.{all_vars['RPM_GCP_PROJECT'].replace('-','_')}_rpm_data_set_tbl\"\n",
        "  if not local_context.get(\"RPM_MODEL_NAME\"):\n",
        "      local_context[\"rpm_bqml_model\"] = \"rpm_bqml_model\"\n",
        "  if not local_context.get(\"rpm_bqml_model_export_path\"):\n",
        "      local_context[\"rpm_bqml_model_export_path\"] = \"bqml/model/export/V_1/\"\n",
        "  if not local_context.get(\"rpm_model_version\"):\n",
        "      local_context[\"rpm_model_version\"] = \"V_1\"\n",
        "  if not local_context[\"rpm_model_uri\"]:\n",
        "      local_context[\"rpm_model_uri\"] = f\"gs://{all_vars['RPM_GCP_PROJECT']}_retail_propensity_model_assets/rpm_data_set/bqml/model/export/V_1/\"\n",
        "  if not local_context[\"rpm_pred_table_id\"]:\n",
        "      local_context[\"rpm_pred_table_id\"] = f\"{all_vars['RPM_GCP_PROJECT']}.{all_vars['RPM_GCP_PROJECT'].replace('-','_')}_rpm_data_set.{all_vars['RPM_GCP_PROJECT'].replace('-','_')}_rpm_data_set_pred_tbl\"\n",
        "\n",
        "  print (local_context)\n",
        "  return local_context\n",
        "\n",
        "def test_comp_local(func):\n",
        "  local_context = get_local_context()\n",
        "  import json\n",
        "  new_local_context_str = func(json.dumps(local_context))\n",
        "  print(f'type: {type(new_local_context_str)}; new_local_context_str:{new_local_context_str}')\n",
        "  local_context = json.loads(new_local_context_str)\n",
        "\n",
        "def update_local_context(output):\n",
        "  print(f'type: {type(output)}; new_local_context_str:{output}')\n",
        "  local_context = json.loads(output[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a8KutqBIwapB",
        "colab_type": "text"
      },
      "source": [
        "# Instantiate the KFP Client"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aL3IakRywapB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create a KFP Client and Validate that you are able to access the KFP Pipelines\n",
        "# You will be using the KFP HOST to deploy the KFP pipeline (experiment) and lauch the experiment\n",
        "import kfp\n",
        "kfp_client = kfp.Client(host=RPM_GCP_KFP_HOST)\n",
        "kfp_client.LOCAL_KFP_CONTEXT"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fXvMko3wapD",
        "colab_type": "text"
      },
      "source": [
        "# Create Google Cloud Storage bucket and folder - function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEF0LnhUwapD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create_gcs_bucket_folder\n",
        "from typing import NamedTuple\n",
        "def create_gcs_bucket_folder(ctx: str,\n",
        "                             RPM_GCP_STORAGE_BUCKET: str,\n",
        "                             RPM_GCP_PROJECT: str,\n",
        "                             RPM_DEFAULT_BUCKET_EXT: str,\n",
        "                             RPM_GCP_STORAGE_BUCKET_FOLDER: str,\n",
        "                             RPM_DEFAULT_BUCKET_FOLDER_NAME: str\n",
        "                            ) -> NamedTuple('Outputs', [\n",
        "       ('rpm_context', str),\n",
        "       ('rpm_gcs_rpm_ds_url', str),\n",
        "    ]):\n",
        "  \"\"\"The function (also used as a base for a KFP Component) creates a\n",
        "    Google Cloud Storage bucket and a folder if they don't exist.\n",
        "\n",
        "    The idea is to create the bucket and folder only on the first\n",
        "      run of the pipeline.\n",
        "    The pipeline uses the same storage account and the folder\n",
        "      for repeated runs.\n",
        "    Args:\n",
        "      ctx(:obj:`str`): The dict object with all the variables\n",
        "        used in the pipeline\n",
        "      RPM_GCP_STORAGE_BUCKET(:obj:`str`): User supplied Storage Bucket name\n",
        "      RPM_GCP_PROJECT:(:obj:`str`): Google Cloud project for deployment\n",
        "      RPM_DEFAULT_BUCKET_EXT:(:obj:`str`): Name of the bucket,\n",
        "        when user hasn't supplied a bucket name\n",
        "      RPM_GCP_STORAGE_BUCKET_FOLDER:(:obj:`str`): User supplied folder name\n",
        "      RPM_DEFAULT_BUCKET_FOLDER_NAME:(:obj:`str`): Name for creating a\n",
        "        bucket, when User hasn't supplied a folder name\n",
        "\n",
        "    Returns:\n",
        "      Outputs(:obj: `tuple`): Returns the below outputs:\n",
        "        rpm_context(:obj:`str`): All variables used in the pipeline\n",
        "        rpm_gcs_rpm_ds_url(:obj:`str`): Full Google Cloud Storage path with\n",
        "          bucket name and folder name\n",
        "  \"\"\"\n",
        "  # loading rpm_context string\n",
        "  import json\n",
        "  rpm_context = json.loads(ctx)\n",
        "  print(rpm_context)\n",
        "  RPM_GCP_STORAGE_BUCKET = rpm_context['RPM_GCP_STORAGE_BUCKET']\n",
        "  RPM_GCP_PROJECT = rpm_context['RPM_GCP_PROJECT']\n",
        "  RPM_DEFAULT_BUCKET_EXT = rpm_context['RPM_DEFAULT_BUCKET_EXT']\n",
        "  RPM_GCP_STORAGE_BUCKET_FOLDER = rpm_context['RPM_GCP_STORAGE_BUCKET_FOLDER']\n",
        "  RPM_DEFAULT_BUCKET_FOLDER_NAME = rpm_context['RPM_DEFAULT_BUCKET_FOLDER_NAME']\n",
        "\n",
        "  if RPM_GCP_STORAGE_BUCKET:\n",
        "    gcs_storage_bucket_name = RPM_GCP_STORAGE_BUCKET\n",
        "  else:\n",
        "    gcs_storage_bucket_name = RPM_GCP_PROJECT + RPM_DEFAULT_BUCKET_EXT\n",
        "\n",
        "  if RPM_GCP_STORAGE_BUCKET_FOLDER:\n",
        "    gcs_folder_name = RPM_GCP_STORAGE_BUCKET_FOLDER + '/'\n",
        "  else:\n",
        "    gcs_folder_name = RPM_DEFAULT_BUCKET_FOLDER_NAME + '/'\n",
        "  print(f\"{gcs_storage_bucket_name} bucket and {gcs_folder_name} will be used in the project.\")\n",
        "\n",
        "  rpm_gcs_rpm_ds_url = f\"gs://{gcs_storage_bucket_name}/{gcs_folder_name}\"\n",
        "  print(rpm_gcs_rpm_ds_url)\n",
        "  rpm_context['rpm_gcs_rpm_ds_url'] = rpm_gcs_rpm_ds_url\n",
        "\n",
        "  # defining the install function\n",
        "  import subprocess\n",
        "  def install(name):\n",
        "    subprocess.call(['pip', 'install', name])\n",
        "  pacakages_to_install = ['google-cloud', 'google-cloud-storage']\n",
        "  for each_package in pacakages_to_install:\n",
        "    install(each_package)\n",
        "    print(f\"'{each_package}' package installed :)\")\n",
        "\n",
        "  cmd = f\"gcloud config set project {rpm_context['RPM_GCP_PROJECT']}\"\n",
        "  print(cmd)\n",
        "  process = subprocess.run(cmd, shell=True, check=True,\n",
        "                           stdout=subprocess.PIPE, universal_newlines=True)\n",
        "  print(f\"'output from {cmd}': {process.stdout}\")\n",
        "\n",
        "  from google.cloud import storage\n",
        "  from google.cloud.exceptions import NotFound\n",
        "  from google.cloud.exceptions import Forbidden\n",
        "  import traceback\n",
        "\n",
        "  def check_storage_bucket_and_folder(bucket_name, folder_name):\n",
        "    if not bucket_name or not folder_name:\n",
        "      return(False, False)\n",
        "    client = storage.Client()\n",
        "    try:  # check if the bucket exists and that we have the proper permission\n",
        "      bucket = client.get_bucket(bucket_name)\n",
        "      print(f\"Bucket: {bucket_name} exists.\")\n",
        "      bucket_exists = True\n",
        "      try:\n",
        "        blob = bucket.get_blob(folder_name)\n",
        "        if blob is None:\n",
        "          print(f\"Folder name {folder_name} does not exist!\")\n",
        "          folder_exists = False\n",
        "        else:\n",
        "          print(f\"Folder name {folder_name} exist.\")\n",
        "          folder_exists = True\n",
        "      except:\n",
        "          print(f\"Folder name {folder_name} doest not exist!\")\n",
        "          folder_exists = False\n",
        "    except Forbidden as e:\n",
        "      print(f\"Sorry, you don't have access to the bucket: {bucket_name}!\")\n",
        "      print(e)\n",
        "      error = traceback.format_exc()\n",
        "      print(error)\n",
        "      bucket_exists = False\n",
        "      folder_exists = False\n",
        "    except NotFound as e:\n",
        "      print(f\"Sorry, the bucket: {bucket_name} does not exist!\")\n",
        "      print(e)\n",
        "      error = traceback.format_exc()\n",
        "      print(error)\n",
        "      bucket_exists = False\n",
        "      folder_exists = False\n",
        "    return(bucket_exists, folder_exists)\n",
        "\n",
        "  # Create a bucket if it doesn't exists\n",
        "  def create_storage_bucket(bucket_name):\n",
        "    if bucket_name:\n",
        "      client = storage.Client()\n",
        "      try:\n",
        "        bucket = client.create_bucket(bucket_name)\n",
        "        print(f\"Bucket {bucket.name} created\")\n",
        "        return True\n",
        "      except Exception as e:\n",
        "        print(f\"Bucket {bucket_name} couldn't be created\")\n",
        "        print(e)\n",
        "        error = traceback.format_exc()\n",
        "        print(error)\n",
        "        return False\n",
        "    else:\n",
        "      print(f\"Bucket {bucket_name} couldn't be created. Name is empty.\")\n",
        "      return False\n",
        "\n",
        "  # Create the folder in the bucket\n",
        "  def create_storage_folder(bucket_name, folder_name):\n",
        "    if len(bucket_name) == 0 or len(folder_name) == 0:\n",
        "      print(f\"Folder {folder_name} couldn't be created. Name is empty.\")\n",
        "      return False\n",
        "    else:\n",
        "      client = storage.Client()\n",
        "      try:\n",
        "        bucket = client.get_bucket(bucket_name)\n",
        "        blob = bucket.blob(folder_name)\n",
        "        blob.upload_from_string('')\n",
        "        print(f\"Folder {blob.name} created\")\n",
        "        return True\n",
        "      except Exception as e:\n",
        "        print(f\"Folder {folder_name} couldn't be created\")\n",
        "        print(e)\n",
        "        error = traceback.format_exc()\n",
        "        print(error)\n",
        "        return False\n",
        "\n",
        "  result = check_storage_bucket_and_folder(gcs_storage_bucket_name,\n",
        "                                           gcs_folder_name)\n",
        "  if result[0] == False:\n",
        "    create_storage_bucket(gcs_storage_bucket_name)\n",
        "  if result[1] == False:\n",
        "    create_storage_folder(gcs_storage_bucket_name, gcs_folder_name)\n",
        "\n",
        "  return (\n",
        "      json.dumps(rpm_context),\n",
        "      rpm_context['rpm_gcs_rpm_ds_url']\n",
        "      )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wg5AdXbuwapF",
        "colab_type": "text"
      },
      "source": [
        "# *Test locally create_gcs_bucket_folder*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "kLTlHVH6wapG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# test locally create_gcs_bucket_folder\n",
        "# You could unit test the above code in your local environment.\n",
        "# You don't need to execute the code, when simply building or running the KFP pipeline (experiment)\n",
        "local_context = get_local_context()\n",
        "import json\n",
        "update_local_context(create_gcs_bucket_folder(\n",
        "    json.dumps(local_context),\n",
        "    local_context['RPM_GCP_STORAGE_BUCKET'],\n",
        "    local_context['RPM_GCP_PROJECT'],\n",
        "    local_context['RPM_DEFAULT_BUCKET_EXT'],\n",
        "    local_context['RPM_GCP_STORAGE_BUCKET_FOLDER'],\n",
        "    local_context['RPM_DEFAULT_BUCKET_FOLDER_NAME']\n",
        "))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IEvTkyPhwapH",
        "colab_type": "text"
      },
      "source": [
        "# Create BigQuery dataset - function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A2OwWAnBwapI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create_bq_ds\n",
        "from typing import NamedTuple\n",
        "def create_bq_ds(ctx: str,\n",
        "                 RPM_GCP_PROJECT: str,\n",
        "                 RPM_BQ_DATASET_NAME: str,\n",
        "                 RPM_LOCATION: str\n",
        "                 ) -> NamedTuple('Outputs', [\n",
        "       ('rpm_context', str),\n",
        "       ('rpm_bq_ds_name', str),\n",
        "    ]):\n",
        "  \"\"\"The function(also used as a base for a KFP Component) creates a\n",
        "        BigQuery dataset if don't exist.\n",
        "\n",
        "      The idea is to create DataSet only on the first run of the pipeline.\n",
        "      The pipeline uses the same DataSet for repeated runs.\n",
        "      Args:\n",
        "        ctx(:obj:`str`): The dict object with all the variables\n",
        "          used in the pipeline\n",
        "        RPM_GCP_PROJECT:(:obj:`str`): Google Cloud project for deployment\n",
        "        RPM_BQ_DATASET_NAME:(:obj:`str`): Name of the dataset.\n",
        "        RPM_LOCATION:(:obj:`str`): Location of the Google Cloud region\n",
        "          of the BigQuery dataset\n",
        "      Returns:\n",
        "        Outputs(:obj: `tuple`): Returns the below outputs:\n",
        "          rpm_context(:obj:`str`): All variables used in the pipeline\n",
        "          rpm_bq_ds_name(:obj:`str`): The dataset name\n",
        "            used in the rest of the pipeline\n",
        "  \"\"\"\n",
        "  import json\n",
        "  rpm_context = json.loads(ctx)\n",
        "  print(rpm_context)\n",
        "\n",
        "  rpm_bq_ds_name = rpm_context['RPM_BQ_DATASET_NAME']\n",
        "  if not rpm_bq_ds_name:\n",
        "    rpm_bq_ds_name = \\\n",
        "        f\"{rpm_context['RPM_GCP_PROJECT']}{rpm_context['RPM_DEFAULT_DATA_SET_NAME_EXT']}\"\n",
        "  rpm_bq_ds_name = rpm_bq_ds_name.replace('-', '_')\n",
        "\n",
        "  rpm_context['rpm_bq_ds_name'] = rpm_bq_ds_name\n",
        "\n",
        "  import subprocess\n",
        "  import traceback\n",
        "  def exec_cmd(cmd):\n",
        "    try:\n",
        "      print(cmd)\n",
        "      process = subprocess.run(cmd, shell=True, check=True,\n",
        "                               stdout=subprocess.PIPE, universal_newlines=True)\n",
        "      print(f\"'output from {cmd}': {process.stdout}\")\n",
        "    except subprocess.CalledProcessError as e:\n",
        "      error = traceback.format_exc()\n",
        "      print(error)\n",
        "      print(e.output)\n",
        "\n",
        "  exec_cmd(f\"gcloud config set project {rpm_context['RPM_GCP_PROJECT']}\")\n",
        "\n",
        "  def install(name):\n",
        "    subprocess.call(['pip', 'install', name])\n",
        "  pacakages_to_install = ['google-cloud', 'google-cloud-bigquery']\n",
        "  for each_package in pacakages_to_install:\n",
        "    install(each_package)\n",
        "    print(f\"'{each_package}' package installed :)\")\n",
        "\n",
        "  from google.cloud import bigquery\n",
        "  from google.cloud.exceptions import NotFound\n",
        "  client = bigquery.Client()\n",
        "  dataset_id = f\"{rpm_context['RPM_GCP_PROJECT']}.{rpm_bq_ds_name}\"\n",
        "  ds_found = True\n",
        "  try:\n",
        "    client.get_dataset(dataset_id)  # Make an API request.\n",
        "    print('Dataset {} already exists'.format(dataset_id))\n",
        "  except NotFound:\n",
        "    print('Dataset {} is not found'.format(dataset_id))\n",
        "    ds_found = False\n",
        "\n",
        "  if ds_found is False:\n",
        "    try:\n",
        "      # Construct a full Dataset object to send to the API.\n",
        "      dataset = bigquery.Dataset(dataset_id)\n",
        "      dataset.location = rpm_context['RPM_LOCATION'].split('-')[0].upper()\n",
        "      dataset = client.create_dataset(dataset)  # Make an API request.\n",
        "      print('Created dataset {}.{} in location: {}.'.\\\n",
        "            format(client.project, dataset.dataset_id, dataset.location))\n",
        "    except Exception as e:\n",
        "      error = traceback.format_exc()\n",
        "      print(error)\n",
        "      print(e)\n",
        "      raise RuntimeError(f\"Can't create the BigQuery DS {dataset_id}\")\n",
        "\n",
        "  return (\n",
        "      json.dumps(rpm_context),\n",
        "      rpm_context['rpm_bq_ds_name']\n",
        "      )\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Depc_dw5wapK",
        "colab_type": "text"
      },
      "source": [
        "# *Test locally create_bq_ds*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "xpJX3dTwwapK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# test locally create_bq_ds\n",
        "# You could unit test the above code in your local environment.\n",
        "# You don't need to execute the code, when simply building or running the KFP pipeline (experiment)\n",
        "local_context = get_local_context()\n",
        "import json\n",
        "update_local_context(create_bq_ds(\n",
        "    json.dumps(local_context),\n",
        "    local_context['RPM_GCP_PROJECT'],\n",
        "    local_context['RPM_BQ_DATASET_NAME'],\n",
        "    local_context['RPM_LOCATION']))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3UuyNqskwapN",
        "colab_type": "text"
      },
      "source": [
        "# Load the data to BigQuery - function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wkXoZqDAwapN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from typing import NamedTuple\n",
        "def load_bq_ds(ctx: str,\n",
        "               RPM_GCP_PROJECT: str,\n",
        "               RPM_BQ_TABLE_NAME: str,\n",
        "               RPM_DEFAULT_BQ_TABLE_NAME_EXT: str,\n",
        "               rpm_bq_ds_name: str, ) -> NamedTuple('Outputs', [\n",
        "       ('rpm_context', str),\n",
        "       ('rpm_table_id', str),\n",
        "    ]):\n",
        "  \"\"\"The function(also used as a base for a KFP Component)\n",
        "        loads the data to a BigQuery table.\n",
        "\n",
        "    You need to replace the component with your data source\n",
        "    e.g. you might download the data from a different source,\n",
        "      in which case you to code those steps\n",
        "    Decide on your load strategy here such add or append.\n",
        "    Furthermore you could cache this KFP component\n",
        "      if the load is just a onetime thing.\n",
        "    Args:\n",
        "      ctx(:obj:`str`): The dict object with all the variables\n",
        "        used in the pipeline\n",
        "      RPM_GCP_PROJECT:(:obj:`str`): Google Cloud project for deployment\n",
        "      RPM_BQ_TABLE_NAME:(:obj:`str`): Name of the table.\n",
        "      RPM_DEFAULT_BQ_TABLE_NAME_EXT:(:obj:`str`): Default table name\n",
        "        if the user didn't provide one(RPM_BQ_TABLE_NAME)\n",
        "      rpm_bq_ds_name(:obj:`str`): The dataset name\n",
        "        used in the rest of the pipeline\n",
        "    Returns:\n",
        "      Outputs(:obj: `tuple`): Returns the below outputs:\n",
        "        rpm_context(:obj:`str`): All variables used in the pipeline\n",
        "        rpm_table_id(:obj:`str`): The table name\n",
        "          used in the rest of the pipeline\n",
        "  \"\"\"\n",
        "  import json\n",
        "  rpm_context = json.loads(ctx)\n",
        "  print(rpm_context)\n",
        "\n",
        "  rpm_bq_ds_name = rpm_context['rpm_bq_ds_name']\n",
        "  dataset_id = f\"{rpm_context['RPM_GCP_PROJECT']}.{rpm_bq_ds_name}\"\n",
        "\n",
        "  if not rpm_context['RPM_BQ_TABLE_NAME']:\n",
        "    rpm_table_id = f\"{dataset_id}.{rpm_bq_ds_name}{rpm_context['RPM_DEFAULT_BQ_TABLE_NAME_EXT']}\"\n",
        "  else:\n",
        "    rpm_table_id = f\"{dataset_id}.{rpm_context['RPM_BQ_TABLE_NAME']}\"\n",
        "  rpm_context['rpm_table_id'] = rpm_table_id\n",
        "\n",
        "  import subprocess\n",
        "  def install(name):\n",
        "    subprocess.call(['pip', 'install', name])\n",
        "  pacakages_to_install = ['google-cloud', 'google-cloud-bigquery']\n",
        "  for each_package in pacakages_to_install:\n",
        "    install(each_package)\n",
        "    print(f\"'{each_package}' package installed :)\")\n",
        "\n",
        "  query = f\"\"\"\n",
        "    # select initial features and label to feed into our model\n",
        "    CREATE OR REPLACE TABLE {rpm_table_id}\n",
        "    OPTIONS(\n",
        "    description=\"Google Store curated Data\"\n",
        "    ) AS \n",
        "    SELECT\n",
        "    fullVisitorId,\n",
        "    bounces,\n",
        "    time_on_site,\n",
        "    will_buy_on_return_visit # <--- our label\n",
        "    FROM\n",
        "    # features\n",
        "    (SELECT\n",
        "        fullVisitorId,\n",
        "        IFNULL(totals.bounces, 0) AS bounces,\n",
        "        IFNULL(totals.timeOnSite, 0) AS time_on_site\n",
        "    FROM\n",
        "        `bigquery-public-data.google_analytics_sample.*`\n",
        "    WHERE\n",
        "        totals.newVisits = 1\n",
        "        AND date BETWEEN '20160801' AND '20170430') # train on first 9 months\n",
        "    JOIN\n",
        "    (SELECT\n",
        "        fullvisitorid,\n",
        "        IF(COUNTIF(totals.transactions > 0 AND totals.newVisits IS NULL) > 0, 1, 0) AS will_buy_on_return_visit\n",
        "    FROM\n",
        "        `bigquery-public-data.google_analytics_sample.*`\n",
        "    GROUP BY fullvisitorid)\n",
        "    USING (fullVisitorId)\n",
        "    ORDER BY time_on_site DESC # order by most time spent first\n",
        "  \"\"\"\n",
        "  print(query)\n",
        "  import traceback\n",
        "  from google.cloud import bigquery\n",
        "  try:\n",
        "    client = bigquery.Client()\n",
        "    print(query)\n",
        "    query_job = client.query(query)  # Make an API request.\n",
        "    print(f\"Table {rpm_table_id} created.\")\n",
        "  except Exception as e:\n",
        "    error = traceback.format_exc()\n",
        "    print(error)\n",
        "    print(e)\n",
        "    raise RuntimeError(f\"Can't create the table {rpm_table_id}\")\n",
        "  destination_table = rpm_table_id\n",
        "  print(f\"{destination_table}\")\n",
        "\n",
        "  return (\n",
        "      json.dumps(rpm_context),\n",
        "      rpm_context['rpm_table_id']\n",
        "      )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PUrom3OYwapP",
        "colab_type": "text"
      },
      "source": [
        "# *Test locally load_bq_ds*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "-EVZQFLEwapQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# test locally load_bq_ds\n",
        "# You could unit test the above code in your local environment.\n",
        "# You don't need to execute the code, when simply building or running the KFP pipeline (experiment)\n",
        "local_context = get_local_context()\n",
        "import json\n",
        "update_local_context(load_bq_ds(\n",
        "    json.dumps(local_context),\n",
        "    local_context['RPM_GCP_PROJECT'],\n",
        "    local_context['RPM_BQ_TABLE_NAME'],\n",
        "    local_context['RPM_DEFAULT_BQ_TABLE_NAME_EXT'],\n",
        "    local_context['rpm_bq_ds_name'],\n",
        "    ))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sUsmunohwapS",
        "colab_type": "text"
      },
      "source": [
        "# Create the BigQuery ML model - function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dx31iHRcwapS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create_bq_ml\n",
        "from typing import NamedTuple\n",
        "def create_bq_ml(ctx: str,\n",
        "                  RPM_GCP_PROJECT: str,\n",
        "                  RPM_MODEL_NAME: str,\n",
        "                  RPM_DEFAULT_MODEL_NAME: str,\n",
        "                  rpm_bq_ds_name: str,\n",
        "                  rpm_table_id: str ) -> NamedTuple('Outputs', [\n",
        "       ('rpm_context', str),\n",
        "       ('rpm_bqml_model', str),\n",
        "    ]):\n",
        "  \"\"\" The function(also used as a base for a KFP Component) creates a model\n",
        "      from the data that you have already loaded previously.\n",
        "\n",
        "    You need to adjust the model type, model hyperparamters, features,\n",
        "      and label depending on your need.\n",
        "    Args:\n",
        "      ctx(:obj:`str`): The dict object with all the variables\n",
        "        used in the pipeline\n",
        "      RPM_GCP_PROJECT:(:obj:`str`): Google Cloud project for deployment\n",
        "      RPM_MODEL_NAME:(:obj:`str`): Name of the model.\n",
        "      RPM_DEFAULT_MODEL_NAME:(:obj:`str`): Default model name\n",
        "        if the user didn't provide one(RPM_MODEL_NAME)\n",
        "      rpm_bq_ds_name(:obj:`str`): The dataset name\n",
        "        used in the rest of the pipeline\n",
        "      rpm_table_id(:obj:`str`): The table name\n",
        "        used in the rest of the pipeline\n",
        "    Returns:\n",
        "      Outputs(:obj: `tuple`): Returns the below outputs:\n",
        "        rpm_context(:obj:`str`): All variables used in the pipeline\n",
        "        rpm_bqml_model(:obj:`str`): The model name\n",
        "          used in the rest of the pipeline\n",
        "  \"\"\"\n",
        "  import json\n",
        "  rpm_context = json.loads(ctx)\n",
        "  print(rpm_context)\n",
        "\n",
        "  rpm_bqml_model = rpm_context['RPM_MODEL_NAME']\n",
        "  if not rpm_bqml_model:\n",
        "    rpm_bqml_model = rpm_context['RPM_DEFAULT_MODEL_NAME']\n",
        "  rpm_bqml_model = rpm_bqml_model.replace('-', '_')\n",
        "  rpm_context['rpm_bqml_model'] = rpm_bqml_model\n",
        "\n",
        "  import subprocess\n",
        "  import traceback\n",
        "  def exec_cmd(cmd):\n",
        "    try:\n",
        "      print(cmd)\n",
        "      process = subprocess.run(cmd, shell=True, check=True,\n",
        "                               stdout=subprocess.PIPE, universal_newlines=True)\n",
        "      print(f\"'output from {cmd}': {process.stdout}\")\n",
        "      return process.stdout\n",
        "    except subprocess.CalledProcessError as e:\n",
        "      error = traceback.format_exc()\n",
        "      print(error)\n",
        "      print(e.output)\n",
        "      return e.output\n",
        "\n",
        "  exec_cmd(f\"gcloud config set project {rpm_context['RPM_GCP_PROJECT']}\")\n",
        "\n",
        "  bqml_create_sql = f\"\"\"\n",
        "        CREATE OR REPLACE MODEL\n",
        "        {rpm_context['rpm_bq_ds_name']}.{rpm_bqml_model}\n",
        "        OPTIONS\n",
        "        ( model_type='LOGISTIC_REG',\n",
        "          auto_class_weights=TRUE,\n",
        "          input_label_cols=['will_buy_on_return_visit']) AS\n",
        "        SELECT\n",
        "        * EXCEPT(fullVisitorId)\n",
        "        FROM\n",
        "        {rpm_context['rpm_table_id'].replace(RPM_GCP_PROJECT+'.', '')}\n",
        "  \"\"\"\n",
        "  # you can uncomment the below query to try out the XGBoost model\n",
        "  # bqml_create_sql = f\"\"\"\n",
        "  #     CREATE OR REPLACE MODEL\n",
        "  #     \\`{rpm_context['rpm_bq_ds_name']}.{rpm_bqml_model}\\`\n",
        "  #     OPTIONS(MODEL_TYPE='BOOSTED_TREE_CLASSIFIER',\n",
        "  #             BOOSTER_TYPE = 'GBTREE',\n",
        "  #             NUM_PARALLEL_TREE = 1,\n",
        "  #             MAX_ITERATIONS = 50,\n",
        "  #             TREE_METHOD = 'HIST',\n",
        "  #             EARLY_STOP = FALSE,\n",
        "  #             SUBSAMPLE = 0.85,\n",
        "  #             INPUT_LABEL_COLS = ['will_buy_on_return_visit'])\n",
        "  #     AS\n",
        "  #     SELECT\n",
        "  #     * EXCEPT(fullVisitorId)\n",
        "  #     FROM\n",
        "  #     \\`{rpm_context['rpm_table_id']}\\`\n",
        "  # \"\"\"\n",
        "  exec_cmd(f'bq query --use_legacy_sql=false \"{bqml_create_sql}\"')\n",
        "  bq_model_created = exec_cmd(f\"bq ls -m --format=pretty {rpm_context['rpm_bq_ds_name']} | grep {rpm_bqml_model}\")\n",
        "  if not bq_model_created:\n",
        "    raise RuntimeError(f\"Please check if the model {rpm_context['rpm_bq_ds_name']} created.\")\n",
        "\n",
        "  return (\n",
        "      json.dumps(rpm_context),\n",
        "      rpm_context['rpm_bqml_model']\n",
        "      )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z2Zjf895wapU",
        "colab_type": "text"
      },
      "source": [
        "# *Test locally create_bq_ml*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "4S8dNWQ2wapV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# test locally create_bq_ml\n",
        "# You could unit test the above code in your local environment.\n",
        "# You don't need to execute the code, when simply building or running the KFP pipeline (experiment)\n",
        "local_context = get_local_context()\n",
        "import json\n",
        "update_local_context(create_bq_ml(\n",
        "    json.dumps(local_context),\n",
        "    local_context['RPM_GCP_PROJECT'],\n",
        "    local_context['RPM_MODEL_NAME'],\n",
        "    local_context['RPM_DEFAULT_MODEL_NAME'],\n",
        "    local_context['rpm_bq_ds_name'],\n",
        "    local_context['rpm_table_id']))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hiw0V-JQwapW",
        "colab_type": "text"
      },
      "source": [
        "# Evaluate the BigQuery ML model - function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zUD6ucM2wapX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# evaluate_ml_model\n",
        "from typing import NamedTuple\n",
        "def evaluate_ml_model(ctx: str,\n",
        "                       RPM_GCP_PROJECT: str,\n",
        "                       rpm_bq_ds_name: str,\n",
        "                       rpm_bqml_model: str, ) -> NamedTuple('Outputs', [\n",
        "       ('rpm_context', str),\n",
        "       ('rpm_eval_query', str),\n",
        "       ('rpm_eval_result', str),\n",
        "    ]):\n",
        "  \"\"\" The function(also used as a base for a KFP Component) evaluates\n",
        "    the model you created.\n",
        "\n",
        "    Update your selection criteria and stop the pipeline\n",
        "      if the model didn't meet the criteria\n",
        "    You can raise an exception to stop the pipeline\n",
        "    Args:\n",
        "      ctx(:obj:`str`): The dict object with all the variables\n",
        "        used in the pipeline\n",
        "      RPM_GCP_PROJECT:(:obj:`str`): Google Cloud project for deployment\n",
        "      rpm_bq_ds_name(:obj:`str`): The dataset name\n",
        "        used in the rest of the pipeline\n",
        "      rpm_bqml_model(:obj:`str`): The model name\n",
        "        used in the rest of the pipeline\n",
        "    Returns:\n",
        "      Outputs(:obj: `tuple`): Returns the below outputs:\n",
        "        rpm_context(:obj:`str`): All variables used in the pipeline\n",
        "        rpm_eval_query(:obj:`str`): The evaluate sql query,\n",
        "          which is saved for auditing purpose in the pipeline artifacts\n",
        "        rpm_eval_result(:obj:`str`): The result of the evaluated query,\n",
        "          which is saved for auditing purpose in the pipeline artifacts\n",
        "  \"\"\"\n",
        "  import json\n",
        "  rpm_context = json.loads(ctx)\n",
        "  print(rpm_context)\n",
        "\n",
        "  import subprocess\n",
        "  import traceback\n",
        "  def exec_cmd(cmd):\n",
        "    try:\n",
        "      print(cmd)\n",
        "      process = subprocess.run(cmd, shell=True, check=True,\n",
        "                               stdout=subprocess.PIPE, universal_newlines=True)\n",
        "      print(f\"'output from {cmd}': {process.stdout}\")\n",
        "      return(process.stdout, 0)\n",
        "    except subprocess.CalledProcessError as e:\n",
        "      error = traceback.format_exc()\n",
        "      print(error)\n",
        "      print(e.output)\n",
        "      return(e.output, 1)\n",
        "\n",
        "  exec_cmd(f\"gcloud config set project {rpm_context['RPM_GCP_PROJECT']}\")\n",
        "\n",
        "  bqml_eval_query = f\"\"\"\n",
        "    SELECT\n",
        "    roc_auc, CASE WHEN roc_auc > .9 THEN 'good'\n",
        "    WHEN roc_auc > .8 THEN 'fair' WHEN roc_auc > .7 THEN 'decent'\n",
        "    WHEN roc_auc > .6 THEN 'not great' ELSE 'poor' END AS modelquality\n",
        "    FROM\n",
        "    ML.EVALUATE(MODEL {rpm_bq_ds_name}.{rpm_bqml_model})\n",
        "  \"\"\"\n",
        "  rpm_eval_result = exec_cmd(f'bq query --use_legacy_sql=false --format=json \"{bqml_eval_query}\"')\n",
        "  print(rpm_eval_result)\n",
        "\n",
        "  rpm_context['bqml_eval_query'] = bqml_eval_query\n",
        "  rpm_context['rpm_eval_result'] = rpm_eval_result\n",
        "\n",
        "  return (\n",
        "      json.dumps(rpm_context),\n",
        "      f\"\"\"{bqml_eval_query}\"\"\",\n",
        "      f\"\"\"{rpm_eval_result}\"\"\",\n",
        "      )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "snaVs3CwwapZ",
        "colab_type": "text"
      },
      "source": [
        "# *Test locally evaluate_ml_model*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "SUFHCPfRwapa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# test locally evaluate_ml_model\n",
        "# You could unit test the above code in your local environment.\n",
        "# You don't need to execute the code, when simply building or running the KFP pipeline (experiment)\n",
        "local_context = get_local_context()\n",
        "import json\n",
        "update_local_context(evaluate_ml_model(\n",
        "    json.dumps(local_context),\n",
        "    local_context['RPM_GCP_PROJECT'],\n",
        "    local_context['rpm_bq_ds_name'],\n",
        "    local_context['rpm_bqml_model'],\n",
        "    ))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ledhuYLIwapb",
        "colab_type": "text"
      },
      "source": [
        "# Prepare dataset for batch prediction with BigQuery ML - function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "btqXpW9ywapc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create_batch_prediction_dataset\n",
        "from typing import NamedTuple\n",
        "def create_batch_prediction_dataset(ctx: str,\n",
        "                                    RPM_GCP_PROJECT: str,\n",
        "                                    rpm_bq_ds_name: str,\n",
        "                                    rpm_table_id: str ) -> NamedTuple('Outputs', [\n",
        "       ('rpm_context', str),\n",
        "       ('rpm_pred_table_id', str),\n",
        "    ]):\n",
        "  \"\"\" The function(also used as a base for a KFP Component)\n",
        "        creates a BigQuery table which contains the input data for\n",
        "        which we want predictions.\n",
        "\n",
        "    You might not need this this componenet with\n",
        "      your input table if already exists.\n",
        "    You might need some transformation or filteration on your input data,\n",
        "    in which case you need to make appropriate code change.\n",
        "    Args:\n",
        "      ctx(:obj:`str`): The dict object with all the variables\n",
        "        used in the pipeline\n",
        "      RPM_GCP_PROJECT:(:obj:`str`): Google Cloud project for deployment\n",
        "      rpm_bq_ds_name(:obj:`str`): The dataset name used in the rest of the pipeline\n",
        "      rpm_table_id(:obj:`str`): The table name used in the rest of the pipeline\n",
        "    Returns:\n",
        "      Outputs(:obj: `tuple`): Returns the below outputs:\n",
        "        rpm_context(:obj:`str`): All variables used in the pipeline\n",
        "        rpm_pred_table_id(:obj:`str`): The table that contains the input data,\n",
        "          which we want batch predict later\n",
        "  \"\"\"\n",
        "  import json\n",
        "  rpm_context = json.loads(ctx)\n",
        "  print(rpm_context)\n",
        "\n",
        "  import subprocess\n",
        "  def install(name):\n",
        "    subprocess.call(['pip', 'install', name])\n",
        "  pacakages_to_install = ['google-cloud', 'google-cloud-bigquery']\n",
        "  for each_package in pacakages_to_install:\n",
        "    install(each_package)\n",
        "    print(f\"'{each_package}' package installed :)\")\n",
        "\n",
        "  rpm_pred_table_id = rpm_table_id.replace('_tbl', '_pred_tbl')\n",
        "\n",
        "  query = f\"\"\"\n",
        "      # create the input table to conduct batch predict\n",
        "      CREATE OR REPLACE TABLE {rpm_pred_table_id}\n",
        "      OPTIONS(\n",
        "      description=\"Input data for prediction\"\n",
        "      ) AS\n",
        "      SELECT *\n",
        "      FROM {rpm_context['rpm_table_id']}\n",
        "      LIMIT 10\n",
        "  \"\"\"\n",
        "  print(query)\n",
        "  import traceback\n",
        "  from google.cloud import bigquery\n",
        "  try:\n",
        "    client = bigquery.Client()\n",
        "    query_job = client.query(query)  # Make an API request.\n",
        "    print(f\"Table {rpm_pred_table_id} created.\")\n",
        "  except Exception as e:\n",
        "    error = traceback.format_exc()\n",
        "    print(error)\n",
        "    print(e)\n",
        "    raise RuntimeError(f\"Can't create the table {rpm_pred_table_id}\")\n",
        "\n",
        "  rpm_context['rpm_pred_table_id'] = rpm_pred_table_id\n",
        "\n",
        "  return (json.dumps(rpm_context),\n",
        "         rpm_context['rpm_pred_table_id'],\n",
        "         )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3qCL6izYwapd",
        "colab_type": "text"
      },
      "source": [
        "# *Test locally create batch prediction dataset*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "AIz9L4p5wape",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#test locally create batch prediction dataset\n",
        "# You could unit test the above code in your local environment.\n",
        "# You don't need to execute the code, when simply building or running the KFP pipeline (experiment)\n",
        "local_context = get_local_context()\n",
        "import json\n",
        "update_local_context(create_batch_prediction_dataset(\n",
        "    json.dumps(local_context),\n",
        "    local_context['RPM_GCP_PROJECT'],\n",
        "    local_context['rpm_bq_ds_name'],\n",
        "    local_context['rpm_table_id'],\n",
        "    ))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TlW2h_ppwapg",
        "colab_type": "text"
      },
      "source": [
        "# Make batch prediction - function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "od7AKx6Wwaph",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# predict_batch_ml_model\n",
        "from typing import NamedTuple\n",
        "def predict_batch_ml_model(ctx: str,\n",
        "                           RPM_GCP_PROJECT: str,\n",
        "                           rpm_bq_ds_name: str,\n",
        "                           rpm_bqml_model: str,\n",
        "                           rpm_pred_table_id: str, ) -> NamedTuple('Outputs', [\n",
        "      ('rpm_context', str),\n",
        "      ('rpm_predict_batch_output', str),\n",
        "    ]):\n",
        "  \"\"\"\n",
        "    The function(also used as a base for a KFP Component) uses the model\n",
        "      to predict the data in mass.\n",
        "\n",
        "    You migth also need to save the predicted values\n",
        "      at an appropriate repository of your choice.\n",
        "    Currently the predicted value is printed on the console\n",
        "      and returned as an output from the function.\n",
        "    Args:\n",
        "      ctx(:obj:`str`): The dict object with all the variables\n",
        "        used in the pipeline\n",
        "      RPM_GCP_PROJECT:(:obj:`str`): Google Cloud project for deployment\n",
        "      rpm_bq_ds_name(:obj:`str`): The dataset name\n",
        "        used in the rest of the pipeline\n",
        "      rpm_bqml_model(:obj:`str`): The model name\n",
        "        used in the rest of the pipeline\n",
        "      rpm_pred_table_id(:obj:`str`): The table that contains the input data\n",
        "        which we want batch predict later\n",
        "    Returns:\n",
        "      Outputs(:obj: `tuple`): Returns the below outputs:\n",
        "        rpm_context(:obj:`str`): All variables used in the pipeline\n",
        "        rpm_predict_batch_output(:obj:`str`): The output f\n",
        "          rom the batch prediction\n",
        "  \"\"\"\n",
        "  import json\n",
        "  rpm_context = json.loads(ctx)\n",
        "  print(rpm_context)\n",
        "\n",
        "  import subprocess\n",
        "  def install(name):\n",
        "    subprocess.call(['pip', 'install', name])\n",
        "  pacakages_to_install = [\"google-cloud\", \"google-cloud-bigquery\"]\n",
        "  for each_package in pacakages_to_install:\n",
        "    install(each_package)\n",
        "    print(f\"'{each_package}' package installed :)\")\n",
        "\n",
        "  query = f\"\"\"\n",
        "      # predict the inputs (rows) from the input table\n",
        "      SELECT\n",
        "      fullVisitorId, predicted_will_buy_on_return_visit\n",
        "      FROM\n",
        "      ML.PREDICT(MODEL {rpm_bq_ds_name}.{rpm_bqml_model},\n",
        "      (\n",
        "          SELECT\n",
        "          fullVisitorId,\n",
        "          bounces,\n",
        "          time_on_site\n",
        "          from {rpm_pred_table_id}\n",
        "      ))\n",
        "  \"\"\"\n",
        "  print(query)\n",
        "  import traceback\n",
        "  from google.cloud import bigquery\n",
        "  output = \"\"\n",
        "  try:\n",
        "    client = bigquery.Client()\n",
        "    query_job = client.query(query)  # Make an API request.\n",
        "    print(\"The query data:\")\n",
        "    for row in query_job:\n",
        "      # Row values can be accessed by field name or index.\n",
        "      print(f\"row data: {row}\")\n",
        "      output += str(row)\n",
        "  except Exception as e:\n",
        "    error = traceback.format_exc()\n",
        "    print(error)\n",
        "    print(e)\n",
        "    raise RuntimeError(f\"Can't batch predict\")\n",
        "\n",
        "  rpm_context['rpm_predict_output'] = output\n",
        "\n",
        "  return (\n",
        "      json.dumps(rpm_context),\n",
        "      rpm_context['rpm_predict_output'],\n",
        "      )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qm8PZE-lwapj",
        "colab_type": "text"
      },
      "source": [
        "# *Test locally batch prediction*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "SYo6XsMKwapj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#test locally batch prediction\n",
        "# You could unit test the above code in your local environment.\n",
        "# You don't need to execute the code, when simply building or running the KFP pipeline (experiment)\n",
        "local_context = get_local_context()\n",
        "import json\n",
        "update_local_context(predict_batch_ml_model(\n",
        "    json.dumps(local_context),\n",
        "    local_context['RPM_GCP_PROJECT'],\n",
        "    local_context['rpm_bq_ds_name'],\n",
        "    local_context['rpm_bqml_model'],\n",
        "    local_context['rpm_pred_table_id'],\n",
        "    ))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sYQBKwJRwapm",
        "colab_type": "text"
      },
      "source": [
        "# Determine the new revision of the model - function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z7KEFuPawapn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# get_bqml_model_version\n",
        "from typing import NamedTuple\n",
        "def get_bqml_model_version(ctx: str,\n",
        "                           RPM_GCP_PROJECT: str,\n",
        "                           RPM_MODEL_EXPORT_PATH: str,\n",
        "                           RPM_DEFAULT_MODEL_EXPORT_PATH: str,\n",
        "                           RPM_MODEL_VER_PREFIX: str,\n",
        "                           rpm_gcs_rpm_ds_url: str ) -> NamedTuple('Outputs', [\n",
        "        ('rpm_context', str),\n",
        "        ('rpm_bqml_model_export_path', str),\n",
        "        ('rpm_model_version', str),\n",
        "    ]):\n",
        "  \"\"\"\n",
        "    The function(also used as a base for a KFP Component) determines\n",
        "      the revision of the models.\n",
        "\n",
        "    It checkes the current version and increments by 1.\n",
        "    It prepares the folder for the BigQuery ML to export the model.\n",
        "    Args:\n",
        "      ctx(:obj:`str`): The dict object with all the variables\n",
        "        used in the pipeline\n",
        "      RPM_GCP_PROJECT:(:obj:`str`): Google Cloud project for deployment\n",
        "      RPM_MODEL_EXPORT_PATH(:obj:`str`): User supplied model export path\n",
        "      RPM_DEFAULT_MODEL_EXPORT_PATH(:obj:`str`): Uses the default path,\n",
        "        if the user didn't provide a path\n",
        "      RPM_MODEL_VER_PREFIX(:obj:`str`): The folder with prefix\n",
        "      rpm_pred_table_id(:obj:`str`): The table that contains the input data\n",
        "        which we want batch predict later\n",
        "    Returns:\n",
        "      Outputs(:obj: `tuple`): Returns the below outputs:\n",
        "        rpm_context(:obj:`str`): All variables used in the pipeline\n",
        "        rpm_bqml_model_export_path(:obj:`str`): the path\n",
        "          to which we can export the model\n",
        "        rpm_model_version(:obj:`str`): the version which we will use when\n",
        "          we deploy the model to Cloud AI Prediction\n",
        "  \"\"\"\n",
        "  import json\n",
        "  rpm_context = json.loads(ctx)\n",
        "  print(rpm_context)\n",
        "\n",
        "  # defining the install function\n",
        "  import subprocess\n",
        "  import os\n",
        "  def install(name):\n",
        "    subprocess.call(['pip', 'install', name])\n",
        "  pacakages_to_install = ['google-cloud', 'google-cloud-storage']\n",
        "  for each_package in pacakages_to_install:\n",
        "    install(each_package)\n",
        "    print(f\"'{each_package}' package installed :)\")\n",
        "\n",
        "  cmd = f\"gcloud config set project {rpm_context['RPM_GCP_PROJECT']}\"\n",
        "  print(cmd)\n",
        "  process = subprocess.run(cmd, shell=True, check=True,\n",
        "                           stdout=subprocess.PIPE, universal_newlines=True)\n",
        "  print(f\"'output from {cmd}': {process.stdout}\")\n",
        "\n",
        "  from google.cloud import storage\n",
        "  from google.cloud.exceptions import NotFound\n",
        "  from google.cloud.exceptions import Forbidden\n",
        "  import traceback\n",
        "\n",
        "  client = storage.Client()\n",
        "  try:\n",
        "    bucket_name= rpm_context['rpm_gcs_rpm_ds_url'].split('/')[2]\n",
        "    rpm_bq_ds_name = rpm_context['rpm_gcs_rpm_ds_url'].split('/')[3]\n",
        "    rpm_bqml_model_export_path = rpm_context['RPM_MODEL_EXPORT_PATH']\n",
        "    if not rpm_bqml_model_export_path:\n",
        "      rpm_bqml_model_export_path = rpm_context['RPM_DEFAULT_MODEL_EXPORT_PATH']\n",
        "    bucket = client.get_bucket(bucket_name)\n",
        "    print(f'Details: {bucket}')\n",
        "    folder_name = os.path.join(rpm_bq_ds_name, rpm_bqml_model_export_path)\n",
        "    blob = bucket.get_blob(folder_name)\n",
        "    model_version = 0\n",
        "    if blob is None:\n",
        "      print(f\"Folder name {folder_name} does not exist!\")\n",
        "      print(f\"{bucket_name}, {folder_name}\")\n",
        "      blob = bucket.blob(folder_name)\n",
        "      blob.upload_from_string('')\n",
        "      print(f\"Folder name {folder_name} created.\")\n",
        "    else:\n",
        "      print(f\"Folder name {folder_name} exist.\")\n",
        "      client = storage.Client()\n",
        "      blobs = client.list_blobs(bucket_name, prefix=folder_name)\n",
        "      print('Blobs:')\n",
        "      for blob in blobs:\n",
        "        print(f\"blob name: {blob.name}\")\n",
        "        curr_ver = blob.name.replace(folder_name, '')\n",
        "        print(f\"folder_name: {folder_name}\")\n",
        "        print(f\"after folder_name replace: {curr_ver}\")\n",
        "        if rpm_context['RPM_MODEL_VER_PREFIX'] in curr_ver \\\n",
        "            and len(curr_ver.split('/')) == 2 and  \\\n",
        "            len(curr_ver.split('/')[1]) == 0:\n",
        "          curr_ver = curr_ver.replace(rpm_context['RPM_MODEL_VER_PREFIX'], '').replace('/','').split('/')[0]\n",
        "          model_version = max(model_version, int(curr_ver))\n",
        "    # increment the model version\n",
        "    model_version += 1\n",
        "    model_version_full_name = f\"{rpm_context['RPM_MODEL_VER_PREFIX']}{model_version}/\"\n",
        "    folder_name = os.path.join(folder_name, model_version_full_name)\n",
        "    print(f\"Going to create folder {folder_name} created.\")\n",
        "    blob = bucket.get_blob(folder_name)\n",
        "    blob = bucket.blob(folder_name)\n",
        "    blob.upload_from_string('')\n",
        "    print(f\"Folder name {folder_name} created.\")\n",
        "    rpm_context['rpm_bqml_model_export_path'] = os.path.join(rpm_bqml_model_export_path, model_version_full_name)\n",
        "    rpm_context['rpm_model_version'] = model_version_full_name.rstrip('/')\n",
        "  except Forbidden as e:\n",
        "    print(f\"Sorry, you don't have access to the bucket: {bucket_name}!\")\n",
        "    print(e)\n",
        "    error = traceback.format_exc()\n",
        "    print(error)\n",
        "  except NotFound as e:\n",
        "    print(f\"Sorry, the bucket: {bucket_name} does not exist!\")\n",
        "    print(e)\n",
        "    error = traceback.format_exc()\n",
        "    print(error)\n",
        "\n",
        "  return (\n",
        "      json.dumps(rpm_context),\n",
        "      rpm_context['rpm_bqml_model_export_path'],\n",
        "      rpm_context['rpm_model_version']\n",
        "      )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uoyANmHLwapo",
        "colab_type": "text"
      },
      "source": [
        "# *Test locally get_bqml_model_version*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "D2NAf7qgwapp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# test locally get_bqml_model_version\n",
        "# You could unit test the above code in your local environment.\n",
        "# You don't need to execute the code, when simply building or running the KFP pipeline (experiment)\n",
        "local_context = get_local_context()\n",
        "import json\n",
        "update_local_context(get_bqml_model_version(\n",
        "    json.dumps(local_context),\n",
        "    local_context['RPM_GCP_PROJECT'],\n",
        "    local_context['RPM_MODEL_EXPORT_PATH'],\n",
        "    local_context['RPM_DEFAULT_MODEL_EXPORT_PATH'],\n",
        "    local_context['RPM_MODEL_VER_PREFIX'],\n",
        "    local_context['rpm_gcs_rpm_ds_url']))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qkX573wzwapq",
        "colab_type": "text"
      },
      "source": [
        "# Export the BigQuery ML model to the Google Cloud Storage bucket - function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBVWPGXBwapr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# export_bqml_model_to_gcs\n",
        "from typing import NamedTuple\n",
        "def export_bqml_model_to_gcs(ctx: str,\n",
        "                             RPM_GCP_PROJECT: str,\n",
        "                             RPM_PVC_NAME: str,\n",
        "                             RPM_PVC_DATASET_FOLDER_NAME: str,\n",
        "                             rpm_bqml_model_export_path: str,\n",
        "                             rpm_gcs_rpm_ds_url: str,\n",
        "                             rpm_bq_ds_name: str,\n",
        "                             rpm_bqml_model: str, ) -> NamedTuple('Outputs', [\n",
        "        ('rpm_context', str),\n",
        "        ('rpm_model_uri', str),\n",
        "    ]):\n",
        "  \"\"\" The function(also used as a base for a KFP Component) exports\n",
        "      the BigQuery ML model.\n",
        "\n",
        "    It also saves the the details used in the model\n",
        "      e.g. losses, learning rate adjustment, #of iterations.\n",
        "    It also saves the evaluation details e.g. roc, accuracy, etc.\n",
        "    Args:\n",
        "      ctx(:obj:`str`): The dict object with all the variables\n",
        "        used in the pipeline\n",
        "      RPM_GCP_PROJECT:(:obj:`str`): Google Cloud project for deployment\n",
        "      RPM_PVC_NAME:(:obj:`str`): Ther persitent volume name\n",
        "      RPM_MODEL_EXPORT_PATH(:obj:`str`): The path to store the temporary files\n",
        "        before we upload to Google Cloud Storage\n",
        "      RPM_PVC_DATASET_FOLDER_NAME(:obj:`str`): The folder name to store\n",
        "        the temporary files before we upload to Google Cloud Storage\n",
        "      rpm_bqml_model_export_path(:obj:`str`): The path\n",
        "        to which we can export the model\n",
        "      rpm_gcs_rpm_ds_url(:obj:`str`): Full Google Cloud Storage path\n",
        "        with bucket name and folder name\n",
        "      rpm_bq_ds_name(:obj:`str`): The dataset name\n",
        "        used in the rest of the pipeline\n",
        "      rpm_bqml_model(:obj:`str`): The model name\n",
        "        used in the rest of the pipeline\n",
        "    Returns:\n",
        "      Outputs(:obj: `tuple`): Returns the below outputs:\n",
        "        rpm_context(:obj:`str`): All variables used in the pipeline\n",
        "        rpm_model_uri(:obj:`str`): The path to where we export the model\n",
        "  \"\"\"\n",
        "  import json\n",
        "  rpm_context = json.loads(ctx)\n",
        "  print(rpm_context)\n",
        "\n",
        "  import subprocess\n",
        "  import traceback\n",
        "  def exec_cmd(cmd):\n",
        "    try:\n",
        "      print(cmd)\n",
        "      process = subprocess.run(cmd, shell=True, check=True,\n",
        "                               stdout=subprocess.PIPE, universal_newlines=True)\n",
        "      print(f\"'output from {cmd}': {process.stdout}\")\n",
        "      return process.stdout\n",
        "    except subprocess.CalledProcessError as e:\n",
        "      error = traceback.format_exc()\n",
        "      print(error)\n",
        "      print(e.output)\n",
        "      return e.output\n",
        "  import os\n",
        "  if not rpm_context['rpm_bqml_model_export_path']:\n",
        "    raise RuntimeError(\"Can't export the BigQuery model: export destination is empty!\")\n",
        "\n",
        "  import os\n",
        "  path_to_ds = os.path.join(rpm_context['RPM_PVC_NAME'],\n",
        "  rpm_context['RPM_PVC_DATASET_FOLDER_NAME'])\n",
        "  # check if that the dataset directory already exists\n",
        "  exec_cmd(f\"test -d {path_to_ds} && echo 'Exists' || echo 'Does not exist'\")\n",
        "  # create the datset directory\n",
        "  exec_cmd(f\"mkdir -p {path_to_ds}\")\n",
        "  # validate that the dataset directory has been created\n",
        "  exec_cmd(f\"test -d {path_to_ds} && echo 'Exists' || echo 'Does not exist'\")\n",
        "\n",
        "\n",
        "  rpm_bqml_model_export_path = os.path.join(rpm_context['rpm_gcs_rpm_ds_url'],\n",
        "  rpm_context['rpm_bqml_model_export_path'])\n",
        "  rpm_bqml_model_export_path=rpm_bqml_model_export_path.rstrip('/')\n",
        "\n",
        "  exec_cmd(f\"gcloud config set project {rpm_context['RPM_GCP_PROJECT']}\")\n",
        "  cmd = f\"bq extract -m {rpm_context['rpm_bq_ds_name']}.{rpm_context['rpm_bqml_model']} {rpm_bqml_model_export_path}\"\n",
        "  print(cmd)\n",
        "  exec_cmd(cmd)\n",
        "\n",
        "  rpm_context['rpm_model_uri'] = \\\n",
        "      os.path.join(rpm_context['rpm_gcs_rpm_ds_url'],\n",
        "                   rpm_context['rpm_bqml_model_export_path'])\n",
        "\n",
        "  bqml_eval_query = f\"\"\"\n",
        "      SELECT\n",
        "      *\n",
        "      FROM\n",
        "      ML.EVALUATE(MODEL `{rpm_bq_ds_name}.{rpm_bqml_model}`)\n",
        "  \"\"\"\n",
        "  rpm_eval_output = exec_cmd(f\"bq query --use_legacy_sql=false --format=json '{bqml_eval_query}'\")\n",
        "  print(rpm_eval_output)\n",
        "\n",
        "  bqml_train_detail_query = f\"\"\"\n",
        "      SELECT\n",
        "      *\n",
        "      FROM\n",
        "      ML.TRAINING_INFO(MODEL `{rpm_bq_ds_name}.{rpm_bqml_model}`)\n",
        "  \"\"\"\n",
        "  bqml_train_detail_query_output = exec_cmd(f\"bq query --use_legacy_sql=false --format=json '{bqml_train_detail_query}'\")\n",
        "  print(bqml_train_detail_query_output)\n",
        "\n",
        "  path_to_ds = f\"/{rpm_context['RPM_PVC_NAME']}/{rpm_context['RPM_PVC_DATASET_FOLDER_NAME']}/\"\n",
        "  import os\n",
        "  # export the eval model output in a file called evalu_details.txt\n",
        "\n",
        "  rpm_eval_output_filename = os.path.join(path_to_ds, 'eval_detail.txt')\n",
        "  with open(rpm_eval_output_filename, 'w') as outfile:\n",
        "    outfile.write(rpm_eval_output)\n",
        "  exec_cmd(f\"cat {rpm_eval_output_filename}\")\n",
        "  exec_cmd(f\"gcloud storage cp {rpm_eval_output_filename} {rpm_context['rpm_model_uri']}\")\n",        "\n",
        "  # export the training details in a file called train_details.txt\n",
        "  bqml_train_detail_query_filename = os.path.join(path_to_ds, 'train_detail.txt')\n",
        "  with open(bqml_train_detail_query_filename, 'w') as outfile:\n",
        "    outfile.write(bqml_train_detail_query_output)\n",
        "  exec_cmd(f\"cat {bqml_train_detail_query_filename}\")\n",
        "  exec_cmd(f\"gcloud storage cp {bqml_train_detail_query_filename} {rpm_context['rpm_model_uri']}\")\n",        "\n",
        "  return (json.dumps(rpm_context),\n",
        "         rpm_context['rpm_model_uri'],\n",
        "         )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sM8KC14uwapu",
        "colab_type": "text"
      },
      "source": [
        "# *Test locally export_bqml_model_to_gcs*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "BNJMaey2wapu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# test locally export_bqml_model_to_gcs\n",
        "# You could unit test the above code in your local environment.\n",
        "# You don't need to execute the code, when simply building or running the KFP pipeline (experiment)\n",
        "local_context = get_local_context()\n",
        "import json\n",
        "update_local_context(export_bqml_model_to_gcs(\n",
        "    json.dumps(local_context),\n",
        "    local_context['RPM_GCP_PROJECT'],\n",
        "    local_context['RPM_PVC_NAME'],\n",
        "    local_context['RPM_PVC_DATASET_FOLDER_NAME'],\n",
        "    local_context['rpm_bqml_model_export_path'],\n",
        "    local_context['rpm_gcs_rpm_ds_url'],\n",
        "    local_context['rpm_bq_ds_name'],\n",
        "    local_context['rpm_bqml_model'],\n",
        "    ))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HfVhjsLAwapw",
        "colab_type": "text"
      },
      "source": [
        "# Deploy the ML model - function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UY57cWHcwapx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from typing import NamedTuple\n",
        "def deploy_ml_model_online_pred(ctx: str,\n",
        "                                RPM_GCP_PROJECT: str,\n",
        "                                RPM_LOCATION: str,\n",
        "                                RPM_RUNTIME_VERSION: str,\n",
        "                                RPM_PYTHON_VERSION: str,\n",
        "                                rpm_model_uri: str,\n",
        "                                rpm_bqml_model: str,\n",
        "                                rpm_model_version: str,\n",
        "                                rpm_gcs_rpm_ds_url: str, ) -> NamedTuple('Outputs', [\n",
        "        ('rpm_context', str),\n",
        "        ('rpm_url_to_monitor', str),\n",
        "        ('rpm_model_region', str),\n",
        "    ]):\n",
        "  \"\"\" The function(also used as a base for a KFP Component) deploys\n",
        "    the model that is exported above to Cloud AI Platform Prediction\n",
        "    Args:\n",
        "      ctx(:obj:`str`): The dict object with all the variables\n",
        "        used in the pipeline\n",
        "      RPM_GCP_PROJECT:(:obj:`str`): Google Cloud project for deployment\n",
        "      RPM_LOCATION:(:obj:`str`): Google Cloud region\n",
        "        where we are going to deploy the model\n",
        "      RPM_RUNTIME_VERSION(:obj:`str`): The runtime version\n",
        "        of the caip predicted\n",
        "      RPM_PYTHON_VERSION(:obj:`str`): The python version\n",
        "        of the caip predicted\n",
        "      rpm_model_uri(:obj:`str`): The path to where we export the model\n",
        "      rpm_bqml_model(:obj:`str`): The model name\n",
        "        used in the rest of the pipeline\n",
        "      rpm_model_version(:obj:`str`): The version which we will use when\n",
        "        we deploy the model to caip prediction\n",
        "      rpm_gcs_rpm_ds_url(:obj:`str`): Full Google Cloud Storage path\n",
        "        with bucket name and folder name\n",
        "    Returns:\n",
        "      Outputs(:obj: `tuple`): Returns the below outputs:\n",
        "        rpm_context(:obj:`str`): All variables used in the pipeline\n",
        "        rpm_url_to_monitor(:obj:`str`): The url in the Google Cloud Console\n",
        "          which you can use to monitor\n",
        "        rpm_model_region(:obj:`str`): The Google Cloud region\n",
        "          where you are going to deploy the model\n",
        "  \"\"\"\n",
        "  import json\n",
        "  rpm_context = json.loads(ctx)\n",
        "  print(rpm_context)\n",
        "  rpm_context['rpm_url_to_monitor'] = f\"https://console.cloud.google.com/ai-platform/models/{rpm_bqml_model}/versions\"\n",
        "\n",
        "  model_region = RPM_LOCATION[:-2]\n",
        "  rpm_context['rpm_model_region'] = model_region\n",
        "\n",
        "  import subprocess\n",
        "  import traceback\n",
        "  def exec_cmd(cmd):\n",
        "    try:\n",
        "      print(cmd)\n",
        "      process = subprocess.run(cmd, shell=True, check=True,\n",
        "                               stdout=subprocess.PIPE, universal_newlines=True)\n",
        "      print(f\"'output from {cmd}': {process.stdout}\")\n",
        "      return(process.stdout, 0)\n",
        "    except subprocess.CalledProcessError as e:\n",
        "      error = traceback.format_exc()\n",
        "      print(error)\n",
        "      print(e.output)\n",
        "      return(e.output, 1)\n",
        "\n",
        "  exec_cmd(f\"gcloud config set project {rpm_context['RPM_GCP_PROJECT']}\")\n",
        "\n",
        "  (output, returncode) = \\\n",
        "      exec_cmd(f\"gcloud ai-platform models list --format='value(name)' | grep {rpm_bqml_model}\")\n",
        "  print(f'output:{output}, returncode:{returncode}')\n",
        "  if returncode == 0: #grep returns 1 if nothing is found\n",
        "    print(f\"{rpm_bqml_model} already exists\")\n",
        "  else:\n",
        "    print(f\"{rpm_bqml_model} doesn't exists. Creating...\")\n",
        "    (output_create, returncode_create) = \\\n",
        "        exec_cmd(f'gcloud ai-platform models create --regions={model_region} {rpm_bqml_model}')\n",
        "    print(f\"output:{output_create}, returncode:{returncode_create}\")\n",
        "    if returncode_create != 0:\n",
        "      raise RuntimeError(f\"Can't create the ML Model {rpm_bqml_model}\")\n",
        "    print(f\"{rpm_bqml_model} created.\")\n",
        "\n",
        "  (output, returncode) = \\\n",
        "      exec_cmd(f\"gcloud ai-platform versions list --model {rpm_bqml_model} --format='value(name)' | grep {rpm_model_version}\")\n",
        "  if returncode == 0: #grep returns 1 if nothing is found\n",
        "    print(f\"{rpm_bqml_model} with version {rpm_model_version} already exists\")\n",
        "  else:\n",
        "    print(f\"{rpm_bqml_model} with version {rpm_model_version} doesn't exists. Creating...\")\n",
        "    cmd = f\"\"\"\n",
        "    gcloud ai-platform versions create --model={rpm_bqml_model} \\\n",
        "        {rpm_model_version} \\\n",
        "     --framework=tensorflow --python-version={RPM_PYTHON_VERSION} \\\n",
        "     --runtime-version={RPM_RUNTIME_VERSION} \\\n",
        "     --origin={rpm_model_uri} \\\n",
        "     --staging-bucket=gs://{rpm_gcs_rpm_ds_url.split('/')[2]}\n",
        "    \"\"\"\n",
        "    (output_create, returncode_create) = exec_cmd(cmd)\n",
        "    print(f\"output:{output_create}, returncode:{returncode_create}\")\n",
        "    if returncode_create != 0:\n",
        "      raise RuntimeError(f\"Can't create the ML Model {rpm_bqml_model} with version {rpm_model_version}!!!\")\n",
        "    print(f\"{rpm_bqml_model} with version {rpm_model_version} created.\")\n",
        "\n",
        "  print(f\"Monitor models at {rpm_context['rpm_url_to_monitor']}\")\n",
        "\n",
        "  return (\n",
        "      json.dumps(rpm_context),\n",
        "      rpm_context['rpm_url_to_monitor'],\n",
        "      rpm_context['rpm_model_region']\n",
        "      )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YlIz8PPiwapz",
        "colab_type": "text"
      },
      "source": [
        "# *Test locally deploy_ml_model_online_pred*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "dRbMYIkvwap0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# test locally deploy_ml_model_online_pred\n",
        "# You could unit test the above code in your local environment.\n",
        "# You don't need to execute the code, when simply building or running the KFP pipeline (experiment)\n",
        "local_context = get_local_context()\n",
        "import json\n",
        "update_local_context(deploy_ml_model_online_pred(\n",
        "    json.dumps(local_context),\n",
        "    local_context['RPM_GCP_PROJECT'],\n",
        "    local_context['RPM_LOCATION'],\n",
        "    local_context['RPM_RUNTIME_VERSION'],\n",
        "    local_context['RPM_PYTHON_VERSION'],\n",
        "    local_context['rpm_model_uri'], \n",
        "    local_context['rpm_bqml_model'],\n",
        "    local_context['rpm_model_version'],\n",
        "    local_context['rpm_gcs_rpm_ds_url'],\n",
        "    ))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aAjOpTf6wap2",
        "colab_type": "text"
      },
      "source": [
        "# Make online prediction - function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "l6KCqVzDwap2"
      },
      "outputs": [],
      "source": [
        "# predict_online_ml_model\n",
        "from typing import NamedTuple\n",
        "def predict_online_ml_model(ctx: str,\n",
        "                            RPM_GCP_PROJECT: str,\n",
        "                            RPM_PVC_NAME: str,\n",
        "                            RPM_PVC_DATASET_FOLDER_NAME: str,\n",
        "                            rpm_bqml_model: str,\n",
        "                            rpm_model_version: str, ) -> NamedTuple('Outputs', [\n",
        "        ('rpm_context', str),\n",
        "        ('rpm_predict_online_output', str),\n",
        "    ]):\n",
        "  \"\"\" The function(also used as a base for a KFP Component)\n",
        "    does the online prediction.\n",
        "\n",
        "    This is to confirm that the endpoint is available and ready to serve.\n",
        "    Args:\n",
        "      ctx(:obj:`str`): The dict object with all the variables\n",
        "        used in the pipeline\n",
        "      RPM_GCP_PROJECT:(:obj:`str`): Google Cloud project for deployment\n",
        "      RPM_PVC_NAME:(:obj:`str`): Ther persitent volume name\n",
        "      RPM_PVC_DATASET_FOLDER_NAME(:obj:`str`): The folder name to store\n",
        "        the temporary files before we upload to Google Cloud Storage\n",
        "      rpm_bqml_model(:obj:`str`): The model name\n",
        "        used in the rest of the pipeline\n",
        "      rpm_model_version(:obj:`str`): The version which we will use\n",
        "        when we deploy the model to Cloud AI Platform Prediction\n",
        "    Returns:\n",
        "      Outputs(:obj: `tuple`): Returns the below outputs:\n",
        "        rpm_context(:obj:`str`): All variables used in the pipeline\n",
        "        rpm_url_to_monitor(:obj:`str`): The url in the Google Cloud Console\n",
        "          which you can use to monitor\n",
        "        rpm_predict_online_output(:obj:`str`): The output\n",
        "          from the online prediction\n",
        "  \"\"\"\n",
        "  import json\n",
        "  rpm_context = json.loads(ctx)\n",
        "  print(rpm_context)\n",
        "\n",
        "  import subprocess\n",
        "  import traceback\n",
        "  def exec_cmd(cmd):\n",
        "    try:\n",
        "      print(cmd)\n",
        "      process = subprocess.run(cmd, shell=True, check=True,\n",
        "                               stdout=subprocess.PIPE, universal_newlines=True)\n",
        "      print(f\"'output from {cmd}': {process.stdout}\")\n",
        "      return(process.stdout, 0)\n",
        "    except subprocess.CalledProcessError as e:\n",
        "      error = traceback.format_exc()\n",
        "      print(error)\n",
        "      print(e.output)\n",
        "      return(e.output, 1)\n",
        "\n",
        "  exec_cmd(f\"gcloud config set project {rpm_context['RPM_GCP_PROJECT']}\")\n",
        "\n",
        "  import os\n",
        "  path_to_ds = os.path.join(rpm_context['RPM_PVC_NAME'],\n",
        "  rpm_context['RPM_PVC_DATASET_FOLDER_NAME'])\n",
        "  # check if that the dataset directory already exists\n",
        "  exec_cmd(f\"test -d {path_to_ds} && echo 'Exists' || echo 'Does not exist'\")\n",
        "  # create the datset directory\n",
        "  exec_cmd(f\"mkdir -p {path_to_ds}\")\n",
        "  # validate that the dataset directory has been created\n",
        "  exec_cmd(f\"test -d {path_to_ds} && echo 'Exists' || echo 'Does not exist'\")\n",
        "\n",
        "  input_data = \"\"\"{\"bounces\": 0, \"time_on_site\": 7363}\"\"\"\n",
        "  filename = os.path.join(path_to_ds, 'input.json')\n",
        "  with open(filename, 'w') as outfile:\n",
        "    outfile.write(input_data)\n",
        "\n",
        "  cmd = f\"\"\"\n",
        "  gcloud ai-platform predict --model {rpm_bqml_model} \\\n",
        "     --version {rpm_model_version} --json-instances {filename}\n",
        "  \"\"\"\n",
        "  (output, returncode) = exec_cmd(cmd)\n",
        "  print(f\"Predicted results for {input_data} is {output} \")\n",
        "  rpm_context['rpm_predict_online_output'] = output\n",
        "\n",
        "  return (\n",
        "      json.dumps(rpm_context),\n",
        "      rpm_context['rpm_predict_online_output'],\n",
        "      )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gQjqEpt5wap5",
        "colab_type": "text"
      },
      "source": [
        "# *Test locally predict_online_ml_model*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "qQfawPl5wap5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# test locally predict_online_ml_model\n",
        "# You could unit test the above code in your local environment.\n",
        "# You don't need to execute the code, when simply building or running the KFP pipeline (experiment)\n",
        "local_context = get_local_context()\n",
        "import json\n",
        "update_local_context(predict_online_ml_model(\n",
        "    json.dumps(local_context),\n",
        "    local_context['RPM_GCP_PROJECT'],\n",
        "    local_context['RPM_PVC_NAME'],\n",
        "    local_context['RPM_PVC_DATASET_FOLDER_NAME'],\n",
        "    local_context['rpm_bqml_model'],\n",
        "    local_context['rpm_model_version'],\n",
        "    ))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TXsOJsaVwap7",
        "colab_type": "text"
      },
      "source": [
        "# Define the KubeFlow Pipeline (KFP)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "Rc0X4C35wap7"
      },
      "outputs": [],
      "source": [
        "# define the pipeline\n",
        "import kfp.components as comp\n",
        "def create_kfp_comp(rpm_comp):\n",
        "  \"\"\" Converts a Python function to a component\n",
        "    and returns a task(ContainerOp) factory\n",
        "\n",
        "    Returns:\n",
        "      Outputs (:obj: `ContainerOp`): returns the operation\n",
        "  \"\"\"\n",
        "  return comp.func_to_container_op(\n",
        "      func=rpm_comp,\n",
        "      base_image=\"google/cloud-sdk:latest\"\n",
        "      )\n",
        "\n",
        "# reload the properties; undo any properties set to test component locally\n",
        "all_vars = load_params()\n",
        "\n",
        "from kfp.dsl import pipeline, VolumeOp\n",
        "import kfp.dsl as dsl\n",
        "import json\n",
        "# define the pipeline metadata\n",
        "@pipeline(\n",
        "  name='Propensity to purchase using BigQuery ML',\n",
        "  description='Propensity model if a customer is likely to purchase'\n",
        ")\n",
        "\n",
        "# define the pipeline\n",
        "def bq_googlestr_dataset_to_bq_to_caip_pipeline(\n",
        "  data_path = all_vars['RPM_PVC_NAME'] #you can pass input variables\n",
        "):\n",
        "  \"\"\" The function defines the pipeline.\n",
        "\n",
        "    Args:\n",
        "        data_path:(:obj:`str`): the volume to store the temporary files\n",
        "  \"\"\"\n",
        "  rpm_context = json.dumps(all_vars)\n",
        "  gcs_bucket_folder_op = create_kfp_comp(create_gcs_bucket_folder)(\n",
        "      rpm_context,\n",
        "      all_vars['RPM_GCP_STORAGE_BUCKET'],\n",
        "      all_vars['RPM_GCP_PROJECT'],\n",
        "      all_vars['RPM_DEFAULT_BUCKET_EXT'],\n",
        "      all_vars['RPM_GCP_STORAGE_BUCKET_FOLDER'],\n",
        "      all_vars['RPM_DEFAULT_BUCKET_FOLDER_NAME']\n",
        "      )\n",
        "\n",
        "  create_bq_ds_op = create_kfp_comp(create_bq_ds)(\n",
        "      gcs_bucket_folder_op.outputs['rpm_context'],\n",
        "      all_vars['RPM_GCP_PROJECT'],\n",
        "      all_vars['RPM_BQ_DATASET_NAME'],\n",
        "      all_vars['RPM_LOCATION']\n",
        "      )\n",
        "\n",
        "\n",
        "  load_bq_ds_op = create_kfp_comp(load_bq_ds)(\n",
        "      create_bq_ds_op.outputs['rpm_context'],\n",
        "      all_vars['RPM_GCP_PROJECT'],\n",
        "      all_vars['RPM_BQ_TABLE_NAME'],\n",
        "      all_vars['RPM_DEFAULT_BQ_TABLE_NAME_EXT'],\n",
        "      create_bq_ds_op.outputs['rpm_bq_ds_name'],\n",
        "      )\n",
        "\n",
        "  create_bq_ml_op = create_kfp_comp(create_bq_ml)(\n",
        "      load_bq_ds_op.outputs['rpm_context'],\n",
        "      all_vars['RPM_GCP_PROJECT'],\n",
        "      all_vars['RPM_MODEL_NAME'],\n",
        "      all_vars['RPM_DEFAULT_MODEL_NAME'],\n",
        "      create_bq_ds_op.outputs['rpm_bq_ds_name'],\n",
        "      load_bq_ds_op.outputs['rpm_table_id']\n",
        "      )\n",
        "\n",
        "  evaluate_ml_model_op = create_kfp_comp(evaluate_ml_model)(\n",
        "      create_bq_ml_op.outputs['rpm_context'],\n",
        "      all_vars['RPM_GCP_PROJECT'],\n",
        "      create_bq_ds_op.outputs['rpm_bq_ds_name'],\n",
        "      create_bq_ml_op.outputs['rpm_bqml_model'],\n",
        "      )\n",
        "\n",
        "  create_batch_prediction_dataset_op = create_kfp_comp(create_batch_prediction_dataset)(\n",
        "      evaluate_ml_model_op.outputs['rpm_context'],\n",
        "      all_vars['RPM_GCP_PROJECT'],\n",
        "      create_bq_ds_op.outputs['rpm_bq_ds_name'],\n",
        "      load_bq_ds_op.outputs['rpm_table_id'],\n",
        "      )\n",
        "\n",
        "  predict_batch_ml_model_op = create_kfp_comp(predict_batch_ml_model)(\n",
        "    evaluate_ml_model_op.outputs['rpm_context'],\n",
        "    all_vars['RPM_GCP_PROJECT'],\n",
        "    create_bq_ds_op.outputs['rpm_bq_ds_name'],\n",
        "    create_bq_ml_op.outputs['rpm_bqml_model'],\n",
        "    create_batch_prediction_dataset_op.outputs['rpm_pred_table_id'],\n",
        "    )\n",
        "\n",
        "  get_versioned_bqml_model_export_path_op = create_kfp_comp(get_bqml_model_version)(\n",
        "      create_bq_ml_op.outputs['rpm_context'],\n",
        "      all_vars['RPM_GCP_PROJECT'],\n",
        "      all_vars['RPM_MODEL_EXPORT_PATH'],\n",
        "      all_vars['RPM_DEFAULT_MODEL_EXPORT_PATH'],\n",
        "      all_vars['RPM_MODEL_VER_PREFIX'],\n",
        "      gcs_bucket_folder_op.outputs['rpm_gcs_rpm_ds_url']\n",
        "      )\n",
        "\n",
        "  # create a volume where the dataset will be temporarily stored.\n",
        "  pvc_op = VolumeOp(\n",
        "      name=all_vars['RPM_PVC_NAME'],\n",
        "      resource_name=all_vars['RPM_PVC_NAME'],\n",
        "      size=\"20Gi\",\n",
        "      modes=dsl.VOLUME_MODE_RWO\n",
        "      )\n",
        "\n",
        "  export_bqml_model_to_gcs_op = create_kfp_comp(export_bqml_model_to_gcs)(get_versioned_bqml_model_export_path_op.outputs['rpm_context'],\n",
        "      all_vars['RPM_GCP_PROJECT'],\n",
        "      all_vars['RPM_PVC_NAME'],\n",
        "      all_vars['RPM_PVC_DATASET_FOLDER_NAME'],\n",
        "      get_versioned_bqml_model_export_path_op.outputs['rpm_bqml_model_export_path'],\n",
        "      gcs_bucket_folder_op.outputs['rpm_gcs_rpm_ds_url'],\n",
        "      create_bq_ds_op.outputs['rpm_bq_ds_name'],\n",
        "      create_bq_ml_op.outputs['rpm_bqml_model'],\n",
        "  )\n",
        "  export_bqml_model_to_gcs_op.add_pvolumes({data_path: pvc_op.volume})\n",
        "\n",
        "  model_deploy_op = create_kfp_comp(deploy_ml_model_online_pred)(\n",
        "      export_bqml_model_to_gcs_op.outputs['rpm_context'],\n",
        "      all_vars['RPM_GCP_PROJECT'],\n",
        "      all_vars['RPM_LOCATION'],\n",
        "      all_vars['RPM_RUNTIME_VERSION'],\n",
        "      all_vars['RPM_PYTHON_VERSION'],\n",
        "      export_bqml_model_to_gcs_op.outputs['rpm_model_uri'],\n",
        "      create_bq_ml_op.outputs['rpm_bqml_model'],\n",
        "      get_versioned_bqml_model_export_path_op.outputs['rpm_model_version'],\n",
        "      gcs_bucket_folder_op.outputs['rpm_gcs_rpm_ds_url'],\n",
        "  )\n",
        "\n",
        "  predict_online_ml_model_op = create_kfp_comp(predict_online_ml_model)(\n",
        "      model_deploy_op.outputs['rpm_context'],\n",
        "      all_vars['RPM_GCP_PROJECT'],\n",
        "      all_vars['RPM_PVC_NAME'],\n",
        "      all_vars['RPM_PVC_DATASET_FOLDER_NAME'],\n",
        "      create_bq_ml_op.outputs['rpm_bqml_model'],\n",
        "      get_versioned_bqml_model_export_path_op.outputs['rpm_model_version'],\n",
        "  )\n",
        "  predict_online_ml_model_op.add_pvolumes({data_path: pvc_op.volume})\n",
        "\n",
        "  # don't cache the following comps\n",
        "  # the below is for model versioning only\n",
        "  get_versioned_bqml_model_export_path_op.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
        "  export_bqml_model_to_gcs_op.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
        "  model_deploy_op.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
        "  predict_online_ml_model_op.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
        "\n",
        "  # don't cache any comps\n",
        "  # you don't want to cache any comps when you are repetatively integration testing\n",
        "  gcs_bucket_folder_op.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
        "  create_bq_ds_op.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
        "  load_bq_ds_op.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
        "  create_bq_ml_op.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
        "  evaluate_ml_model_op.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
        "  create_batch_prediction_dataset_op.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
        "  predict_batch_ml_model_op.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
        "  get_versioned_bqml_model_export_path_op.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
        "  export_bqml_model_to_gcs_op.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
        "  model_deploy_op.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
        "  predict_online_ml_model_op.execution_options.caching_strategy.max_cache_staleness = \"P0D\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RbateePuwap9",
        "colab_type": "text"
      },
      "source": [
        "# Compile, watch out for errors in the pipeline composition"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYXb7UJtwap9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#compile the pipeline\n",
        "def complie_pipeline(pipeline_func):\n",
        "  \"\"\" Compile the pipeline, watch out for errors in the pipeline composition.\n",
        "\n",
        "    Args:\n",
        "      pipeline_func (:obj:`bq_googlestr_dataset_to_bq_to_caip_pipeline`):\n",
        "        The pipeline definition\n",
        "    Returns:\n",
        "      pipeline_filename (:obj:`str`): the compressed file compipled file\n",
        "        to upload to CloudAI Platform Prediction\n",
        "      pipeline_func (:obj:`str`): bq_googlestr_dataset_to_bq_to_caip_pipeline,\n",
        "        name of the the pipeline\n",
        "      arguments (:obj:`str`): the arguments to pass to the pipeline\n",
        "        when you launch it\n",
        "  \"\"\"\n",
        "  pipeline_func = pipeline_func\n",
        "  pipeline_filename = pipeline_func.__name__ + '.zip'\n",
        "\n",
        "  import kfp.compiler as compiler\n",
        "  compiler.Compiler().compile(pipeline_func, pipeline_filename)\n",
        "\n",
        "  arguments = {}\n",
        "  return (pipeline_filename, pipeline_func, arguments)\n",
        "pipeline_filename, pipeline_func, arguments = complie_pipeline(bq_googlestr_dataset_to_bq_to_caip_pipeline)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ACMErXIwap_",
        "colab_type": "text"
      },
      "source": [
        "# Create an experiment and run the pipeline immediately\n",
        "Please use the links in the output to go directly to the experiment/run launched in the browser"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iwRZDx0zwaqA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create and run an experiment\n",
        "def create_experiment_and_run():\n",
        "  \"\"\" Create an experiment and run the pipeline immediately.\n",
        "    Please use the links in the output to go directly to the experiment/run launched in the browser\n",
        "  \"\"\"\n",
        "  client = kfp.Client(RPM_GCP_KFP_HOST)\n",
        "  experiment = client.create_experiment(RPM_DS_DOWNLOAD_EXPERIMENT_NAME)\n",
        "  #Submit a pipeline run\n",
        "  run_name = pipeline_func.__name__ + ' run'\n",
        "  run_result = client.run_pipeline(\n",
        "      experiment_id=experiment.id,\n",
        "      job_name=run_name,\n",
        "      pipeline_package_path=pipeline_filename,\n",
        "      params=arguments)\n",
        "create_experiment_and_run()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wzw4YDNs_iA9",
        "colab_type": "text"
      },
      "source": [
        "If there is error in the pipeline, you will see that in the KubeFlow Pipelines UI in the Experiments section. If you encounter any errors, identify the issue, fix it in the Python function, unit test the function, update the pipeline defintion, compile, create an experiment, and run the experiment. Iterate through the process until you successfully run a pipeline.\n",
        "\n",
        "You have run successfully run a KubeFlow Pipelines. The pipeline created a model using BigQuery ML. You could now make use of the model while exploring or analyzing the data. You use SQL to use the model for prediction and then use the result as you wish. You could visualize the result using your favorite visualization package such as matplot lib.\n",
        "\n",
        "The section, below, demonstrates how you can use the BigQuery library to execute a sql in the BigQuey and collect the result in a pandas data frame. You can use any query you want including that of a query to predict using the model which the pipeline created."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CGMI9l4qwaqC",
        "colab_type": "text"
      },
      "source": [
        "# Data Exploraton"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "JgvYUrK1waqC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Data Exploration !!! BE CAREFUL !!! adjust the query to sample the data.\n",
        "# 1. Get pandas df from BigQuery\n",
        "# 2. plot histogram using matplotlib\n",
        "#########################\n",
        "from google.cloud import bigquery as bq\n",
        "import pandas as pd\n",
        "\n",
        "rpm_context = get_local_context()\n",
        "client = bq.Client(project=rpm_context[\"RPM_GCP_PROJECT\"])\n",
        "\n",
        "# adjust the below query to grab only a sample dataset e.g. use a where clause.\n",
        "df = client.query('''\n",
        "  SELECT *\n",
        "  FROM `%s.%s`\n",
        "  LIMIT 10\n",
        "''' % (rpm_context[\"rpm_bq_ds_name\"], rpm_context[\"rpm_table_id\"].split('.')[2])).to_dataframe()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xTFMsyMVwaqE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jTIG7BckwaqG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "UGqVZmILwaqJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7urbxPZZwaqL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cjoZwvmYwaqM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "plt.close('all') \n",
        "df.hist(bins=50, figsize=(20,15))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "E75eIJfKwaqO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# takes a bit of time...BE CAREFUL!!!\n",
        "# works on local Jupyter instance.\n",
        "import pandas_profiling as pp\n",
        "pp.ProfileReport(df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lzznZNc0_ntW",
        "colab_type": "text"
      },
      "source": [
        "The utilities method in the section below provides convinient way to delete the Google Cloud resources. You can use the methods  while developing your pipeline components.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46nuAibTwaqP",
        "colab_type": "text"
      },
      "source": [
        "# Clean up - !!! BE CAREFUL!!!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0DOWwgC5waqP",
        "colab_type": "text"
      },
      "source": [
        "## Delete the PODs and the PVCs in the KFP (Kubernetes Cluster)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MR42JjY_waqP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# delete_pod_pvc\n",
        "def delete_pod_pvc(ctx: str) -> str:\n",
        "  \"\"\" Removes the Pods and Persistence Volume (PVCs) created in the pipeline,\n",
        "    This is not recommendated to use it in a production enviornment.\n",
        "    Comes handy in the iterative development and testing phases of the SDLC.\n",
        "    !!! BE CAREFUL !!!!\n",
        "    Args:\n",
        "      ctx(:obj:`str`): The dict object\n",
        "      with all the variables in the local context\n",
        "  \"\"\"\n",
        "\n",
        "  # loading rpm_context string\n",
        "  import json\n",
        "  rpm_context = json.loads(ctx)\n",
        "  print(rpm_context)\n",
        "  import subprocess\n",
        "  def exec_cmd (cmd):\n",
        "    try:\n",
        "      print(cmd)\n",
        "      process = subprocess.run(cmd, shell=True, check=True, stdout=subprocess.PIPE, universal_newlines=True)\n",
        "      print(f\"'output from {cmd}': {process.stdout}\")\n",
        "    except subprocess.CalledProcessError as e:\n",
        "      error = traceback.format_exc()\n",
        "      print(error)\n",
        "      print(e)\n",
        "\n",
        "  exec_cmd(f\"gcloud config set project {rpm_context['RPM_GCP_PROJECT']}\")\n",
        "  exec_cmd(f\"gcloud container clusters get-credentials {rpm_context['RPM_CLUSTER_NAME']} --zone {rpm_context['RPM_LOCATION']} --project {rpm_context['RPM_GCP_PROJECT']}\")\n",
        "  exec_cmd(''' for pod in `kubectl get pod | grep 'bq-public-google-ds-to-bq-' | awk -F ' ' '{print $1}'`; do echo kubectl delete pod $pod; kubectl delete pod $pod; done ''')\n",
        "  exec_cmd(''' for pvc in `kubectl get pvc | grep 'bq-public-google-ds-to-bq-' | awk -F ' ' '{print $1}'`; do echo kubectl delete pvc $pvc; kubectl delete pvc $pvc; done ''')\n",
        "  exec_cmd(''' for pod in `kubectl get pod | grep 'bq-public-google-ds-to-bq-' | awk -F ' ' '{print $1}'`; do echo kubectl patch pod $pod -p '{\"metadata\":{\"finalizers\":null}}'; kubectl patch pod $pod -p '{\"metadata\":{\"finalizers\":null}}'; done ''')\n",
        "  exec_cmd(''' for pvc in `kubectl get pvc | grep 'bq-public-google-ds-to-bq-' | awk -F ' ' '{print $1}'`; do echo kubectl patch pvc $pvc -p '{\"metadata\":{\"finalizers\":null}}'; kubectl patch pvc $pvc -p '{\"metadata\":{\"finalizers\":null}}'; done ''')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "oXB1DLIKwaqR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_comp_local(delete_pod_pvc)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ICHm_tGlwaqS",
        "colab_type": "text"
      },
      "source": [
        "## Delete Google Cloud Storage folder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-NrsZqu5waqT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# delete the storage folder\n",
        "from google.cloud import storage\n",
        "from google.cloud.exceptions import NotFound\n",
        "from google.cloud.exceptions import Forbidden\n",
        "import traceback\n",
        "def delete_storage_folder(bucket_name, folder_name):\n",
        "  \"\"\"Deletes a folder in the Google Cloust Storage,\n",
        "    This is not recommendated to use it in a production enviornment.\n",
        "    Comes handy in the iterative development and testing phases of the SDLC.\n",
        "    !!! BE CAREFUL !!!!\n",
        "    Args:\n",
        "      bucket_name(:obj:`str`): The Cloud Storage bucket name,\n",
        "        where the folder exists\n",
        "      folder_name(:obj:`str`): The folder that we want to delete\n",
        "    Returns:\n",
        "      (:obj:`boolean`): True if we are able to scucessfully delete the folder\n",
        "  \"\"\"\n",
        "  if len(bucket_name) == 0 or len(folder_name) == 0:\n",
        "    print(f\"Folder {folder_name} couldn't be deleted. Name is empty.\")\n",
        "    return False\n",
        "  else:\n",
        "    client = storage.Client()\n",
        "    try:\n",
        "      bucket = client.get_bucket(bucket_name)\n",
        "      blob = bucket.get_blob(folder_name)\n",
        "      if blob is None:\n",
        "        print(f\"Folder name {folder_name} does not exist!\")\n",
        "        return False\n",
        "      else:\n",
        "        bucket.delete_blobs(blobs=bucket.list_blobs(prefix=folder_name))\n",
        "        print(f\"Folder {folder_name} deleted\")\n",
        "      return True\n",
        "    except Exception as e:\n",
        "      print(f\"Folder {folder_name} couldn't be deleted\")\n",
        "      print(e)\n",
        "      error = traceback.format_exc()\n",
        "      print(error)\n",
        "      return False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "EEcjbftawaqU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# delete storage folder if desired...!!!BE CAREFUL!!!!\n",
        "local_context = get_local_context()\n",
        "delete_storage_folder(local_context['rpm_gcs_rpm_ds_url'].split('/')[2],\n",
        "                      local_context['rpm_gcs_rpm_ds_url'].split('/')[3]+'/')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o0Bp9ZrGwaqW",
        "colab_type": "text"
      },
      "source": [
        "## Delete Google Cloud Storage bucket"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pYNul6qWwaqX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#delete the bucket\n",
        "from google.cloud import storage\n",
        "from google.cloud.exceptions import NotFound\n",
        "from google.cloud.exceptions import Forbidden\n",
        "import traceback\n",
        "def delete_storage_bucket (bucket_name):\n",
        "  \"\"\"Deletes a folder in the Google Cloust Storage,\n",
        "    This is not recommendated to use it in a production enviornment.\n",
        "    Comes handy in the iterative development and testing phases of the SDLC.\n",
        "    !!! BE CAREFUL !!!!\n",
        "    Args:\n",
        "      bucket_name(:obj:`str`): The Cloud Storage bucket name,\n",
        "        that we want to delete\n",
        "    Returns:\n",
        "      (:obj:`boolean`): True if we are able to scucessfully delete the folder\n",
        "  \"\"\"\n",
        "  if bucket_name:\n",
        "    client = storage.Client()\n",
        "    try:\n",
        "      bucket = client.get_bucket(bucket_name)\n",
        "      bucket.delete()\n",
        "      print(f\"Bucket {bucket.name} deleted\")\n",
        "      return True\n",
        "    except Exception as e:\n",
        "      print(f\"Bucket {bucket_name} couldn't be deleted\")\n",
        "      print(e)\n",
        "      error = traceback.format_exc()\n",
        "      print(error)\n",
        "      return False\n",
        "  else:\n",
        "    print(f\"Bucket {bucket_name} couldn't be deleted. Name is empty.\")\n",
        "    return False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "id_bfu_owaqY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# delete storage bucket if desired...!!! BE CAREFUL !!!!\n",
        "delete_storage_bucket(get_local_context()['rpm_gcs_rpm_ds_url'].split('/')[2])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CF2ebPbXwaqb",
        "colab_type": "text"
      },
      "source": [
        "## Delete the table in BigQuery"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ipzP_M-5waqb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#delete BigQuery table if not needed...!!! BE CAREFUL !!!\n",
        "def delete_table(table_id):\n",
        "  \"\"\"Deletes a BigQuery table\n",
        "    This is not recommendated to use it in a production enviornment.\n",
        "    Comes handy in the iterative development and testing phases of the SDLC.\n",
        "    !!! BE CAREFUL !!!!\n",
        "    Args:\n",
        "      table_id(:obj:`str`): The BigQuery table name that we want to delete\n",
        "  \"\"\"\n",
        "  from google.cloud import bigquery\n",
        "  # Construct a BigQuery client object.\n",
        "  client = bigquery.Client()\n",
        "  # client.delete_table(table_id, not_found_ok=True)  # Make an API request.\n",
        "  client.delete_table(table_id)  # Make an API request.\n",
        "  print(\"Deleted table '{}'.\".format(table_id))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "GGKE8kV9waqd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#delete the table in the BigQuery\n",
        "delete_table(get_local_context()['rpm_table_id'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W2XMVpwXwaqe",
        "colab_type": "text"
      },
      "source": [
        "## Delete the dataset in BigQuery"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VWcKti2dwaqe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def delete_dataset(dataset_id):\n",
        "  \"\"\"Deletes a BigQuery dataset\n",
        "    This is not recommendated to use it in a production enviornment.\n",
        "    Comes handy in the iterative development and testing phases of the SDLC.\n",
        "    !!! BE CAREFUL !!!!\n",
        "    Args:\n",
        "      dataset_id(:obj:`str`): The BigQuery dataset name that we want to delete\n",
        "  \"\"\"\n",
        "    # [START bigquery_delete_dataset]\n",
        "    from google.cloud import bigquery\n",
        "    # Construct a BigQuery client object.\n",
        "    client = bigquery.Client()\n",
        "    # dataset_id = 'your-project.your_dataset'\n",
        "    # Use the delete_contents parameter to delete a dataset and its contents.\n",
        "    # Use the not_found_ok parameter to not receive an error if the\n",
        "    #     dataset has already been deleted.\n",
        "    client.delete_dataset(\n",
        "        dataset_id, delete_contents=True, not_found_ok=True\n",
        "    )  # Make an API request.\n",
        "    print(\"Deleted dataset '{}'.\".format(dataset_id))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "n5MlUKnPwaqf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#delete the BigQuery dataset\n",
        "rpm_context = get_local_context()\n",
        "delete_dataset(f\"{rpm_context['RPM_GCP_PROJECT']}.{rpm_context['rpm_bq_ds_name']}\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ULFMAURwaqg",
        "colab_type": "text"
      },
      "source": [
        "## Delete the Google Cloud Storage folders which contains exported model artifacts "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "N-ZSWu60waqh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# delete the Cloud Storage folders where the models are saved\n",
        "local_context = get_local_context()\n",
        "bucket_name= local_context['rpm_gcs_rpm_ds_url'].split('/')[2]\n",
        "rpm_bq_ds_name = local_context['rpm_gcs_rpm_ds_url'].split('/')[3]\n",
        "rpm_bqml_model_export_path = local_context['RPM_MODEL_EXPORT_PATH']\n",
        "if not rpm_bqml_model_export_path:\n",
        "  rpm_bqml_model_export_path = local_context[\"RPM_DEFAULT_MODEL_EXPORT_PATH\"]\n",
        "folder_name = os.path.join(rpm_bq_ds_name, rpm_bqml_model_export_path)\n",
        "assert delete_storage_folder(bucket_name, folder_name) == True"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CEPJZGwzwaqi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#exec a cmd in python; an utility func\n",
        "import subprocess\n",
        "import traceback\n",
        "def exec_cmd(cmd):\n",
        "  \"\"\"Executes an OS command.\n",
        "    Args:\n",
        "      cmd(:obj:`str`): The OS command\n",
        "    Returns:\n",
        "      (:obj:`str`): The output of the execution of the OS command\n",
        "      (:obj:`str`): The returned code of the excecution of the OS command\n",
        "  \"\"\"\n",
        "  try:\n",
        "    print(cmd)\n",
        "    process = subprocess.run(cmd, shell=True, check=True, \n",
        "                             stdout=subprocess.PIPE, universal_newlines=True)\n",
        "    print(f\"'output from {cmd}': {process.stdout}\")\n",
        "    return (process.stdout, 0)\n",
        "  except subprocess.CalledProcessError as e:\n",
        "    error = traceback.format_exc()\n",
        "    print(error)\n",
        "    print(e.output)\n",
        "    return (e.output, 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "00LXbnRRwaqj",
        "colab_type": "text"
      },
      "source": [
        "## Delete the Cloud AI Platform Prediction models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "IJ_QwrYVwaql",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#delete the model\n",
        "def delete_caip_model():\n",
        "  \"\"\"Deletes the models from the Cloud AI Platform Prediction\n",
        "  \"\"\"\n",
        "  local_context = get_local_context()\n",
        "  (output, returncode) = exec_cmd(f\"gcloud ai-platform versions list --model {local_context['rpm_bqml_model']} --format='value(name)'\")\n",
        "  for each_ver in output.split('\\n'):\n",
        "    print(each_ver)\n",
        "    cmd = f\"gcloud ai-platform versions delete {each_ver} --model={local_context['rpm_bqml_model']}\"\n",
        "    exec_cmd(cmd)\n",
        "  cmd = f'gcloud ai-platform models delete {local_context[\"rpm_bqml_model\"]}'\n",
        "  exec_cmd(cmd)\n",
        "delete_caip_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HluVXlKzwaqm",
        "colab_type": "text"
      },
      "source": [
        "## Delete the GCP Project\n",
        "To avoid incurring charges to your Google Cloud Platform account for the resources used in this tutorial is to **Delete the project**.\n",
        "\n",
        "The easiest way to eliminate billing is to delete the project you created for the tutorial.\n",
        "\n",
        "**Caution**: Deleting a project has the following effects:\n",
        "* *Everything in the project is deleted.* If you used an existing project for this tutorial, when you delete it, you also delete any other work you've done in the project.\n",
        "* <b>Custom project IDs are lost. </b>When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com</b> URL, delete selected resources inside the project instead of deleting the whole project. \n",
        "\n",
        "If you plan to explore multiple tutorials and quickstarts, reusing projects can help you avoid exceeding project quota limits.\n",
        "<br>\n",
        "<ol type=\"1\">\n",
        "    <li>In the Cloud Console, go to the <b>Manage resources</b> page.</li>\n",
        "    Go to the <a href=\"https://console.cloud.google.com/iam-admin/projects\">Manage resources page</a>\n",
        "    <li>In the project list, select the project that you want to delete and then click <b>Delete</b> Trash icon.</li>\n",
        "    <li>In the dialog, type the project ID and then click <b>Shut down</b> to delete the project. </li>\n",
        "</ol>\n"
      ]
    }
  ]
}

================================================
FILE: retail/recommendation-system/bqml/README.md
================================================
## License
```
Copyright 2020 Google LLC

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

     https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
```
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)

# How to build a recommendation system on e-commerce data using BigQuery ML
With your data in BigQuery, machine learning workflows are now easier than ever with BigQuery ML. In this [notebook](bqml_retail_recommendation_system.ipynb) you’ll learn how to build a product recommendation system in a retail scenario using matrix factorization, and how to use the predicted recommendations to drive marketing activation. 

## Questions? Feedback?
If you have any questions or feedback, please open up a [new issue](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/issues).

================================================
FILE: retail/recommendation-system/bqml/bqml_retail_recommendation_system.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ur8xi4C7S06n"
   },
   "outputs": [],
   "source": [
    "# Copyright 2020 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eHLV0D7Y5jtU"
   },
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/analytics-componentized-patterns/blob/master/notebooks/bqml_matrix_factorization_retail_ecommerce.ipynb\">\n",
    "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
    "    </a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://console.cloud.google.com/ai-platform/notebooks/deploy-notebook?name=BigQuery%20ML%20-%20Retail%20Recommendation%20System&download_url=https%3A%2F%2Fgithub.com%2FGoogleCloudPlatform%2Fanalytics-componentized-patterns%2Fraw%2Fmaster%2Fretail%2Frecommendation-system%2Fbqml%2Fbqml_retail_recommendation_system.ipynb&url=https%3A%2F%2Fgithub.com%2FGoogleCloudPlatform%2Fanalytics-componentized-patterns%2Fblob%2Fmaster%2Fretail%2Frecommendation-system%2Fbqml%2Fbqml_retail_recommendation_system.ipynb\">\n",
    "      <img src=\"https://cloud.google.com/images/products/ai/ai-solutions-icon.svg\" alt=\"AI Platform Notebooks\">Run on AI Platform Notebooks</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/blob/master/notebooks/bqml_matrix_factorization_retail_ecommerce.ipynb\">\n",
    "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
    "      View on GitHub\n",
    "    </a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tvgnzT1CKxrO"
   },
   "source": [
    "# Overview\n",
    "In this notebook, you’ll learn how to build a product recommendation system in a retail scenario using matrix factorization, and how to use the predicted recommendations to drive marketing activation.\n",
    "\n",
    "#### Why are recommendation systems so important?\n",
    "\n",
    "The majority of consumers today expect personalization — to see products and services relevant to their interests. Naturally, they can help businesses too. By learning from user behaviours and preferences, businesses can deliver their recommendations in a variety of ways, including personalized coupons, marketing emails, and search results, or targeted ads. Ultimately, this enables businesses to attract more customer spending with targeted cross-selling or upselling, while reducing unnecessary costs by marketing irrelevant products.\n",
    "\n",
    ">>\n",
    "_\"Companies that fail to show customers they know them and their buying preferences risk losing business to competitors who are more attuned to what their customers want.\"_  \n",
    ">_Harvard Business Review. “The Age of Personalization”. September 2018_\n",
    "\n",
    "\n",
    "#### How does matrix factorization work?\n",
    "Based on user preferences, matrix factorization (collaborative filtering) is one of the most common and effective methods of creating recommendation systems. For more information about how they work, see [this introduction to recommendation systems here](https://developers.google.com/machine-learning/recommendation/collaborative/matrix). \n",
    "\n",
    "#### What is BigQuery ML?\n",
    "[BigQuery ML](https://cloud.google.com/bigquery-ml/docs/bigqueryml-intro) enables users to create and execute machine learning models in BigQuery by using standard SQL queries. This means, if your data is already in BigQuery, you don’t need to export your data to train and deploy machine learning models — by training, you’re also deploying in the same step. Combined with BigQuery’s auto-scaling of compute resources, you won’t have to worry about spinning up a cluster or building a model training and deployment pipeline. This means you’ll be saving time building your machine learning pipeline, enabling your business to focus more on the value of machine learning instead of spending time setting up the infrastructure.\n",
    "\n",
    "You may have also heard of [Recommendations AI](https://cloud.google.com/recommendations), a Google Cloud product purpose-built for real-time recommendations on a website using state-of-the-art deep learning models. Matrix factorization with BigQuery ML, on the other hand, is a more generic ML algorithm that can be used for offline and online recommendations (e.g. personalized e-mail campaigns).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5Sym0_-GsDkd"
   },
   "source": [
    "## Scope of this notebook\n",
    "### Dataset\n",
    "\n",
    "The [Google Analytics Sample](https://console.cloud.google.com/marketplace/details/obfuscated-ga360-data/obfuscated-ga360-data?filter=solution-type:dataset) dataset, which is hosted publicly on BigQuery, is a dataset that provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the [Google Merchandise Store](https://www.googlemerchandisestore.com/), a real e-commerce store that sells Google-branded merchandise.\n",
    "\n",
    "### Objective\n",
    "\n",
    "By the end of this notebook, you will know how to:\n",
    "* _pre-process data_ into the correct format needed to create a recommender system using BigQuery ML\n",
    "* _train (and deploy) the matrix factorization model_ in BigQuery ML\n",
    "* _evaluate the model_\n",
    "* _make predictions using the model_\n",
    "* _take action on the predicted recommendations:_\n",
    "  * for activation via Google Ads, Display & Video 360 and Search Ads 360\n",
    "  * for activation via emails\n",
    "  * export predictions to a pandas dataframe\n",
    "  * export predictions into Google Cloud Storage\n",
    "  \n",
    "\n",
    "### Costs \n",
    "\n",
    "This tutorial uses billable components of Google Cloud Platform (GCP):\n",
    "\n",
    "* BigQuery\n",
    "* BigQuery ML\n",
    "\n",
    "Learn about [BigQuery pricing](https://cloud.google.com/bigquery/pricing), [BigQuery ML\n",
    "pricing](https://cloud.google.com/bigquery-ml/pricing) and use the [Pricing\n",
    "Calculator](https://cloud.google.com/products/calculator/)\n",
    "to generate a cost estimate based on your projected usage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i7EUnXsZhAGF"
   },
   "source": [
    "## PIP Install Packages and dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wyy5Lbnzg5fi"
   },
   "outputs": [],
   "source": [
    "!pip install google-cloud-bigquery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9luQpONrzPb6"
   },
   "outputs": [],
   "source": [
    "# Automatically restart kernel after installs\n",
    "import IPython\n",
    "app = IPython.Application.instance()\n",
    "app.kernel.do_shutdown(True)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BF1j6f9HApxa"
   },
   "source": [
    "### Set up your GCP project\n",
    "\n",
    "_The following steps are required, regardless of your notebook environment._\n",
    "\n",
    "1. [Select or create a GCP project.](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
    "\n",
    "1. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n",
    "\n",
    "1. Enter your project ID and region in the cell below. Then run the  cell to make sure the\n",
    "Cloud SDK uses the right project for all the commands in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dgWHxHS-sDkl"
   },
   "outputs": [],
   "source": [
    "PROJECT_ID = \"your_project_id\"\n",
    "REGION = 'US'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XoEqT2Y4DJmf"
   },
   "source": [
    "### Import libraries and define constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pRUOFELefqf1"
   },
   "outputs": [],
   "source": [
    "from google.cloud import bigquery\n",
    "import time\n",
    "import pandas as pd\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "znIaF2QGsDkr"
   },
   "source": [
    "### Creating a BigQuery dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "H1waFXGXsDkr"
   },
   "source": [
    "In this notebook, you will need to create a dataset in your project called `bqml`. To create it, run the following cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pIBm-lrVsDks"
   },
   "outputs": [],
   "source": [
    "!bq mk --location=$REGION --dataset $PROJECT_ID:bqml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QB7wXYDpsDkv"
   },
   "source": [
    "## Raw data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MsxSG7yfsDkv"
   },
   "source": [
    "Before beginning, take a look at the raw data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Z3G-0P1vsDkv"
   },
   "source": [
    "_Note_: Jupyter runs cells starting with %%bigquery as SQL queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YbCrRUyFsDkw"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "## follows the Google Analytics schema: \n",
    "#https://support.google.com/analytics/answer/3437719?hl=en\n",
    "\n",
    "SELECT \n",
    "  CONCAT(fullVisitorID,'-',CAST(visitNumber AS STRING)) AS visitorId,\n",
    "  hitNumber,\n",
    "  time,\n",
    "  page.pageTitle,\n",
    "  type,\n",
    "  productSKU,\n",
    "  v2ProductName,\n",
    "  v2ProductCategory,\n",
    "  productPrice/1000000 as productPrice_USD\n",
    "\n",
    "FROM \n",
    "  `bigquery-public-data.google_analytics_sample.ga_sessions_20160801`, \n",
    "  UNNEST(hits) AS hits,\n",
    "  UNNEST(hits.product) AS hits_product\n",
    "LIMIT 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9dMO82QnsDky"
   },
   "source": [
    "## Pre-process the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iA90j6LqsDkz"
   },
   "source": [
    "With collaborative filtering (matrix factorization), the dataset must indicate a user's preference for a product, like a rating between 1 and 5 stars. However, in the retail industry, there is usually no or insufficient explicit feedback on how much a user liked a product. Thus, other behavioral metrics need to be used to infer their implicit \"rating\".  One way to infer user interest in a product is to look at the total time spent on a product detail page (e.g., session duration).\n",
    "\n",
    "With matrix factorization, in order to train the model, you will need a table with `userId`, `itemId`, and the `rating`. In this notebook example, session duration will be used as the implicit rating. If you have other metrics (e.g., frequency of pageviews), you can simply combine the metrics together using a weighted sum to compute a rating value.\n",
    "\n",
    "|userId|itemId|rating|\n",
    "|-|-|-|\n",
    "|visitor1|productSKU_1|3000|\n",
    "|visitor1|productSKU_4|15000|\n",
    "|visitor1|productSKU_9|920|\n",
    "|visitor2|productSKU_12|0|\n",
    "\n",
    "Notice how every row is a unique combination of userId and itemId, along with the (implicit) rating."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YetLiwPgsDkz"
   },
   "source": [
    "The query below will pre-process the data by calculating the total pageview duration per product per user, and materialize the data in a new table, `aggregate_web_stats`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "E6ppE7imft-y"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "## follows schema from https://support.google.com/analytics/answer/3437719?hl=en&ref_topic=3416089\n",
    "CREATE OR REPLACE TABLE bqml.aggregate_web_stats AS (\n",
    "  WITH\n",
    "    durations AS (\n",
    "      --calculate pageview durations\n",
    "      SELECT\n",
    "        CONCAT(fullVisitorID,'-', \n",
    "             CAST(visitNumber AS STRING),'-', \n",
    "             CAST(hitNumber AS STRING) ) AS visitorId_session_hit,\n",
    "        LEAD(time, 1) OVER (\n",
    "          PARTITION BY CONCAT(fullVisitorID,'-',CAST(visitNumber AS STRING))\n",
    "          ORDER BY\n",
    "          time ASC ) - time AS pageview_duration\n",
    "      FROM\n",
    "        `bigquery-public-data.google_analytics_sample.ga_sessions_2016*`,\n",
    "        UNNEST(hits) AS hit \n",
    "    ),\n",
    "      \n",
    "    prodview_durations AS (\n",
    "      --filter for product detail pages only\n",
    "      SELECT\n",
    "        CONCAT(fullVisitorID,'-',CAST(visitNumber AS STRING)) AS visitorId,\n",
    "        productSKU AS itemId,\n",
    "        IFNULL(dur.pageview_duration,\n",
    "          1) AS pageview_duration,\n",
    "      FROM\n",
    "        `bigquery-public-data.google_analytics_sample.ga_sessions_2016*` t,\n",
    "        UNNEST(hits) AS hits,\n",
    "        UNNEST(hits.product) AS hits_product\n",
    "      JOIN\n",
    "        durations dur\n",
    "      ON\n",
    "        CONCAT(fullVisitorID,'-',\n",
    "               CAST(visitNumber AS STRING),'-',\n",
    "               CAST(hitNumber AS STRING)) = dur.visitorId_session_hit\n",
    "      WHERE\n",
    "      #action_type: Product detail views = 2\n",
    "      eCommerceAction.action_type = \"2\" \n",
    "    ),\n",
    "    \n",
    "    aggregate_web_stats AS(\n",
    "      --sum pageview durations by visitorId, itemId\n",
    "      SELECT\n",
    "        visitorId,\n",
    "        itemId,\n",
    "        SUM(pageview_duration) AS session_duration\n",
    "      FROM\n",
    "        prodview_durations\n",
    "      GROUP BY\n",
    "        visitorId,\n",
    "        itemId )\n",
    "    SELECT\n",
    "      *\n",
    "    FROM\n",
    "      aggregate_web_stats\n",
    ");\n",
    "-- Show table\n",
    "SELECT\n",
    "  *\n",
    "FROM\n",
    "  bqml.aggregate_web_stats\n",
    "LIMIT\n",
    "  10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0UmMwUzfsDk0"
   },
   "source": [
    "### The training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xenhAS06sDk0"
   },
   "source": [
    "With the data stored in an output table in the correct format for matrix factorization, the data is now ready for training a matrix factorization model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_WyrXCRUsDk1"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "SELECT\n",
    "  *\n",
    "FROM\n",
    "  bqml.aggregate_web_stats\n",
    "LIMIT \n",
    "  10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WRUv_AVosDk4"
   },
   "source": [
    "## Train the matrix factorization model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "r8TSRjl5sDk4"
   },
   "source": [
    "To train the matrix factorization model (with implicit feedback), you will need to set the options:\n",
    "* `model_type`: `'matrix_factorization'` \n",
    "* `user_col`: \\<user column name>\n",
    "* `item_col`: \\<item column name>\n",
    "* `rating_col`: \\<rating column name>\n",
    "* `feedback_type`: `'implicit'` (default is 'explicit')\n",
    "\n",
    "To learn more about the parameters when training a model, read the [documentation on the CREATE MODEL statement for Matrix Factorization](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-matrix-factorization)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RbaQgPZQ70Uu"
   },
   "source": [
    "_Note: You may need to setup slot reservations. For more information, you can read up on how to set up flex slots [programmatically](https://medium.com/google-cloud/optimize-bigquery-costs-with-flex-slots-e06ec5e4aa90) or via the [BigQuery UI](https://cloud.google.com/bigquery/docs/reservations-workload-management#getting-started-with-bigquery-reservations)._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SOgEf1OasDk6"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "CREATE OR REPLACE MODEL bqml.retail_recommender\n",
    "OPTIONS(model_type='matrix_factorization', \n",
    "        user_col='visitorId', \n",
    "        item_col='itemId',\n",
    "        rating_col='session_duration',\n",
    "        feedback_type='implicit'\n",
    "        )\n",
    "AS\n",
    "SELECT * FROM bqml.aggregate_web_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vsG6GXlnsDlK"
   },
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BQDuG8GPsDlK"
   },
   "source": [
    "Inspect the resulting metrics from model evaluation.\n",
    "\n",
    "For more information on these metrics, read the [ML.EVALUATE documentation here](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "q_Qi2PmbsDlL"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "SELECT\n",
    "  *\n",
    "FROM\n",
    "  ML.EVALUATE(MODEL bqml.retail_recommender)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RWNXE406sDlN"
   },
   "source": [
    "## Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Pl1J2024sDlN"
   },
   "source": [
    "If you want to improve your model,  some of the hyperparameters you can tune are:\n",
    "* `NUM_FACTORS`: Specifies the number of latent factors to use for matrix factorization models (int64_value)\n",
    "* `L2_REG`: The amount of L2 regularization applied (float64_value)\n",
    "* `WALS_ALPHA`: A hyperparameter for 'IMPLICIT' matrix factorization model (float64_value)\n",
    "\n",
    "See the official documentation on [CREATE MODEL (matrix factorization)](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-matrix-factorization) for more information on hyperparameter tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eCQ_-wyMsDlN"
   },
   "source": [
    "## Make predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jxQ4HIeasDlO"
   },
   "source": [
    "### Inspect the predicted recommendations for a single user"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xaoYyCHSsDlO"
   },
   "source": [
    "#### What are the top 5 items you could recommend to a specific visitorId?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0kl0uSKmsDlO"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "#check for a single visitor\n",
    "DECLARE MY_VISITORID STRING DEFAULT \"0824461277962362623-1\";\n",
    "\n",
    "SELECT\n",
    "  *\n",
    "FROM\n",
    "  ML.RECOMMEND(MODEL `bqml.retail_recommender`,\n",
    "      (SELECT MY_VISITORID as visitorID)\n",
    "              )\n",
    "ORDER BY predicted_session_duration_confidence DESC\n",
    "LIMIT 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eWbAurz4sDlQ"
   },
   "source": [
    "What are the names of the recommended products? Discover the product names by joining the resulting productSKU recommendations back with the product names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WaalmFivsDlQ"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "DECLARE MY_VISITORID STRING DEFAULT \"6499749315992064304-2\";\n",
    "\n",
    "WITH product_details AS(\n",
    "    SELECT \n",
    "        productSKU,\n",
    "        v2ProductName,\n",
    "    FROM \n",
    "        `bigquery-public-data.google_analytics_sample.ga_sessions_2016*`,\n",
    "        UNNEST(hits) AS hits,\n",
    "        UNNEST(hits.product) AS hits_product\n",
    "    GROUP BY 2,1 \n",
    ")\n",
    "\n",
    "SELECT\n",
    "  r.*,\n",
    "  d.v2ProductName\n",
    "FROM\n",
    "  ML.RECOMMEND(MODEL `bqml.retail_recommender`,\n",
    "      (\n",
    "      SELECT\n",
    "       MY_VISITORID as visitorId)) r\n",
    "JOIN\n",
    "  product_details d\n",
    "ON\n",
    "  r.itemId = d.productSKU\n",
    "ORDER BY predicted_session_duration_confidence DESC\n",
    "LIMIT 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "znIO7dFVsDlS"
   },
   "source": [
    "### Batch predictions for all users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9BYe_15fsDlT"
   },
   "source": [
    "To retrieve the top 5 recommended products for all existing users, run the following query. As the result can be large (num_users * num_products * top N), this also outputs the recommendations to a separate table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2gCkde_gsDlT"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "-- Create output table\n",
    "CREATE OR REPLACE TABLE bqml.prod_recommendations AS (\n",
    "WITH predictions AS (\n",
    "    SELECT \n",
    "      visitorId, \n",
    "      ARRAY_AGG(STRUCT(itemId, \n",
    "                       predicted_session_duration_confidence)\n",
    "                ORDER BY \n",
    "                  predicted_session_duration_confidence DESC\n",
    "                LIMIT 5) as recommended\n",
    "    FROM ML.RECOMMEND(MODEL bqml.retail_recommender)\n",
    "    GROUP BY visitorId\n",
    ")\n",
    "    \n",
    "\n",
    "SELECT\n",
    "  visitorId,\n",
    "  itemId,\n",
    "  predicted_session_duration_confidence\n",
    "FROM\n",
    "  predictions p,\n",
    "  UNNEST(recommended)\n",
    ");\n",
    "\n",
    "-- Show table\n",
    "SELECT\n",
    "  *\n",
    "FROM\n",
    "  bqml.prod_recommendations\n",
    "ORDER BY \n",
    "  visitorId\n",
    "LIMIT\n",
    "  20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NfffZ2FksDlV"
   },
   "source": [
    "## Using the predicted recommendations in production"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2-ddW8pasDlV"
   },
   "source": [
    "Once you have the recommendations, plugging into your production pipeline will depend on your use case. \n",
    "\n",
    "Here are a few possible ways to help you get started:\n",
    "1. [Export recommendations for marketing activation:](#export_ga360)\n",
    "    1. For activation via Google Ads, Display & Video 360 and Search Ads 360\n",
    "    1. For activation via emails\n",
    "1. [Other ways to export recommendations from BigQuery](#export_other)\n",
    "    1. BigQuery to pandas dataframes\n",
    "    1. Export the predictions to Google Cloud Storage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uR_p3Jt2sDlW"
   },
   "source": [
    "<a id=\"export_ga360\"></a>\n",
    "### 1-1. Export recommendations to Google Analytics 360 (Google Marketing Platform)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "54zMANFfsDlW"
   },
   "source": [
    "By exporting the resulting predictions from BigQuery ML back to Google Analytics, you will be able to generate custom remarketing audiences and target customers more effectively with ads, search, or email activation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RsfdKkA5sDlW"
   },
   "source": [
    "##### Formatting the data for Google Analytics 360"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MON2xqDYsDlX"
   },
   "source": [
    "You may need to format the data output into something that Google Analytics, for example:\n",
    "\n",
    "|clientId | LikelyToBuyProductA |  \n",
    "|-|-|  \n",
    "| 123 |  0.70 |  \n",
    "| 345 |  0.90 |  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kLPaIJil8gQ8"
   },
   "source": [
    "Here's a sample query for an itemId \"GGOEYOLR018699\", that normalizes the confidence scores between 0 and 1, using [ML.MIN_MAX_SCALER](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-preprocessing-functions#mlmin_max_scaler):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Dlna9gd78iQ_"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "WITH predictions AS (\n",
    "    SELECT \n",
    "      visitorId, \n",
    "      ARRAY_AGG(STRUCT(itemId, \n",
    "                       predicted_session_duration_confidence)\n",
    "                ORDER BY \n",
    "                  predicted_session_duration_confidence) as recommended\n",
    "    FROM ML.RECOMMEND(MODEL bqml.retail_recommender)\n",
    "    WHERE itemId = \"GGOEYOLR018699\"\n",
    "    GROUP BY visitorId\n",
    ")\n",
    "  \n",
    "SELECT\n",
    "  visitorId,\n",
    "  ML.MIN_MAX_SCALER(\n",
    "    predicted_session_duration_confidence\n",
    "    ) OVER() as GGOEYOLR018699\n",
    "FROM\n",
    "  predictions p,\n",
    "  UNNEST(recommended)\n",
    "ORDER BY GGOEYOLR018699 DESC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k9xvYYTOsDlX"
   },
   "source": [
    "To create a column per product, you can use the pivot() function as described in [this blogpost](https://towardsdatascience.com/easy-pivot-in-bigquery-one-step-5a1f13c6c710).\n",
    "\n",
    "For Google Analytics Data Import, it's recommended that you use  `clientId` as the key, along with individual columns that show some propensity score. In other words, you may need to create a new column for each product that you are interested in recommending, and create a custom dimension in Google Analytics that can be then used to build your audiences. It's also likely best to ensure that you have one row per `clientId`. If you know you will be exporting predictions to Google Analytics, it's recommended that you train your models using `clientId` directly instead of `visitorId`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RQyG7oTosDlX"
   },
   "source": [
    "##### Exporting the data from BigQuery into Google Analytics 360"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q4PLyv_AsDlY"
   },
   "source": [
    "The easiest way to export your BigQuery ML predictions from a BigQuery table to Google Analytics 360 is to use the [MoDeM (Model Deployment for Marketing)](https://github.com/google/modem) reference implementation. MoDeM helps you load data into Google Analytics for eventual activation in Google Ads, Display & Video 360 and Search Ads 360.  \n",
    "\n",
    "To export to Google Analytics 360 from BigQuery:\n",
    "- Follow the [step-by-step instructions here](https://github.com/google/modem/tree/master/bqml) to build your ETL pipeline from BigQuery ML to Google Analytics using MoDeM. You can also view the interactive instructions in this [notebook](https://colab.research.google.com/github/google/modem/blob/master/bqml/utils/BQML_Deployment_Template_Cloud_Function.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "B1Lb8eKAsDlY"
   },
   "source": [
    "### 1-2. Email activation using Salesforce Marketing Cloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6ezUUPtnsDlY"
   },
   "source": [
    "As Google Analytics does not contain email addresses, you may need to integrate with a 3rd-party platform like Salesforce Marketing Cloud for email activations.\n",
    "\n",
    "Google Analytics 360 customers can  activate their Analytics 360 audiences in Marketing Cloud on Salesforce direct marketing channels (email and SMS). This enables your marketing team to build audiences based on online web behavior and engage with those customers via emails and SMS.\n",
    "\n",
    "Follow the [step-by-step instructions here](https://support.google.com/analytics/answer/9250031?hl=en) to integrate Google Analytics 360 with Salesforce Marketing Cloud, or learn more about [Audience Activation through Salesforce Trailhead](https://trailhead.salesforce.com/content/learn/modules/google-analytics-360-integration-for-marketing-cloud)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "56KmAjV8sDlZ"
   },
   "source": [
    "<a id=\"export_other\"></a>\n",
    "### 2. Other ways to export recommendations from BigQuery\n",
    "If you want to use the predicted recommendations in other services, two ways to leverage the results are to export the data from BigQuery as a pandas dataframe, or if you want to store the result on Google Cloud Storage, you can also export the table directly as a CSV file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wTeO6BYSsDlZ"
   },
   "source": [
    "#### 2-1. Read from the predictions directly from BigQuery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Gr091SfdsDlZ"
   },
   "source": [
    "With the predictions stored in a separate table, you can export the data into a Pandas dataframe using the BigQuery Storage API (see [documentation and code samples](https://cloud.google.com/bigquery/docs/bigquery-storage-python-pandas#download_table_data_using_the_client_library)). You can also use other [BigQuery client libraries](https://cloud.google.com/bigquery/docs/reference/libraries). \n",
    "\n",
    "Alternatively you can also export directly into pandas in a notebook using the `%%bigquery <variable name>` as in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yrarIhXy9pP-"
   },
   "outputs": [],
   "source": [
    "%%bigquery df --project $PROJECT_ID\n",
    "SELECT\n",
    "  *\n",
    "FROM \n",
    "  bqml.prod_recommendations\n",
    "LIMIT 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dkCf_l-v953l"
   },
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ENDbm3HksDlb"
   },
   "source": [
    "#### 2-2. Export predictions table to Google Cloud Storage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ms9dYeutsDlc"
   },
   "source": [
    "There are several ways to export the predictions table to Google Cloud Storage (GCS), so that you can use them in a separate service. Perhaps the easiest way is to export directly to GCS using SQL ([documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/other-statements#export_data_statement))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wmBRzUdY9aKB"
   },
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "EXPORT DATA OPTIONS (\n",
    "uri=\"gs://mybucket/myfile/recommendations_*.csv\", \n",
    "  format=CSV\n",
    ") AS \n",
    "SELECT\n",
    "  *\n",
    "FROM \n",
    "  bqml.prod_recommendations"
   ]
  },
  {
   "source": [
    "## Questions? Feedback?\n",
    "If you have any questions or feedback, please open up a [new issue](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/issues)."
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "bqml_matrix_factorization_retail_ecommerce.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "environment": {
   "name": "common-cpu.m55",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/base-cpu:m55"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.6.8 64-bit"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  },
  "interpreter": {
   "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
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 "nbformat": 4,
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}

================================================
FILE: retail/recommendation-system/bqml-mlops/README.md
================================================
```python
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
```

## Tutorial Overview 

This is a three part tutorial where part one will walk you through a complete end to end Machine Learning use case using Google Cloud Platform. You will learn how to build a hybrid recommendation model with embedding technique with Google BigQuery Machine Learning from book [“BigQuery: The Definitive Guide”](https://www.amazon.com/Google-BigQuery-Definitive-Warehousing-Analytics/dp/1492044466), a highly recommended book written by BigQuery and ML expert Valliappa Lakshmanan. We will not cover in detail on typical machine learining steps such data exploration and cleaning, feature selection, and feature engineering (other than embedding technique we show here). We encourage the readers to do so and see if you can improve the model quality and performance. Instead we will mostly focus on show you how to orchestrate the entire machine learning process with Kubeflow on Google AI Platform Pipelines. In [PART TWO](part_2/README.md), you will learn how to setup a CI/CD pipeline with Google Cloud Source Repositories and Google Cloud Build.  In [PART THREE](part_3/README.md), you will learn how to run the same code in Part One (with minor changes) in Google's new Vertex AI pipeline.   

The use case is to predict the the propensity of booking for any user/hotel combination. The intuition behind the embedding layer with Matrix Factorization is if we can find similar hotels that are close in the embedding space, we will achieve a higher accuracy to predict whether the user will book the hotel. 

![Pipeline](pipeline.png)

## Getting Started
Use this [notebook](kfp_tutorial.ipynb) to get started. 

## Questions? Feedback?
If you have any questions or feedback, please open up a [new issue](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/issues).


================================================
FILE: retail/recommendation-system/bqml-mlops/dockerfile
================================================
FROM gcr.io/deeplearning-platform-release/base-cpu 

RUN apt-get update -y && apt-get -y install kubectl 

RUN python -m pip install --no-cache -I kfp gcsfs

================================================
FILE: retail/recommendation-system/bqml-mlops/kfp_tutorial.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright 2020 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial Overview \n",
    "\n",
    "This is a two part tutorial where part one will walk you through a complete end to end Machine Learning use case using Google Cloud Platform. You will learn how to build a hybrid recommendation model with embedding technique with Google BigQuery Machine Learning from book [“BigQuery: The Definitive Guide”](https://www.amazon.com/Google-BigQuery-Definitive-Warehousing-Analytics/dp/1492044466), a highly recommended book written by BigQuery and ML expert Valliappa Lakshmanan. We will not cover in detail on typical machine learining steps such data exploration and cleaning, feature selection, and feature engineering (other than embedding technique we show here). We encourage the readers to do so and see if you can improve the model quality and performance. Instead we will mostly focus on show you how to orchestrate the entire machine learning process with Kubeflow on Google AI Platform Pipelines. In [PART TWO](part_2/README.md), you will learn how to setup a CI/CD pipeline with Google Cloud Source Repositories and Google Cloud Build.  \n",
    "\n",
    "The use case is to predict the the propensity of booking for any user/hotel combination. The intuition behind the embedding layer with Matrix Factorization is if we can find similar hotels that are close in the embedding space, we will achieve a higher accuracy to predict whether the user will book the hotel. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Pipeline](pipeline.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "* Download the [Expedia Hotel Recommendation Dataset](https://www.kaggle.com/c/expedia-hotel-recommendations) from Kaggle. You will be mostly working with the train.csv dataset for this tutorial\n",
    "* Upload the dataset to BigQuery by following the how-to guide [Loading CSV Data](https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-csv)\n",
    "* Follow the how-to guide [create flex slots, reservation and assignment in BigQuery](https://cloud.google.com/bigquery/docs/reservations-get-started) for training ML models. <strong>Make sure to create Flex slots and not month/year slots so you can delete them after the tutorial.</strong> \n",
    "* Build and push a docker image using [this dockerfile](dockerfile) as the base image for the Kubeflow pipeline components. \n",
    "*  Create an instance of AI Platform Pipelines by following the how-to guide [Setting up AI Platform Pipelines](https://cloud.google.com/ai-platform/pipelines/docs/setting-up). \n",
    "* Create or use a [Google Cloud Storage](https://console.cloud.google.com/storage) bucket to export the finalized model to.\n",
    "* Change the following cell to reflect your setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CHANGE the following settings\n",
    "BASE_IMAGE='gcr.io/your-image-name'\n",
    "MODEL_STORAGE = 'gs://your-bucket-name/folder-name' #Must include a folder in the bucket, otherwise, model export will fail\n",
    "BQ_DATASET_NAME=\"hotel_recommendations\" #This is the name of the target dataset where you model and predictions will be stored\n",
    "PROJECT_ID=\"your-project-id\" #This is your GCP project ID that can be found in the GCP console\n",
    "KFPHOST=\"your-ai-platform-pipeline-url\" # Kubeflow Pipelines URL, can be found from settings button in CAIP Pipelines\n",
    "REGION='your-project-region' #For example, us-central1, note that Vertex AI endpoint deployment region must match MODEL_STORAGE bucket region\n",
    "ENDPOINT_NAME='your-vertex-ai-endpoint-name'\n",
    "DEPLOY_COMPUTE='your-endpoint-compute-size'#For example, n1-standard-4\n",
    "DEPLOY_IMAGE='us-docker.pkg.dev/vertex-ai/prediction/xgboost-cpu.0-82:latest' #Do not change, BQML XGBoost is currently compatible with 0.82"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create BigQuery function\n",
    "\n",
    "Create a generic BigQuery function that runs a BigQuery query and returns the table/model created. This will be re-used to return BigQuery results for all the different segments of the BigQuery process in the Kubeflow Pipeline. You will see later in the tutorial where this function is being passed as parameter (ddlop) to other functions to perform certain BigQuery operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import NamedTuple\n",
    "import json\n",
    "import os\n",
    "\n",
    "def run_bigquery_ddl(project_id: str, query_string: str, location: str) -> NamedTuple(\n",
    "    'DDLOutput', [('created_table', str), ('query', str)]):\n",
    "    \"\"\"\n",
    "    Runs BigQuery query and returns a table/model name\n",
    "    \"\"\"\n",
    "    print(query_string)\n",
    "        \n",
    "    from google.cloud import bigquery\n",
    "    from google.api_core.future import polling\n",
    "    from google.cloud import bigquery\n",
    "    from google.cloud.bigquery import retry as bq_retry\n",
    "    \n",
    "    bqclient = bigquery.Client(project=project_id, location=location)\n",
    "    job = bqclient.query(query_string, retry=bq_retry.DEFAULT_RETRY)\n",
    "    job._retry = polling.DEFAULT_RETRY\n",
    "    \n",
    "    while job.running():\n",
    "        from time import sleep\n",
    "        sleep(0.1)\n",
    "        print('Running ...')\n",
    "        \n",
    "    tblname = job.ddl_target_table\n",
    "    tblname = '{}.{}'.format(tblname.dataset_id, tblname.table_id)\n",
    "    print('{} created in {}'.format(tblname, job.ended - job.started))\n",
    "    \n",
    "    from collections import namedtuple\n",
    "    result_tuple = namedtuple('DDLOutput', ['created_table', 'query'])\n",
    "    return result_tuple(tblname, query_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the model\n",
    "\n",
    "We will start by training a matrix factorization model that will allow us to understand the latent relationship between user and hotel clusters. The reason why we are doing this is because matrix factorization approach can only find latent relationship between a user and a hotel. However, there are other intuitive useful predictors (such as `is_mobile`, `location`, etc.) that can improve the model performance. So together, we can feed the resulting weights/factors as features among with other features to train the final XGBoost model. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_matrix_factorization_model(ddlop, project_id, dataset):\n",
    "    query = \"\"\"\n",
    "        CREATE OR REPLACE MODEL `{project_id}.{dataset}.my_implicit_mf_model_quantiles_demo_binary_prod`\n",
    "        OPTIONS\n",
    "          (model_type='matrix_factorization',\n",
    "           feedback_type='implicit',\n",
    "           user_col='user_id',\n",
    "           item_col='hotel_cluster',\n",
    "           rating_col='rating',\n",
    "           l2_reg=30,\n",
    "           num_factors=15) AS\n",
    "\n",
    "        SELECT\n",
    "          user_id,\n",
    "          hotel_cluster,\n",
    "          if(sum(is_booking) > 0, 1, sum(is_booking)) AS rating\n",
    "        FROM `{project_id}.{dataset}.hotel_train`\n",
    "        group by 1,2\n",
    "    \"\"\".format(project_id = project_id, dataset = dataset)\n",
    "    return ddlop(project_id, query, 'US')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_matrix_factorization_model(project_id, mf_model, location='US')-> NamedTuple('MFMetrics', [('msqe', float)]):\n",
    "    \n",
    "    query = \"\"\"\n",
    "        SELECT * FROM ML.EVALUATE(MODEL `{project_id}.{mf_model}`)\n",
    "    \"\"\".format(project_id = project_id, mf_model = mf_model)\n",
    "\n",
    "    print(query)\n",
    "\n",
    "    from google.cloud import bigquery\n",
    "    import json\n",
    "\n",
    "    bqclient = bigquery.Client(project=project_id, location=location)\n",
    "    job = bqclient.query(query)\n",
    "    metrics_df = job.result().to_dataframe()\n",
    "    from collections import namedtuple\n",
    "    result_tuple = namedtuple('MFMetrics', ['msqe'])\n",
    "    return result_tuple(metrics_df.loc[0].to_dict()['mean_squared_error'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating embedding features for users and hotels \n",
    "\n",
    "We will use the matrix factorization model to create corresponding user factors, hotel factors and embed them together with additional features such as total visits and distinct cities to create a new training dataset to an XGBoost classifier which will try to predict the the likelihood of booking for any user/hotel combination. Also note that we aggregated and grouped the original dataset by `user_id`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_user_features(ddlop, project_id, dataset, mf_model):\n",
    "    #Feature engineering for useres\n",
    "    query = \"\"\"\n",
    "        CREATE OR REPLACE TABLE  `{project_id}.{dataset}.user_features_prod` AS\n",
    "        WITH u as \n",
    "        (\n",
    "            select\n",
    "            user_id,\n",
    "            count(*) as total_visits,\n",
    "            count(distinct user_location_city) as distinct_cities,\n",
    "            sum(distinct site_name) as distinct_sites,\n",
    "            sum(is_mobile) as total_mobile,\n",
    "            sum(is_booking) as total_bookings,\n",
    "            FROM `{project_id}.{dataset}.hotel_train`\n",
    "            GROUP BY 1\n",
    "        )\n",
    "        SELECT\n",
    "            u.*,\n",
    "            (SELECT ARRAY_AGG(weight) FROM UNNEST(factor_weights)) AS user_factors\n",
    "        FROM\n",
    "            u JOIN ML.WEIGHTS( MODEL `{mf_model}`) w\n",
    "            ON processed_input = 'user_id' AND feature = CAST(u.user_id AS STRING)\n",
    "    \"\"\".format(project_id = project_id, dataset = dataset, mf_model=mf_model)\n",
    "    return ddlop(project_id, query, 'US')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_hotel_features(ddlop, project_id, dataset, mf_model):\n",
    "    #Feature eingineering for hotels\n",
    "    query = \"\"\"\n",
    "        CREATE OR REPLACE TABLE  `{project_id}.{dataset}.hotel_features_prod` AS\n",
    "        WITH h as \n",
    "        (\n",
    "            select\n",
    "            hotel_cluster,\n",
    "            count(*) as total_cluster_searches,\n",
    "            count(distinct hotel_country) as distinct_hotel_countries,\n",
    "            sum(distinct hotel_market) as distinct_hotel_markets,\n",
    "            sum(is_mobile) as total_mobile_searches,\n",
    "            sum(is_booking) as total_cluster_bookings,\n",
    "            FROM `{project_id}.{dataset}.hotel_train`\n",
    "            group by 1\n",
    "        )\n",
    "        SELECT\n",
    "            h.*,\n",
    "            (SELECT ARRAY_AGG(weight) FROM UNNEST(factor_weights)) AS hotel_factors\n",
    "        FROM\n",
    "            h JOIN ML.WEIGHTS( MODEL `{mf_model}`) w\n",
    "            ON processed_input = 'hotel_cluster' AND feature = CAST(h.hotel_cluster AS STRING)\n",
    "    \"\"\".format(project_id = project_id, dataset = dataset, mf_model=mf_model)\n",
    "    return ddlop(project_id, query, 'US')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function below combines all the features selected (`total_mobile_searches`) and engineered (user factors and hotel factors) into a training dataset for the XGBoost classifier. Note the target variable is rating which is converted into a binary classfication."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def combine_features(ddlop, project_id, dataset, mf_model, hotel_features, user_features):\n",
    "    #Combine user and hotel embedding features with the rating associated with each combination\n",
    "    query = \"\"\"\n",
    "        CREATE OR REPLACE TABLE `{project_id}.{dataset}.total_features_prod` AS\n",
    "        with ratings as(\n",
    "          SELECT\n",
    "            user_id,\n",
    "            hotel_cluster,\n",
    "            if(sum(is_booking) > 0, 1, sum(is_booking)) AS rating\n",
    "          FROM `{project_id}.{dataset}.hotel_train`\n",
    "          group by 1,2\n",
    "        )\n",
    "        select\n",
    "          h.* EXCEPT(hotel_cluster),\n",
    "          u.* EXCEPT(user_id),\n",
    "          IFNULL(rating,0) as rating\n",
    "        from `{hotel_features}` h, `{user_features}` u\n",
    "        LEFT OUTER JOIN ratings r\n",
    "        ON r.user_id = u.user_id AND r.hotel_cluster = h.hotel_cluster\n",
    "    \"\"\".format(project_id = project_id, dataset = dataset, mf_model=mf_model, hotel_features=hotel_features, user_features=user_features)\n",
    "    return ddlop(project_id, query, 'US')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will create a couple of BigQuery user-defined functions (UDF) to convert arrays to a struct and its array elements are the fields in the struct. <strong>Be sure to change the BigQuery dataset name to your dataset name. </strong>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "CREATE OR REPLACE FUNCTION `hotel_recommendations.arr_to_input_15_hotels`(h ARRAY<FLOAT64>)\n",
    "RETURNS \n",
    "    STRUCT<\n",
    "        h1 FLOAT64,\n",
    "        h2 FLOAT64,\n",
    "        h3 FLOAT64,\n",
    "        h4 FLOAT64,\n",
    "        h5 FLOAT64,\n",
    "        h6 FLOAT64,\n",
    "        h7 FLOAT64,\n",
    "        h8 FLOAT64,\n",
    "        h9 FLOAT64,\n",
    "        h10 FLOAT64,\n",
    "        h11 FLOAT64,\n",
    "        h12 FLOAT64,\n",
    "        h13 FLOAT64,\n",
    "        h14 FLOAT64,\n",
    "        h15 FLOAT64\n",
    "    > AS (STRUCT(\n",
    "        h[OFFSET(0)],\n",
    "        h[OFFSET(1)],\n",
    "        h[OFFSET(2)],\n",
    "        h[OFFSET(3)],\n",
    "        h[OFFSET(4)],\n",
    "        h[OFFSET(5)],\n",
    "        h[OFFSET(6)],\n",
    "        h[OFFSET(7)],\n",
    "        h[OFFSET(8)],\n",
    "        h[OFFSET(9)],\n",
    "        h[OFFSET(10)],\n",
    "        h[OFFSET(11)],\n",
    "        h[OFFSET(12)],\n",
    "        h[OFFSET(13)],\n",
    "        h[OFFSET(14)]\n",
    "));\n",
    "\n",
    "CREATE OR REPLACE FUNCTION `hotel_recommendations.arr_to_input_15_users`(u ARRAY<FLOAT64>)\n",
    "RETURNS \n",
    "    STRUCT<\n",
    "        u1 FLOAT64,\n",
    "        u2 FLOAT64,\n",
    "        u3 FLOAT64,\n",
    "        u4 FLOAT64,\n",
    "        u5 FLOAT64,\n",
    "        u6 FLOAT64,\n",
    "        u7 FLOAT64,\n",
    "        u8 FLOAT64,\n",
    "        u9 FLOAT64,\n",
    "        u10 FLOAT64,\n",
    "        u11 FLOAT64,\n",
    "        u12 FLOAT64,\n",
    "        u13 FLOAT64,\n",
    "        u14 FLOAT64,\n",
    "        u15 FLOAT64\n",
    "    > AS (STRUCT(\n",
    "        u[OFFSET(0)],\n",
    "        u[OFFSET(1)],\n",
    "        u[OFFSET(2)],\n",
    "        u[OFFSET(3)],\n",
    "        u[OFFSET(4)],\n",
    "        u[OFFSET(5)],\n",
    "        u[OFFSET(6)],\n",
    "        u[OFFSET(7)],\n",
    "        u[OFFSET(8)],\n",
    "        u[OFFSET(9)],\n",
    "        u[OFFSET(10)],\n",
    "        u[OFFSET(11)],\n",
    "        u[OFFSET(12)],\n",
    "        u[OFFSET(13)],\n",
    "        u[OFFSET(14)]\n",
    "));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train XGBoost model and evaluate it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_xgboost_model(ddlop, project_id, dataset, total_features):\n",
    "    #Combine user and hotel embedding features with the rating associated with each combination\n",
    "    query = \"\"\"\n",
    "        CREATE OR REPLACE MODEL `{project_id}.{dataset}.recommender_hybrid_xgboost_prod` \n",
    "        OPTIONS(model_type='boosted_tree_classifier', input_label_cols=['rating'], AUTO_CLASS_WEIGHTS=True)\n",
    "        AS\n",
    "\n",
    "        SELECT\n",
    "          * EXCEPT(user_factors, hotel_factors),\n",
    "            {dataset}.arr_to_input_15_users(user_factors).*,\n",
    "            {dataset}.arr_to_input_15_hotels(hotel_factors).*\n",
    "        FROM\n",
    "          `{total_features}`\n",
    "    \"\"\".format(project_id = project_id, dataset = dataset, total_features=total_features)\n",
    "    return ddlop(project_id, query, 'US')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_class(project_id, dataset, class_model, total_features, location='US')-> NamedTuple('ClassMetrics', [('roc_auc', float)]):\n",
    "     \n",
    "    query = \"\"\"\n",
    "        SELECT\n",
    "          *\n",
    "        FROM ML.EVALUATE(MODEL `{class_model}`, (\n",
    "          SELECT\n",
    "          * EXCEPT(user_factors, hotel_factors),\n",
    "            {dataset}.arr_to_input_15_users(user_factors).*,\n",
    "            {dataset}.arr_to_input_15_hotels(hotel_factors).*\n",
    "        FROM\n",
    "          `{total_features}`\n",
    "        ))\n",
    "    \"\"\".format(dataset = dataset, class_model = class_model, total_features = total_features)\n",
    "\n",
    "    print(query)\n",
    "\n",
    "    from google.cloud import bigquery\n",
    "\n",
    "    bqclient = bigquery.Client(project=project_id, location=location)\n",
    "    job = bqclient.query(query)\n",
    "    metrics_df = job.result().to_dataframe()\n",
    "    from collections import namedtuple\n",
    "    result_tuple = namedtuple('ClassMetrics', ['roc_auc'])\n",
    "    return result_tuple(metrics_df.loc[0].to_dict()['roc_auc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export XGBoost model and host it as a model endpoint on Vertex AI\n",
    "\n",
    "One of the nice features of BigQuery ML is the ability to import and export machine learning models. In the function defined below, we are going to export the trained XGBoost model to a Google Cloud Storage bucket. We will later have Google Cloud AI Platform host this model as an endpoint for predictions. It is worth mentioning that you can host this model on any platform that supports Booster (XGBoost 0.82). Check out [the documentation](https://cloud.google.com/bigquery-ml/docs/exporting-models) for more information on exporting BigQuery ML models and their formats.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def export_bqml_model(project_id, model, destination) -> NamedTuple('ModelExport', [('destination', str)]):\n",
    "    import subprocess\n",
    "    #command='bq extract -destination_format=ML_XGBOOST_BOOSTER -m {}:{} {}'.format(project_id, model, destination)\n",
    "    model_name = '{}:{}'.format(project_id, model)\n",
    "    print (model_name)\n",
    "    subprocess.run(['bq', 'extract', '-destination_format=ML_XGBOOST_BOOSTER', '-m', model_name, destination], check=True)\n",
    "    from collections import namedtuple\n",
    "    result_tuple = namedtuple('ModelExport', ['destination'])\n",
    "    return result_tuple(destination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deploy_bqml_model_vertexai(project_id, region, model_name, endpoint_name, model_dir, deploy_image, deploy_compute):\n",
    "    from google.cloud import aiplatform\n",
    "    \n",
    "    parent = \"projects/\" + project_id + \"/locations/\" + region\n",
    "    client_options = {\"api_endpoint\": \"{}-aiplatform.googleapis.com\".format(region)}\n",
    "    clients = {}\n",
    "\n",
    "    #upload the model to Vertex AI\n",
    "    clients['model'] = aiplatform.gapic.ModelServiceClient(client_options=client_options)\n",
    "    model = {\n",
    "        \"display_name\": model_name,\n",
    "        \"metadata_schema_uri\": \"\",\n",
    "        \"artifact_uri\": model_dir,\n",
    "        \"container_spec\": {\n",
    "            \"image_uri\": deploy_image,\n",
    "            \"command\": [],\n",
    "            \"args\": [],\n",
    "            \"env\": [],\n",
    "            \"ports\": [{\"container_port\": 8080}],\n",
    "            \"predict_route\": \"\",\n",
    "            \"health_route\": \"\"\n",
    "        }\n",
    "    }\n",
    "    upload_model_response = clients['model'].upload_model(parent=parent, model=model)\n",
    "    print(\"Long running operation on uploading the model:\", upload_model_response.operation.name)\n",
    "    model_info = clients['model'].get_model(name=upload_model_response.result(timeout=180).model)\n",
    "\n",
    "    #Create an endpoint on Vertex AI to host the model\n",
    "    clients['endpoint'] = aiplatform.gapic.EndpointServiceClient(client_options=client_options)\n",
    "    create_endpoint_response = clients['endpoint'].create_endpoint(parent=parent, endpoint={\"display_name\": endpoint_name})\n",
    "    print(\"Long running operation on creating endpoint:\", create_endpoint_response.operation.name)\n",
    "    endpoint_info = clients['endpoint'].get_endpoint(name=create_endpoint_response.result(timeout=180).name)\n",
    "\n",
    "    #Deploy the model to the endpoint\n",
    "    dmodel = {\n",
    "            \"model\": model_info.name,\n",
    "            \"display_name\": 'deployed_'+model_name,\n",
    "            \"dedicated_resources\": {\n",
    "                \"min_replica_count\": 1,\n",
    "                \"max_replica_count\": 1,\n",
    "                \"machine_spec\": {\n",
    "                        \"machine_type\": deploy_compute,\n",
    "                        \"accelerator_count\": 0,\n",
    "                    }\n",
    "            }   \n",
    "    }\n",
    "\n",
    "    traffic = {\n",
    "        '0' : 100\n",
    "    }\n",
    "\n",
    "    deploy_model_response = clients['endpoint'].deploy_model(endpoint=endpoint_info.name, deployed_model=dmodel, traffic_split=traffic)\n",
    "    print(\"Long running operation on deploying the model:\", deploy_model_response.operation.name)\n",
    "    deploy_model_result = deploy_model_response.result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the Kubeflow Pipelines (KFP)\n",
    "\n",
    "Now we have the necessary functions defined, we are now ready to create a workflow using Kubeflow Pipeline. The workflow implemented by the pipeline is defined using a Python based Domain Specific Language (DSL). \n",
    "The pipeline's DSL has been designed to avoid hardcoding any environment specific settings like file paths or connection strings. These settings are provided to the pipeline code through a set of environment variables.\n",
    "\n",
    "The pipeline performs the following steps - \n",
    "* Trains a Matrix Factorization model \n",
    "* Evaluates the trained Matrix Factorization model and if the Mean Square Error is less than threadshold, it will continue to the next step, otherwise, the pipeline will stop\n",
    "* Engineers new user factors feature with the Matrix Factorization model\n",
    "* Engineers new hotel factors feature with the Matrix Factorization model\n",
    "* Combines all the features selected (total_mobile_searches) and engineered (user factors and hotel factors) into a training dataset for the XGBoost classifier \n",
    "* Trains a XGBoost classifier\n",
    "* Evalutes the trained XGBoost model and if the ROC AUC score is more than threadshold, it will continue to the next step, otherwise, the pipeline will stop\n",
    "* Exports the XGBoost model to a Google Cloud Storage bucket\n",
    "* Deploys the XGBoost model from the Google Cloud Storage bucket to Google Cloud AI Platform for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import kfp.dsl as dsl\n",
    "import kfp.components as comp\n",
    "import time\n",
    "\n",
    "@dsl.pipeline(\n",
    "    name='Training pipeline for hotel recommendation prediction',\n",
    "    description='Training pipeline for hotel recommendation prediction'\n",
    ")\n",
    "def training_pipeline(project_id = PROJECT_ID):\n",
    "\n",
    "    import json\n",
    "    \n",
    "    #Minimum threshold for model metric to determine if model will be deployed for prediction \n",
    "    mf_msqe_threshold = 0.5\n",
    "    class_auc_threshold = 0.8\n",
    "    \n",
    "    #Defining function containers\n",
    "    ddlop = comp.func_to_container_op(run_bigquery_ddl, base_image=BASE_IMAGE, packages_to_install=['google-cloud-bigquery'])\n",
    "    \n",
    "    evaluate_class_op = comp.func_to_container_op(evaluate_class, base_image=BASE_IMAGE, packages_to_install=['google-cloud-bigquery','pandas'])\n",
    "    \n",
    "    evaluate_mf_op = comp.func_to_container_op(evaluate_matrix_factorization_model, base_image=BASE_IMAGE, packages_to_install=['google-cloud-bigquery','pandas'])\n",
    "    \n",
    "    export_bqml_model_op = comp.func_to_container_op(export_bqml_model, base_image=BASE_IMAGE, packages_to_install=['google-cloud-bigquery'])\n",
    "    \n",
    "    deploy_bqml_model_op = comp.func_to_container_op(deploy_bqml_model_vertexai, base_image=BASE_IMAGE, packages_to_install=['google-cloud-aiplatform'])\n",
    "\n",
    "    \n",
    "    #############################                                 \n",
    "    #Defining pipeline execution graph\n",
    "    dataset = BQ_DATASET_NAME\n",
    "    \n",
    "    #Train matrix factorization model\n",
    "    mf_model_output = train_matrix_factorization_model(ddlop, PROJECT_ID, dataset).set_display_name('train matrix factorization model')\n",
    "    mf_model_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'\n",
    "    mf_model = mf_model_output.outputs['created_table']\n",
    "    \n",
    "    #Evaluate matrix factorization model\n",
    "    mf_eval_output = evaluate_mf_op(PROJECT_ID, mf_model).set_display_name('evaluate matrix factorization model')\n",
    "    mf_eval_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'\n",
    "    \n",
    "    with dsl.Condition(mf_eval_output.outputs['msqe'] < mf_msqe_threshold):\n",
    "    \n",
    "        #Create features for classification model\n",
    "        user_features_output = create_user_features(ddlop, PROJECT_ID, dataset, mf_model).set_display_name('create user factors features')\n",
    "        user_features = user_features_output.outputs['created_table']\n",
    "        user_features_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'\n",
    "        \n",
    "        hotel_features_output = create_hotel_features(ddlop, PROJECT_ID, dataset, mf_model).set_display_name('create hotel factors features')\n",
    "        hotel_features = hotel_features_output.outputs['created_table']\n",
    "        hotel_features_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'\n",
    "\n",
    "        total_features_output = combine_features(ddlop, PROJECT_ID, dataset, mf_model, hotel_features, user_features).set_display_name('combine all features')\n",
    "        total_features = total_features_output.outputs['created_table']\n",
    "        total_features_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'\n",
    "        \n",
    "        #Train XGBoost model\n",
    "        class_model_output = train_xgboost_model(ddlop, PROJECT_ID, dataset, total_features).set_display_name('train XGBoost model')\n",
    "        class_model = class_model_output.outputs['created_table']\n",
    "        class_model_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'\n",
    "\n",
    "        class_eval_output = evaluate_class_op(project_id, dataset, class_model, total_features).set_display_name('evaluate XGBoost model')\n",
    "        class_eval_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'\n",
    "        \n",
    "        with dsl.Condition(class_eval_output.outputs['roc_auc'] > class_auc_threshold):\n",
    "            #Export model\n",
    "            export_destination_output = export_bqml_model_op(project_id, class_model, MODEL_STORAGE).set_display_name('export XGBoost model')\n",
    "            export_destination_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'\n",
    "            export_destination = export_destination_output.outputs['destination']\n",
    "            deploy_model = deploy_bqml_model_op(PROJECT_ID, REGION, class_model, ENDPOINT_NAME, MODEL_STORAGE, DEPLOY_IMAGE, DEPLOY_COMPUTE).set_display_name('Deploy XGBoost model')\n",
    "            deploy_model.execution_options.caching_strategy.max_cache_staleness = 'P0D'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submitting pipeline runs  \n",
    "\n",
    "You can trigger pipeline runs using an API from the KFP SDK or using KFP CLI. To submit the run using KFP CLI, execute the following commands. Notice how the pipeline's parameters are passed to the pipeline run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_func = training_pipeline\n",
    "pipeline_filename = pipeline_func.__name__ + '.zip'\n",
    "import kfp.compiler as compiler\n",
    "import kfp\n",
    "compiler.Compiler().compile(pipeline_func, pipeline_filename)\n",
    "\n",
    "#Specify pipeline argument values\n",
    "arguments = {}\n",
    "\n",
    "#Get or create an experiment and submit a pipeline run\n",
    "client = kfp.Client(KFPHOST)\n",
    "experiment = client.create_experiment('hotel_recommender_experiment')\n",
    "\n",
    "#Submit a pipeline run\n",
    "run_name = pipeline_func.__name__ + ' run'\n",
    "run_result = client.run_pipeline(experiment.id, run_name, pipeline_filename, arguments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monitoring the run \n",
    "The Pipeline will take several hours to train two models, you can monitor the run using KFP UI. Follow the instructor who will walk you through the KFP UI and monitoring techniques.\n",
    "\n",
    "To access the KFP UI in your environment use the following URI and <strong>replace KFPHOST</strong> variable with your Cloud AI Platform Pipelines host name \n",
    "* https://[KFPHOST]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning up\n",
    "\n",
    "* Delete the Model in [Cloud AI Platform](https://console.cloud.google.com/ai-platform/models), note you may have to delete all versions first before deleting the model.\n",
    "* Delete the bucket or model content in [Google Cloud Storage](https://console.cloud.google.com/storage).\n",
    "* Delete the dataset in [BigQuery](https://console.cloud.google.com/bigquery), it will delete the models, tables and UDFs created in BigQuery.\n",
    "* Follow how-to guide to [delete Flex commitment](https://console.cloud.google.com/bigquery/docs/reservations-get-started#cleaning-up)\n",
    "* Delete the container from the [Google Container Registry](https://console.cloud.google.com/gcr/images)\n",
    "* Delete the [Cloud AI Platform Pipeline](https://console.cloud.google.com/ai-platform/pipelines), select <strong>Delete Cluster</strong> check box to delete the underlying Google Kubernetes Cluster. \n",
    "* Delete the [Vertex AI](https://console.cloud.google.com/vertex-ai), undeploy the model within Endpoint first, then delete the Endpoint and finally delete the Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "environment": {
   "name": "tf2-2-3-gpu.2-3.m59",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/tf2-2-3-gpu.2-3:m59"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}

================================================
FILE: retail/recommendation-system/bqml-mlops/part_2/Dockerfile
================================================
FROM python:3.7

# ensure local python is preferred over distribution python
ENV PATH /usr/local/bin:$PATH

RUN pip3 install fire pyyaml pathlib
RUN pip3 install --upgrade kfp
RUN pip3 install google-cloud-bigquery




================================================
FILE: retail/recommendation-system/bqml-mlops/part_2/README.md
================================================
```
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
```

## Tutorial Overview 

This is part two of the tutorial where you will learn how to setup a CI/CD pipeline with Google Cloud Source Repositories and Google Cloud Build. Google Cloud Source Repositorie is a fully managed private Git Repositories with integrations for continuous integration, delivery & deployment. Google Cloud Build is a serverless CI/CD platform to build, test and deploy code. In this tutorial, you will create a new repository in Google Cloud Source Respositories, create a new Google Cloud Build trigger that will compile and deploy a KubeFlow Pipeline automatically when you check in new code to the source repository.

![CI/CD](cicd.png)

## Prerequisites
* Follow instructions in [Part One Prerequisites](../README.md) to ensure your enviornment is properly setup correctly before continuing, i.e. datasets in BigQuery, AI Platform Pipelines, Google Cloud Storage and etc.

## Setup Instructions
1. Clone this repository and add only the content in this folder (part_2) into a new Google Cloud Source Repositories. Follow instructions [here](https://cloud.google.com/source-repositories/docs/quickstart) to create a new Google Cloud Source Repositories. 

1. Open [pipeline.py](pipeline.py) and update the following line of code ```base_image = 'YOUR BASE IMAGE CONTAINER URL FOR COMPONENTS BELOW'```. This container is built as part of [Part One Prerequisites](../README.md).  

1. Build and push a docker image using [this dockerfile](Dockerfile) as the base image. This is used by the Google Cloud Build to compile and deploy a KubeFlow Pipeline. To help you simplify this step, run the [dockerbuild.sh](dockerbuild.sh) script.  

1. Create a new Google Cloud Build Trigger by following instructions [here](https://cloud.google.com/build/docs/automating-builds/create-manage-triggers)
    - In the Source Repository section, select the newly created repository in previous step
    - In the Configuration section, enter cloudbuild.yaml in Cloud Build configuration file location. This file instructs the Google Cloud Build to download the container built earlier and execute the [pipeline.py](pipeline.py) python file
    - In the Advanced section, add the following Substitution variables. These variables are passed by cloudbuild.yaml at runtime to pipeline for dynamic configuration
        - **_DATASET_NAME** YOUR_BQ_DATASET_NAME
        - **_KFP_HOST** YOUR_AI_PLATFORM_PIPELINE_URL
        - **_MODEL_STORAGE** YOUR_MODEL_EXPORT_GCS_LOCATION
        - **_PIPELINE_IMAGE** YOUR_CLOUD_BUILD_DOCKER_IMAGE_URL_ABOVE
        - **_TAG** YOUR_CLOUD_BUILD_DOCKER_IMAGE_TAG_ABOVE
        - **_PROJECT_ID** YOUR_GCP_PROJECT_ID
    - Leave everything else default and save the trigger

1. In Google Cloud Build settings, make sure Kubernetes Engines with Kubernetes Engine Developer role is ENABLED. This allows the Cloud Build to deploy the KubeFlow pipeline. To learn more about permissions, see link [here](https://cloud.google.com/build/docs/securing-builds/configure-access-for-cloud-build-service-account).
 
1. You can now either update the source code and check it in to trigger the Google Cloud Build process **OR** from the Google Cloud Build Trigger console, click the Run button.


## Cleaning up

* Delete the Google Cloud Trigger
* Delete the Google Cloud Source Repository
* Follow instructions in [Part One Clean Up](../README.md) section to delete the rest of cloud services


================================================
FILE: retail/recommendation-system/bqml-mlops/part_2/cloudbuild.yaml
================================================
# Copyright 2020 Google Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


steps:
- name: '${_PIPELINE_IMAGE}:${_TAG}'
  entrypoint: 'python3'
  args: ['pipeline.py',
         '--project_id', '${_PROJECT_ID}',
         '--dataset_name', '${_DATASET_NAME}',
         '--model_storage', '${_MODEL_STORAGE}',
         '--kfp_host', '${_KFP_HOST}']
  id:  'compile pipeline'

================================================
FILE: retail/recommendation-system/bqml-mlops/part_2/dockerbuild.sh
================================================
export PROJECT_ID=$(gcloud config list project --format "value(core.project)")
export IMAGE_REPO_NAME=hotel_recommender_pipeline_container
export IMAGE_TAG=python3
export IMAGE_URI=gcr.io/$PROJECT_ID/$IMAGE_REPO_NAME:$IMAGE_TAG

docker build --no-cache -f Dockerfile -t $IMAGE_URI ./
docker push $IMAGE_URI

================================================
FILE: retail/recommendation-system/bqml-mlops/part_2/pipeline.py
================================================
from typing import NamedTuple
import json
import os
import fire

def run_bigquery_ddl(project_id: str, query_string: str, location: str) -> NamedTuple(
    'DDLOutput', [('created_table', str), ('query', str)]):
    """
    Runs BigQuery query and returns a table/model name
    """
    print(query_string)
        
    from google.cloud import bigquery
    from google.api_core.future import polling
    from google.cloud import bigquery
    from google.cloud.bigquery import retry as bq_retry
    
    bqclient = bigquery.Client(project=project_id, location=location)
    job = bqclient.query(query_string, retry=bq_retry.DEFAULT_RETRY)
    job._retry = polling.DEFAULT_RETRY
    
    while job.running():
        from time import sleep
        sleep(0.1)
        print('Running ...')
        
    tblname = job.ddl_target_table
    tblname = '{}.{}'.format(tblname.dataset_id, tblname.table_id)
    print('{} created in {}'.format(tblname, job.ended - job.started))
    
    from collections import namedtuple
    result_tuple = namedtuple('DDLOutput', ['created_table', 'query'])
    return result_tuple(tblname, query_string)

def train_matrix_factorization_model(ddlop, project_id: str, dataset: str):
    query = """
        CREATE OR REPLACE MODEL `{project_id}.{dataset}.my_implicit_mf_model_quantiles_demo_binary_prod`
        OPTIONS
          (model_type='matrix_factorization',
           feedback_type='implicit',
           user_col='user_id',
           item_col='hotel_cluster',
           rating_col='rating',
           l2_reg=30,
           num_factors=15) AS

        SELECT
          user_id,
          hotel_cluster,
          if(sum(is_booking) > 0, 1, sum(is_booking)) AS rating
        FROM `{project_id}.{dataset}.hotel_train`
        group by 1,2
    """.format(project_id = project_id, dataset = dataset)
    return ddlop(project_id, query, 'US')

def evaluate_matrix_factorization_model(project_id:str, mf_model:str, location:str='US')-> NamedTuple('MFMetrics', [('msqe', float)]):
    
    query = """
        SELECT * FROM ML.EVALUATE(MODEL `{project_id}.{mf_model}`)
    """.format(project_id = project_id, mf_model = mf_model)

    print(query)

    from google.cloud import bigquery
    import json

    bqclient = bigquery.Client(project=project_id, location=location)
    job = bqclient.query(query)
    metrics_df = job.result().to_dataframe()
    from collections import namedtuple
    result_tuple = namedtuple('MFMetrics', ['msqe'])
    return result_tuple(metrics_df.loc[0].to_dict()['mean_squared_error'])

def create_user_features(ddlop, project_id:str, dataset:str, mf_model:str):
    #Feature engineering for useres
    query = """
        CREATE OR REPLACE TABLE  `{project_id}.{dataset}.user_features_prod` AS
        WITH u as 
        (
            select
            user_id,
            count(*) as total_visits,
            count(distinct user_location_city) as distinct_cities,
            sum(distinct site_name) as distinct_sites,
            sum(is_mobile) as total_mobile,
            sum(is_booking) as total_bookings,
            FROM `{project_id}.{dataset}.hotel_train`
            GROUP BY 1
        )
        SELECT
            u.*,
            (SELECT ARRAY_AGG(weight) FROM UNNEST(factor_weights)) AS user_factors
        FROM
            u JOIN ML.WEIGHTS( MODEL `{mf_model}`) w
            ON processed_input = 'user_id' AND feature = CAST(u.user_id AS STRING)
    """.format(project_id = project_id, dataset = dataset, mf_model=mf_model)
    return ddlop(project_id, query, 'US')

def create_hotel_features(ddlop, project_id:str, dataset:str, mf_model:str):
    #Feature eingineering for hotels
    query = """
        CREATE OR REPLACE TABLE  `{project_id}.{dataset}.hotel_features_prod` AS
        WITH h as 
        (
            select
            hotel_cluster,
            count(*) as total_cluster_searches,
            count(distinct hotel_country) as distinct_hotel_countries,
            sum(distinct hotel_market) as distinct_hotel_markets,
            sum(is_mobile) as total_mobile_searches,
            sum(is_booking) as total_cluster_bookings,
            FROM `{project_id}.{dataset}.hotel_train`
            group by 1
        )
        SELECT
            h.*,
            (SELECT ARRAY_AGG(weight) FROM UNNEST(factor_weights)) AS hotel_factors
        FROM
            h JOIN ML.WEIGHTS( MODEL `{mf_model}`) w
            ON processed_input = 'hotel_cluster' AND feature = CAST(h.hotel_cluster AS STRING)
    """.format(project_id = project_id, dataset = dataset, mf_model=mf_model)
    return ddlop(project_id, query, 'US')

def combine_features(ddlop, project_id:str, dataset:str, mf_model:str, hotel_features:str, user_features:str):
    #Combine user and hotel embedding features with the rating associated with each combination
    query = """
        CREATE OR REPLACE TABLE `{project_id}.{dataset}.total_features_prod` AS
        with ratings as(
          SELECT
            user_id,
            hotel_cluster,
            if(sum(is_booking) > 0, 1, sum(is_booking)) AS rating
          FROM `{project_id}.{dataset}.hotel_train`
          group by 1,2
        )
        select
          h.* EXCEPT(hotel_cluster),
          u.* EXCEPT(user_id),
          IFNULL(rating,0) as rating
        from `{hotel_features}` h, `{user_features}` u
        LEFT OUTER JOIN ratings r
        ON r.user_id = u.user_id AND r.hotel_cluster = h.hotel_cluster
    """.format(project_id = project_id, dataset = dataset, mf_model=mf_model, hotel_features=hotel_features, user_features=user_features)
    return ddlop(project_id, query, 'US')

def train_xgboost_model(ddlop, project_id:str, dataset:str, total_features:str):
    #Combine user and hotel embedding features with the rating associated with each combination
    query = """
        CREATE OR REPLACE MODEL `{project_id}.{dataset}.recommender_hybrid_xgboost_prod` 
        OPTIONS(model_type='boosted_tree_classifier', input_label_cols=['rating'], AUTO_CLASS_WEIGHTS=True)
        AS

        SELECT
          * EXCEPT(user_factors, hotel_factors),
            {dataset}.arr_to_input_15_users(user_factors).*,
            {dataset}.arr_to_input_15_hotels(hotel_factors).*
        FROM
          `{total_features}`
    """.format(project_id = project_id, dataset = dataset, total_features=total_features)
    return ddlop(project_id, query, 'US')

def evaluate_class(project_id:str, dataset:str, class_model:str, total_features:str, location:str='US')-> NamedTuple('ClassMetrics', [('roc_auc', float)]):     
    query = """
        SELECT
          *
        FROM ML.EVALUATE(MODEL `{class_model}`, (
          SELECT
          * EXCEPT(user_factors, hotel_factors),
            {dataset}.arr_to_input_15_users(user_factors).*,
            {dataset}.arr_to_input_15_hotels(hotel_factors).*
        FROM
          `{total_features}`
        ))
    """.format(dataset = dataset, class_model = class_model, total_features = total_features)

    print(query)

    from google.cloud import bigquery

    bqclient = bigquery.Client(project=project_id, location=location)
    job = bqclient.query(query)
    metrics_df = job.result().to_dataframe()
    from collections import namedtuple
    result_tuple = namedtuple('ClassMetrics', ['roc_auc'])
    return result_tuple(metrics_df.loc[0].to_dict()['roc_auc'])

def export_bqml_model(project_id:str, model:str, destination:str) -> NamedTuple('ModelExport', [('destination', str)]):
    import subprocess
    #command='bq extract -destination_format=ML_XGBOOST_BOOSTER -m {}:{} {}'.format(project_id, model, destination)
    model_name = '{}:{}'.format(project_id, model)
    print (model_name)
    subprocess.run(['bq', 'extract', '-destination_format=ML_XGBOOST_BOOSTER', '-m', model_name, destination], check=True)
    from collections import namedtuple
    result_tuple = namedtuple('ModelExport', ['destination'])
    return result_tuple(destination)

import kfp.dsl as dsl
import kfp.components as comp
import time

@dsl.pipeline(
    name='Training pipeline for hotel recommendation prediction',
    description='Training pipeline for hotel recommendation prediction'
)
def training_pipeline(project_id:str, dataset_name:str, model_storage:str, base_image_path:str):

    import json
        
    #############################                                 
    #Defining pipeline execution graph
    dataset = dataset_name
    base_image = 'YOUR BASE IMAGE CONTAINER URL FOR COMPONENTS BELOW'
    
    #Minimum threshold for model metric to determine if model will be deployed to inference: 0.5 is a basically a coin toss with 50/50 chance
    mf_msqe_threshold = 0.5
    class_auc_threshold = 0.8
    
    #Defining function containers
    ddlop = comp.func_to_container_op(run_bigquery_ddl, packages_to_install=['google-cloud-bigquery'])
    evaluate_mf_op = comp.func_to_container_op(evaluate_matrix_factorization_model, base_image=base_image, packages_to_install=['google-cloud-bigquery','pandas'])
    evaluate_class_op = comp.func_to_container_op(evaluate_class, base_image=base_image, packages_to_install=['google-cloud-bigquery','pandas'])
    export_bqml_model_op = comp.func_to_container_op(export_bqml_model, base_image=base_image, packages_to_install=['google-cloud-bigquery'])
    
    #Train matrix factorization model
    mf_model_output = train_matrix_factorization_model(ddlop, project_id, dataset).set_display_name('train matrix factorization model')
    mf_model_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'
    mf_model = mf_model_output.outputs['created_table']
    
    #Evaluate matrix factorization model
    mf_eval_output = evaluate_mf_op(project_id, mf_model).set_display_name('evaluate matrix factorization model')
    mf_eval_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'
    
    #mean squared quantization error 
    with dsl.Condition(mf_eval_output.outputs['msqe'] < mf_msqe_threshold):
    
        #Create features for Classification model
        user_features_output = create_user_features(ddlop, project_id, dataset, mf_model).set_display_name('create user factors features')
        user_features = user_features_output.outputs['created_table']
        user_features_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'
        
        hotel_features_output = create_hotel_features(ddlop, project_id, dataset, mf_model).set_display_name('create hotel factors features')
        hotel_features = hotel_features_output.outputs['created_table']
        hotel_features_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'

        total_features_output = combine_features(ddlop, project_id, dataset, mf_model, hotel_features, user_features).set_display_name('combine all features')
        total_features = total_features_output.outputs['created_table']
        total_features_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'

        #Train XGBoost model
        class_model_output = train_xgboost_model(ddlop, project_id, dataset, total_features).set_display_name('train XGBoost model')
        class_model = class_model_output.outputs['created_table']
        class_model_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'

        class_model = 'hotel_recommendations.recommender_hybrid_xgboost_prod'
    
        #Evaluate XGBoost model
        class_eval_output = evaluate_class_op(project_id, dataset, class_model, total_features).set_display_name('evaluate XGBoost model')
        class_eval_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'
        
        with dsl.Condition(class_eval_output.outputs['roc_auc'] > class_auc_threshold):
            #Export model
            export_destination_output = export_bqml_model_op(project_id, class_model, model_storage).set_display_name('export XGBoost model')
            export_destination_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'
            export_destination = export_destination_output.outputs['destination']

def main(**args):
    #Specify pipeline argument values
    arguments = {
        'project_id': args['project_id'],
        'dataset_name': args['dataset_name'],
        'model_storage': args['model_storage']
    }
    
    pipeline_func = training_pipeline
    pipeline_filename = pipeline_func.__name__ + '.zip'
    import kfp.compiler as compiler
    import kfp
    compiler.Compiler().compile(pipeline_func, pipeline_filename)


    #Get or create an experiment and submit a pipeline run
    client = kfp.Client(args['kfp_host'])
    experiment = client.create_experiment('hotel_recommender_experiment')

    #Submit a pipeline run
    run_name = pipeline_func.__name__ + ' run'
    run_result = client.run_pipeline(experiment.id, run_name, pipeline_filename, arguments)
    
if __name__ == '__main__':
  fire.Fire(main)

================================================
FILE: retail/recommendation-system/bqml-mlops/part_3/Dockerfile
================================================
FROM python:3.7

# Install Scikit-Learn
# Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.
# Scikit-learn now requires Python 3.6 or newer.
RUN python -m pip install --no-cache -I scikit-learn==0.23.2

# Install pandas
RUN python -m pip install --no-cache -I pandas==1.0.5

# Install Google SDK
RUN echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] http://packages.cloud.google.com/apt cloud-sdk main" | tee -a /etc/apt/sources.list.d/google-cloud-sdk.list && curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key --keyring /usr/share/keyrings/cloud.google.gpg  add - && apt-get update -y && apt-get install google-cloud-sdk -y

================================================
FILE: retail/recommendation-system/bqml-mlops/part_3/README.md
================================================
```python
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
```

## Tutorial Overview 

This is part three of the tutorial where you will learn how to run same code in [Part One](../README.md) (with minor changes) in Google's new Vertex AI pipeline. Vertex Pipelines helps you to automate, monitor, and govern your ML systems by orchestrating your ML workflow in a serverless manner, and storing your workflow's artifacts using Vertex ML Metadata. By storing the artifacts of your ML workflow in Vertex ML Metadata, you can analyze the lineage of your workflow's artifacts — for example, an ML model's lineage may include the training data, hyperparameters, and code that were used to create the model.

You will also learn how to export the final BQML model and hosted on the Google Vertex AI Endpoint. 

![Pipeline](pipeline.png)

## Getting Started
Use this [notebook](vertex_ai_pipeline.ipynb) to get started. 

## Questions? Feedback?
If you have any questions or feedback, please open up a [new issue](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/issues).


================================================
FILE: retail/recommendation-system/bqml-mlops/part_3/dockerbuild.sh
================================================
export PROJECT_ID=$(gcloud config list project --format "value(core.project)")
export IMAGE_REPO_NAME=hotel_recommender_vertexai_container
export IMAGE_TAG=latest
export IMAGE_URI=gcr.io/$PROJECT_ID/$IMAGE_REPO_NAME:$IMAGE_TAG

docker build --no-cache -f Dockerfile -t $IMAGE_URI ./
docker push $IMAGE_URI

================================================
FILE: retail/recommendation-system/bqml-mlops/part_3/vertex_ai_pipeline.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright 2020 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial Overview \n",
    "\n",
    "This is part three of the tutorial where you will learn how to run same code in [Part One](../README.md) (with minor changes) in Google's new Vertex AI pipeline. Vertex Pipelines helps you to automate, monitor, and govern your ML systems by orchestrating your ML workflow in a serverless manner, and storing your workflow's artifacts using Vertex ML Metadata. By storing the artifacts of your ML workflow in Vertex ML Metadata, you can analyze the lineage of your workflow's artifacts — for example, an ML model's lineage may include the training data, hyperparameters, and code that were used to create the model.\n",
    "\n",
    "You will also learn how to export the final BQML model and hosted on the Google Vertex AI Endpoint. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Pipeline](pipeline.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "* Download the [Expedia Hotel Recommendation Dataset](https://www.kaggle.com/c/expedia-hotel-recommendations) from Kaggle. You will be mostly working with the train.csv dataset for this tutorial\n",
    "* Upload the dataset to BigQuery by following the how-to guide [Loading CSV Data](https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-csv)\n",
    "* Follow the how-to guide [create flex slots, reservation and assignment in BigQuery](https://cloud.google.com/bigquery/docs/reservations-get-started) for training ML models. <strong>Make sure to create Flex slots and not month/year slots so you can delete them after the tutorial.</strong> \n",
    "* Build and push a docker image using [this dockerfile](dockerfile) as the base image for the Kubeflow pipeline components. \n",
    "* Create or use a [Google Cloud Storage](https://console.cloud.google.com/storage) bucket to export the finalized model to. <strong>Make sure to create the bucket in the same region where you will create Vertex AI Endpoint to host your model.</strong> \n",
    "* If you do not specify a service account, Vertex Pipelines uses the Compute Engine default service account to run your pipelines. The Compute Engine default service account has the Project Editor role by default so it should have access to BigQuery as well as Google Cloud Storage.\n",
    "* Change the following cell to reflect your setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH=%env PATH\n",
    "%env PATH={PATH}:/home/jupyter/.local/bin\n",
    "\n",
    "# CHANGE the following settings\n",
    "BASE_IMAGE='gcr.io/your-image-name' #This is the image built from the Dockfile in the same folder\n",
    "REGION='vertex-ai-region' #For example, us-central1, note that Vertex AI endpoint deployment region must match MODEL_STORAGE bucket region\n",
    "MODEL_STORAGE = 'gs://your-bucket-name/folder-name' #Make sure this bucket is created in the same region defined above\n",
    "BQ_DATASET_NAME=\"hotel_recommendations\" #This is the name of the target dataset where you model and predictions will be stored\n",
    "PROJECT_ID=\"your-project-id\" #This is your GCP project ID that can be found in the GCP console\n",
    "\n",
    "# Required Parameters for Vertex AI\n",
    "USER = 'your-user-name'\n",
    "BUCKET_NAME = 'your-bucket-name' \n",
    "PIPELINE_ROOT = 'gs://{}/pipeline_root/{}'.format(BUCKET_NAME, USER) #Cloud Storage URI that your pipelines service account can access.\n",
    "ENDPOINT_NAME='bqml-hotel-recommendations' #Vertex AI Endpoint Name\n",
    "DEPLOY_COMPUTE='n1-standard-4' #Could be any supported Vertex AI Instance Types\n",
    "DEPLOY_IMAGE='us-docker.pkg.dev/vertex-ai/prediction/xgboost-cpu.0-82:latest'#Do not change, BQML XGBoost is currently compatible with 0.82\n",
    "\n",
    "\n",
    "print('PIPELINE_ROOT: {}'.format(PIPELINE_ROOT))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the KFP version, The KFP version should be >= 1.6. If lower, run !pip3 install --user kfp --upgrade, then restart the kernel\n",
    "!python3 -c \"import kfp; print('KFP version: {}'.format(kfp.__version__))\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create BigQuery function\n",
    "\n",
    "Create a generic BigQuery function that runs a BigQuery query and returns the table/model created. This will be re-used to return BigQuery results for all the different segments of the BigQuery process in the Kubeflow Pipeline. You will see later in the tutorial where this function is being passed as parameter (ddlop) to other functions to perform certain BigQuery operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import NamedTuple\n",
    "import json\n",
    "import os\n",
    "\n",
    "def run_bigquery_ddl(project_id: str, query_string: str, location: str) -> NamedTuple(\n",
    "    'DDLOutput', [('created_table', str), ('query', str)]):\n",
    "    \"\"\"\n",
    "    Runs BigQuery query and returns a table/model name\n",
    "    \"\"\"\n",
    "    print(query_string)\n",
    "        \n",
    "    from google.cloud import bigquery\n",
    "    from google.api_core.future import polling\n",
    "    from google.cloud import bigquery\n",
    "    from google.cloud.bigquery import retry as bq_retry\n",
    "    \n",
    "    bqclient = bigquery.Client(project=project_id, location=location)\n",
    "    job = bqclient.query(query_string, retry=bq_retry.DEFAULT_RETRY)\n",
    "    job._retry = polling.DEFAULT_RETRY\n",
    "    \n",
    "    print('bq version: {}'.format(bigquery.__version__))\n",
    "    \n",
    "    while job.running():\n",
    "        from time import sleep\n",
    "        sleep(30)\n",
    "        print('Running ...')\n",
    "    \n",
    "    tblname = '{}.{}'.format(job.ddl_target_table.dataset_id, job.ddl_target_table.table_id)\n",
    "    print('{} created in {}'.format(tblname, job.ended - job.started))\n",
    "    \n",
    "    from collections import namedtuple\n",
    "    result_tuple = namedtuple('DDLOutput', ['created_table', 'query'])\n",
    "    return result_tuple(tblname, query_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Matrix Factorization model and evaluate it\n",
    "\n",
    "We will start by training a matrix factorization model that will allow us to understand the latent relationship between user and hotel clusters. The reason why we are doing this is because matrix factorization approach can only find latent relationship between a user and a hotel. However, there are other intuitive useful predictors (such as is_mobile, location, and etc) that can improve the model performance. So togther, we can feed the resulting weights/factors as features among with other features to train the final XGBoost model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_matrix_factorization_model(ddlop, project_id: str, dataset: str):\n",
    "    query = \"\"\"\n",
    "        CREATE OR REPLACE MODEL `{project_id}.{dataset}.my_implicit_mf_model_quantiles_demo_binary_prod`\n",
    "        OPTIONS\n",
    "          (model_type='matrix_factorization',\n",
    "           feedback_type='implicit',\n",
    "           user_col='user_id',\n",
    "           item_col='hotel_cluster',\n",
    "           rating_col='rating',\n",
    "           l2_reg=30,\n",
    "           num_factors=15) AS\n",
    "\n",
    "        SELECT\n",
    "          user_id,\n",
    "          hotel_cluster,\n",
    "          if(sum(is_booking) > 0, 1, sum(is_booking)) AS rating\n",
    "        FROM `{project_id}.{dataset}.hotel_train`\n",
    "        group by 1,2\n",
    "    \"\"\".format(project_id = project_id, dataset = dataset)\n",
    "    return ddlop(project_id, query, 'US')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_matrix_factorization_model(project_id:str, mf_model: str, location: str='US')-> NamedTuple('MFMetrics', [('msqe', float)]):\n",
    "    \n",
    "    query = \"\"\"\n",
    "        SELECT * FROM ML.EVALUATE(MODEL `{project_id}.{mf_model}`)\n",
    "    \"\"\".format(project_id = project_id, mf_model = mf_model)\n",
    "\n",
    "    print(query)\n",
    "\n",
    "    from google.cloud import bigquery\n",
    "    import json\n",
    "\n",
    "    bqclient = bigquery.Client(project=project_id, location=location)\n",
    "    job = bqclient.query(query)\n",
    "    metrics_df = job.result().to_dataframe()\n",
    "    from collections import namedtuple\n",
    "    result_tuple = namedtuple('MFMetrics', ['msqe'])\n",
    "    return result_tuple(metrics_df.loc[0].to_dict()['mean_squared_error'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating embedding features for users and hotels \n",
    "\n",
    "We will use the matrix factorization model to create corresponding user factors, hotel factors and embed them together with additional features such as total visits and distinct cities to create a new training dataset to an XGBoost classifier which will try to predict the the likelihood of booking for any user/hotel combination. Also note that we aggregated and grouped the orginal dataset by user_id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_user_features(ddlop, project_id:str, dataset:str, mf_model:str):\n",
    "    #Feature engineering for useres\n",
    "    query = \"\"\"\n",
    "        CREATE OR REPLACE TABLE  `{project_id}.{dataset}.user_features_prod` AS\n",
    "        WITH u as \n",
    "        (\n",
    "            select\n",
    "            user_id,\n",
    "            count(*) as total_visits,\n",
    "            count(distinct user_location_city) as distinct_cities,\n",
    "            sum(distinct site_name) as distinct_sites,\n",
    "            sum(is_mobile) as total_mobile,\n",
    "            sum(is_booking) as total_bookings,\n",
    "            FROM `{project_id}.{dataset}.hotel_train`\n",
    "            GROUP BY 1\n",
    "        )\n",
    "        SELECT\n",
    "            u.*,\n",
    "            (SELECT ARRAY_AGG(weight) FROM UNNEST(factor_weights)) AS user_factors\n",
    "        FROM\n",
    "            u JOIN ML.WEIGHTS( MODEL `{mf_model}`) w\n",
    "            ON processed_input = 'user_id' AND feature = CAST(u.user_id AS STRING)\n",
    "    \"\"\".format(project_id = project_id, dataset = dataset, mf_model=mf_model)\n",
    "    return ddlop(project_id, query, 'US')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_hotel_features(ddlop, project_id:str, dataset:str, mf_model:str):\n",
    "    #Feature eingineering for hotels\n",
    "    query = \"\"\"\n",
    "        CREATE OR REPLACE TABLE  `{project_id}.{dataset}.hotel_features_prod` AS\n",
    "        WITH h as \n",
    "        (\n",
    "            select\n",
    "            hotel_cluster,\n",
    "            count(*) as total_cluster_searches,\n",
    "            count(distinct hotel_country) as distinct_hotel_countries,\n",
    "            sum(distinct hotel_market) as distinct_hotel_markets,\n",
    "            sum(is_mobile) as total_mobile_searches,\n",
    "            sum(is_booking) as total_cluster_bookings,\n",
    "            FROM `{project_id}.{dataset}.hotel_train`\n",
    "            group by 1\n",
    "        )\n",
    "        SELECT\n",
    "            h.*,\n",
    "            (SELECT ARRAY_AGG(weight) FROM UNNEST(factor_weights)) AS hotel_factors\n",
    "        FROM\n",
    "            h JOIN ML.WEIGHTS( MODEL `{mf_model}`) w\n",
    "            ON processed_input = 'hotel_cluster' AND feature = CAST(h.hotel_cluster AS STRING)\n",
    "    \"\"\".format(project_id = project_id, dataset = dataset, mf_model=mf_model)\n",
    "    return ddlop(project_id, query, 'US')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function below combines all the features selected (total_mobile_searches) and engineered (user factors and hotel factors) into a training dataset for the XGBoost classifier. Note the target variable is rating which is converted into a binary classfication."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def combine_features(ddlop, project_id:str, dataset:str, mf_model:str, hotel_features:str, user_features:str):\n",
    "    #Combine user and hotel embedding features with the rating associated with each combination\n",
    "    query = \"\"\"\n",
    "        CREATE OR REPLACE TABLE `{project_id}.{dataset}.total_features_prod` AS\n",
    "        with ratings as(\n",
    "          SELECT\n",
    "            user_id,\n",
    "            hotel_cluster,\n",
    "            if(sum(is_booking) > 0, 1, sum(is_booking)) AS rating\n",
    "          FROM `{project_id}.{dataset}.hotel_train`\n",
    "          group by 1,2\n",
    "        )\n",
    "        select\n",
    "          h.* EXCEPT(hotel_cluster),\n",
    "          u.* EXCEPT(user_id),\n",
    "          IFNULL(rating,0) as rating\n",
    "        from `{hotel_features}` h, `{user_features}` u\n",
    "        LEFT OUTER JOIN ratings r\n",
    "        ON r.user_id = u.user_id AND r.hotel_cluster = h.hotel_cluster\n",
    "    \"\"\".format(project_id = project_id, dataset = dataset, mf_model=mf_model, hotel_features=hotel_features, user_features=user_features)\n",
    "    return ddlop(project_id, query, 'US')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will create a couple of BigQuery user-defined functions (UDF) to convert arrays to a struct and its array elements are the fields in the struct. <strong>Be sure to change the BigQuery dataset name to your dataset name. </strong>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "CREATE OR REPLACE FUNCTION `hotel_recommendations.arr_to_input_15_hotels`(h ARRAY<FLOAT64>)\n",
    "RETURNS \n",
    "    STRUCT<\n",
    "        h1 FLOAT64,\n",
    "        h2 FLOAT64,\n",
    "        h3 FLOAT64,\n",
    "        h4 FLOAT64,\n",
    "        h5 FLOAT64,\n",
    "        h6 FLOAT64,\n",
    "        h7 FLOAT64,\n",
    "        h8 FLOAT64,\n",
    "        h9 FLOAT64,\n",
    "        h10 FLOAT64,\n",
    "        h11 FLOAT64,\n",
    "        h12 FLOAT64,\n",
    "        h13 FLOAT64,\n",
    "        h14 FLOAT64,\n",
    "        h15 FLOAT64\n",
    "    > AS (STRUCT(\n",
    "        h[OFFSET(0)],\n",
    "        h[OFFSET(1)],\n",
    "        h[OFFSET(2)],\n",
    "        h[OFFSET(3)],\n",
    "        h[OFFSET(4)],\n",
    "        h[OFFSET(5)],\n",
    "        h[OFFSET(6)],\n",
    "        h[OFFSET(7)],\n",
    "        h[OFFSET(8)],\n",
    "        h[OFFSET(9)],\n",
    "        h[OFFSET(10)],\n",
    "        h[OFFSET(11)],\n",
    "        h[OFFSET(12)],\n",
    "        h[OFFSET(13)],\n",
    "        h[OFFSET(14)]\n",
    "));\n",
    "\n",
    "CREATE OR REPLACE FUNCTION `hotel_recommendations.arr_to_input_15_users`(u ARRAY<FLOAT64>)\n",
    "RETURNS \n",
    "    STRUCT<\n",
    "        u1 FLOAT64,\n",
    "        u2 FLOAT64,\n",
    "        u3 FLOAT64,\n",
    "        u4 FLOAT64,\n",
    "        u5 FLOAT64,\n",
    "        u6 FLOAT64,\n",
    "        u7 FLOAT64,\n",
    "        u8 FLOAT64,\n",
    "        u9 FLOAT64,\n",
    "        u10 FLOAT64,\n",
    "        u11 FLOAT64,\n",
    "        u12 FLOAT64,\n",
    "        u13 FLOAT64,\n",
    "        u14 FLOAT64,\n",
    "        u15 FLOAT64\n",
    "    > AS (STRUCT(\n",
    "        u[OFFSET(0)],\n",
    "        u[OFFSET(1)],\n",
    "        u[OFFSET(2)],\n",
    "        u[OFFSET(3)],\n",
    "        u[OFFSET(4)],\n",
    "        u[OFFSET(5)],\n",
    "        u[OFFSET(6)],\n",
    "        u[OFFSET(7)],\n",
    "        u[OFFSET(8)],\n",
    "        u[OFFSET(9)],\n",
    "        u[OFFSET(10)],\n",
    "        u[OFFSET(11)],\n",
    "        u[OFFSET(12)],\n",
    "        u[OFFSET(13)],\n",
    "        u[OFFSET(14)]\n",
    "));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train XGBoost model and evaluate it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_xgboost_model(ddlop, project_id:str, dataset:str, total_features:str):\n",
    "    #Combine user and hotel embedding features with the rating associated with each combination\n",
    "    query = \"\"\"\n",
    "        CREATE OR REPLACE MODEL `{project_id}.{dataset}.recommender_hybrid_xgboost_prod` \n",
    "        OPTIONS(model_type='boosted_tree_classifier', input_label_cols=['rating'], AUTO_CLASS_WEIGHTS=True)\n",
    "        AS\n",
    "\n",
    "        SELECT\n",
    "          * EXCEPT(user_factors, hotel_factors),\n",
    "            {dataset}.arr_to_input_15_users(user_factors).*,\n",
    "            {dataset}.arr_to_input_15_hotels(hotel_factors).*\n",
    "        FROM\n",
    "          `{total_features}`\n",
    "    \"\"\".format(project_id = project_id, dataset = dataset, total_features=total_features)\n",
    "    return ddlop(project_id, query, 'US')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_class(project_id:str, dataset:str, class_model:str, total_features:str, location:str='US')-> NamedTuple('ClassMetrics', [('roc_auc', float)]):\n",
    "     \n",
    "    query = \"\"\"\n",
    "        SELECT\n",
    "          *\n",
    "        FROM ML.EVALUATE(MODEL `{class_model}`, (\n",
    "          SELECT\n",
    "          * EXCEPT(user_factors, hotel_factors),\n",
    "            {dataset}.arr_to_input_15_users(user_factors).*,\n",
    "            {dataset}.arr_to_input_15_hotels(hotel_factors).*\n",
    "        FROM\n",
    "          `{total_features}`\n",
    "        ))\n",
    "    \"\"\".format(dataset = dataset, class_model = class_model, total_features = total_features)\n",
    "\n",
    "    print(query)\n",
    "\n",
    "    from google.cloud import bigquery\n",
    "\n",
    "    bqclient = bigquery.Client(project=project_id, location=location)\n",
    "    job = bqclient.query(query)\n",
    "    metrics_df = job.result().to_dataframe()\n",
    "    from collections import namedtuple\n",
    "    result_tuple = namedtuple('ClassMetrics', ['roc_auc'])\n",
    "    return result_tuple(metrics_df.loc[0].to_dict()['roc_auc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export XGBoost model and host it on Vertex AI\n",
    "\n",
    "One of the nice features of BigQuery ML is the ability to import and export machine learning models. In the function defined below, we are going to export the trained XGBoost model to a Google Cloud Storage bucket. We will later have Google Cloud AI Platform host this model for predictions. It is worth mentioning that you can host this model on any platform that supports Booster (XGBoost 0.82). Check out [the documentation](https://cloud.google.com/bigquery-ml/docs/exporting-models) for more information on exporting BigQuery ML models and their formats.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def export_bqml_model(project_id:str, model:str, destination:str) -> NamedTuple('ModelExport', [('destination', str)]):\n",
    "    import subprocess\n",
    "    import shutil\n",
    "    #command='bq extract -destination_format=ML_XGBOOST_BOOSTER -m {}:{} {}'.format(project_id, model, destination)\n",
    "    model_name = '{}:{}'.format(project_id, model)\n",
    "    print (model_name)\n",
    "    #subprocess.run(['bq', 'extract', '-destination_format=ML_XGBOOST_BOOSTER', '-m', model_name, destination], check=True)\n",
    "    subprocess.run(\n",
    "        (\n",
    "            shutil.which(\"bq\"),\n",
    "            \"extract\",\n",
    "            \"-destination_format=ML_XGBOOST_BOOSTER\",\n",
    "            \"--project_id=\" + project_id,\n",
    "            \"-m\",\n",
    "            model_name,\n",
    "            destination\n",
    "        ),\n",
    "        stderr=subprocess.PIPE,\n",
    "        check=True)\n",
    "\n",
    "    from collections import namedtuple\n",
    "    result_tuple = namedtuple('ModelExport', ['destination'])\n",
    "    return result_tuple(destination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deploy_bqml_model_vertexai(project_id:str, region:str, model_name:str, endpoint_name:str, model_dir:str, deploy_image:str, deploy_compute:str):\n",
    "    from google.cloud import aiplatform\n",
    "    \n",
    "    parent = \"projects/\" + project_id + \"/locations/\" + region\n",
    "    client_options = {\"api_endpoint\": \"{}-aiplatform.googleapis.com\".format(region)}\n",
    "    clients = {}\n",
    "\n",
    "    #upload the model to Vertex AI\n",
    "    clients['model'] = aiplatform.gapic.ModelServiceClient(client_options=client_options)\n",
    "    model = {\n",
    "        \"display_name\": model_name,\n",
    "        \"metadata_schema_uri\": \"\",\n",
    "        \"artifact_uri\": model_dir,\n",
    "        \"container_spec\": {\n",
    "            \"image_uri\": deploy_image,\n",
    "            \"command\": [],\n",
    "            \"args\": [],\n",
    "            \"env\": [],\n",
    "            \"ports\": [{\"container_port\": 8080}],\n",
    "            \"predict_route\": \"\",\n",
    "            \"health_route\": \"\"\n",
    "        }\n",
    "    }\n",
    "    upload_model_response = clients['model'].upload_model(parent=parent, model=model)\n",
    "    print(\"Long running operation on uploading the model:\", upload_model_response.operation.name)\n",
    "    model_info = clients['model'].get_model(name=upload_model_response.result(timeout=180).model)\n",
    "\n",
    "    #Create an endpoint on Vertex AI to host the model\n",
    "    clients['endpoint'] = aiplatform.gapic.EndpointServiceClient(client_options=client_options)\n",
    "    create_endpoint_response = clients['endpoint'].create_endpoint(parent=parent, endpoint={\"display_name\": endpoint_name})\n",
    "    print(\"Long running operation on creating endpoint:\", create_endpoint_response.operation.name)\n",
    "    endpoint_info = clients['endpoint'].get_endpoint(name=create_endpoint_response.result(timeout=180).name)\n",
    "\n",
    "    #Deploy the model to the endpoint\n",
    "    dmodel = {\n",
    "            \"model\": model_info.name,\n",
    "            \"display_name\": 'deployed_'+model_name,\n",
    "            \"dedicated_resources\": {\n",
    "                \"min_replica_count\": 1,\n",
    "                \"max_replica_count\": 1,\n",
    "                \"machine_spec\": {\n",
    "                        \"machine_type\": deploy_compute,\n",
    "                        \"accelerator_count\": 0,\n",
    "                    }\n",
    "            }   \n",
    "    }\n",
    "\n",
    "    traffic = {\n",
    "        '0' : 100\n",
    "    }\n",
    "\n",
    "    deploy_model_response = clients['endpoint'].deploy_model(endpoint=endpoint_info.name, deployed_model=dmodel, traffic_split=traffic)\n",
    "    print(\"Long running operation on deploying the model:\", deploy_model_response.operation.name)\n",
    "    deploy_model_result = deploy_model_response.result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the Kubeflow Pipelines\n",
    "\n",
    "Now we have the necessary functions defined, we are now ready to create a workflow using Kubeflow Pipeline. The workflow implemented by the pipeline is defined using a Python based Domain Specific Language (DSL). \n",
    "The pipeline's DSL has been designed to avoid hardcoding any environment specific settings like file paths or connection strings. These settings are provided to the pipeline code through a set of environment variables.\n",
    "\n",
    "The pipeline performs the following steps - \n",
    "* Trains a Matrix Factorization model \n",
    "* Evaluates the trained Matrix Factorization model and if the Mean Square Error is less than threadshold, it will continue to the next step, otherwise, the pipeline will stop\n",
    "* Engineers new user factors feature with the Matrix Factorization model\n",
    "* Engineers new hotel factors feature with the Matrix Factorization model\n",
    "* Combines all the features selected (total_mobile_searches) and engineered (user factors and hotel factors) into a training dataset for the XGBoost classifier \n",
    "* Trains a XGBoost classifier\n",
    "* Evalutes the trained XGBoost model and if the ROC AUC score is more than threadshold, it will continue to the next step, otherwise, the pipeline will stop\n",
    "* Exports the XGBoost model to a Google Cloud Storage bucket\n",
    "* Deploys the XGBoost model from the Google Cloud Storage bucket to Google Cloud AI Platform for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import kfp.v2.dsl as dsl\n",
    "import kfp.v2.components as comp\n",
    "import time\n",
    "\n",
    "@dsl.pipeline(\n",
    "    name='hotel-recs-pipeline',\n",
    "    description='training pipeline for hotel recommendation prediction'\n",
    ")\n",
    "def training_pipeline():\n",
    "\n",
    "    import json\n",
    "    \n",
    "    #Minimum threshold for model metric to determine if model will be deployed to inference: 0.5 is a basically a coin toss with 50/50 chance\n",
    "    mf_msqe_threshold = 0.5\n",
    "    class_auc_threshold = 0.8\n",
    "    \n",
    "    #Defining function containers\n",
    "    ddlop = comp.func_to_container_op(run_bigquery_ddl, base_image=BASE_IMAGE, packages_to_install=['google-cloud-bigquery'])\n",
    "    \n",
    "    evaluate_mf_op = comp.func_to_container_op(evaluate_matrix_factorization_model, base_image=BASE_IMAGE, packages_to_install=['google-cloud-bigquery', 'google-cloud-bigquery-storage', 'pandas', 'pyarrow'], output_component_file='mf_eval.yaml')\n",
    "    \n",
    "    evaluate_class_op = comp.func_to_container_op(evaluate_class, base_image=BASE_IMAGE, packages_to_install=['google-cloud-bigquery','pandas', 'pyarrow'])\n",
    "    \n",
    "    export_bqml_model_op = comp.func_to_container_op(export_bqml_model, base_image=BASE_IMAGE, output_component_file='export_bqml.yaml')\n",
    "    \n",
    "    deploy_bqml_model_op = comp.func_to_container_op(deploy_bqml_model_vertexai, base_image=BASE_IMAGE, packages_to_install=['google-cloud-aiplatform'])\n",
    "\n",
    "        \n",
    "    #############################                                 \n",
    "    #Defining pipeline execution graph\n",
    "    dataset = BQ_DATASET_NAME\n",
    "    \n",
    "    #Train matrix factorization model\n",
    "    mf_model_output = train_matrix_factorization_model(ddlop, PROJECT_ID, dataset).set_display_name('train matrix factorization model')\n",
    "    mf_model_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'\n",
    "    mf_model = mf_model_output.outputs['created_table']\n",
    "    \n",
    "    #Evaluate matrix factorization model\n",
    "    mf_eval_output = evaluate_mf_op(PROJECT_ID, mf_model).set_display_name('evaluate matrix factorization model')\n",
    "    mf_eval_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'\n",
    "    \n",
    "    #mean squared quantization error \n",
    "    with dsl.Condition(mf_eval_output.outputs['msqe'] < mf_msqe_threshold):\n",
    "    \n",
    "        #Create features for Classification model\n",
    "        user_features_output = create_user_features(ddlop, PROJECT_ID, dataset, mf_model).set_display_name('create user factors features')\n",
    "        user_features = user_features_output.outputs['created_table']\n",
    "        user_features_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'\n",
    "        \n",
    "        hotel_features_output = create_hotel_features(ddlop, PROJECT_ID, dataset, mf_model).set_display_name('create hotel factors features')\n",
    "        hotel_features = hotel_features_output.outputs['created_table']\n",
    "        hotel_features_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'\n",
    "\n",
    "        total_features_output = combine_features(ddlop, PROJECT_ID, dataset, mf_model, hotel_features, user_features).set_display_name('combine all features')\n",
    "        total_features = total_features_output.outputs['created_table']\n",
    "        total_features_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'\n",
    "\n",
    "        #Train XGBoost model\n",
    "        class_model_output = train_xgboost_model(ddlop, PROJECT_ID, dataset, total_features).set_display_name('train XGBoost model')\n",
    "        class_model = class_model_output.outputs['created_table']\n",
    "        class_model_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'\n",
    "\n",
    "        #Evaluate XGBoost model\n",
    "        class_eval_output = evaluate_class_op(PROJECT_ID, dataset, class_model, total_features).set_display_name('evaluate XGBoost model')\n",
    "        class_eval_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'\n",
    "        \n",
    "        with dsl.Condition(class_eval_output.outputs['roc_auc'] > class_auc_threshold):\n",
    "            #Export model\n",
    "            export_destination_output = export_bqml_model_op(PROJECT_ID, class_model, MODEL_STORAGE).set_display_name('export XGBoost model')\n",
    "            export_destination_output.execution_options.caching_strategy.max_cache_staleness = 'P0D'\n",
    "            export_destination = export_destination_output.outputs['destination']\n",
    "            deploy_model = deploy_bqml_model_op(PROJECT_ID, REGION, class_model, ENDPOINT_NAME, MODEL_STORAGE, DEPLOY_IMAGE, DEPLOY_COMPUTE).set_display_name('Deploy XGBoost model')\n",
    "            deploy_model.execution_options.caching_strategy.max_cache_staleness = 'P0D'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submitting pipeline runs  \n",
    "\n",
    "You can trigger pipeline runs using an API from the KFP SDK or using KFP CLI. To submit the run using KFP CLI, execute the following commands. Notice how the pipeline's parameters are passed to the pipeline run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import kfp.v2 as kfp\n",
    "from kfp.v2 import compiler\n",
    "\n",
    "pipeline_func = training_pipeline\n",
    "compiler.Compiler().compile(pipeline_func=pipeline_func, \n",
    "                            package_path='hotel_rec_pipeline_job.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kfp.v2.google.client import AIPlatformClient\n",
    "\n",
    "api_client = AIPlatformClient(project_id=PROJECT_ID, region=REGION)\n",
    "\n",
    "response = api_client.create_run_from_job_spec(\n",
    "    job_spec_path='hotel_rec_pipeline_job.json', \n",
    "    enable_caching=False,\n",
    "    pipeline_root=PIPELINE_ROOT  # optional- use if want to override compile-time value\n",
    "    #parameter_values={'text': 'Hello world!'}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monitoring the run \n",
    "The Pipeline will take several hours to train two models, you can monitor the run using [Vertex AI Pipelins Console](https://console.cloud.google.com/vertex-ai/pipelines). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning up\n",
    "\n",
    "* Delete the Model in [Cloud AI Platform](https://console.cloud.google.com/ai-platform/models), note you may have to delete all versions first before deleting the model.\n",
    "* Delete the bucket or model content in [Google Cloud Storage](https://console.cloud.google.com/storage).\n",
    "* Delete the dataset in [BigQuery](https://console.cloud.google.com/bigquery), it will delete the models, tables and UDFs created in BigQuery.\n",
    "* Follow how-to guide to [delete Flex commitment](https://console.cloud.google.com/bigquery/docs/reservations-get-started#cleaning-up)\n",
    "* Delete the container from the [Google Container Registry](https://console.cloud.google.com/gcr/images)\n",
    "* Delete the [Vertex AI](https://console.cloud.google.com/vertex-ai), undeploy the model within Endpoint first, then delete the Endpoint and finally delete the Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "environment": {
   "name": "tf2-2-3-gpu.2-3.m59",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/tf2-2-3-gpu.2-3:m59"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: retail/recommendation-system/bqml-scann/.gitignore
================================================
.ipynb_checkpoints/
.DS_Store
.idea/
*.pyc
*.egg-info/
workspace/

================================================
FILE: retail/recommendation-system/bqml-scann/00_prep_bq_and_datastore.ipynb
================================================
{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"00_prep_bq_and_datastore.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"environment":{"name":"tf2-gpu.2-3.m61","type":"gcloud","uri":"gcr.io/deeplearning-platform-release/tf2-gpu.2-3:m61"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.9"}},"cells":[{"cell_type":"markdown","metadata":{"id":"KAdAvk8LquRK"},"source":["# Import the sample data into BigQuery and Datastore\n","\n","This notebook is the first of two notebooks that guide you through completing the prerequisites for running the [Real-time Item-to-item Recommendation with BigQuery ML Matrix Factorization and ScaNN](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/tree/master/retail/recommendation-system/bqml-scann) solution.\n","\n","Use this notebook to complete the following tasks:\n","\n","1. Importing the `playlist` table from the public BigQuery dataset to your BigQuery dataset.\n","1. Creating the `vw_item_groups` view that contains the item data used to compute item co-occurence.\n","1. Exporting song title and artist information to Datastore to make it available for lookup when making similar song recommendations. \n","\n","Before starting this notebook, you must [set up the GCP environment](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/tree/master/retail/recommendation-system/bqml-scann#set-up-the-gcp-environment).\n","\n","After completing this notebook, run the [00_prep_bq_procedures](00_prep_bq_procedures.ipynb) notebook to complete the solution prerequisites."]},{"cell_type":"markdown","metadata":{"id":"C9tnKiq4q6as"},"source":["## Setup\r\n","\r\n","Install the required Python packages, configure the environment variables, and authenticate your GCP account. "]},{"cell_type":"code","metadata":{"id":"Bm8pqhsarF9L"},"source":["!pip install -q -U apache-beam[gcp]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ns9KcpcW-dQx"},"source":["# Automatically restart kernel after installs\n","import IPython\n","app = IPython.Application.instance()\n","app.kernel.do_shutdown(True)  "],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xSNZnVZbEVO_"},"source":["### Import libraries"]},{"cell_type":"code","metadata":{"id":"EMtPGiyVtZTj"},"source":["import os\n","from datetime import datetime\n","import apache_beam as beam\n","from apache_beam.io.gcp.datastore.v1new.datastoreio import WriteToDatastore"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"bHAObQoaEfC2"},"source":["### Configure GCP environment settings\r\n","\r\n","Update the following variables to reflect the values for your GCP environment:\r\n","\r\n","+ `PROJECT_ID`: The ID of the Google Cloud project you are using to implement this solution.\r\n","+ `BUCKET`: The name of the Cloud Storage bucket you created to use with this solution. The `BUCKET` value should be just the bucket name, so `myBucket` rather than `gs://myBucket`.\r\n","+ `BQ_REGION`: The region to use for the BigQuery dataset.\r\n","+ `DF_REGION`: The region to use for the Dataflow job. Choose the same region that you used for the `BQ_REGION` variable to avoid issues around reading/writing in different locations."]},{"cell_type":"code","metadata":{"id":"81apxD89q6Co"},"source":["PROJECT_ID = 'yourProject' # Change to your project.\n","BUCKET = 'yourBucketName' # Change to the bucket you created.\n","BQ_REGION = 'yourBigQueryRegion' # Change to your BigQuery region.\n","DF_REGION = 'yourDataflowRegion' # Change to your Dataflow region.\n","BQ_DATASET_NAME = 'recommendations'\n","BQ_TABLE_NAME = 'playlist'\n","DS_KIND = 'song'\n","\n","!gcloud config set project $PROJECT_ID"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"kgjoyd3CrBQi"},"source":["### Authenticate your GCP account\n","This is required if you run the notebook in Colab. If you use an AI Platform notebook, you should already be authenticated."]},{"cell_type":"code","metadata":{"id":"hU_altC3pTmd"},"source":["try:\n","  from google.colab import auth\n","  auth.authenticate_user()\n","  print(\"Colab user is authenticated.\")\n","except: pass"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"4XaUBOKTkzKi"},"source":["## Copy the public playlist data into your BigQuery dataset"]},{"cell_type":"markdown","metadata":{"id":"lBlQ3RrinurU"},"source":["### Create the BigQuery dataset"]},{"cell_type":"code","metadata":{"id":"d5OHoo3Eowfh"},"source":["!bq mk --dataset \\\n","    --location={BQ_REGION} \\\n","    --project_id={PROJECT_ID} \\\n","    --headless=True \\\n","    {PROJECT_ID}:{BQ_DATASET_NAME}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"WLLs69WFEqTU"},"source":["### Define the Dataflow pipeline\r\n","\r\n","The pipeline selects songs where the `track_data_title` field isn't NULL and the `track_data_id` field is greater than 0."]},{"cell_type":"code","metadata":{"id":"cSwEWeWSkzSA"},"source":["def run_copy_bq_data_pipeline(args):\n","\n","  schema = 'list_Id:INT64, track_Id:INT64, track_title:STRING, track_artist:STRING'\n","\n","  query = '''\n","    SELECT \n","      id list_Id, \n","      tracks_data_id track_Id, \n","      tracks_data_title track_title,\n","      tracks_data_artist_name track_artist\n","    FROM `bigquery-samples.playlists.playlist`\n","    WHERE tracks_data_title IS NOT NULL AND tracks_data_id > 0\n","    GROUP BY list_Id, track_Id, track_title, track_artist;\n","  '''\n","\n","  pipeline_options = beam.options.pipeline_options.PipelineOptions(**args)\n","  with beam.Pipeline(options=pipeline_options) as pipeline:\n","\n","    _ = (\n","        pipeline\n","        | 'ReadFromBigQuery' >> beam.io.Read(beam.io.BigQuerySource(\n","            project=PROJECT_ID, query=query, use_standard_sql=True))\n","        | 'WriteToBigQuery' >> beam.io.WriteToBigQuery(\n","            table=BQ_TABLE_NAME, dataset=BQ_DATASET_NAME, project=PROJECT_ID,\n","            schema=schema, \n","            create_disposition='CREATE_IF_NEEDED',\n","            write_disposition='WRITE_TRUNCATE'\n","        )\n","    )\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"G-PAwjZmEwq8"},"source":["### Run the Dataflow pipeline\r\n","\r\n","This pipeline takes approximately 15 minutes to run."]},{"cell_type":"code","metadata":{"id":"gLvUGHUMnVDm"},"source":["DATASET = 'playlist'\n","RUNNER = 'DataflowRunner'\n","\n","job_name = f'copy-bigquery-{datetime.utcnow().strftime(\"%y%m%d%H%M%S\")}'\n","\n","args = {\n","    'job_name': job_name,\n","    'runner': RUNNER,\n","    'project': PROJECT_ID,\n","    'temp_location': f'gs://{BUCKET}/dataflow_tmp',\n","    'region': DF_REGION\n","}\n","\n","print(\"Pipeline args are set.\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"uTpFW5HmnYtq"},"source":["print(\"Running pipeline...\")\n","%time run_copy_bq_data_pipeline(args)\n","print(\"Pipeline is done.\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"EO6yUMUwuxr-"},"source":["## Create the `vw_item_groups` view\r\n","\r\n","Create the `recommendations.vw_item_groups` view to focus on song and playlist data.\r\n","\r\n","To adapt this view to your own data, you would need to map your item identifier, for example product SKU, to `item_Id`, and your context identifier, for example purchase order number, to `group_Id`."]},{"cell_type":"code","metadata":{"id":"z4Qya0Mtux6b"},"source":["%%bigquery  --project $PROJECT_ID\n","\n","CREATE OR REPLACE VIEW `recommendations.vw_item_groups`\n","AS\n","SELECT\n","  list_Id AS group_Id,\n","  track_Id AS item_Id\n","FROM  \n","  `recommendations.playlist` "],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Wr5mm4J3rehj"},"source":["## Export song information to Datastore\r\n","\r\n","Export data from the `track_title` and `artist` fields to Datastore."]},{"cell_type":"markdown","metadata":{"id":"HvBuPJTcresj"},"source":["### Define the Dataflow pipeline"]},{"cell_type":"code","metadata":{"id":"yOANhNR3q_Tc"},"source":["def create_entity(song_info, kind):\n","\n","  from apache_beam.io.gcp.datastore.v1new.types import Entity\n","  from apache_beam.io.gcp.datastore.v1new.types import Key\n","\n","  track_Id = song_info.pop(\"track_Id\")\n","  key = Key([kind, track_Id])\n","  song_entity = Entity(key)\n","  song_entity.set_properties(song_info)\n","  return song_entity\n","\n","def run_export_to_datatore_pipeline(args):\n","\n","    query = f'''\n","      SELECT  \n","        track_Id, \n","        MAX(track_title) track_title, \n","        MAX(track_artist) artist\n","      FROM \n","        `{BQ_DATASET_NAME}.{BQ_TABLE_NAME}`\n","      GROUP BY track_Id\n","    '''\n","\n","    pipeline_options = beam.options.pipeline_options.PipelineOptions(**args)\n","    with beam.Pipeline(options=pipeline_options) as pipeline:\n","\n","      _ = (\n","        pipeline\n","        | 'ReadFromBigQuery' >> beam.io.Read(beam.io.BigQuerySource(\n","            project=PROJECT_ID, query=query, use_standard_sql=True))\n","        | 'ConvertToDatastoreEntity' >> beam.Map(create_entity, DS_KIND)\n","        | 'WriteToDatastore' >> WriteToDatastore(project=PROJECT_ID)\n","      )\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Cs_Fyqu7rdsU"},"source":["### Run the Dataflow pipeline\r\n","\r\n","This pipeline takes approximately 15 minutes to run."]},{"cell_type":"code","metadata":{"id":"D0zcYCsyrdzH"},"source":["import os\n","from datetime import datetime\n","\n","DATASET = 'playlist'\n","RUNNER = 'DataflowRunner'\n","\n","job_name = f'load-datastore-{datetime.utcnow().strftime(\"%y%m%d%H%M%S\")}'\n","\n","args = {\n","    'job_name': job_name,\n","    'runner': RUNNER,\n","    'project': PROJECT_ID,\n","    'temp_location': f'gs://{BUCKET}/dataflow_tmp',\n","    'region': DF_REGION\n","}\n","\n","print(\"Pipeline args are set.\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"74x5kPvQsG7i"},"source":["print(\"Running pipeline...\")\n","%time run_export_to_datatore_pipeline(args)\n","print(\"Pipeline is done.\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"fhQ7HVnCBmqc"},"source":["After running the pipeline, you can view the song entries on the [Datastore Entities page](https://pantheon.corp.google.com/datastore/entities):\r\n","\r\n","\r\n","![song-datastore-data.png](data:image/png;base64,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zCCHV1dWxsbE7duxwc3ObOnWqr6/vxYsXDx06tHfvXpZlJ06c2FZpeuAvyGQy5efnE0K8vb1vOfPR39+/Q4cOLMvKn/lDhw79/PPPtra2r732WlBQUG1t7a5duxITE0tKSv72t7/R7ThZlrWzs3Nzc/v666+ff/75V155paKi4uTJk5cvX96+fbskSdXV1c8880yvXr3Kysp27NiRmpp66NAhV1fXdu3aEULq6+s3btwYHR3ds2fPp556yt7e/vz584cOHfr666/t7e0t1w7fLfrjzcaNGy9fvjx58uRevXoJgnDixImdO3cWFhbOnj07IiKClkxKSlq2bJlKpRo0aNDjjz8uSdLp06f379+vUCh69uwpV1hRUbF79+4dO3Z069Zt/vz5Li4uFy5c2LBhwy+//GJjY9OrV68JEyY8+eST0dHRqampPj4+ixYtUigU7du3p3PElEplYmKiIAhRUVHu7u7Xr18/depUQkKCt7f3pEmTaDStoKBgw4YNx48fHzZs2GOPPaZQKGJiYo4dO/bjjz8uWLDAz8+PEEJ/Ibhy5Up9ff3QoUP9/Pzk+VkAAPcIASwAAGhLJpPp3Llzv//+OyEkMTExMDBQpVL17NmzT58+o0aNsixJxzPyj+oymhPE3t7+gbUZ/ufU1ta+/PLLTz/9tL29vSAIPj4+H3300ZUrVyoqKmgAKzExcd26dd26dXvxxRfDwsJ4nu/WrRvHcVu2bNmxY8egQYNut56OZdmGhobDhw9nZmaKomg0GouLi2/cuFFfXz9+/PgnnnhCLhkbGxsXF2dvb//ee+85OzszDOPj46NWq9etW7dr164uXbqo1eoLFy4sW7Zs7ty5U6ZM8ff35zguNDS0vLz84MGDKSkp5eXlTk5OoaGhJSUlycnJtbW1fn5+lsPXRjIzM1etWmVvbz9x4kSafCcwMNDBweHLL79MSEh47LHHunfvrtfrU1JS6uvrp06d2qdPHxsbG39//27dus2cOTMuLm7AgAEIYMEDIwhCZWUlnUV1y8xKGo3GMlybkZGxevVqW1vbSZMmDR482MbGRhCEgICAJUuWZGVl7d2797XXXpPrMRgMM2bMePbZZ+3s7ARB8PX1/eGHH/R6fVlZ2cKFC8PDwx0cHIxGo0ql+uSTT65evVpWVkYDWAcOHNi9e3e3bt1ef/11Ly8vlmUDAgJYlt25c+fy5cvvJYBVUlKybdu2ixcvzp49e8iQIS4uLpIk+fr6arXa33///ciRI56enh06dDAajTt27OB5Pjg4OCoqysnJSZKkkJAQZ2fnI0eOyPtUmEym5OTkXbt2+fn5zZw508/Pj04j5Xl+9erVCQkJfn5+AQEBhJDTp08nJyfrdLqwsDB6Ls1mJUlSbW3t559/3rVrV5VKFRoa6uzsvGbNmoSEhLFjx9rY2EiS9Mcffxw9enTMmDGTJk2iO0j4+/uXl5enpqYeOHDAz89PXsRdVlb297//PTQ0VK1WIwEWALSW1t/aGQAA4M4VFxefOXOGEDJ37twePXrY2NhwHOfs7BwWFvbCCy9YlvTx8aE/mzeSkJBw9uzZyZMnt9ErAGsnCIKrq+uYMWOcnZ15nlepVB4eHiEhIZIklZeXm81mQkhCQgIdsPXu3ZuOtVQq1ZAhQ9q3b19dXZ2enn67yhmGEUUxMTFx1apVa9asWbt27ZEjR0pKSjQaTVBQkLzWqbq6OjMzs6KiYubMmS4uLnSM5+joGBIS0qVLl927d9MsWkFBQbm5uQsWLOjevTvP8wzDqFSqwYMH0yhtSUmJ/KTkDjZYTE1N1ev1vXv3HjhwIF2iyPN8ZGSkm5tbTU3NtWvXaCV0aaHZbKYzLGxtbQMDA3/88ceoqCh5mSHAg0H3N7CxsbmTkEdKSkplZWVQUFC/fv3op5fjuPbt20+ePLmmpiYjIyMzM1MuXFpaOmbMGAcHB47jlEqlp6dneHi4wWAICwsLDg52dHSkl1v37t3t7e2LiooaGhroifHx8ba2tkOGDOnUqRPNS0XTtHt4eFy9erW4uLjFL7aqqurEiRNBQUF9+vShIXKGYdq1azdixAhnZ+djx47RSz4rKys/P5/2SDqdjiaqd3FxiYyM1Ol0cgK+urq6a9eu6fX6cePGde3alWat0mq1Q4YMUavV6enppaWl8lPfMl1gfX396NGjw8LC6Bexo6Ojr6+vk5PTtWvX5DRemzZt6tKly6OPPirvf6rVakeNGlVTU5OXl1dZWUmrra+vHzFiREREhLOzs42NzZ8megcAuEMIhwMAQFuSB9JPP/20ZXZby0UiFM/zlqlPAO4Q3e/P8tOlUCjc3NxEUayvr6cDs5SUFFtb26qqqlOnTtGQFiGE5/mGhgaWZYuKim5XOc0yM2rUqODgYJpFvqKi4vjx4ykpKd9999348eOffPJJQgjNEq1SqWpqak6cOEFjTyzL5ufnsywrSVJlZaWtra1Wq9VqtTRbc1ZWVmlpqdlsLi8vp3l2aFPv3IULF+zt7cvLy8+dOye/KIVCoVarzWYzHc06Ozv7+fmlpKT861//unTp0qOPPhocHKzRaB555JG7ei6AVkHDH6Io/ml8lhCSnJxsZ2fn5eVFtziQRUZGlpaW1tbW6vV6eX8Ds9lsuSxRo9F4eHjU1dX5+/tbHlcoFJ6enleuXKGXW3l5eW1tLc/z5eXlCQkJNFrEMExFRYXZbNZqtYWFhS2L85rN5ry8PEEQQkJCGrXfwcGhT58+0dHRFRUVhBC9Xk+3EG00AZluGCq/UTSDmL29/c2bN0+ePEnbzzBMTU2Nq6trSUlJfX19800SBKHR9g4ajUan02VkZNC7N2/epPuiZmZm0g0Z6VOUlJSoVKr6+vry8nI6G1oURa1Wi4lXANDq0K0AAEBbamhoOH/+PCHEw8PDctOlpqOXkpKSX3/99XZbwvXq1atPnz73r53wkKHjZIZhWJatrKysrq5mGCY+Pn7fvn3yZ49hGHt7e0mSLGcuNCJJEsdxPXr0oFsTUKNGjVq3bt3GjRsPHjzYuXPn7t27l5SUlJSU2Nvbr1692nIYqVKpNBqNUqnU6/UdO3aUJCk5OXnZsmXXr1/X6XR0HgptZAte49GjR3U6XXJyclxcnOUF5eDgIAgCHRs7OjqOGzfObDYnJCTExMRs3brVZDINGzbsmWeeCQ4ObtnzArQMx3E6nY4QUllZaTQam0nuRsXFxXXs2LFRzIUQwjCMu7t7bW1teXm55fGmXyu3DJNZTk3KycmhF+z27ds3bdokl1coFLa2tjzP0+0aWqChoSEjI4PneWdn50YTlDiOc3V1pVv4mUymqqoqGsBqtJ63UeONRuOlS5fs7e03bdpUX18vP8rzvJ2dXVlZmTynrBnNxw2vXr2q1Wr1ev26dess+zGlUmljY2MwGKqqqu6wKgCAlkEACwAA2hLLsi4uLnl5eX/6n93MzMy33nrrdo8uXLgQASxoGXkeQXBwcJcuXRpNdJIkyd/fv/ka5FU8sgkTJvzrX//S6/Xyuj9CSH19PZ1p2HTkSadg3Lhx4/XXX+/cufPYsWPDw8NdXV1tbW3z8/O/+uqrFrwunudFUfTz86OzwywfEkVR3rasXbt2s2fPfvzxx5OTk3NycoqLiy9cuHD+/Pnp06cPHz78diFjgFbH87yrqyshpKSk5JYBrNL/x959B0Rx5//jn60sLCzLwi5dBGygCKKoWGPH3mv0orl0L6ZeEhM/ubtcLhc1xYtJjJ7R2KNijb0hRURRilRBRJC+vZfZnfn9MZ/vfvYHaBBRSPJ8/KWzs+957zJl5zWv9+utVDITgERGRvJ4PCYU2/LoIwhCp9NJJJLHnCKQ+H9RGBaLNWrUKCZt0/VVkiRbzpbYRiwWi4lbMZmbzTZqtVqZ1FE2m81EsdsSD2Kz2RRFjRkzxlmX3clutz+okF/bMZlx3t7eI0eOFIvFrl2iadrd3V0ikSBuBQBPFAJYAADQmQQCQe/evW/dulVWVta9e3embAdBECwWq1mFDn9//y+//LLV22mKopzjRAAeCU3TPj4+zFCXYcOGJSUldUizXC6Xz+fbbDaSJAmCEIlEIpGopKRkxowZzC16q06ePCkSiRISElasWCEWi5mFHh4edrv9V1NFWoqKiqqsrBw0aNCCBQsetI7JZDIajQKBIDo6Ojo6mqKoO3fuXL58+ejRo7/88kvv3r2joqLa+pkBHg+fzw8ICGCxWAUFBZMnT242ZtzhcGRlZa1atWr8+PHr1q3z9vaOjo4uLi52zfph6PV6i8Xi7e3d7uiSk0wmY6JgkyZN6t+//2O25so5kLmpqclqtTI1vBh2u726upr5NphaVDwejyTJhoYGZ+UpgiCaJUhyudyQkJDGxsZp06Y9oQtiaGiowWAICAiYNWsWM/1FS87RygAATwICWAAA0JmYOtYHDx7ct2/fqFGjhEIhs9xsNt+7d891zbCwsLfffrsTugi/a0xgqEePHnl5eQUFBSNGjPD09HS+2tDQYLfbQ0JCHt5Iy0BSQUEBm8329vZmxjf5+fmJxWJPT89Tp06tWLHCdeu1tbVeXl7MvXptbS2bzZZIJK6zat69e5fD4bTcBFN5/SG9io2NLSoqun37drP7Xq1Wq1KpmInt79y5c+TIkcjIyNmzZwuFQjab3atXL7FYfObMmfv375vN5od/cIAOxOFwunXrFhMTc+vWrdzcXH9/f9exdXK5PD8/PywsbMyYMUxJu/j4+Ozs7IqKitraWmdGIUEQKSkpYrHYy8srKCjoMbsklUqFQmFTU1NWVlbfvn2dj1gIgqiurvbw8GDGPLYDj8cLDAwUCoW5ubkTJkxwLYNVW1ubk5PTrVs35lwUHBzM5XKNRmNOTs6UKVOcq6lUKrPZ7Axjubm5RURE3LlzJy8vLywszPVhT2NjI5vNZiZVdGpHpCkkJITD4VRVVZWUlISGhrqelMrLywMCAloO5wQA6FgobQAAAJ3JOZP3rl27jh49Wlt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)"]},{"cell_type":"markdown","metadata":{"id":"y9AGcKZRfSc-"},"source":["## License\n","\n","Copyright 2020 Google LLC\n","\n","Licensed under the Apache License, Version 2.0 (the \"License\");\n","you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0\n","\n","Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n","\n","See the License for the specific language governing permissions and limitations under the License.\n","\n","**This is not an official Google product but sample code provided for an educational purpose**"]}]}

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FILE: retail/recommendation-system/bqml-scann/00_prep_bq_procedures.ipynb
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{"nbformat":4,"nbformat_minor":0,"metadata":{"environment":{"name":"tf2-gpu.2-3.m61","type":"gcloud","uri":"gcr.io/deeplearning-platform-release/tf2-gpu.2-3:m61"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.9"},"colab":{"name":"00_prep_bq_procedures.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true}},"cells":[{"cell_type":"markdown","metadata":{"id":"T7ya4ArX8p1h"},"source":["# Create BigQuery stored procedures\n","\n","This notebook is the second of two notebooks that guide you through completing the prerequisites for running the [Real-time Item-to-item Recommendation with BigQuery ML Matrix Factorization and ScaNN](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/tree/master/retail/recommendation-system/bqml-scann) solution.\n","\n","Use this notebook to create the following stored procedures that are needed by the solution:\n","\n","+ `sp_ComputePMI` - Computes [pointwise mutual information (PMI)](https://en.wikipedia.org/wiki/Pointwise_mutual_information) from item co-occurence data. This data is used by a matrix factorization model to learn item embeddings.\n","+ `sp_TrainItemMatchingModel` - Creates the `item_embedding_model` [matrix factorization](https://en.wikipedia.org/wiki/Matrix_factorization_(recommender_systems)) model. This model learns item embeddings based on the PMI data computed by `sp_ComputePMI`. \n","+ `sp_ExractEmbeddings` - Extracts the item embedding values from the `item_embedding_model` model, aggregates these values to produce a single embedding vector for each item, and stores these vectors in the `item_embeddings` table. The vector data is later exported to Cloud Storage to be used for item embedding lookup.\n","\n","Before starting this notebook, you must run the [00_prep_bq_and_datastore](00_prep_bq_and_datastore.ipynb) notebook to complete the first part of the prerequisites.\n","\n","After completing this notebook, you can run the solution either step-by-step or with a TFX pipeline:\n","\n","+ To start running the solution step-by-step, run the [01_train_bqml_mf_pmi](01_train_bqml_mf_pmi.ipynb) notebook to create item embeddings.\n","+ To run the solution by using a TFX pipeline, run the [tfx01_interactive](tfx01_interactive.ipynb) notebook to create the pipeline."]},{"cell_type":"markdown","metadata":{"id":"8XDNl5508p1q"},"source":["## Setup\r\n","\r\n","Install the required Python packages, configure the environment variables, and authenticate your GCP account."]},{"cell_type":"code","metadata":{"id":"ciAORQac8p1r"},"source":["!pip install -q -U google-cloud-bigquery pyarrow"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"dn791d8i8p1s"},"source":["### Import libraries"]},{"cell_type":"code","metadata":{"id":"XdHb5Au58p1t"},"source":["import os\n","from google.cloud import bigquery"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"9UFVH4xM8p1u"},"source":["### Configure GCP environment settings\r\n","\r\n","Update the following variables to reflect the values for your GCP environment:\r\n","\r\n","+ `PROJECT_ID`: The ID of the Google Cloud project you are using to implement this solution.\r\n","+ `BUCKET`: The name of the Cloud Storage bucket you created to use with this solution. The `BUCKET` value should be just the bucket name, so `myBucket` rather than `gs://myBucket`.\r\n"]},{"cell_type":"code","metadata":{"id":"gZDEzHun8p1v"},"source":["PROJECT_ID = 'yourProject' # Change to your project.\n","BUCKET = 'yourBucketName' # Change to the bucket you created.\n","SQL_SCRIPTS_DIR = 'sql_scripts'\n","BQ_DATASET_NAME = 'recommendations'\n","\n","!gcloud config set project $PROJECT_ID"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"kiCcnPua8p1v"},"source":["### Authenticate your GCP account\n","This is required if you run the notebook in Colab. If you use an AI Platform notebook, you should already be authenticated."]},{"cell_type":"code","metadata":{"id":"iSg7I1e38p1w"},"source":["try:\n","  from google.colab import auth\n","  auth.authenticate_user()\n","  print(\"Colab user is authenticated.\")\n","except: pass"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"gvIYbnii8p1x"},"source":["## Create the stored procedure dependencies"]},{"cell_type":"code","metadata":{"id":"NxvKwbdf8p1y"},"source":["%%bigquery --project $PROJECT_ID\n","\n","CREATE TABLE IF NOT EXISTS recommendations.item_cooc\n","AS SELECT 0 AS item1_Id, 0 AS item2_Id, 0 AS cooc, 0 AS pmi;"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qc5rdQap8p1z"},"source":["%%bigquery --project $PROJECT_ID\n","\n","CREATE MODEL IF NOT EXISTS recommendations.item_matching_model\n","OPTIONS(\n","    MODEL_TYPE='matrix_factorization', \n","    USER_COL='item1_Id', \n","    ITEM_COL='item2_Id',\n","    RATING_COL='score'\n",")\n","AS\n","SELECT 0 AS item1_Id, 0 AS item2_Id, 0 AS score;"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"-W2Rajhs8p1z"},"source":["## Create the stored procedures\r\n","\r\n","Run the scripts that create the BigQuery stored procedures."]},{"cell_type":"code","metadata":{"id":"Cp87zCIu8p10"},"source":["client = bigquery.Client(project=PROJECT_ID)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"VKXjLIqU8p11"},"source":["sql_scripts = dict()\n","\n","for script_file in [file for file in os.listdir(SQL_SCRIPTS_DIR) if '.sql' in file]:\n","  script_file_path = os.path.join(SQL_SCRIPTS_DIR, script_file)\n","  sql_script = open(script_file_path, 'r').read()\n","  sql_script = sql_script.replace('@DATASET_NAME', BQ_DATASET_NAME)\n","  sql_scripts[script_file] = sql_script"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"mHq4rJYf8p12"},"source":["for script_file in sql_scripts:\n","  print(f'Executing {script_file} script...')\n","  query = sql_scripts[script_file]\n","  query_job = client.query(query)\n","  result = query_job.result()\n","\n","print('Done.')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"1a3kqq5Q8p12"},"source":["### List the stored procedures"]},{"cell_type":"code","metadata":{"id":"Jm5crAur8p13"},"source":["query = f'SELECT * FROM {BQ_DATASET_NAME}.INFORMATION_SCHEMA.ROUTINES;'\n","query_job = client.query(query)\n","query_job.result().to_dataframe()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"qeJmhunE961u"},"source":["You can also verify that the stored procedures have been created by viewing them in the [BigQuery console](https://pantheon.corp.google.com/bigquery).\r\n"]},{"cell_type":"markdown","metadata":{"id":"mxd9Wvpi8p13"},"source":["## License\n","\n","Copyright 2020 Google LLC\n","\n","Licensed under the Apache License, Version 2.0 (the \"License\");\n","you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0\n","\n","Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n","\n","See the License for the specific language governing permissions and limitations under the License.\n","\n","**This is not an official Google product but sample code provided for an educational purpose**"]}]}

================================================
FILE: retail/recommendation-system/bqml-scann/01_train_bqml_mf_pmi.ipynb
================================================
{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"01_train_bqml_mf_pmi.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"environment":{"name":"tf2-2-3-gpu.2-3.m59","type":"gcloud","uri":"gcr.io/deeplearning-platform-release/tf2-2-3-gpu.2-3:m59"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.8"}},"cells":[{"cell_type":"markdown","metadata":{"id":"wlOPkKEQsVhe"},"source":["# Part 1: Learn item embeddings based on song co-occurrence\n","\n","This notebook is the first of five notebooks that guide you through running the [Real-time Item-to-item Recommendation with BigQuery ML Matrix Factorization and ScaNN](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/tree/master/retail/recommendation-system/bqml-scann) solution.\n","\n","Use this notebook to complete the following tasks:\n","\n","1. Explore the sample playlist data.\n","2. Compute [Pointwise mutual information (PMI)](https://en.wikipedia.org/wiki/Pointwise_mutual_information) that represents the co-occurence of songs on playlists. \n","3. Train a [matrix factorization](https://en.wikipedia.org/wiki/Matrix_factorization_(recommender_systems)) model using BigQuery ML to learn item embeddings based on the PMI data.\n","4. Explore the learned embeddings.\n","\n","Before starting this notebook, you must run the [00_prep_bq_procedures](00_prep_bq_procedures.ipynb) notebook to complete the solution prerequisites.\n","\n","After completing this notebook, run the [02_export_bqml_mf_embeddings](02_export_bqml_mf_embeddings.ipynb) notebook to process the item embedding data.\n"]},{"cell_type":"markdown","metadata":{"id":"yLOYZMkfsW58"},"source":["## Setup\r\n","\r\n","Import the required libraries, configure the environment variables, and authenticate your GCP account."]},{"cell_type":"markdown","metadata":{"id":"7zFymQUxs_kC"},"source":["### Import libraries"]},{"cell_type":"code","metadata":{"id":"LGGgQSQQs_sp"},"source":["from google.cloud import bigquery\n","from datetime import datetime\n","import matplotlib.pyplot as plt, seaborn as sns"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"aVulMWXysc8m"},"source":["### Configure GCP environment settings\r\n","\r\n","Update the `PROJECT_ID` variable to reflect the ID of the Google Cloud project you are using to implement this solution."]},{"cell_type":"code","metadata":{"id":"1_3BTCjAsdHm"},"source":["PROJECT_ID = 'yourProject' # Change to your project.\n","\n","!gcloud config set project $PROJECT_ID"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FoVPZUFxsdOm"},"source":["### Authenticate your GCP account\n","This is required if you run the notebook in Colab. If you use an AI Platform notebook, you should already be authenticated."]},{"cell_type":"code","metadata":{"id":"drQzoF07sdiN"},"source":["try:\n","  from google.colab import auth\n","  auth.authenticate_user()\n","  print(\"Colab user is authenticated.\")\n","except: pass"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"AeehVjyYbMak"},"source":["## Explore the sample data\r\n","\r\n","Use visualizations to explore the data in the `vw_item_groups` view that you created in the `00_prep_bq_and_datastore.ipynb` notebook.\r\n","\r\n","Import libraries for data visualization:"]},{"cell_type":"code","metadata":{"id":"hQr0UVFMBylx"},"source":["import matplotlib.pyplot as plt, seaborn as sns"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Tk3c9PybBylx"},"source":["Count the number of songs that occur in at least 15 groups:"]},{"cell_type":"code","metadata":{"id":"yBdEEFf0-Qy7"},"source":["%%bigquery  --project $PROJECT_ID\n","\n","CREATE OR REPLACE TABLE recommendations.valid_items\n","AS\n","SELECT \n","  item_Id, \n","  COUNT(group_Id) AS item_frequency\n","FROM recommendations.vw_item_groups\n","GROUP BY item_Id\n","HAVING item_frequency >= 15;\n","\n","SELECT COUNT(*) item_count FROM recommendations.valid_items;"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3Vcv-jqfByly"},"source":["Count the number of playlists that have between 2 and 100 items:"]},{"cell_type":"code","metadata":{"id":"NePi9cOjEAaA"},"source":["%%bigquery  --project $PROJECT_ID\n","\n","CREATE OR REPLACE TABLE recommendations.valid_groups\n","AS\n","SELECT \n","  group_Id, \n","  COUNT(item_Id) AS group_size\n","FROM recommendations.vw_item_groups\n","WHERE item_Id IN (SELECT item_Id FROM recommendations.valid_items)\n","GROUP BY group_Id\n","HAVING group_size BETWEEN 2 AND 100;\n","\n","SELECT COUNT(*) group_count FROM recommendations.valid_groups;"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Uy38zSdyBylz"},"source":["Count the number of records with valid songs and playlists:"]},{"cell_type":"code","metadata":{"id":"xyuWCHvnYnqI"},"source":["%%bigquery  --project $PROJECT_ID\n","\n","SELECT COUNT(*) record_count\n","FROM `recommendations.vw_item_groups`\n","WHERE item_Id IN (SELECT item_Id FROM recommendations.valid_items)\n","AND group_Id IN (SELECT group_Id FROM recommendations.valid_groups);"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"HcpeCnSfByl0"},"source":["Show the playlist size distribution:"]},{"cell_type":"code","metadata":{"id":"T3Vzdt0gctkq"},"source":["%%bigquery size_distribution --project $PROJECT_ID\n","\n","WITH group_sizes\n","AS\n","(\n","  SELECT \n","    group_Id, \n","    ML.BUCKETIZE(\n","      COUNT(item_Id), [10, 20, 30, 40, 50, 101])\n","     AS group_size\n","  FROM `recommendations.vw_item_groups`\n","  WHERE item_Id IN (SELECT item_Id FROM recommendations.valid_items)\n","  AND group_Id IN (SELECT group_Id FROM recommendations.valid_groups)\n","  GROUP BY group_Id\n",")\n","\n","SELECT \n","  CASE \n","    WHEN group_size = 'bin_1' THEN '[1 - 10]'\n","    WHEN group_size = 'bin_2' THEN '[10 - 20]'\n","    WHEN group_size = 'bin_3' THEN '[20 - 30]'\n","    WHEN group_size = 'bin_4' THEN '[30 - 40]'\n","    WHEN group_size = 'bin_5' THEN '[40 - 50]'\n","    ELSE '[50 - 100]'\n","  END AS group_size,\n","  CASE \n","    WHEN group_size = 'bin_1' THEN 1\n","    WHEN group_size = 'bin_2' THEN 2\n","    WHEN group_size = 'bin_3' THEN 3\n","    WHEN group_size = 'bin_4' THEN 4\n","    WHEN group_size = 'bin_5' THEN 5\n","    ELSE 6\n","  END AS bucket_Id,\n","  COUNT(group_Id) group_count\n","FROM group_sizes\n","GROUP BY group_size, bucket_Id\n","ORDER BY bucket_Id "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"diSmT8oMduma"},"source":["plt.figure(figsize=(20,5))\n","q = sns.barplot(x='group_size', y='group_count', data=size_distribution)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"P3gparz9Byl2"},"source":["Show the song occurrence distribution:"]},{"cell_type":"code","metadata":{"id":"KEyeUhGNeyfU"},"source":["%%bigquery occurrence_distribution --project $PROJECT_ID\n","\n","WITH item_frequency\n","AS\n","(\n","  SELECT \n","    Item_Id, \n","    ML.BUCKETIZE(\n","      COUNT(group_Id)\n","      , [15, 30, 50, 100, 200, 300, 400]) AS group_count\n","  FROM `recommendations.vw_item_groups`\n","  WHERE item_Id IN (SELECT item_Id FROM recommendations.valid_items)\n","  AND group_Id IN (SELECT group_Id FROM recommendations.valid_groups)\n","  GROUP BY Item_Id\n",")\n","\n","\n","SELECT \n","  CASE \n","    WHEN group_count = 'bin_1' THEN '[15 - 30]'\n","    WHEN group_count = 'bin_2' THEN '[30 - 50]'\n","    WHEN group_count = 'bin_3' THEN '[50 - 100]'\n","    WHEN group_count = 'bin_4' THEN '[100 - 200]'\n","    WHEN group_count = 'bin_5' THEN '[200 - 300]'\n","    WHEN group_count = 'bin_6' THEN '[300 - 400]'\n","    ELSE '[400+]'\n","  END AS group_count,\n","  CASE \n","    WHEN group_count = 'bin_1' THEN 1\n","    WHEN group_count = 'bin_2' THEN 2\n","    WHEN group_count = 'bin_3' THEN 3\n","    WHEN group_count = 'bin_4' THEN 4\n","    WHEN group_count = 'bin_5' THEN 5\n","    WHEN group_count = 'bin_6' THEN 6\n","    ELSE 7\n","  END AS bucket_Id,\n","  COUNT(Item_Id) item_count\n","FROM item_frequency\n","GROUP BY group_count, bucket_Id\n","ORDER BY bucket_Id "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Z-T1fZIDh_Nt"},"source":["plt.figure(figsize=(20, 5))\n","q = sns.barplot(x='group_count', y='item_count', data=occurrence_distribution)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"xU48zbXN1Mvq"},"source":["%%bigquery --project $PROJECT_ID\n","\n","DROP TABLE IF EXISTS recommendations.valid_items;"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"3jtzZV_L1yqv"},"source":["%%bigquery --project $PROJECT_ID\n","\n","DROP TABLE IF EXISTS recommendations.valid_groups;"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"1tTZYlTKtuhu"},"source":["## Compute song PMI data\r\n","\r\n","You run the [sp_ComputePMI](sql_scripts/sp_ComputePMI.sql) stored procedure to compute song PMI data. This PMI data is what you'll use to train the matrix factorization model in the next section.\r\n","\r\n","This stored procedure accepts the following parameters:\r\n","\r\n","+ `min_item_frequency` — Sets the minimum number of times that a song must appear on playlists.\r\n","+ `max_group_size` — Sets the maximum number of songs that a playlist can contain.\r\n","\r\n","These parameters are used together to select records where the song occurs on a number of playlists equal to or greater than the `min_item_frequency` value and the playlist contains a number of songs between 2 and the `max_group_size` value. These are the records that get processed to make the training dataset.\r\n","\r\n","The stored procedure works as follows:\r\n","\r\n","1. Selects a `valid_item_groups1 table and populates it with records from the\r\n","   `vw_item_groups` view that meet the following criteria:\r\n","\r\n","    + The song occurs on a number of playlists equal to or greater than the\r\n","      `min_item_frequency` value\r\n","    + The playlist contains a number of songs between 2 and the `max_group_size`\r\n","      value.\r\n","\r\n","1. Creates the `item_cooc` table and populates it with co-occurrence data that\r\n","   identifies pairs of songs that occur on the same playlist. It does this by:\r\n","\r\n","    1. Self-joining the `valid_item_groups` table on the `group_id` column.\r\n","    1. Setting the `cooc` column to 1.\r\n","    1. Summing the `cooc` column for the `item1_Id` and `item2_Id` columns.\r\n","\r\n","1. Creates an `item_frequency` table and populates it with data that identifies\r\n","   how many playlists each song occurs in.\r\n","1. Recreates the `item_cooc` table to include the following record sets:\r\n","\r\n","    + The `item1_Id`, `item2_Id`, and `cooc` data from the original `item_cooc`\r\n","      table. The PMI values calculated from these song pairs lets the solution\r\n","      calculate the embeddings for the rows in the feedback matrix.\r\n","\r\n","     <img src=\"figures/feedback-matrix-rows.png\" alt=\"Embedding matrix that shows the matrix rows calculated by this step.\" style=\"width: 400px;\"/>\r\n","\r\n","    + The same data as in the previous bullet, but with the `item1_Id` data\r\n","      written to the `item2_Id` column and the `item2_Id` data written to the\r\n","      `item1_Id` column. This data provides the mirror values of the initial\r\n","      entities in the feedback matrix. The PMI values calculated from these\r\n","      song pairs lets the solution calculate the embeddings for the columns in\r\n","      the feedback matrix.\r\n","\r\n","     <img src=\"figures/feedback-matrix-columns.png\" alt=\"Embedding matrix that shows the matrix columns calculated by this step.\" style=\"width: 400px;\"/>\r\n","\r\n","    + The data from the `item_frequency` table. The `item_Id` data is written\r\n","      to both the `item1_Id` and `item2_Id` columns and the `frequency` data is\r\n","      written to the `cooc` column. This data provides the diagonal entries of\r\n","      the feedback matrix. The PMI values calculated from these song pairs lets\r\n","      the solution calculate the embeddings for the diagonals in the feedback\r\n","      matrix.\r\n","\r\n","     <img src=\"figures/feedback-matrix-diagonals.png\" alt=\"Embedding matrix that shows the matrix diagonals calculated by this step.\" style=\"width: 400px;\"/>\r\n","\r\n","1. Computes the PMI for item pairs in the `item_cooc` table, then recreates the\r\n","   `item_cooc` table to include this data in the `pmi` column."]},{"cell_type":"markdown","metadata":{"id":"Id6-wKlpdD09"},"source":["### Run the `sp_ComputePMI` stored procedure"]},{"cell_type":"code","metadata":{"id":"Gw8QwUhaF15_"},"source":["%%bigquery --project $PROJECT_ID\n","\n","DECLARE min_item_frequency INT64;\n","DECLARE max_group_size INT64;\n","\n","SET min_item_frequency = 15;\n","SET max_group_size = 100;\n","\n","CALL recommendations.sp_ComputePMI(min_item_frequency, max_group_size);"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ve4DjWj4dRqU"},"source":["### View the song PMI data"]},{"cell_type":"code","metadata":{"id":"2hwRjMOqGCl9"},"source":["%%bigquery --project $PROJECT_ID\n","\n","SELECT \n","  a.item1_Id, \n","  a.item2_Id, \n","  b.frequency AS freq1,\n","  c.frequency AS freq2,\n","  a.cooc,\n","  a.pmi,\n","  a.cooc * a.pmi AS score\n","FROM recommendations.item_cooc a\n","JOIN recommendations.item_frequency b\n","ON a.item1_Id = b.item_Id\n","JOIN recommendations.item_frequency c \n","ON a.item2_Id = c.item_Id\n","WHERE a.item1_Id != a.item2_Id\n","ORDER BY score DESC\n","LIMIT 10;"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"DbPg-dllGjth"},"source":["%%bigquery --project $PROJECT_ID\n","\n","SELECT COUNT(*) records_count \n","FROM recommendations.item_cooc"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"o2VcN4iwc8e6"},"source":["## Train the BigQuery ML matrix factorization model\n","\n","You run the [sp_TrainItemMatchingModel](sql_scripts/sp_TrainItemMatchingModel.sql) stored procedure to train the `item_matching_model` matrix factorization model on the song PMI data. The model builds a feedback matrix, which in turn is used to calculate item embeddings for the songs. For more information about how this process works, see [Understanding item embeddings](https://cloud.google.com/solutions/real-time-item-matching#understanding_item_embeddings).\n","\n","This stored procedure accepts the `dimensions` parameter, which provides the value for the [NUM_FACTORS](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-matrix-factorization#num_factors) parameter of the [CREATE MODEL](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-matrix-factorization) statement. The `NUM_FACTORS` parameter lets you set the number of latent factors to use in the model. Higher values for this parameter can increase model performance, but will also increase the time needed to train the model. Using the default `dimensions` value of 50, the model takes around 120 minutes to train.\n"]},{"cell_type":"markdown","metadata":{"id":"XnMPdHARBymC"},"source":["### Run the `sp_TrainItemMatchingModel` stored procedure\r\n","\r\n","After the `item_matching_model model` is created successfully, you can use the the [BigQuery console](https://console.cloud.google.com/bigquery) to investigate the loss through the training iterations, and also see the final evaluation metrics."]},{"cell_type":"code","metadata":{"id":"W8SDQi3SiBhW"},"source":["%%bigquery --project $PROJECT_ID\n","\n","DECLARE dimensions INT64 DEFAULT 50;\n","CALL recommendations.sp_TrainItemMatchingModel(dimensions)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OR9mOYivM9Qg"},"source":["### Explore the trained embeddings"]},{"cell_type":"code","metadata":{"id":"0a_hI3-sNA_Q"},"source":["%%bigquery song_embeddings --project $PROJECT_ID\n","\n","SELECT \n","  feature,\n","  processed_input,\n","  factor_weights,\n","  intercept\n","FROM\n","  ML.WEIGHTS(MODEL recommendations.item_matching_model)\n","WHERE \n","  feature IN ('2114406',\n","              '2114402',\n","              '2120788',\n","              '2120786',\n","              '1086322',\n","              '3129954',\n","              '53448',\n","              '887688',\n","              '562487',\n","              '833391',\n","              '1098069',\n","              '910683',\n","              '1579481',\n","              '2675403',\n","              '2954929',\n","              '625169')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Ug9us_TbGjYy"},"source":["songs = {\n","    '2114406': 'Metallica: Nothing Else Matters',\n","    '2114402': 'Metallica: The Unforgiven',\n","    '2120788': 'Limp Bizkit: My Way',\n","    '2120786': 'Limp Bizkit: My Generation',\n","    '1086322': 'Jacques Brel: Ne Me Quitte Pas',\n","    '3129954': 'Édith Piaf: Non, Je Ne Regrette Rien',\n","    '53448': 'France Gall: Ella, Elle l\\'a',\n","    '887688': 'Enrique Iglesias: Tired Of Being Sorry',\n","    '562487': 'Shakira: Hips Don\\'t Lie',\n","    '833391': 'Ricky Martin: Livin\\' la Vida Loca',\n","    '1098069': 'Snoop Dogg: Drop It Like It\\'s Hot',\n","    '910683': '2Pac: California Love',\n","    '1579481': 'Dr. Dre: The Next Episode',\n","    '2675403': 'Eminem: Lose Yourself',\n","    '2954929': 'Black Sabbath: Iron Man',\n","    '625169': 'Black Sabbath: Paranoid',\n","}"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Zfv3Rk1DQuwh"},"source":["import numpy as np\n","from sklearn.metrics.pairwise import cosine_similarity\n","\n","def process_results(results):\n","  items = list(results['feature'].unique())\n","  item_embeddings = dict()\n","  for item in items:\n","    emebedding = [0.0] * 100\n","    embedding_pair = results[results['feature'] == item]\n","\n","    for _, row in embedding_pair.iterrows():\n","      factor_weights = list(row['factor_weights'])\n","      for _, element in enumerate(factor_weights):\n","        emebedding[element['factor'] - 1] += element['weight']\n","\n","    item_embeddings[item] = emebedding\n","    \n","  return item_embeddings\n","\n","item_embeddings = process_results(song_embeddings)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"WdkYwCnRSTVe"},"source":["item_ids = list(item_embeddings.keys())\n","for idx1 in range(0, len(item_ids) - 1):\n","  item1_Id = item_ids[idx1]\n","  title1 = songs[item1_Id]\n","  print(title1)\n","  print(\"==================\")\n","  embedding1 = np.array(item_embeddings[item1_Id])\n","  similar_items = []\n","  for idx2 in range(len(item_ids)):\n","    item2_Id = item_ids[idx2]\n","    title2 = songs[item2_Id]\n","    embedding2 = np.array(item_embeddings[item2_Id])\n","    similarity = round(cosine_similarity([embedding1], [embedding2])[0][0], 5)\n","    similar_items.append((title2, similarity))\n","  \n","  similar_items = sorted(similar_items, key=lambda item: item[1], reverse=True)\n","  for element in similar_items[1:]:\n","    print(f\"- {element[0]}' = {element[1]}\")\n","  print()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"GEpzwhG8f_cG"},"source":["## License\n","\n","Copyright 2020 Google LLC\n","\n","Licensed under the Apache License, Version 2.0 (the \"License\");\n","you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0\n","\n","Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n","\n","See the License for the specific language governing permissions and limitations under the License.\n","\n","**This is not an official Google product but sample code provided for an educational purpose**"]}]}

================================================
FILE: retail/recommendation-system/bqml-scann/02_export_bqml_mf_embeddings.ipynb
================================================
{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"02_export_bqml_mf_embeddings.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"environment":{"name":"tf2-2-3-gpu.2-3.m59","type":"gcloud","uri":"gcr.io/deeplearning-platform-release/tf2-2-3-gpu.2-3:m59"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.8"}},"cells":[{"cell_type":"markdown","metadata":{"id":"dkgce5cdOcW7"},"source":["# Part 2: Process the item embedding data in BigQuery and export it to Cloud Storage\n","\n","This notebook is the second of five notebooks that guide you through running the [Real-time Item-to-item Recommendation with BigQuery ML Matrix Factorization and ScaNN](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/tree/master/retail/recommendation-system/bqml-scann) solution.\n","\n","Use this notebook to complete the following tasks:\n","\n","1. Process the song embeddings data in BigQuery to generate a single embedding vector for each song.\n","1. Use a Dataflow pipeline to write the embedding vector data to CSV files and export the files to a Cloud Storage bucket. \n","\n","Before starting this notebook, you must run the [01_train_bqml_mf_pmi](01_train_bqml_mf_pmi.ipynb) notebook to calculate item PMI data and then train a matrix factorization model with it.\n","\n","After completing this notebook, run the [03_create_embedding_lookup_model](03_create_embedding_lookup_model.ipynb) notebook to create a model to serve the item embedding data.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"SW1RHsqGPNzE"},"source":["## Setup\r\n","\r\n","Import the required libraries, configure the environment variables, and authenticate your GCP account.\r\n","\r\n"]},{"cell_type":"code","metadata":{"id":"Mp6ETYF2R-0q"},"source":["!pip install -U -q apache-beam[gcp]"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"zdSKSzqvR_qY"},"source":["### Import libraries"]},{"cell_type":"code","metadata":{"id":"OcUKzLnuR_wa"},"source":["import os\n","import numpy as np\n","import tensorflow.io as tf_io\n","import apache_beam as beam\n","from datetime import datetime"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"22rDpO3JPcy9"},"source":["### Configure GCP environment settings\r\n","\r\n","Update the following variables to reflect the values for your GCP environment:\r\n","\r\n","+ `PROJECT_ID`: The ID of the Google Cloud project you are using to implement this solution.\r\n","+ `BUCKET`: The name of the Cloud Storage bucket you created to use with this solution. The `BUCKET` value should be just the bucket name, so `myBucket` rather than `gs://myBucket`.\r\n","+ `REGION`: The region to use for the Dataflow job."]},{"cell_type":"code","metadata":{"id":"Nyx4vEd7Oa9I"},"source":["PROJECT_ID = 'yourProject' # Change to your project.\n","BUCKET = 'yourBucketName' # Change to the bucket you created.\n","REGION = 'yourDataflowRegion' # Change to your Dataflow region.\n","BQ_DATASET_NAME = 'recommendations'\n","\n","!gcloud config set project $PROJECT_ID"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3d89ZwydPhQX"},"source":["### Authenticate your GCP account\n","This is required if you run the notebook in Colab. If you use an AI Platform notebook, you should already be authenticated."]},{"cell_type":"code","metadata":{"id":"6ICvdRicPhl8"},"source":["try:\n","  from google.colab import auth\n","  auth.authenticate_user()\n","  print(\"Colab user is authenticated.\")\n","except: pass"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"R1gmEmHbSaQD"},"source":["## Process the item embeddings data\r\n","\r\n","You run the [sp_ExractEmbeddings](sql_scripts/sp_ExractEmbeddings.sql) stored procedure to process the item embeddings data and write the results to the `item_embeddings` table.\r\n","\r\n","This stored procedure works as follows:\r\n","\r\n","1. Uses the [ML.WEIGHTS](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-weights) function to extract the item embedding matrices from the `item_matching_model` model.\r\n","1. Aggregates these matrices to generate a single embedding vector for each item.\r\n","\r\n","    Because BigQuery ML matrix factorization models are designed for user-item recommendation use cases, they generate two embedding matrices, one for users, and the other of items. However, in this use case, both embedding matrices represent items, but in different axes of the feedback matrix. For more information about how the feedback matrix is calculated, see [Understanding item embeddings](https://cloud.google.com/solutions/real-time-item-matching#understanding_item_embeddings).\r\n"]},{"cell_type":"markdown","metadata":{"id":"utkyuwJUyTlb"},"source":["### Run the `sp_ExractEmbeddings` stored procedure"]},{"cell_type":"code","metadata":{"id":"DK0olptba8qi"},"source":["%%bigquery --project $PROJECT_ID\n","\n","CALL recommendations.sp_ExractEmbeddings() "],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0UvHD7BJ8Gk0"},"source":["Get a count of the records in the `item_embeddings` table:"]},{"cell_type":"code","metadata":{"id":"pQsJenNFzVJ7"},"source":["%%bigquery --project $PROJECT_ID\n","\n","SELECT COUNT(*) embedding_count\n","FROM recommendations.item_embeddings;"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"sx8JNJbA8PxC"},"source":["See a sample of the data in the `item_embeddings` table:"]},{"cell_type":"code","metadata":{"id":"Y4kTGcaRzVJ7"},"source":["%%bigquery --project $PROJECT_ID\n","\n","SELECT *\n","FROM recommendations.item_embeddings\n","LIMIT 5;"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"i3LKaxlNSkrv"},"source":["## Export the item embedding vector data\n","\n","Export the item embedding data to Cloud Storage by using a Dataflow pipeline. This pipeline does the following:\n","\n","1. Reads the item embedding records from the `item_embeddings` table in BigQuery.\n","1. Writes each item embedding record to a CSV file.\n","1. Writes the item embedding CSV files to a Cloud Storage bucket.\n","\n","The pipeline in implemented in the [embeddings_exporter/pipeline.py](embeddings_exporter/pipeline.py) module."]},{"cell_type":"markdown","metadata":{"id":"G8HLFGGl5oac"},"source":["### Configure the pipeline variables\r\n","\r\n","Configure the variables needed by the pipeline:"]},{"cell_type":"code","metadata":{"id":"2ZKaoBwnSk6U"},"source":["runner = 'DataflowRunner'\n","timestamp = datetime.utcnow().strftime('%y%m%d%H%M%S')\n","job_name = f'ks-bqml-export-embeddings-{timestamp}'\n","bq_dataset_name = BQ_DATASET_NAME\n","embeddings_table_name = 'item_embeddings'\n","output_dir = f'gs://{BUCKET}/bqml/item_embeddings'\n","project = PROJECT_ID\n","temp_location = os.path.join(output_dir, 'tmp')\n","region = REGION\n","\n","print(f'runner: {runner}')\n","print(f'job_name: {job_name}')\n","print(f'bq_dataset_name: {bq_dataset_name}')\n","print(f'embeddings_table_name: {embeddings_table_name}')\n","print(f'output_dir: {output_dir}')\n","print(f'project: {project}')\n","print(f'temp_location: {temp_location}')\n","print(f'region: {region}')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"OyiIh-ATzVJ8"},"source":["try: os.chdir(os.path.join(os.getcwd(), 'embeddings_exporter'))\n","except: pass"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"AHxODaoFzVJ8"},"source":["### Run the pipeline"]},{"cell_type":"markdown","metadata":{"id":"OBarPrE_-LJr"},"source":["It takes about 5 minutes to run the pipeline. You can see the graph for the running pipeline in the [Dataflow Console](https://console.cloud.google.com/dataflow/jobs)."]},{"cell_type":"code","metadata":{"id":"WngoWnt2zVJ9"},"source":["if tf_io.gfile.exists(output_dir):\n","  print(\"Removing {} contents...\".format(output_dir))\n","  tf_io.gfile.rmtree(output_dir)\n","\n","print(\"Creating output: {}\".format(output_dir))\n","tf_io.gfile.makedirs(output_dir)\n","\n","!python runner.py \\\n","  --runner={runner} \\\n","  --job_name={job_name} \\\n","  --bq_dataset_name={bq_dataset_name} \\\n","  --embeddings_table_name={embeddings_table_name} \\\n","  --output_dir={output_dir} \\\n","  --project={project} \\\n","  --temp_location={temp_location} \\\n","  --region={region}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PLXGq4CA_Oz0"},"source":["### List the CSV files that were written to Cloud Storage"]},{"cell_type":"code","metadata":{"id":"Ee89jHK5zVJ9"},"source":["!gcloud storage ls {output_dir}/embeddings-*.csv"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Fp1bOyVCgBnH"},"source":["## License\n","\n","Copyright 2020 Google LLC\n","\n","Licensed under the Apache License, Version 2.0 (the \"License\");\n","you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0\n","\n","Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n","\n","See the License for the specific language governing permissions and limitations under the License.\n","\n","**This is not an official Google product but sample code provided for an educational purpose**"]}]}

================================================
FILE: retail/recommendation-system/bqml-scann/03_create_embedding_lookup_model.ipynb
================================================
{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"03_create_embedding_lookup_model.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"environment":{"name":"tf2-2-3-gpu.2-3.m59","type":"gcloud","uri":"gcr.io/deeplearning-platform-release/tf2-2-3-gpu.2-3:m59"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.8"}},"cells":[{"cell_type":"markdown","metadata":{"id":"2WVKxCwTsFVK"},"source":["# Part 3: Create a model to serve the item embedding data\n","\n","This notebook is the third of five notebooks that guide you through running the [Real-time Item-to-item Recommendation with BigQuery ML Matrix Factorization and ScaNN](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/tree/master/retail/recommendation-system/bqml-scann) solution.\n","\n","Use this notebook to wrap the item embeddings data in a Keras model that can act as an item-embedding lookup, then export the model as a SavedModel.\n","\n","Before starting this notebook, you must run the [02_export_bqml_mf_embeddings](02_export_bqml_mf_embeddings.ipynb) notebook to process the item embeddings data and export it to Cloud Storage.\n","\n","After completing this notebook, run the [04_build_embeddings_scann](04_build_embeddings_scann.ipynb) notebook to create an approximate nearest neighbor index for the item embeddings.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"vLLtPpTQSQM-"},"source":["## Setup\r\n","\r\n","Import the required libraries, configure the environment variables, and authenticate your GCP account."]},{"cell_type":"code","metadata":{"id":"ZlnTyUeAdfnO"},"source":["!pip install -q -U pip\n","!pip install -q tensorflow==2.2.0\n","!pip install -q -U google-auth google-api-python-client google-api-core"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"lq0qelfbSSnR"},"source":["### Import libraries"]},{"cell_type":"code","metadata":{"id":"bup8vvpRSWg2"},"source":["import os\n","import tensorflow as tf\n","import numpy as np\n","print(f'Tensorflow version: {tf.__version__}')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Ty3pxeh3Sej9"},"source":["### Configure GCP environment settings\r\n","\r\n","Update the following variables to reflect the values for your GCP environment:\r\n","\r\n","+ `PROJECT_ID`: The ID of the Google Cloud project you are using to implement this solution.\r\n","+ `BUCKET`: The name of the Cloud Storage bucket you created to use with this solution. The `BUCKET` value should be just the bucket name, so `myBucket` rather than `gs://myBucket`."]},{"cell_type":"code","metadata":{"id":"Yx83a_PasCBa"},"source":["PROJECT_ID = 'yourProject' # Change to your project.\n","BUCKET = 'yourBucketName' # Change to the bucket you created.\n","EMBEDDING_FILES_PATH = f'gs://{BUCKET}/bqml/item_embeddings/embeddings-*'\n","MODEL_OUTPUT_DIR = f'gs://{BUCKET}/bqml/embedding_lookup_model'\n","\n","!gcloud config set project $PROJECT_ID"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"My7F5vUCShdv"},"source":["### Authenticate your GCP account\n","This is required if you run the notebook in Colab. If you use an AI Platform notebook, you should already be authenticated."]},{"cell_type":"code","metadata":{"id":"PZAUnfyFShls"},"source":["try:\n","  from google.colab import auth\n","  auth.authenticate_user()\n","  print(\"Colab user is authenticated.\")\n","except: pass"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"hCgB7fjuV9fP"},"source":["## Create the embedding lookup model\r\n","\r\n","You use the `EmbeddingLookup` class to create the item embedding lookup model. The `EmbeddingLookup` class inherits from [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model), and is implemented in the\r\n","[lookup_creator.py](embeddings_lookup/lookup_creator.py)\r\n","module.\r\n","\r\n","The `EmbeddingLookup `class works as follows:\r\n","\r\n","1. Accepts the `embedding_files_prefix` variable in the class constructor. This variable points to the Cloud Storage location of the CSV files containing the item embedding data.     \r\n","1. Reads and parses the item embedding CSV files.\r\n","1. Populates the `vocabulary` and `embeddings` class variables. `vocabulary` is an array of item IDs, while `embeddings` is a Numpy array with the shape (*number of embeddings*, *embedding dimensions*). \r\n","1. Appends the `oov_embedding` variable to the `embeddings` variable. The `oov_embedding` variable value is all zeros, and it represents the out of vocabulary (OOV) embedding vector. The `oov_embedding` variable is used when an invalid (\"out of vocabulary\", or OOV) item ID is submitted, in which case an embedding vector of zeros is returned.\r\n","1. Writes the `vocabulary` value to a file, one array element per line, so it can be used as a model asset by the SavedModel.\r\n","1. Uses `token_to_idx`, a `tf.lookup.StaticHashTable` object, to map the\r\n","   item ID to the index of the embedding vector in the `embeddings` Numpy array.\r\n","1. Accepts a list of strings with the `__call__` method of the model. Each string represents the item ID(s) for which the embeddings are to be retrieved. If the input list contains _N_ strings, then _N_ embedding vectors are returned. \r\n","\r\n","    Note that each string in the input list may contain one or more space-separated item IDs. If multiple item IDs are present, the embedding   vectors of these item IDs are retrieved and _combined_ (by averaging)   into a single embedding vector. This makes it possible to fetch an embedding vector representing a set of items (like a playlist) rather than just a single item."]},{"cell_type":"markdown","metadata":{"id":"x-zb5lLRKUbr"},"source":["### Clear the model export directory"]},{"cell_type":"code","metadata":{"id":"koSO5kd7V9fP"},"source":["if tf.io.gfile.exists(MODEL_OUTPUT_DIR):\n","  print(\"Removing {} contents...\".format(MODEL_OUTPUT_DIR))\n","  tf.io.gfile.rmtree(MODEL_OUTPUT_DIR)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"vYnm0eqkK2kx"},"source":["### Create the model and export the SavedModel file\r\n","\r\n","Call the `export_saved_model` method, which uses the `EmbeddingLookup` class to create the model and then exports the resulting SavedModel file:"]},{"cell_type":"code","metadata":{"id":"IW1amfSCYMn5"},"source":["from embeddings_lookup import lookup_creator\n","lookup_creator.export_saved_model(EMBEDDING_FILES_PATH, MODEL_OUTPUT_DIR)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0MjA6nlUgAM3"},"source":["Inspect the exported SavedModel using the `saved_model_cli` command line tool:\r\n"]},{"cell_type":"code","metadata":{"id":"3Y1o5lVCZqbY"},"source":["!saved_model_cli show --dir {MODEL_OUTPUT_DIR} --tag_set serve --signature_def serving_default"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"q2waV_yOOluG"},"source":["### Test the SavedModel file"]},{"cell_type":"markdown","metadata":{"id":"yX9jAKkkgbyE"},"source":["Test the SavedModel by loading it and then calling it with input item IDs:\r\n"]},{"cell_type":"code","metadata":{"id":"serXfA5jfy0h"},"source":["loaded_model = tf.saved_model.load(MODEL_OUTPUT_DIR)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"oVveXWDIqE1V"},"source":["input_items = ['2114406', '2114402 2120788', 'abc123']\n","output = loaded_model(input_items)\n","print(f'Embeddings retrieved: {output.shape}')\n","for idx, embedding in enumerate(output):\n","  print(f'{input_items[idx]}: {embedding[:5]}')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"4Azup3yqgjnJ"},"source":["The output shows the output embedding vector (the first five elements of each vector) for each input item. Note the following:\r\n","\r\n","+ The second entry in the input list contains two item IDs, `2114402` and `2120788`. The returned vector is the average of the embeddings of these two items.\r\n","+ The third entry in the input list, `abc123`, is an invalid item ID, so the returned embedding vector contains zeros.\r\n"]},{"cell_type":"markdown","metadata":{"id":"2zkAH5zH5n4g"},"source":["## License\n","\n","Copyright 2020 Google LLC\n","\n","Licensed under the Apache License, Version 2.0 (the \"License\");\n","you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0\n","\n","Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n","\n","See the License for the specific language governing permissions and limitations under the License.\n","\n","**This is not an official Google product but sample code provided for an educational purpose**"]}]}

================================================
FILE: retail/recommendation-system/bqml-scann/04_build_embeddings_scann.ipynb
================================================
{"nbformat":4,"nbformat_minor":0,"metadata":{"environment":{"name":"tf2-2-3-gpu.2-3.m59","type":"gcloud","uri":"gcr.io/deeplearning-platform-release/tf2-2-3-gpu.2-3:m59"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.8"},"colab":{"name":"04_build_embeddings_scann.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true}},"cells":[{"cell_type":"markdown","metadata":{"id":"Ai1o-fGapfNV"},"source":["# Part 4: Create an approximate nearest neighbor index for the item embeddings\n","\n","This notebook is the fourth of five notebooks that guide you through running the [Real-time Item-to-item Recommendation with BigQuery ML Matrix Factorization and ScaNN](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/tree/master/retail/recommendation-system/bqml-scann) solution.\n","\n","Use this notebook to create an approximate nearest neighbor (ANN) index for the item embeddings by using the [ScaNN](https://github.com/google-research/google-research/tree/master/scann) framework. You create the index as a model, train the model on AI Platform Training, then export the index to Cloud Storage so that it can serve ANN information.\n","\n","Before starting this notebook, you must run the [03_create_embedding_lookup_model](03_create_embedding_lookup_model.ipynb) notebook to process the item embeddings data and export it to Cloud Storage.\n","\n","After completing this notebook, run the [05_deploy_lookup_and_scann_caip](05_deploy_lookup_and_scann_caip.ipynb) notebook to deploy the solution. Once deployed, you can submit song IDs to the solution and get similar song recommendations in return, based on the ANN index.\n"]},{"cell_type":"markdown","metadata":{"id":"Pk9Wij8ppfNY"},"source":["## Setup\r\n","\r\n","Import the required libraries, configure the environment variables, and authenticate your GCP account."]},{"cell_type":"code","metadata":{"id":"M-H_wPdmpfNY"},"source":["!pip install -q scann"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"gzoXTCD5pfNZ"},"source":["### Import libraries"]},{"cell_type":"code","metadata":{"id":"_7vHSotqpfNa"},"source":["import tensorflow as tf\n","import numpy as np\n","from datetime import datetime"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"4_tk1WOppfNa"},"source":["### Configure GCP environment settings\r\n","\r\n","Update the following variables to reflect the values for your GCP environment:\r\n","\r\n","+ `PROJECT_ID`: The ID of the Google Cloud project you are using to implement this solution.\r\n","+ `BUCKET`: The name of the Cloud Storage bucket you created to use with this solution. The `BUCKET` value should be just the bucket name, so `myBucket` rather than `gs://myBucket`.\r\n","+ `REGION`: The region to use for the AI Platform Training job."]},{"cell_type":"code","metadata":{"id":"FxhEWsL4pfNb"},"source":["PROJECT_ID = 'yourProject' # Change to your project.\n","BUCKET = 'yourBucketName' # Change to the bucket you created.\n","REGION = 'yourTrainingRegion' # Change to your AI Platform Training region.\n","EMBEDDING_FILES_PREFIX = f'gs://{BUCKET}/bqml/item_embeddings/embeddings-*'\n","OUTPUT_INDEX_DIR = f'gs://{BUCKET}/bqml/scann_index'"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"wPdz87S3pfNb"},"source":["### Authenticate your GCP account\n","This is required if you run the notebook in Colab. If you use an AI Platform notebook, you should already be authenticated."]},{"cell_type":"code","metadata":{"id":"fBXROib7pfNc"},"source":["try:\n","  from google.colab import auth\n","  auth.authenticate_user()\n","  print(\"Colab user is authenticated.\")\n","except: pass"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xaiIfZw9pfNc"},"source":["## Build the ANN index\r\n","\r\n","Use the `build` method implemented in the [indexer.py](index_builder/builder/indexer.py) module to load the embeddings from the CSV files, create the ANN index model and train it on the embedding data, and save the SavedModel file to Cloud Storage. You pass the following three parameters to this method:\r\n","\r\n","+ `embedding_files_path`, which specifies the Cloud Storage location from which to load the embedding vectors.\r\n","+ `num_leaves`, which provides the value for a hyperparameter that tunes the model based on the trade-off between retrieval latency and recall. A higher `num_leaves` value will use more data and provide better recall, but will also increase latency. If `num_leaves` is set to `None` or `0`, the `num_leaves` value is the square root of the number of items.\r\n","+ `output_dir`, which specifies the Cloud Storage location to write the ANN index SavedModel file to.\r\n","\r\n","Other configuration options for the model are set based on the [rules-of-thumb](https://github.com/google-research/google-research/blob/master/scann/docs/algorithms.md#rules-of-thumb) provided by ScaNN."]},{"cell_type":"markdown","metadata":{"id":"PwcdyrDiGcep"},"source":["### Build the index locally"]},{"cell_type":"code","metadata":{"id":"l5TGzINqpfNc"},"source":["from index_builder.builder import indexer\n","indexer.build(EMBEDDING_FILES_PREFIX, OUTPUT_INDEX_DIR)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"aonq2MptpfNd"},"source":["### Build the index using AI Platform Training\r\n","\r\n","Submit an AI Platform Training job to build the ScaNN index at scale. The [index_builder](index_builder) directory contains the expected [training application packaging structure](https://cloud.google.com/ai-platform/training/docs/packaging-trainer) for submitting the AI Platform Training job."]},{"cell_type":"code","metadata":{"id":"uCVEI3mjpfNd"},"source":["if tf.io.gfile.exists(OUTPUT_INDEX_DIR):\n","  print(\"Removing {} contents...\".format(OUTPUT_INDEX_DIR))\n","  tf.io.gfile.rmtree(OUTPUT_INDEX_DIR)\n","\n","print(\"Creating output: {}\".format(OUTPUT_INDEX_DIR))\n","tf.io.gfile.makedirs(OUTPUT_INDEX_DIR)\n","\n","timestamp = datetime.utcnow().strftime('%y%m%d%H%M%S')\n","job_name = f'ks_bqml_build_scann_index_{timestamp}'\n","\n","!gcloud ai-platform jobs submit training {job_name} \\\n","  --project={PROJECT_ID} \\\n","  --region={REGION} \\\n","  --job-dir={OUTPUT_INDEX_DIR}/jobs/ \\\n","  --package-path=index_builder/builder \\\n","  --module-name=builder.task \\\n","  --config='index_builder/config.yaml' \\\n","  --runtime-version=2.2 \\\n","  --python-version=3.7 \\\n","  --\\\n","  --embedding-files-path={EMBEDDING_FILES_PREFIX} \\\n","  --output-dir={OUTPUT_INDEX_DIR} \\\n","  --num-leaves=500"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"MzS533hgpfNe"},"source":["After the AI Platform Training job finishes, check that the `scann_index` folder has been created in your Cloud Storage bucket:"]},{"cell_type":"code","metadata":{"id":"hgEdM632pfNe"},"source":["!gcloud storage ls {OUTPUT_INDEX_DIR}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"KcSPTZeDpfNf"},"source":["## Test the ANN index\r\n","\r\n","Test the ANN index by using the `ScaNNMatcher` class implemented in the [index_server/matching.py](index_server/matching.py) module.\r\n","\r\n","Run the following code snippets to create an item embedding from random generated values and pass it to `scann_matcher`, which returns the items IDs for the five items that are the approximate nearest neighbors of the embedding you submitted."]},{"cell_type":"code","metadata":{"id":"nQXdRKV4pfNf"},"source":["from index_server.matching import ScaNNMatcher\n","scann_matcher = ScaNNMatcher(OUTPUT_INDEX_DIR)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"xlUc_GsBpfNf"},"source":["vector = np.random.rand(50)\n","scann_matcher.match(vector, 5)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"CHXed9gdpfNg"},"source":["## License\n","\n","Copyright 2020 Google LLC\n","\n","Licensed under the Apache License, Version 2.0 (the \"License\");\n","you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0\n","\n","Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n","\n","See the License for the specific language governing permissions and limitations under the License.\n","\n","**This is not an official Google product but sample code provided for an educational purpose**"]}]}

================================================
FILE: retail/recommendation-system/bqml-scann/05_deploy_lookup_and_scann_caip.ipynb
================================================
{"nbformat":4,"nbformat_minor":0,"metadata":{"environment":{"name":"tf2-2-3-gpu.2-3.m59","type":"gcloud","uri":"gcr.io/deeplearning-platform-release/tf2-2-3-gpu.2-3:m59"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.8"},"colab":{"name":"05_deploy_lookup_and_scann_caip.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true}},"cells":[{"cell_type":"markdown","metadata":{"id":"59DnLe66_RSq"},"source":["# Part 5: Deploy the solution to AI Platform Prediction\n","\n","This notebook is the fifth of five notebooks that guide you through running the [Real-time Item-to-item Recommendation with BigQuery ML Matrix Factorization and ScaNN](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/tree/master/retail/recommendation-system/bqml-scann) solution.\n","\n","Use this notebook to complete the following tasks:\n","\n","1. Deploy the embedding lookup model to AI Platform Prediction. \n","2. Deploy the ScaNN matching service to AI Platform Prediction by using a custom container. The ScaNN matching service is an application that wraps the ANN index model and provides additional functionality, like mapping item IDs to item embeddings.\n","3. Optionally, export and deploy the matrix factorization model to AI Platform for exact matching.\n","\n","Before starting this notebook, you must run the [04_build_embeddings_scann](04_build_embeddings_scann.ipynb) notebook to build an approximate nearest neighbor (ANN) index for the item embeddings.\n"]},{"cell_type":"markdown","metadata":{"id":"--twfVSH_RSx"},"source":["## Setup\r\n","\r\n","Import the required libraries, configure the environment variables, and authenticate your GCP account."]},{"cell_type":"markdown","metadata":{"id":"hTf9yuUI_RSy"},"source":["### Import libraries"]},{"cell_type":"code","metadata":{"id":"-74hbUcn_RSy"},"source":["import numpy as np\n","import tensorflow as tf"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"G0-pepnt_RSz"},"source":["### Configure GCP environment settings\r\n","\r\n","Update the following variables to reflect the values for your GCP environment:\r\n","\r\n","+ `PROJECT_ID`: The ID of the Google Cloud project you are using to implement this solution.\r\n","+ `PROJECT_NUMBER`: The number of the Google Cloud project you are using to implement this solution. You can find this in the **Project info** card on the [project dashboard page](https://pantheon.corp.google.com/home/dashboard).\r\n","+ `BUCKET`: The name of the Cloud Storage bucket you created to use with this solution. The `BUCKET` value should be just the bucket name, so `myBucket` rather than `gs://myBucket`.\r\n","+ `REGION`: The region to use for the AI Platform Prediction job."]},{"cell_type":"code","metadata":{"id":"7JijghSw_RSz"},"source":["PROJECT_ID = 'yourProject' # Change to your project.\n","PROJECT_NUMBER = 'yourProjectNumber' # Change to your project number\n","BUCKET = 'yourBucketName' # Change to the bucket you created.\n","REGION = 'yourPredictionRegion' # Change to your AI Platform Prediction region.\n","ARTIFACTS_REPOSITORY_NAME = 'ml-serving'\n","\n","EMBEDDNIG_LOOKUP_MODEL_OUTPUT_DIR = f'gs://{BUCKET}/bqml/embedding_lookup_model'\n","EMBEDDNIG_LOOKUP_MODEL_NAME = 'item_embedding_lookup'\n","EMBEDDNIG_LOOKUP_MODEL_VERSION = 'v1'\n","\n","INDEX_DIR = f'gs://{BUCKET}/bqml/scann_index'\n","SCANN_MODEL_NAME = 'index_server'\n","SCANN_MODEL_VERSION = 'v1'\n","\n","KIND = 'song'\n","\n","!gcloud config set project $PROJECT_ID"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"sRgHeeDH_RS0"},"source":["### Authenticate your GCP account\n","This is required if you run the notebook in Colab. If you use an AI Platform notebook, you should already be authenticated."]},{"cell_type":"code","metadata":{"id":"mCRUZhqy_RS1"},"source":["try:\n","  from google.colab import auth\n","  auth.authenticate_user()\n","  print(\"Colab user is authenticated.\")\n","except: pass"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"dTdkkpNL_RS1"},"source":["## Deploy the embedding lookup model to AI Platform Prediction\r\n","\r\n","Create the embedding lookup model resource in AI Platform:"]},{"cell_type":"code","metadata":{"id":"e_4hhSxr_RS2"},"source":["!gcloud ai-platform models create {EMBEDDNIG_LOOKUP_MODEL_NAME} --region={REGION}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"nOLmODpvPSvk"},"source":["Next, deploy the model:"]},{"cell_type":"code","metadata":{"id":"o8QANnrC_RS2"},"source":["!gcloud ai-platform versions create {EMBEDDNIG_LOOKUP_MODEL_VERSION} \\\n","  --region={REGION} \\\n","  --model={EMBEDDNIG_LOOKUP_MODEL_NAME} \\\n","  --origin={EMBEDDNIG_LOOKUP_MODEL_OUTPUT_DIR} \\\n","  --runtime-version=2.2 \\\n","  --framework=TensorFlow \\\n","  --python-version=3.7 \\\n","  --machine-type=n1-standard-2\n","\n","print(\"The model version is deployed to AI Platform Prediction.\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"p9Y0KD-CPXw1"},"source":["Once the model is deployed, you can verify it in the [AI Platform console](https://pantheon.corp.google.com/ai-platform/models)."]},{"cell_type":"markdown","metadata":{"id":"6gwpUx9q_RS3"},"source":["### Test the deployed embedding lookup AI Platform Prediction model\r\n","\r\n","Set the AI Platform Prediction API information:"]},{"cell_type":"code","metadata":{"id":"6a_V-ueo_RS3"},"source":["import googleapiclient.discovery\n","from google.api_core.client_options import ClientOptions\n","\n","api_endpoint = f'https://{REGION}-ml.googleapis.com'\n","client_options = ClientOptions(api_endpoint=api_endpoint)\n","service = googleapiclient.discovery.build(\n","    serviceName='ml', version='v1', client_options=client_options)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FTucyodISTFi"},"source":["Run the `caip_embedding_lookup` method to retrieve item embeddings. This method accepts item IDs, calls the embedding lookup model in AI Platform Prediction, and returns the appropriate embedding vectors.\r\n"]},{"cell_type":"code","metadata":{"id":"y0PYGzvV_RS4"},"source":["def caip_embedding_lookup(input_items):\n","  request_body = {'instances': input_items}\n","  service_name = f'projects/{PROJECT_ID}/models/{EMBEDDNIG_LOOKUP_MODEL_NAME}/versions/{EMBEDDNIG_LOOKUP_MODEL_VERSION}'\n","  print(f'Calling : {service_name}')\n","  response = service.projects().predict(\n","    name=service_name, body=request_body).execute()\n","\n","  if 'error' in response:\n","    raise RuntimeError(response['error'])\n","\n","  return response['predictions']"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"NBNQXyIkEz-q"},"source":["Test the `caip_embedding_lookup` method with three item IDs:"]},{"cell_type":"code","metadata":{"id":"180KDniX_RS4"},"source":["input_items = ['2114406', '2114402 2120788', 'abc123']\n","\n","embeddings = caip_embedding_lookup(input_items)\n","print(f'Embeddings retrieved: {len(embeddings)}')\n","for idx, embedding in enumerate(embeddings):\n","  print(f'{input_items[idx]}: {embedding[:5]}')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"kZD4MK5k-VGD"},"source":["## ScaNN matching service\r\n","\r\n","The ScaNN matching service performs the following steps:\r\n","\r\n","1. Receives one or more item IDs from the client.\r\n","1. Calls the embedding lookup model to fetch the embedding vectors of those item IDs.\r\n","1. Uses these embedding vectors to query the ANN index to find approximate nearest neighbor embedding vectors.\r\n","1. Maps the approximate nearest neighbors embedding vectors to their corresponding item IDs.\r\n","1. Sends the item IDs back to the client.\r\n","\r\n","When the client receives the item IDs of the matches, the song title and artist information is fetched from Datastore in real-time to be displayed and served to the client application.\r\n","\r\n","Note: In practice, recommendation systems combine matches (from one or more indices) with user-provided filtering clauses (like where price <= *value* and colour =red), as well as other item metadata (like item categories, popularity, and recency) to ensure recommendation freshness and diversity. In addition, ranking is commonly applied after generating the matches to decide the order in which they are served to the user. "]},{"cell_type":"markdown","metadata":{"id":"P0L5qYS1hpl3"},"source":["### ScaNN matching service implementation\r\n","\r\n","The ScaNN matching service is implemented as a [Flask](https://flask.palletsprojects.com/en/1.1.x/quickstart/) application that runs on a [gunicorn](https://gunicorn.org/) web server. This application is implemented in the [main.py](index_server/main.py) module.\r\n","\r\n","The ScaNN matching service application works as follows:\r\n","\r\n","1. Uses environmental variables to set configuration information, such as the Google Cloud location of the ScaNN index to load.\r\n","1. Loads the ScaNN index as the `ScaNNMatcher` object is initiated.\r\n","1. As [required by AI Platform Prediction](https://cloud.google.com/ai-platform/prediction/docs/custom-container-requirements), exposes two HTTP endpoints:\r\n","    \r\n","    + `health`: a `GET` method to which AI Platform Prediction sends health checks.\r\n","    + `predict`: a `POST` method to which AI Platform Prediction forwards prediction requests.\r\n","\r\n","    The `predict` method expects JSON requests in the form `{\"instances\":[{\"query\": \"item123\", \"show\": 10}]}`, where `query` represents the item ID to retrieve matches for, and `show` represents the number of matches to retrieve.\r\n","    \r\n","    The `predict` method works as follows:\r\n","\r\n","        1. Validates the received request object.\r\n","        1. Extracts the `query` and `show` values from the request object.\r\n","        1. Calls `embedding_lookup.lookup` with the given query item ID to get its embedding vector from the embedding lookup model.\r\n","        1. Calls `scann_matcher.match` with the query item embedding vector to retrieve its approximate nearest neighbor item IDs from the ANN Index.\r\n","The list of matching item IDs are put into JSON format and returned as the response of the `predict` method."]},{"cell_type":"markdown","metadata":{"id":"eaeXo9prFNpL"},"source":["## Deploy the ScaNN matching service to AI Platform Prediction\r\n","\r\n","Package the ScaNN matching service application in a custom container and deploy it to AI Platform Prediction."]},{"cell_type":"markdown","metadata":{"id":"qkpwIGnb_RS5"},"source":["### Create an Artifact Registry for the Docker container image"]},{"cell_type":"code","metadata":{"id":"ru4oMDt2_RS5"},"source":["!gcloud beta artifacts repositories create {ARTIFACTS_REPOSITORY_NAME} \\\n","  --location={REGION} \\\n","  --repository-format=docker"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"fIhDsllM_RS6"},"source":["!gcloud beta auth configure-docker {REGION}-docker.pkg.dev --quiet"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"eTG7nvvt_RS6"},"source":["### Use Cloud Build to build the Docker container image\n","\n","The container runs the gunicorn HTTP web server and executes the Flask [app](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/blob/315040032df26d7cef3a26e5def35ca50dd559d6/retail/recommendation-system/bqml-scann/index_server/main.py#L35) variable defined in the `main.py` module.\n","\n","The container image to deploy to AI Platform Prediction is defined in a [Dockerfile](index_server/Dockerfile), as shown in the following code snippet:\n","\n","```\n","FROM python:3.8-slim\n","\n","COPY requirements.txt .\n","RUN pip install -r requirements.txt\n","\n","COPY . ./\n","\n","ARG PORT\n","ENV PORT=$PORT\n","\n","CMD exec gunicorn --bind :$PORT main:app  --workers=1 --threads 8 --timeout 1800\n","```\n","\n","Build the container image by using Cloud Build and specifying the [cloudbuild.yaml](index_server/cloudbuild.yaml) file:\n"]},{"cell_type":"code","metadata":{"id":"QVopE8-0_RS6"},"source":["IMAGE_URL = f'{REGION}-docker.pkg.dev/{PROJECT_ID}/{ARTIFACTS_REPOSITORY_NAME}/{SCANN_MODEL_NAME}:{SCANN_MODEL_VERSION}'\n","PORT=5001\n","\n","SUBSTITUTIONS = ''\n","SUBSTITUTIONS += f'_IMAGE_URL={IMAGE_URL},'\n","SUBSTITUTIONS += f'_PORT={PORT}'\n","\n","!gcloud builds submit --config=index_server/cloudbuild.yaml \\\n","  --substitutions={SUBSTITUTIONS} \\\n","  --timeout=1h"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"TPk9Qi93G56d"},"source":["Run the following command to verify the container image has been built:\r\n"]},{"cell_type":"code","metadata":{"id":"PvSIUzD9_RS7"},"source":["repository_id = f'{REGION}-docker.pkg.dev/{PROJECT_ID}/{ARTIFACTS_REPOSITORY_NAME}'\n","\n","!gcloud beta artifacts docker images list {repository_id}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"UAr0Vsft_RS7"},"source":["### Create a service account for AI Platform Prediction\r\n","\r\n","Create a service account to run the custom container. This [is required](https://cloud.google.com/ai-platform/prediction/docs/custom-service-account#container-default) in cases where you want to grant specific permissions to the service account."]},{"cell_type":"code","metadata":{"id":"cBa56i5g_RS8"},"source":["SERVICE_ACCOUNT_NAME = 'caip-serving'\n","SERVICE_ACCOUNT_EMAIL = f'{SERVICE_ACCOUNT_NAME}@{PROJECT_ID}.iam.gserviceaccount.com'\n","!gcloud iam service-accounts create {SERVICE_ACCOUNT_NAME} \\\n","  --description=\"Service account for AI Platform Prediction to access cloud resources.\" "],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0-5BUPqT_RS8"},"source":["Grant the `Cloud ML Engine (AI Platform)` service account the `iam.serviceAccountAdmin` privilege, and grant the `caip-serving` service account the privileges required by the ScaNN matching service, which are `storage.objectViewer` and `ml.developer`."]},{"cell_type":"code","metadata":{"id":"qUoaWCVJ_RS8"},"source":["!gcloud projects describe {PROJECT_ID} --format=\"value(projectNumber)\""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"LzMJ60X-_RS9"},"source":["!gcloud projects add-iam-policy-binding {PROJECT_ID} \\\n","  --role=roles/iam.serviceAccountAdmin \\\n","  --member=serviceAccount:service-{PROJECT_NUMBER}@cloud-ml.google.com.iam.gserviceaccount.com\n","\n","!gcloud projects add-iam-policy-binding {PROJECT_ID} \\\n","  --role=roles/storage.objectViewer \\\n","  --member=serviceAccount:{SERVICE_ACCOUNT_EMAIL}\n","    \n","!gcloud projects add-iam-policy-binding {PROJECT_ID} \\\n","  --role=roles/ml.developer \\\n","  --member=serviceAccount:{SERVICE_ACCOUNT_EMAIL}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"1-x11wBH_RS9"},"source":["### Deploy the custom container to AI Platform Prediction\r\n","\r\n","Create the ANN index model resource in AI Platform:"]},{"cell_type":"code","metadata":{"id":"CfeleaCW_RS-"},"source":["!gcloud ai-platform models create {SCANN_MODEL_NAME} --region={REGION}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"I9TcigBmHOin"},"source":["Deploy the custom container to AI Platform prediction. Note that you use the `env-vars` parameter to pass environmental variables to the Flask application in the container. "]},{"cell_type":"code","metadata":{"id":"fg9wtS2__RS_"},"source":["HEALTH_ROUTE=f'/v1/models/{SCANN_MODEL_NAME}/versions/{SCANN_MODEL_VERSION}'\n","PREDICT_ROUTE=f'/v1/models/{SCANN_MODEL_NAME}/versions/{SCANN_MODEL_VERSION}:predict'\n","\n","ENV_VARIABLES = f'PROJECT_ID={PROJECT_ID},'\n","ENV_VARIABLES += f'REGION={REGION},'\n","ENV_VARIABLES += f'INDEX_DIR={INDEX_DIR},'\n","ENV_VARIABLES += f'EMBEDDNIG_LOOKUP_MODEL_NAME={EMBEDDNIG_LOOKUP_MODEL_NAME},'\n","ENV_VARIABLES += f'EMBEDDNIG_LOOKUP_MODEL_VERSION={EMBEDDNIG_LOOKUP_MODEL_VERSION}'\n","\n","!gcloud beta ai-platform versions create {SCANN_MODEL_VERSION} \\\n","  --region={REGION} \\\n","  --model={SCANN_MODEL_NAME} \\\n","  --image={IMAGE_URL} \\\n","  --ports={PORT} \\\n","  --predict-route={PREDICT_ROUTE} \\\n","  --health-route={HEALTH_ROUTE} \\\n","  --machine-type=n1-standard-4 \\\n","  --env-vars={ENV_VARIABLES} \\\n","  --service-account={SERVICE_ACCOUNT_EMAIL}\n","\n","print(\"The model version is deployed to AI Platform Prediction.\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"WzCcA58l_RS_"},"source":["### Test the Deployed ScaNN Index Service\r\n","\r\n","After deploying the custom container, test it by running the `caip_scann_match` method. This method accepts the parameter `query_items`, whose value is converted into a space-separated string of item IDs and treated as a single query. That is, a single embedding vector is retrieved from the embedding lookup model, and similar item IDs are retrieved from the ScaNN index given this embedding vector."]},{"cell_type":"code","metadata":{"id":"w5OlMGuD_RS_"},"source":["from google.cloud import datastore\n","import requests\n","client = datastore.Client(PROJECT_ID)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"WwzuijuH_RTA"},"source":["def caip_scann_match(query_items, show=10):\n","  request_body = {\n","      'instances': [{\n","          'query':' '.join(query_items), \n","          'show':show\n","      }]\n","   }\n","  \n","  service_name = f'projects/{PROJECT_ID}/models/{SCANN_MODEL_NAME}/versions/{SCANN_MODEL_VERSION}'\n","  print(f'Calling: {service_name}')  \n","  response = service.projects().predict(\n","    name=service_name, body=request_body).execute()\n","\n","  if 'error' in response:\n","    raise RuntimeError(response['error'])\n","\n","  match_tokens = response['predictions']\n","  keys = [client.key(KIND, int(key)) for key in match_tokens]\n","  items = client.get_multi(keys)\n","  return items\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ctXsOxhDHsX_"},"source":["Call the `caip_scann_match` method with five item IDs and request five match items for each:"]},{"cell_type":"code","metadata":{"id":"620GvSzr_RTA"},"source":["songs = {\n","    '2120788': 'Limp Bizkit: My Way',\n","    '1086322': 'Jacques Brel: Ne Me Quitte Pas',\n","    '833391': 'Ricky Martin: Livin\\' la Vida Loca',\n","    '1579481': 'Dr. Dre: The Next Episode',\n","    '2954929': 'Black Sabbath: Iron Man'\n","}"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"YiYAYVG9_RTB"},"source":["for item_Id, desc in songs.items():\n","  print(desc)\n","  print(\"==================\")\n","  similar_items = caip_scann_match([item_Id], 5)\n","  for similar_item in similar_items:\n","    print(f'- {similar_item[\"artist\"]}: {similar_item[\"track_title\"]}')\n","  print()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"lt7-YDSe_RTB"},"source":["## (Optional) Deploy the matrix factorization model to AI Platform Prediction\r\n","\r\n","Optionally, you can deploy the matrix factorization model in order to perform exact item matching. The model takes `Item1_Id` as an input and outputs the top 50 recommended `item2_Ids`.\r\n","\r\n","Exact matching returns better results, but takes significantly longer than approximate nearest neighbor matching. You might want to use exact item matching in cases where you are working with a very small data set and where latency isn't a primary concern."]},{"cell_type":"markdown","metadata":{"id":"YSDzSk4T_RTC"},"source":["### Export the model from BigQuery ML to Cloud Storage as a SavedModel"]},{"cell_type":"code","metadata":{"id":"YVmmFvLk_RTC"},"source":["BQ_DATASET_NAME = 'recommendations'\n","BQML_MODEL_NAME = 'item_matching_model'\n","BQML_MODEL_VERSION = 'v1' \n","BQML_MODEL_OUTPUT_DIR = f'gs://{BUCKET}/bqml/item_matching_model'\n","\n","!bq --quiet extract -m {BQ_DATASET_NAME}.{BQML_MODEL_NAME} {BQML_MODEL_OUTPUT_DIR}"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Q4xzyUte_RTC"},"source":["!saved_model_cli show --dir {BQML_MODEL_OUTPUT_DIR} --tag_set serve --signature_def serving_default"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"kWbEnUnO_RTC"},"source":["### Deploy the exact matching model to AI Platform Prediction"]},{"cell_type":"code","metadata":{"id":"Es8Tp9HM_RTD"},"source":["!gcloud ai-platform models create {BQML_MODEL_NAME} --region={REGION}"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"nNOVgOD9_RTD"},"source":["!gcloud ai-platform versions create {BQML_MODEL_VERSION} \\\n","  --region={REGION} \\\n","  --model={BQML_MODEL_NAME} \\\n","  --origin={BQML_MODEL_OUTPUT_DIR} \\\n","  --runtime-version=2.2 \\\n","  --framework=TensorFlow \\\n","  --python-version=3.7 \\\n","  --machine-type=n1-standard-2\n","\n","print(\"The model version is deployed to AI Platform Predicton.\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"n9MDaT3P_RTD"},"source":["def caip_bqml_matching(input_items, show):\n","  request_body = {'instances': input_items}\n","  service_name = f'projects/{PROJECT_ID}/models/{BQML_MODEL_NAME}/versions/{BQML_MODEL_VERSION}'\n","  print(f'Calling : {service_name}')\n","  response = service.projects().predict(\n","    name=service_name, body=request_body).execute()\n","\n","  if 'error' in response:\n","    raise RuntimeError(response['error'])\n","    \n","  \n","  match_tokens = response['predictions'][0][\"predicted_item2_Id\"][:show]\n","  keys = [client.key(KIND, int(key)) for key in match_tokens]\n","  items = client.get_multi(keys)\n","  return items\n","\n","  return response['predictions']"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"bHvFNZ78_RTD"},"source":["for item_Id, desc in songs.items():\n","  print(desc)\n","  print(\"==================\")\n","  similar_items = caip_bqml_matching([int(item_Id)], 5)\n","  for similar_item in similar_items:\n","    print(f'- {similar_item[\"artist\"]}: {similar_item[\"track_title\"]}')\n","  print()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6uUOZgLQ_RTE"},"source":["## License\n","\n","Copyright 2020 Google LLC\n","\n","Licensed under the Apache License, Version 2.0 (the \"License\");\n","you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0\n","\n","Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n","\n","See the License for the specific language governing permissions and limitations under the License.\n","\n","**This is not an official Google product but sample code provided for an educational purpose**"]}]}

================================================
FILE: retail/recommendation-system/bqml-scann/README.md
================================================
# Real-time Item-to-item Recommendation with BigQuery ML Matrix Factorization and ScaNN

This directory contains code samples that demonstrate how to implement a low
latency item-to-item recommendation solution, by training and serving embeddings
that you can use to enable real-time similarity matching. The foundations of the
solution are [BigQuery](https://cloud.google.com/bigquery) and
[ScaNN](https://github.com/google-research/google-research/tree/master/scann),
which is an open source library for efficient vector similarity search at scale.

The series is for data scientists and ML engineers who want to build an
embedding training system and serve for item-item recommendation use cases. It
assumes that you have experience with Google Cloud,
[BigQuery](https://cloud.google.com/bigquery), [AI
Platform](https://cloud.google.com/ai-platform),
[Dataflow](https://cloud.google.com/dataflow),
[Datastore](https://cloud.google.com/datastore), and with
[Tensorflow](https://www.tensorflow.org/) and [TFX
Pipelines](https://www.tensorflow.org/tfx).

## Solution variants

There are two variants of the solution:

-  The first variant utilizes generally available releases of BigQuery and
   AI Platform together with open source components including ScaNN and
   [Kubeflow Pipelines](https://www.kubeflow.org/docs/pipelines/overview/pipelines-overview/).
   To use this variant, follow the instructions in the [Production
   variant](#production-variant) section.
-  The second variant is a fully-managed solution that leverages the
   _experimental_ releases of AI Platform Pipelines and ANN service. To use this
   variant, follow the instructions in the [Experimental
   variant](#experimental-variant) section.

## Dataset

We use the public `bigquery-samples.playlists` BigQuery dataset to demonstrate the
solutions. We use the playlist data to learn embeddings for songs based on their
co-occurrences in different playlists. The learned embeddings can be used to
match and recommend relevant songs to a given song or playlist.

## Production variant

At a high level, the solution works as follows:

1. Computes pointwise mutual information (PMI) between items based on their
   co-occurrences.
1. Trains item embeddings using BigQuery ML Matrix Factorization, with item
   PMI as implicit feedback.
1. Using Cloud Dataflow, post-processes the embeddings into CSV files and
   exports them from the BigQuery ML model to Cloud Storage.
1. Implements an embedding lookup model using TensorFlow Keras, and then
   deploys it to AI Platform Prediction.
1. Serves the embeddings as an approximate nearest neighbor index on AI
   Platform Prediction for real-time similar items matching.

![Diagram showing the architecture of the item embedding solution.](figures/diagram.png)

For a detailed description of the solution architecture, see
[Architecture of a machine learning system for item matching](https://cloud.google.com/solutions/real-time-item-matching).

### Cost

The solution uses the following billable components of Google Cloud:

-  AI Platform Notebooks
-  AI Platform Pipelines
-  AI Platform Prediction
-  AI Platform Training
-  Artifact Registry
-  BigQuery
-  Cloud Build
-  Cloud Storage
-  Dataflow
-  Datastore

To learn about Google Cloud pricing, use the [Pricing
Calculator](https://cloud.google.com/products/calculator) to generate a cost
estimate based on your projected usage.

### Running the solution

You can run the solution step-by-step, or you can run it by using a TFX
pipeline. 

#### Run the solution step-by-step

1. Complete the steps in [Set up the GCP environment](#set-up-the-gcp-environment).
1. Complete the steps in [Set up the AI Platform Notebooks
   environment](#set-up-the-ai-platform-notebooks-environment).
1. In the Jupyterlab environment of the `embeddings-notebooks` instance, open
   the file browser pane and navigate to the
   `analytics-componentized-patterns/retail/recommendation-system/bqml-scann`
   directory.
1. Run the `00_prep_bq_and_datastore.ipynb` notebook to import the `playlist`
   dataset, create the  `vw_item_groups` view with song and playlist data, and
   export song title and artist information to Datastore.
1. Run the `00_prep_bq_procedures` notebook to create stored procedures
   needed by the solution.
1. Run the `01_train_bqml_mf_pmi.ipynb` notebook. This covers computing item
   co-occurrences using PMI, and then training a BigQuery ML matrix
   factorization mode to generate item embeddings.
1. Run the `02_export_bqml_mf_embeddings.ipynb` notebook. This covers using
   Dataflow to request the embeddings from the matrix factorization model,
   format them as CSV files, and export them to Cloud Storage.
1. Run the `03_create_embedding_lookup_model.ipynb` notebook. This covers
   creating a TensorFlow Keras model to wrap the item embeddings, exporting
   that model as a SavedModel, and deploying that SavedModel to act as an
   item-embedding lookup.
1. Run the `04_build_embeddings_scann.ipynb` notebook. This covers building an
   approximate nearest neighbor index for the embeddings using ScaNN and AI
   Platform Training, then exporting the ScaNN index to Cloud Storage.
1. Run the `05_deploy_lookup_and_scann_caip.ipynb` notebook. This covers
   deploying the embedding lookup model and ScaNN index (wrapped in a Flask app
   to add functionality) created by the solution.
1. If you don't want to keep the resources you created for this solution, complete the steps in [Delete the GCP resources](#delete-the-gcp-resources).

#### Run the solution by using a TFX pipeline

In addition to manual steps outlined above, we provide a
[TFX pipeline](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/blob/master/retail/recommendation-system/bqml-scann/tfx_pipeline)
that automates the process of building and deploying the solution. To run the
solution by using the TFX pipeline, follow these steps:

1. Complete the steps in [Set up the GCP environment](#set-up-the-gcp-environment).
1. Complete the steps in [Set up the AI Platform Notebooks
   environment](#set-up-the-ai-platform-notebooks-environment).
1. In the Jupyterlab environment of the `embeddings-notebooks` instance, open
   the file browser pane and navigate to the
   `analytics-componentized-patterns/retail/recommendation-system/bqml-scann`
   directory.
1. Run the `00_prep_bq_and_datastore.ipynb` notebook to import the `playlist`
   dataset, create the  `vw_item_groups` view with song and playlist data, and
   export song title and artist information to Datastore.
1. Run the `00_prep_bq_procedures` notebook to create stored procedures
   needed by the solution.
1. Run the `tfx01_interactive.ipynb` notebook. This covers creating and running
   a TFX pipeline that runs the solution, which includes all of the tasks
   mentioned in the step-by-step notebooks above.
1. Run the `tfx02_deploy_run.ipynb` notebook. This covers deploying the TFX
   pipeline, including building a Docker container image, compiling the
   pipeline, and deploying the pipeline to AI Platform Pipelines.
1. Run the `05_deploy_lookup_and_scann_caip.ipynb` notebook. This covers
   deploying the embedding lookup model and ScaNN index (wrapped in a Flask app
   to add functionality) created by the solution.
1. If you don't want to keep the resources you created for this solution, complete the steps in [Delete the GCP resources](#delete-the-gcp-resources).

## Set up the GCP environment

Before running the solution, you must complete the following steps to prepare an
appropriate environment:

1. Create and configure a GCP project.
1. Create the GCP resources you need.

    Before creating the resources, consider what regions you want to use.
    Creating resources in the same region or multi-region (like US or EU) can
    reduce latency and improve performance.

1. Clone this repo to the AI Platform notebook environment.
1. Install the solution requirements on the notebook environment.
1. Add the sample dataset and some stored procedures to BigQuery.

### Set up the GCP project

1. In the Cloud Console, on the
   [project selector page](https://console.cloud.google.com/projectselector2/home/dashboard),
   select or create a Cloud project.
1. Make sure that
   [billing is enabled](https://cloud.google.com/billing/docs/how-to/modify-project)
   for your Cloud project. 
1. [Enable the Compute Engine, Dataflow, Datastore, AI Platform, AI Platform Notebooks, Artifact Registry, Identity and Access Management, Cloud Build, BigQuery, and BigQuery Reservations APIs](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,iam.googleapis.com,compute.googleapis.com,dataflow.googleapis.com,datastore.googleapis.com,cloudbuild.googleapis.com,bigquery.googleapis.com,artifactregistry.googleapis.com,notebooks.googleapis.com,bigqueryreservation.googleapis.com&redirect=https://console.cloud.google.com).

### Create a BigQuery reservation

If you use
[on-demand pricing](https://cloud.google.com/bigquery/pricing#on_demand_pricing)
for BigQuery, you must purchase flex slots and then create reservations and
assignments for them in order to train a matrix factorization model. You can
skip this section if you use flat-rate pricing with BigQuery.  

You must have the `bigquery.reservations.create` permission in order to purchase
flex slots. This permission is granted to the project owner, and also to the
`bigquery.admin` and `bigquery.resourceAdmin` predefined Identity and Access
Management roles.

1. In the [BigQuery console](https://pantheon.corp.google.com/bigquery),
   click **Reservations**.
1. On the **Reservations** page, click **Buy Slots**.
1. On the **Buy Slots** page, set the options as follows:
  
   1. In **Commitment duration**, choose **Flex**.
   1. In **Location**, choose the region you want to use for BigQuery.
      Depending on the region you choose, you may have to request additional
      slot quota.
   1. In **Number of slots**, choose **500**.
   1. Click **Next**.
   1. In **Purchase confirmation**, type `CONFIRM`.  
     
      **Note:** The console displays an estimated monthly cost of $14,600.00.
      You will delete the unused slots at the end of this tutorial, so you will
      only pay for the slots you use to train the model. Training the model
      takes approximately 2 hours.

1. Click **Purchase**.
1. Click **View Slot Commitments**.
1. Allow up to 20 minutes for the capacity to be provisioned. After the
   capacity is provisioned, the slot commitment status turns green and shows a
   checkmark.
1. Click **Create Reservation**.
1. On the **Create Reservation** page, set the options as follows:
   
   1. In **Reservation name**, type `model`.
   1. In **Location**, choose whatever region you purchased the flex slots in.
   1. In **Number of slots**, type `500`.
   1. Click **Save**. This returns you to the **Reservations** page.

1. Select the **Assignments** tab.
1. In **Select an organization, folder, or project**, click **Browse**.
1. Type the name of the project you are using.
1. Click **Select**.
1. In **Reservation**, choose the **model** reservation you created.
1. Click **Create**.

### Create a Firestore in Datastore Mode database instance

Create a Firestore in Datastore Mode database instance to store song title and
artist information for lookup.

1. [Open the Datastore console](https://console.cloud.google.com/datastore/welcome).
1. Click **Select Datastore Mode**.
1. For **Select a location**, choose the region you want to use for Datastore.
1. Click **Create Database**.

### Create a Cloud Storage bucket

Create a Cloud Storage bucket to store the following objects:

-  The SavedModel files for the models created in the solution.
-  The temp files created by the Dataflow pipeline that processes the song
   embeddings.
-  The CSV files for the processed embeddings.

1. [Open the Cloud Storage console](https://console.cloud.google.com/storage/browser).
1. Click **Create Bucket**.
1. For Name your bucket, type a bucket name. The name must be globally unique.
1. For **Choose where to store your data**, select **Region** and then choose
   the region you want to use for Cloud Storage.
1. Click **Create**.

### Create an AI Platform Notebooks instance

Create an AI Platform Notebooks instance to run the notebooks that walk you
through using the solution.

1. [Open the AI Platform Notebooks console](https://console.cloud.google.com/ai-platform/notebooks/instances).
1. Click **New Instance**.
1. Choose **TensorFlow Enterprise 2.3, Without GPUs**.
1. For **Instance name**, type `embeddings-notebooks`.
1. For **Region**, choose the region you want to use for the AI Platform
   Notebooks instance.
1. Click **Create**. It takes a few minutes for the notebook instance to be
   created.

### Give the Cloud Build service account permissions to interact with Compute Engine 

1. Open the
   [Cloud Build settings page](https://console.cloud.google.com/cloud-build/settings/service-account).
1. In the service account list, find the row for **Compute Engine** and
   change the **Status** column value to **Enabled**.

### Update the Compute Engine service account permissions

Add the Compute Engine service account to the IAM Security Admin role. This is
required so that later this account can set up other service accounts needed by
the solution.

1. Open the [IAM permissions
   page](https://console.cloud.google.com/iam-admin/iam).
1. In the members list, find the row for
   `<projectNumber>-compute@developer.gserviceaccount.com` and click **Edit**.
1. Click **Add another role**.
1. In **Select a role**, choose **IAM** and then choose **Security Admin**.
1. Click **Save**.

### Create an AI Platform pipeline

Create an AI Platform Pipelines instance to run the TensorFlow Extended (TFX)
pipeline that automates the solution workflow. You can skip this step if you are
running the solution using the step-by-step notebooks.

#### Create a Cloud SQL instance

Create a Cloud SQL instance to provide managed storage for the pipeline.

1. [Open the Cloud SQL
   console](https://console.cloud.google.com/sql/instances).
1. Click **Create Instance**.
1. On the **MySQL** card, click **Choose MySQL**.
1. For **Instance ID**, type `pipeline-db`.
1. For **Root Password**, type in the password you want to use for the root
   user.
1. For **Region**, type in the region you want to use for the database instance.
1. Click **Create**.

#### Create the pipeline

1. [Open the AI Platform Pipelines console](https://console.cloud.google.com/ai-platform/pipelines/clusters).
1. In the AI Platform Pipelines toolbar, click **New instance**. Kubeflow
   Pipelines opens in Google Cloud Marketplace.
1. Click **Configure**. The **Deploy Kubeflow Pipelines** form opens.
1. For **Cluster zone**, choose a zone in the region you want to use for AI
   Platform Pipelines.
1. Check **Allow access to the following Cloud APIs** to grant applications
   that run on your GKE cluster access to Google Cloud resources. By checking
   this box, you are granting your cluster access to the
   `https://www.googleapis.com/auth/cloud-platform` access scope. This access
   scope provides full access to the Google Cloud resources that you have
   enabled in your project. Granting your cluster access to Google Cloud
   resources in this manner saves you the effort of
   [creating and managing a service account](https://cloud.google.com/ai-platform/pipelines/docs/configure-gke-cluster#configure-service-account)
   or
   [creating a Kubernetes secret](https://cloud.google.com/ai-platform/pipelines/docs/configure-gke-cluster#grant-access).
1. Click **Create cluster**. This step may take several minutes.
1. Select **Create a namespace** in the **Namespace** drop-down list. Type
   `kubeflow-pipelines` in **New namespace name**.

   To learn more about namespaces, read a blog post about
[organizing Kubernetes with namespaces](https://cloud.google.com/blog/products/gcp/kubernetes-best-practices-organizing-with-namespaces).

1. In the **App instance name** box, type `kubeflow-pipelines`.
1. Select **Use managed storage** and supply the following information:

    -  **Artifact storage Cloud Storage bucket**: Specify the name of the
       bucket you created in the "Create a Cloud Storage bucket" procedure.
    -  **Cloud SQL instance connection name**: Specify the connection name
       for the Cloud SQL instance you created in the "Create a Cloud SQL
       instance" procedure. The instance connection name can be found on the
       instance detail page in the Cloud SQL console.
    -  **Database username**: Leave this field empty to default to **root**.
    -  **Database password**: Specify the root user password for the Cloud
       SQL instance you created in the "Create a Cloud SQL instance" procedure.
    -  **Database name prefix**: Type `embeddings`.

1. Click **Deploy**. This step may take several minutes.

## Set up the AI Platform Notebooks environment

You use notebooks to complete the prerequisites and then run the
solution. To use the notebooks, you must clone the solution's GitHub repo to
your AI Platform Notebooks JupyterLab instance.

1. [Open the AI Platform Notebooks console](https://console.cloud.google.com/ai-platform/notebooks/instances).
1. Click **Open JupyterLab** for the `embeddings-notebooks` instance.
1. In the **Other** section of the JupyterLab Launcher, click **Terminal**.
1. In the terminal, run the following command to clone the
   `analytics-componentized-patterns` Github repository:

    ```
    git clone https://github.com/GoogleCloudPlatform/analytics-componentized-patterns.git
    ```

1. In the terminal, run the following command to install packages required
   by the solution:

    ```
    pip install -r analytics-componentized-patterns/retail/recommendation-system/bqml-scann/requirements.txt
    ```

## Delete the GCP resources

Unless you plan to continue using the resources you created in this solution, you should delete them
to avoid incurring charges to your GCP account. You can either delete the project containing the resources, or
keep the project but delete just those resources.

Either way, you should remove the resources so you won't be billed for them in
the future. The following sections describe how to delete these resources.

### Delete the project

The easiest way to eliminate billing is to delete the project you created for
the solution.

1. In the Cloud Console, go to the [Manage resources page](https://pantheon.corp.google.com/cloud-resource-manager).
1. In the project list, select the project that you want to delete, and then click **Delete**.
1. In the dialog, type the project ID, and then click **Shut down** to delete the project.

### Delete the components

If you don't want to delete the project, delete the billable components of the solution.
These can include:

1. A Bigquery assignment, reservation, and remaining flex slots (if you chose to use flex slots to train the matrix factorization model)
1. A BigQuery dataset
1. Several Cloud Storage buckets
1. Datastore entities
1. An AI Platform Notebooks instance
1. AI Platform models
1. A Kubernetes Engine cluster (if you used a pipeline for automation)
1. An AI Platform pipeline (if you used a pipeline for automation)
1. A Cloud SQL instance (if you used a pipeline for automation)
1. A Container Registry image (if you used a pipeline for automation)

## Experimental variant

The experimental variant of the solution utilizes the new AI Platform and AI
Platform (Unified) Pipelines services. Note that both services are currently in
the Experimental stage and that the provided examples may have to be updated
when the services move to the Preview and eventually to the General
Availability. Setting up the managed ANN service is described in the
[ann_setup.md](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/blob/master/retail/recommendation-system/bqml-scann/ann_setup.md)
file.  
Note: To use the Experimental releases of AI Platform Pipelines and ANN services
you need to allow-list you project and user account. Please contact your Google
representative for more information and support.

### Experimental variant workflow

1. Compute pointwise mutual information (PMI) between items based on their
   co-occurrences.
1. Train item embeddings using BigQuery ML Matrix Factorization, with item
   PMI as implicit feedback.
1. Post-process and export the embeddings from BigQuery ML Matrix
   Factorization Model to Cloud Storage JSONL formatted files.
1. Create an approximate nearest search index using the ANN service and the
   exported embedding files.
1. Deployed to the index as an ANN service endpoint.

Note that the first two steps are the same as the ScaNN library based
solution.

![Workflow Ann](figures/ann-flow.png)

We provide an example TFX pipeline that automates the process of training the
embeddings and deploying the index.  
The pipeline is designed to run on AI Platform (Unified) Pipelines and relies on
features introduced in v0.25 of TFX. Each step of the pipeline is implemented as
a
[TFX Custom Python function component](https://www.tensorflow.org/tfx/guide/custom_function_component).
All steps and their inputs and outputs are tracked in the AI Platform (Unified)
ML Metadata service.

![TFX Ann](figures/ann-tfx.png)

### Run the experimental variant with notebooks

1. [ann01_create_index.ipynb](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/blob/master/retail/recommendation-system/bqml-scann/ann01_create_index.ipynb)
   - This notebook walks you through creating an ANN index, creating an ANN
   endpoint, and deploying the index to the endpoint. It also shows how to call
   the interfaces exposed by the deployed index.
1. [ann02_run_pipeline.ipynb](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/blob/master/retail/recommendation-system/bqml-scann/ann02_run_pipeline.ipynb)
   - This notebook demonstrates how to create and test the TFX pipeline and how
   to submit pipeline runs to AI Platform (Unified) Pipelines.

Before experimenting with the notebooks, make sure that you have prepared the BigQuery environment and trained and extracted item embeddings using the procedures described in the ScaNN library based solution.

## Questions? Feedback?
If you have any questions or feedback, please open up a [new issue](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/issues).

## License

Copyright 2020 Google LLC

Licensed under the Apache License, Version 2.0 (the "License"); you may not use
this file except in compliance with the License. You may obtain a copy of the
License at:
[http://www.apache.org/licenses/LICENSE-2.0](http://www.apache.org/licenses/LICENSE-2.0)

Unless required by applicable law or agreed to in writing, software distributed
under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
CONDITIONS OF ANY KIND, either express or implied.

See the License for the specific language governing permissions and limitations
under the License.

This is not an official Google product but sample code provided for an
educational purpose


================================================
FILE: retail/recommendation-system/bqml-scann/ann01_create_index.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Low-latency item-to-item recommendation system - Creating ANN index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "This notebook is a part of the series that describes the process of implementing a [**Low-latency item-to-item recommendation system**](https://github.com/jarokaz/analytics-componentized-patterns/tree/master/retail/recommendation-system/bqml-scann).\n",
    "\n",
    "The notebook demonstrates how to create and deploy an ANN index using item embeddings created in the preceding notebooks. In this notebook you go through the following steps.\n",
    "\n",
    "1. Exporting embeddings from BigQuery into the JSONL formated file.\n",
    "2. Creating an ANN Index using the exported embeddings.\n",
    "3. Creating and ANN Endpoint. \n",
    "4. Deploying the ANN Index to the ANN Endpoint.\n",
    "5. Testing the deployed ANN Index.\n",
    "\n",
    "This notebook was designed to run on [AI Platform Notebooks](https://cloud.google.com/ai-platform-notebooks). Before running the notebook make sure that you have completed the setup steps as described in the [README file](README.md).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up the notebook's environment\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import notebook dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import base64\n",
    "import datetime\n",
    "import logging\n",
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "import time\n",
    "import sys\n",
    "\n",
    "import grpc\n",
    "\n",
    "import google.auth\n",
    "import numpy as np\n",
    "import tensorflow.io as tf_io\n",
    "\n",
    "from google.cloud import bigquery\n",
    "from typing import List, Optional, Text, Tuple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the experimental release, the *Online Querying API* of the ANN service is exposed throught the GRPC interface. The `ann_grpc` folder contains the grpc client stub to interface to the API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ANN_GRPC_ENDPOINT_STUB = 'ann_grpc'\n",
    "if ANN_GRPC_ENDPOINT_STUB not in sys.path:\n",
    "    sys.path.append(ANN_GRPC_ENDPOINT_STUB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ann_grpc.match_pb2_grpc as match_pb2_grpc\n",
    "import ann_grpc.match_pb2 as match_pb2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configure GCP environment\n",
    "\n",
    "Set the following constants to the values reflecting your environment:\n",
    "\n",
    "* `PROJECT_ID` - your GCP project ID.\n",
    "* `PROJECT_NUMBER` - your GCP project number.\n",
    "* `BQ_DATASET_NAME` - the name of the BigQuery dataset that contains the item embeddings table.\n",
    "* `BQ_LOCATION` - the dataset location\n",
    "* `DATA_LOCATION` - a GCS location for the exported embeddings (JSONL) files.\n",
    "* `VPC_NAME` - a name of the GCP VPC to use for the index deployments. Use the name of the VPC prepared during the initial setup. \n",
    "* `REGION` - a compute region. Don't change the default - `us-central` - while the ANN Service is in the experimental stage\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROJECT_ID = 'jk-mlops-dev' # <-CHANGE THIS\n",
    "PROJECT_NUMBER = '895222332033' # <-CHANGE THIS\n",
    "BQ_DATASET_NAME = 'song_embeddings' # <- CHANGE THIS\n",
    "BQ_LOCATION = 'US' # <- CHANGE THIS\n",
    "DATA_LOCATION = 'gs://jk-ann-staging/embeddings' # <-CHANGE THIS\n",
    "VPC_NAME = 'default' # <-CHANGE THIS\n",
    "\n",
    "EMBEDDINGS_TABLE = 'item_embeddings'\n",
    "REGION = 'us-central1'\n",
    "MATCH_SERVICE_PORT = 10000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exporting the embeddings\n",
    "\n",
    "In the preceeding notebooks you trained the Matrix Factorization BQML model and exported the embeddings to the `item_embeddings` table. \n",
    "\n",
    "In this step you will extract the embeddings to a set of JSONL files in the format required by the ANN service."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verify the number of embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = bigquery.Client(project=PROJECT_ID, location=BQ_LOCATION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>embedding_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   embedding_count\n",
       "0            35519"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = f\"\"\"\n",
    "    SELECT COUNT(*) embedding_count\n",
    "    FROM {BQ_DATASET_NAME}.item_embeddings;\n",
    "\"\"\"\n",
    "\n",
    "query_job = client.query(query)\n",
    "query_job.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export the embeddings\n",
    "\n",
    "You will use the [BigQuery export job](https://cloud.google.com/bigquery/docs/exporting-data) to export the embeddings table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<google.cloud.bigquery.job.ExtractJob at 0x7f2e4841f0d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name_pattern = 'embedding-*.json'\n",
    "destination_uri = f'{DATA_LOCATION}/{file_name_pattern}'\n",
    "table_id = 'item_embeddings'\n",
    "destination_format = 'NEWLINE_DELIMITED_JSON'\n",
    "\n",
    "dataset_ref = bigquery.DatasetReference(PROJECT_ID, BQ_DATASET_NAME)\n",
    "table_ref = dataset_ref.table(table_id)\n",
    "job_config = bigquery.job.ExtractJobConfig()\n",
    "job_config.destination_format = bigquery.DestinationFormat.NEWLINE_DELIMITED_JSON\n",
    "\n",
    "extract_job = client.extract_table(\n",
    "    table_ref,\n",
    "    destination_uris=destination_uri,\n",
    "    job_config=job_config,\n",
    "    #location=BQ_LOCATION,\n",
    ")  \n",
    "\n",
    "extract_job.result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect the extracted files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gs://jk-ann-staging/embeddings/embedding-000000000000.json\n"
     ]
    }
   ],
   "source": [
    "! gcloud storage ls {DATA_LOCATION}"   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating an ANN index deployment\n",
    "\n",
    "Deploying an ANN index is a 3 step process:\n",
    "1. Creating an index from source files\n",
    "2. Creating an endpoint to access the index\n",
    "3. Deploying the index to the endpoint\n",
    "\n",
    "\n",
    "You will use the REST interface to invoke the AI Platform ANN Service Control Plane API that manages indexes, endpoints, and deployments.\n",
    "\n",
    "After the index has been deployed you can submit matching requests using Online Querying API. In the experimental stage this API is only accessible through the gRPC interface.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define helper classes to encapsulate the ANN Service REST API.\n",
    "\n",
    "Currently, there is no Python client that encapsulates the ANN Service Control Plane API. The below code snippet defines a simple wrapper that encapsulates a subset of REST APIs used in this notebook.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import logging\n",
    "import json\n",
    "import time\n",
    "\n",
    "import google.auth\n",
    "\n",
    "class ANNClient(object):\n",
    "    \"\"\"Base ANN Service client.\"\"\"\n",
    "    \n",
    "    def __init__(self, project_id, project_number, region):\n",
    "        credentials, _ = google.auth.default()\n",
    "        self.authed_session = google.auth.transport.requests.AuthorizedSession(credentials)\n",
    "        self.ann_endpoint = f'{region}-aiplatform.googleapis.com'\n",
    "        self.ann_parent = f'https://{self.ann_endpoint}/v1alpha1/projects/{project_id}/locations/{region}'\n",
    "        self.project_id = project_id\n",
    "        self.project_number = project_number\n",
    "        self.region = region\n",
    "        \n",
    "    def wait_for_completion(self, operation_id, message, sleep_time):\n",
    "        \"\"\"Waits for a completion of a long running operation.\"\"\"\n",
    "        \n",
    "        api_url = f'{self.ann_parent}/operations/{operation_id}'\n",
    "\n",
    "        start_time = datetime.datetime.utcnow()\n",
    "        while True:\n",
    "            response = self.authed_session.get(api_url)\n",
    "            if response.status_code != 200:\n",
    "                raise RuntimeError(response.json())\n",
    "            if 'done' in response.json().keys():\n",
    "                logging.info('Operation completed!')\n",
    "                break\n",
    "            elapsed_time = datetime.datetime.utcnow() - start_time\n",
    "            logging.info('{}. Elapsed time since start: {}.'.format(\n",
    "                message, str(elapsed_time)))\n",
    "            time.sleep(sleep_time)\n",
    "    \n",
    "        return response.json()['response']\n",
    "\n",
    "\n",
    "class IndexClient(ANNClient):\n",
    "    \"\"\"Encapsulates a subset of control plane APIs \n",
    "    that manage ANN indexes.\"\"\"\n",
    "\n",
    "    def __init__(self, project_id, project_number, region):\n",
    "        super().__init__(project_id, project_number, region)\n",
    "\n",
    "    def create_index(self, display_name, description, metadata):\n",
    "        \"\"\"Creates an ANN Index.\"\"\"\n",
    "    \n",
    "        api_url = f'{self.ann_parent}/indexes'\n",
    "    \n",
    "        request_body = {\n",
    "            'display_name': display_name,\n",
    "            'description': description,\n",
    "            'metadata': metadata\n",
    "        }\n",
    "    \n",
    "        response = self.authed_session.post(api_url, data=json.dumps(request_body))\n",
    "        if response.status_code != 200:\n",
    "            raise RuntimeError(response.text)\n",
    "        operation_id = response.json()['name'].split('/')[-1]\n",
    "        \n",
    "        return operation_id\n",
    "\n",
    "    def list_indexes(self, display_name=None):\n",
    "        \"\"\"Lists all indexes with a given display name or\n",
    "        all indexes if the display_name is not provided.\"\"\"\n",
    "    \n",
    "        if display_name:\n",
    "            api_url = f'{self.ann_parent}/indexes?filter=display_name=\"{display_name}\"'\n",
    "        else:\n",
    "            api_url = f'{self.ann_parent}/indexes'\n",
    "\n",
    "        response = self.authed_session.get(api_url).json()\n",
    "\n",
    "        return response['indexes'] if response else []\n",
    "    \n",
    "    def delete_index(self, index_id):\n",
    "        \"\"\"Deletes an ANN index.\"\"\"\n",
    "        \n",
    "        api_url = f'{self.ann_parent}/indexes/{index_id}'\n",
    "        response = self.authed_session.delete(api_url)\n",
    "        if response.status_code != 200:\n",
    "            raise RuntimeError(response.text)\n",
    "\n",
    "\n",
    "class IndexDeploymentClient(ANNClient):\n",
    "    \"\"\"Encapsulates a subset of control plane APIs \n",
    "    that manage ANN endpoints and deployments.\"\"\"\n",
    "    \n",
    "    def __init__(self, project_id, project_number, region):\n",
    "        super().__init__(project_id, project_number, region)\n",
    "\n",
    "    def create_endpoint(self, display_name, vpc_name):\n",
    "        \"\"\"Creates an ANN endpoint.\"\"\"\n",
    "    \n",
    "        api_url = f'{self.ann_parent}/indexEndpoints'\n",
    "        network_name = f'projects/{self.project_number}/global/networks/{vpc_name}'\n",
    "\n",
    "        request_body = {\n",
    "            'display_name': display_name,\n",
    "            'network': network_name\n",
    "        }\n",
    "\n",
    "        response = self.authed_session.post(api_url, data=json.dumps(request_body))\n",
    "        if response.status_code != 200:\n",
    "            raise RuntimeError(response.text)\n",
    "        operation_id = response.json()['name'].split('/')[-1]\n",
    "    \n",
    "        return operation_id\n",
    "    \n",
    "    def list_endpoints(self, display_name=None):\n",
    "        \"\"\"Lists all ANN endpoints with a given display name or\n",
    "        all endpoints in the project if the display_name is not provided.\"\"\"\n",
    "        \n",
    "        if display_name:\n",
    "            api_url = f'{self.ann_parent}/indexEndpoints?filter=display_name=\"{display_name}\"'\n",
    "        else:\n",
    "            api_url = f'{self.ann_parent}/indexEndpoints'\n",
    "\n",
    "        response = self.authed_session.get(api_url).json()\n",
    " \n",
    "        return response['indexEndpoints'] if response else []\n",
    "    \n",
    "    def delete_endpoint(self, endpoint_id):\n",
    "        \"\"\"Deletes an ANN endpoint.\"\"\"\n",
    "        \n",
    "        api_url = f'{self.ann_parent}/indexEndpoints/{endpoint_id}'\n",
    "        \n",
    "        response = self.authed_session.delete(api_url)\n",
    "        if response.status_code != 200:\n",
    "            raise RuntimeError(response.text)\n",
    "        \n",
    "        return response.json()\n",
    "    \n",
    "    def create_deployment(self, display_name, deployment_id, endpoint_id, index_id):\n",
    "        \"\"\"Deploys an ANN index to an endpoint.\"\"\"\n",
    "    \n",
    "        api_url = f'{self.ann_parent}/indexEndpoints/{endpoint_id}:deployIndex'\n",
    "        index_name = f'projects/{self.project_number}/locations/{self.region}/indexes/{index_id}'\n",
    "\n",
    "        request_body = {\n",
    "            'deployed_index': {\n",
    "                'id': deployment_id,\n",
    "                'index': index_name,\n",
    "                'display_name': display_name\n",
    "            }\n",
    "        }\n",
    "\n",
    "        response = self.authed_session.post(api_url, data=json.dumps(request_body))\n",
    "        if response.status_code != 200:\n",
    "            raise RuntimeError(response.text)\n",
    "        operation_id = response.json()['name'].split('/')[-1]\n",
    "        \n",
    "        return operation_id\n",
    "    \n",
    "    def get_deployment_grpc_ip(self, endpoint_id, deployment_id):\n",
    "        \"\"\"Returns a private IP address for a gRPC interface to \n",
    "        an Index deployment.\"\"\"\n",
    "  \n",
    "        api_url = f'{self.ann_parent}/indexEndpoints/{endpoint_id}'\n",
    "\n",
    "        response = self.authed_session.get(api_url)\n",
    "        if response.status_code != 200:\n",
    "            raise RuntimeError(response.text)\n",
    "            \n",
    "        endpoint_ip = None\n",
    "        if 'deployedIndexes' in response.json().keys():\n",
    "            for deployment in response.json()['deployedIndexes']:\n",
    "                if deployment['id'] == deployment_id:\n",
    "                    endpoint_ip = deployment['privateEndpoints']['matchGrpcAddress']\n",
    "                    \n",
    "        return endpoint_ip\n",
    "\n",
    "    \n",
    "    def delete_deployment(self, endpoint_id, deployment_id):\n",
    "        \"\"\"Undeployes an index from an endpoint.\"\"\"\n",
    "        \n",
    "        api_url = f'{self.ann_parent}/indexEndpoints/{endpoint_id}:undeployIndex'\n",
    "        \n",
    "        request_body = {\n",
    "            'deployed_index_id': deployment_id\n",
    "        }\n",
    "    \n",
    "        response = self.authed_session.post(api_url, data=json.dumps(request_body))\n",
    "        if response.status_code != 200:\n",
    "            raise RuntimeError(response.text)\n",
    "        \n",
    "        return response\n",
    "    \n",
    "index_client = IndexClient(PROJECT_ID, PROJECT_NUMBER, REGION)\n",
    "deployment_client = IndexDeploymentClient(PROJECT_ID, PROJECT_NUMBER, REGION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create an ANN index\n",
    "\n",
    "#### List all indexes registered with the ANN service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are not any indexes registered with the service\n"
     ]
    }
   ],
   "source": [
    "indexes = index_client.list_indexes()\n",
    "\n",
    "if not indexes:\n",
    "    print('There are not any indexes registered with the service')\n",
    "\n",
    "for index in indexes:\n",
    "    print(index['name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Configure and create a new index based on the exported embeddings\n",
    "\n",
    "Index creation is a long running operation. Be patient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_display_name = 'Song embeddings'\n",
    "index_description = 'Song embeddings created BQML Matrix Factorization model'\n",
    "\n",
    "index_metadata = {\n",
    "    'contents_delta_uri': DATA_LOCATION,\n",
    "    'config': {\n",
    "        'dimensions': 50,\n",
    "        'approximate_neighbors_count': 50,\n",
    "        'distance_measure_type': 'DOT_PRODUCT_DISTANCE',\n",
    "        'feature_norm_type': 'UNIT_L2_NORM',\n",
    "        'tree_ah_config': {\n",
    "            'child_node_count': 1000,\n",
    "            'max_leaves_to_search': 100\n",
    "         }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Creating index. Elapsed time since start: 0:00:00.284307.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:00:20.590597.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:00:40.895456.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:01:01.212661.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:01:21.541146.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:01:41.850597.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:02:02.007845.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:02:22.286154.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:02:42.594481.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:03:02.768118.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:03:23.046792.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:03:43.167440.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:04:03.450286.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:04:23.576374.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:04:43.861362.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:05:04.153150.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:05:24.271262.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:05:44.388816.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:06:04.500550.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:06:24.816035.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:06:44.921801.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:07:05.245659.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:07:25.388260.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:07:45.692861.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:08:05.998090.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:08:26.113222.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:08:46.253836.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:09:06.423070.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:09:26.701742.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:09:46.817432.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:10:07.115610.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:10:27.229846.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:10:47.518691.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:11:07.814059.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:11:28.202194.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:11:48.485845.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:12:08.603703.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:12:28.930780.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:12:49.167657.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:13:09.455059.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:13:29.596488.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:13:49.921589.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:14:10.489026.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:14:30.787925.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:14:51.157541.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:15:11.284806.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:15:31.405137.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:15:51.756367.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:16:12.004751.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:16:32.280723.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:16:52.573849.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:17:12.856906.\n",
      "INFO:root:Creating index. Elapsed time since start: 0:17:33.208577.\n",
      "INFO:root:Operation completed!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'@type': 'type.googleapis.com/google.cloud.aiplatform.v1alpha1.Index', 'name': 'projects/895222332033/locations/us-central1/indexes/4272227196514336768'}\n"
     ]
    }
   ],
   "source": [
    "logging.getLogger().setLevel(logging.INFO)\n",
    "\n",
    "operation_id = index_client.create_index(index_display_name, \n",
    "                                          index_description,\n",
    "                                          index_metadata)\n",
    "\n",
    "response = index_client.wait_for_completion(operation_id, 'Creating index', 20)\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Verify that the index was created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "projects/895222332033/locations/us-central1/indexes/4272227196514336768\n",
      "Index: 4272227196514336768 will be used for deployment\n"
     ]
    }
   ],
   "source": [
    "indexes = index_client.list_indexes(index_display_name)\n",
    "\n",
    "for index in indexes:\n",
    "    print(index['name'])\n",
    "\n",
    "if indexes: \n",
    "    index_id = index['name'].split('/')[-1]\n",
    "    print(f'Index: {index_id} will be used for deployment')\n",
    "else:\n",
    "    print('No indexes available for deployment')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the index deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### List all endpoints registered with the ANN service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are not any endpoints registered with the service\n"
     ]
    }
   ],
   "source": [
    "endpoints = deployment_client.list_endpoints()\n",
    "\n",
    "if not endpoints:\n",
    "    print('There are not any endpoints registered with the service')\n",
    "\n",
    "for endpoint in endpoints:\n",
    "    print(endpoint['name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create an index endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "deployment_display_name = 'Song embeddings endpoint'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Waiting for endpoint. Elapsed time since start: 0:00:00.275412.\n",
      "INFO:root:Waiting for endpoint. Elapsed time since start: 0:00:10.556182.\n",
      "INFO:root:Operation completed!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'@type': 'type.googleapis.com/google.cloud.aiplatform.v1alpha1.IndexEndpoint', 'name': 'projects/895222332033/locations/us-central1/indexEndpoints/4112349409742684160'}\n"
     ]
    }
   ],
   "source": [
    "operation_id = deployment_client.create_endpoint(deployment_display_name, VPC_NAME)\n",
    "\n",
    "response = index_client.wait_for_completion(operation_id, 'Waiting for endpoint', 10)\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Verify that the endpoint was created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "projects/895222332033/locations/us-central1/indexEndpoints/4112349409742684160\n",
      "Endpoint: 4112349409742684160 will be used for deployment\n"
     ]
    }
   ],
   "source": [
    "endpoints = deployment_client.list_endpoints(deployment_display_name)\n",
    "\n",
    "for endpoint in endpoints:\n",
    "    print(endpoint['name'])\n",
    "    \n",
    "if endpoints: \n",
    "    endpoint_id = endpoint['name'].split('/')[-1]\n",
    "    print(f'Endpoint: {endpoint_id} will be used for deployment')\n",
    "else:\n",
    "    print('No endpoints available for deployment')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Deploy the index to the endpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Set the deployed index ID\n",
    "\n",
    "The ID of the deployed index must be unique within your project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "deployment_display_name = 'Song embeddings deployed index'\n",
    "deployed_index_id = 'songs_embeddings_deployed_index'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Deploy the index\n",
    "\n",
    "Be patient. Index deployment is a long running operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:00:00.300974.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'projects/895222332033/locations/us-central1/indexEndpoints/4112349409742684160/operations/9143743205747982336', 'metadata': {'@type': 'type.googleapis.com/google.cloud.aiplatform.v1alpha1.DeployIndexOperationMetadata', 'genericMetadata': {'createTime': '2021-01-14T20:19:24.354833Z', 'updateTime': '2021-01-14T20:19:24.354833Z'}}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:00:10.608720.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'projects/895222332033/locations/us-central1/indexEndpoints/4112349409742684160/operations/9143743205747982336', 'metadata': {'@type': 'type.googleapis.com/google.cloud.aiplatform.v1alpha1.DeployIndexOperationMetadata', 'genericMetadata': {'createTime': '2021-01-14T20:19:24.354833Z', 'updateTime': '2021-01-14T20:19:24.354833Z'}}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:00:20.916550.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'projects/895222332033/locations/us-central1/indexEndpoints/4112349409742684160/operations/9143743205747982336', 'metadata': {'@type': 'type.googleapis.com/google.cloud.aiplatform.v1alpha1.DeployIndexOperationMetadata', 'genericMetadata': {'createTime': '2021-01-14T20:19:24.354833Z', 'updateTime': '2021-01-14T20:19:24.354833Z'}}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:00:31.250512.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'projects/895222332033/locations/us-central1/indexEndpoints/4112349409742684160/operations/9143743205747982336', 'metadata': {'@type': 'type.googleapis.com/google.cloud.aiplatform.v1alpha1.DeployIndexOperationMetadata', 'genericMetadata': {'createTime': '2021-01-14T20:19:24.354833Z', 'updateTime': '2021-01-14T20:19:24.354833Z'}}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:00:41.539311.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'projects/895222332033/locations/us-central1/indexEndpoints/4112349409742684160/operations/9143743205747982336', 'metadata': {'@type': 'type.googleapis.com/google.cloud.aiplatform.v1alpha1.DeployIndexOperationMetadata', 'genericMetadata': {'createTime': '2021-01-14T20:19:24.354833Z', 'updateTime': '2021-01-14T20:19:24.354833Z'}}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:00:51.832651.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'projects/895222332033/locations/us-central1/indexEndpoints/4112349409742684160/operations/9143743205747982336', 'metadata': {'@type': 'type.googleapis.com/google.cloud.aiplatform.v1alpha1.DeployIndexOperationMetadata', 'genericMetadata': {'createTime': '2021-01-14T20:19:24.354833Z', 'updateTime': '2021-01-14T20:19:24.354833Z'}}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:01:02.144021.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'projects/895222332033/locations/us-central1/indexEndpoints/4112349409742684160/operations/9143743205747982336', 'metadata': {'@type': 'type.googleapis.com/google.cloud.aiplatform.v1alpha1.DeployIndexOperationMetadata', 'genericMetadata': {'createTime': '2021-01-14T20:19:24.354833Z', 'updateTime': '2021-01-14T20:19:24.354833Z'}}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:01:12.452319.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'projects/895222332033/locations/us-central1/indexEndpoints/4112349409742684160/operations/9143743205747982336', 'metadata': {'@type': 'type.googleapis.com/google.cloud.aiplatform.v1alpha1.DeployIndexOperationMetadata', 'genericMetadata': {'createTime': '2021-01-14T20:19:24.354833Z', 'updateTime': '2021-01-14T20:19:24.354833Z'}}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:01:22.717381.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'projects/895222332033/locations/us-central1/indexEndpoints/4112349409742684160/operations/9143743205747982336', 'metadata': {'@type': 'type.googleapis.com/google.cloud.aiplatform.v1alpha1.DeployIndexOperationMetadata', 'genericMetadata': {'createTime': '2021-01-14T20:19:24.354833Z', 'updateTime': '2021-01-14T20:19:24.354833Z'}}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:01:33.029002.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'projects/895222332033/locations/us-central1/indexEndpoints/4112349409742684160/operations/9143743205747982336', 'metadata': {'@type': 'type.googleapis.com/google.cloud.aiplatform.v1alpha1.DeployIndexOperationMetadata', 'genericMetadata': {'createTime': '2021-01-14T20:19:24.354833Z', 'updateTime': '2021-01-14T20:19:24.354833Z'}}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:01:43.170426.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'projects/895222332033/locations/us-central1/indexEndpoints/4112349409742684160/operations/9143743205747982336', 'metadata': {'@type': 'type.googleapis.com/google.cloud.aiplatform.v1alpha1.DeployIndexOperationMetadata', 'genericMetadata': {'createTime': '2021-01-14T20:19:24.354833Z', 'updateTime': '2021-01-14T20:19:24.354833Z'}}}\n"
     ]
    },
    {
     "name": "stderr",
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     "output_type": "stream",
     "text": [
      "{'name': 'projects/895222332033/locations/us-central1/indexEndpoints/4112349409742684160/operations/9143743205747982336', 'metadata': {'@type': 'type.googleapis.com/google.cloud.aiplatform.v1alpha1.DeployIndexOperationMetadata', 'genericMetadata': {'createTime': '2021-01-14T20:19:24.354833Z', 'updateTime': '2021-01-14T20:19:24.354833Z'}}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:13:16.369676.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'projects/895222332033/locations/us-central1/indexEndpoints/4112349409742684160/operations/9143743205747982336', 'metadata': {'@type': 'type.googleapis.com/google.cloud.aiplatform.v1alpha1.DeployIndexOperationMetadata', 'genericMetadata': {'createTime': '2021-01-14T20:19:24.354833Z', 'updateTime': '2021-01-14T20:19:24.354833Z'}}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:13:26.658924.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'projects/895222332033/locations/us-central1/indexEndpoints/4112349409742684160/operations/9143743205747982336', 'metadata': {'@type': 'type.googleapis.com/google.cloud.aiplatform.v1alpha1.DeployIndexOperationMetadata', 'genericMetadata': {'createTime': '2021-01-14T20:19:24.354833Z', 'updateTime': '2021-01-14T20:19:24.354833Z'}}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:13:36.765529.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'projects/895222332033/locations/us-central1/indexEndpoints/4112349409742684160/operations/9143743205747982336', 'metadata': {'@type': 'type.googleapis.com/google.cloud.aiplatform.v1alpha1.DeployIndexOperationMetadata', 'genericMetadata': {'createTime': '2021-01-14T20:19:24.354833Z', 'updateTime': '2021-01-14T20:19:24.354833Z'}}}\n"
     ]
    }
   ],
   "source": [
    "response = index_client.wait_for_completion(operation_id, 'Waiting for deployment', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:00:00.309496.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:00:10.618832.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:00:20.953429.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:00:31.276869.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:00:41.582544.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:00:51.860203.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:01:02.145775.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:01:12.413776.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:01:22.769919.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:01:33.043710.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:01:43.331444.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:01:53.635462.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:02:03.913796.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:02:14.246248.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:02:24.438006.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:02:34.737109.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:02:45.061957.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:02:55.214420.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:03:05.503850.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:03:15.619386.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:03:25.763396.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:03:35.926869.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:03:46.206785.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:03:56.344440.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:04:06.661850.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:04:16.790771.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:04:27.100158.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:04:37.478707.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:04:47.790848.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:04:58.108535.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:05:08.379241.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:05:18.693843.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:05:28.969801.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:05:39.122504.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:05:49.430795.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:05:59.568054.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:06:09.933457.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:06:20.214257.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:06:30.500999.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:06:40.779993.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:06:51.158269.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:07:01.475487.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:07:11.603737.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:07:21.890999.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:07:32.167565.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:07:42.449151.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:07:52.745178.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:08:03.047037.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:08:13.190369.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:08:23.500509.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:08:33.640225.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:08:43.755012.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:08:54.051967.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:09:04.331984.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:09:14.495996.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:09:24.617013.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:09:34.892956.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:09:45.165349.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:09:55.271476.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:10:05.551907.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:10:15.696284.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:10:26.041164.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:10:36.310077.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:10:46.466342.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:10:56.574228.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:11:06.863240.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:11:16.965472.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:11:27.089647.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:11:37.181525.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:11:47.483513.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:11:57.588205.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:12:07.875761.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:12:18.003721.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:12:28.269875.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:12:38.527134.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:12:48.796227.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:12:59.091610.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:13:09.193477.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:13:19.279818.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:13:29.388218.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:13:39.487099.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:13:49.628959.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:13:59.883954.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:14:09.984956.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:14:20.108242.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:14:30.236006.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:14:40.592755.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:14:50.703748.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:15:00.847793.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:15:10.953756.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:15:21.107493.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:15:31.231147.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:15:41.334272.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:15:51.630401.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:16:01.733843.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:16:11.842532.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:16:21.949418.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:16:32.219992.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:16:42.518122.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:16:52.624872.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:17:02.896258.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:17:13.163637.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:17:23.462698.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:17:33.614831.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:17:43.734966.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:17:53.879712.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:18:03.987525.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:18:14.091399.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:18:24.196694.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:18:34.323345.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:18:44.498746.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:18:54.604675.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:19:04.730859.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:19:15.034432.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:19:25.186901.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:19:35.300179.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:19:45.578095.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:19:55.854867.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:20:06.188719.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:20:16.458125.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:20:26.759566.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:20:36.884720.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:20:47.229833.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:20:57.353245.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:21:07.469112.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:21:17.749214.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:21:28.010035.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:21:38.291132.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:21:58.556089.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:22:08.847796.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:22:18.973042.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:22:29.256783.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:22:39.523975.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:22:49.647099.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:22:59.779039.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:23:09.901494.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:23:20.024414.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:23:30.281691.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:23:40.411582.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:23:50.715438.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:24:00.825649.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:24:10.927193.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:24:21.204168.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:24:31.455315.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:24:41.641686.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:24:51.917218.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:25:02.204333.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:25:12.299840.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:25:22.597023.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:25:32.730363.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:25:42.868776.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:25:52.975587.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:26:03.290870.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:26:13.585602.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:26:23.726020.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:26:33.998132.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:26:44.283786.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:26:54.546448.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:27:04.661727.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:27:14.793746.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:27:25.076297.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:27:35.348142.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:27:45.497051.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:27:55.625915.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:28:05.748964.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:28:15.877515.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:28:26.164943.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:28:36.284533.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:28:46.414829.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:28:56.514943.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:29:06.662625.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:29:16.779856.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:29:26.917760.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:29:37.052899.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:29:47.182276.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:29:57.472446.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:30:07.592834.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:30:17.753531.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:30:27.850986.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:30:37.976432.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:30:48.098709.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:30:58.218721.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:31:08.348619.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:31:18.482850.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:31:28.594592.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:31:38.880292.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:31:49.007348.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:31:59.094694.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:32:09.304929.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:32:19.584198.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:32:29.706484.\n",
      "INFO:root:Waiting for deployment. Elapsed time since start: 0:32:39.832605.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-599f950d8dc2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m                                                    index_id)\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait_for_completion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moperation_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Waiting for deployment'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-9-0dd7f532d61e>\u001b[0m in \u001b[0;36mwait_for_completion\u001b[0;34m(self, operation_id, message, sleep_time)\u001b[0m\n\u001b[1;32m     34\u001b[0m             logging.info('{}. Elapsed time since start: {}.'.format(\n\u001b[1;32m     35\u001b[0m                 message, str(elapsed_time)))\n\u001b[0;32m---> 36\u001b[0;31m             \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msleep_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'response'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "operation_id = deployment_client.create_deployment(deployment_display_name, \n",
    "                                                   deployed_index_id,\n",
    "                                                   endpoint_id,\n",
    "                                                   index_id)\n",
    "\n",
    "response = index_client.wait_for_completion(operation_id, 'Waiting for deployment', 10)\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying the ANN service\n",
    "\n",
    "You will use the gRPC interface to query the deployed index."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the gRPC private endpoint for the ANN Match service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "deployed_index_ip = deployment_client.get_deployment_grpc_ip(endpoint_id, deployed_index_id)\n",
    "endpoint = f'{deployed_index_ip}:{MATCH_SERVICE_PORT}'\n",
    "print(f'gRPC endpoint for the: {deployed_index_id} deployment is: {endpoint}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a helper wrapper around the Match Service gRPC API.\n",
    "\n",
    "The wrapper uses the pre-generated gRPC stub to the Online Querying gRPC interface. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MatchService(object):\n",
    "    \"\"\"This is a wrapper around Online Querying gRPC interface.\"\"\"\n",
    "    def __init__(self, endpoint, deployed_index_id):\n",
    "        self.endpoint = endpoint\n",
    "        self.deployed_index_id = deployed_index_id\n",
    "\n",
    "    def single_match(\n",
    "        self,\n",
    "        embedding: List[float],\n",
    "        num_neighbors: int) -> List[Tuple[str, float]]:\n",
    "        \"\"\"Requests a match for a single embedding.\"\"\"\n",
    "    \n",
    "        match_request = match_pb2.MatchRequest(deployed_index_id=self.deployed_index_id,\n",
    "                                               float_val=embedding,\n",
    "                                               num_neighbors=num_neighbors)\n",
    "        with grpc.insecure_channel(endpoint) as channel:\n",
    "            stub = match_pb2_grpc.MatchServiceStub(channel)\n",
    "            response = stub.Match(match_request)\n",
    "    \n",
    "        return [(neighbor.id, neighbor.distance) for neighbor in response.neighbor]\n",
    "\n",
    "\n",
    "    def batch_match(\n",
    "        self,\n",
    "        embeddings: List[List[float]],\n",
    "        num_neighbors: int) -> List[List[Tuple[str, float]]]:\n",
    "        \"\"\"Requests matches ofr a list of embeddings.\"\"\"\n",
    "    \n",
    "        match_requests = [\n",
    "            match_pb2.MatchRequest(deployed_index_id=self.deployed_index_id,\n",
    "                                   float_val=embedding,\n",
    "                                   num_neighbors=num_neighbors)\n",
    "            for embedding in embeddings]\n",
    "    \n",
    "        batches_per_index = [\n",
    "            match_pb2.BatchMatchRequest.BatchMatchRequestPerIndex(\n",
    "                deployed_index_id=self.deployed_index_id,\n",
    "                requests=match_requests)]\n",
    "    \n",
    "        batch_match_request = match_pb2.BatchMatchRequest(\n",
    "            requests=batches_per_index)\n",
    "    \n",
    "        with grpc.insecure_channel(endpoint) as channel:\n",
    "            stub = match_pb2_grpc.MatchServiceStub(channel)\n",
    "            response = stub.BatchMatch(batch_match_request)\n",
    "        \n",
    "        matches = []\n",
    "        for batch_per_index in response.responses:\n",
    "            for match in batch_per_index.responses:\n",
    "                matches.append(\n",
    "                    [(neighbor.id, neighbor.distance) for neighbor in match.neighbor])\n",
    "        \n",
    "        return matches\n",
    "    \n",
    "match_service = MatchService(endpoint, deployed_index_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare sample data\n",
    "\n",
    "Retrieve a few embeddings from the BigQuery embedding table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bigquery df_embeddings\n",
    "\n",
    "SELECT id, embedding\n",
    "FROM `recommendations.item_embeddings` \n",
    "LIMIT 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_embeddings = [list(embedding) for embedding in df_embeddings['embedding']]\n",
    "sample_embeddings[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run a single match query\n",
    "\n",
    "The following call requests 10 closest neighbours for a single embedding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time \n",
    "\n",
    "single_match = match_service.single_match(sample_embeddings[0], 10)\n",
    "single_match"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run a batch match query\n",
    "\n",
    "The following call requests 3 closest neighbours for each of the embeddings in a batch of 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time \n",
    "\n",
    "batch_match = match_service.batch_match(sample_embeddings[0:5], 3)\n",
    "batch_match"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean up\n",
    "\n",
    "**WARNING**\n",
    "\n",
    "The below code will delete all ANN deployments, endpoints, and indexes in the configured project.\n",
    "\n",
    "### Delete index deployments and endpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleting endpoint: projects/895222332033/locations/us-central1/indexEndpoints/856246879153815552\n",
      "Deleting endpoint: projects/895222332033/locations/us-central1/indexEndpoints/3621457050359300096\n",
      "Deleting endpoint: projects/895222332033/locations/us-central1/indexEndpoints/108649341010313216\n"
     ]
    }
   ],
   "source": [
    "for endpoint in deployment_client.list_endpoints():\n",
    "    endpoint_id = endpoint['name'].split('/')[-1]\n",
    "    if 'deployedIndexes' in endpoint.keys():\n",
    "        for deployment in endpoint['deployedIndexes']:\n",
    "            print('   Deleting index deployment: {} in the endpoint: {} '.format(deployment['id'], endpoint_id))\n",
    "            deployment_client.delete_deployment(endpoint_id, deployment['id'])\n",
    "    print('Deleting endpoint: {}'.format(endpoint['name']))\n",
    "    deployment_client.delete_endpoint(endpoint_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleting index: projects/895222332033/locations/us-central1/indexes/1335880239468773376\n",
      "Deleting index: projects/895222332033/locations/us-central1/indexes/3544895856694001664\n",
      "Deleting index: projects/895222332033/locations/us-central1/indexes/4886968545650409472\n"
     ]
    }
   ],
   "source": [
    "for index in index_client.list_indexes():\n",
    "    index_id = index['name'].split('/')[-1]\n",
    "    print('Deleting index: {}'.format(index['name']))\n",
    "    index_client.delete_index(index_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License\n",
    "\n",
    "Copyright 2020 Google LLC\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n",
    "\n",
    "See the License for the specific language governing permissions and limitations under the License.\n",
    "\n",
    "**This is not an official Google product but sample code provided for an educational purpose**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "environment": {
   "name": "tf2-gpu.2-4.m61",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-4:m61"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: retail/recommendation-system/bqml-scann/ann02_run_pipeline.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Low-latency item-to-item recommendation system - Orchestrating with TFX\n",
    "\n",
    "## Overview\n",
    "\n",
    "This notebook is a part of the series that describes the process of implementing a [**Low-latency item-to-item recommendation system**](https://github.com/jarokaz/analytics-componentized-patterns/tree/master/retail/recommendation-system/bqml-scann).\n",
    "\n",
    "This notebook demonstrates how to use TFX and AI Platform Pipelines (Unified) to operationalize the workflow that creates embeddings and builds and deploys an ANN Service index. \n",
    "\n",
    "In the notebook you go through the following steps.\n",
    "\n",
    "1. Creating TFX custom components that encapsulate operations on BQ, BQML and ANN Service.\n",
    "2. Creating a TFX pipeline that automates the processes of creating embeddings and deploying an ANN Index \n",
    "3. Testing the pipeline locally using Beam runner.\n",
    "4. Compiling the pipeline to the TFX IR format for execution on AI Platform Pipelines (Unified).\n",
    "5. Submitting pipeline runs.\n",
    "\n",
    "This notebook was designed to run on [AI Platform Notebooks](https://cloud.google.com/ai-platform-notebooks). Before running the notebook make sure that you have completed the setup steps as described in the [README file](README.md).\n",
    "\n",
    "### TFX Pipeline Design\n",
    "\n",
    "The below diagram depicts the TFX pipeline that you will implement in this notebook. Each step of the pipeline is implemented as a [TFX Custom Python function component](https://www.tensorflow.org/tfx/guide/custom_function_component). The components track the relevant metadata in AI Platform (Unfied) ML Metadata using both standard and custom metadata types. \n",
    "\n",
    "![TFX pipeline](figures/ann-tfx.png)\n",
    "\n",
    "1. The first step of the pipeline is to compute item co-occurence. This is done by calling the `sp_ComputePMI` stored procedure created in the preceeding notebooks. \n",
    "2. Next, the BQML Matrix Factorization model is created. The model training code is encapsulated in the `sp_TrainItemMatchingModel` stored procedure.\n",
    "3. Item embeddings are extracted from the trained model weights and stored in a BQ table. The component calls the `sp_ExtractEmbeddings` stored procedure that implements the extraction logic.\n",
    "4. The embeddings are exported in the JSONL format to the GCS location using the BigQuery extract job.\n",
    "5. The embeddings in the JSONL format are used to create an ANN index by calling the ANN Service Control Plane REST API.\n",
    "6. Finally, the ANN index is deployed to an ANN endpoint.\n",
    "\n",
    "All steps and their inputs and outputs are tracked in the AI Platform (Unified) ML Metadata service.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up the notebook's environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install AI Platform Pipelines client library\n",
    "\n",
    "For AI Platform Pipelines (Unified), which is in the Experimental stage, you need to download and install the AI Platform client library on top of the KFP and TFX SDKs that were installed as part of the initial environment setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "AIP_CLIENT_WHEEL = 'aiplatform_pipelines_client-0.1.0.caip20201123-py3-none-any.whl'\n",
    "AIP_CLIENT_WHEEL_GCS_LOCATION = f'gs://cloud-aiplatform-pipelines/releases/20201123/{AIP_CLIENT_WHEEL}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!gcloud storage cp {AIP_CLIENT_WHEEL_GCS_LOCATION} {AIP_CLIENT_WHEEL}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install {AIP_CLIENT_WHEEL}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Restart the kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import IPython\n",
    "app = IPython.Application.instance()\n",
    "app.kernel.do_shutdown(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import notebook dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import tfx\n",
    "import tensorflow as tf\n",
    "\n",
    "from aiplatform.pipelines import client\n",
    "from tfx.orchestration.beam.beam_dag_runner import BeamDagRunner\n",
    "\n",
    "print('TFX Version: ', tfx.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configure GCP environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------\n",
    "\n",
    "**If you're on AI Platform Notebooks**, authenticate with Google Cloud before running the next section, by running\n",
    "```sh\n",
    "gcloud auth login\n",
    "```\n",
    "**in the Terminal window** (which you can open via **File** > **New** in the menu). You only need to do this once per notebook instance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the following constants to the values reflecting your environment:\n",
    "\n",
    "* `PROJECT_ID` - your GCP project ID\n",
    "* `PROJECT_NUMBER` - your GCP project number\n",
    "* `BUCKET_NAME` - a name of the GCS bucket that will be used to host artifacts created by the pipeline\n",
    "* `PIPELINE_NAME_SUFFIX` - a suffix appended to the standard pipeline name. You can change to differentiate between pipelines from different users in a classroom environment\n",
    "* `API_KEY` - a GCP API key\n",
    "* `VPC_NAME` - a name of the GCP VPC to use for the index deployments.  \n",
    "* `REGION` - a compute region. Don't change the default - `us-central` - while the ANN Service is in the experimental stage\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROJECT_ID = ''  # <---CHANGE THIS\n",
    "PROJECT_NUMBER = ''  # <---CHANGE THIS\n",
    "API_KEY = ''  # <---CHANGE THIS\n",
    "USER = 'user' # <---CHANGE THIS\n",
    "BUCKET_NAME = 'jk-ann-staging'  # <---CHANGE THIS\n",
    "VPC_NAME = 'default' # <---CHANGE THIS IF USING A DIFFERENT VPC\n",
    "\n",
    "REGION = 'us-central1'\n",
    "PIPELINE_NAME = \"ann-pipeline-{}\".format(USER)\n",
    "PIPELINE_ROOT = 'gs://{}/pipeline_root/{}'.format(BUCKET_NAME, PIPELINE_NAME)\n",
    "PATH=%env PATH\n",
    "%env PATH={PATH}:/home/jupyter/.local/bin\n",
    "    \n",
    "print('PIPELINE_ROOT: {}'.format(PIPELINE_ROOT))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining custom components\n",
    "\n",
    "In this section of the notebook you define a set of custom TFX components that encapsulate BQ, BQML and ANN Service calls. The components are [TFX Custom Python function components](https://www.tensorflow.org/tfx/guide/custom_function_component). \n",
    "\n",
    "Each component is created as a separate Python module. You also create a couple of helper modules that encapsulate Python functions and classess used across the custom components. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove files created in the previous executions of the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "component_folder = 'bq_components'\n",
    "\n",
    "if tf.io.gfile.exists(component_folder):\n",
    "    print('Removing older file')\n",
    "    tf.io.gfile.rmtree(component_folder)\n",
    "print('Creating component folder')\n",
    "tf.io.gfile.mkdir(component_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd {component_folder}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define custom types for ANN service artifacts\n",
    "\n",
    "This module defines a couple of custom TFX artifacts to track ANN Service indexes and index deployments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile ann_types.py\n",
    "# Copyright 2020 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "\"\"\"Custom types for managing ANN artifacts.\"\"\"\n",
    "\n",
    "from tfx.types import artifact\n",
    "\n",
    "class ANNIndex(artifact.Artifact):\n",
    "    TYPE_NAME = 'ANNIndex'\n",
    "    \n",
    "class DeployedANNIndex(artifact.Artifact):\n",
    "    TYPE_NAME = 'DeployedANNIndex'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a wrapper around ANN Service REST API\n",
    "\n",
    "This module provides a convenience wrapper around ANN Service REST API. In the experimental stage, the ANN Service does not have an \"official\" Python client SDK nor it is supported by the Google Discovery API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile ann_service.py\n",
    "# Copyright 2020 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "\"\"\"Helper classes encapsulating ANN Service REST API.\"\"\"\n",
    "\n",
    "import datetime\n",
    "import logging\n",
    "import json\n",
    "import time\n",
    "\n",
    "import google.auth\n",
    "\n",
    "class ANNClient(object):\n",
    "    \"\"\"Base ANN Service client.\"\"\"\n",
    "    \n",
    "    def __init__(self, project_id, project_number, region):\n",
    "        credentials, _ = google.auth.default()\n",
    "        self.authed_session = google.auth.transport.requests.AuthorizedSession(credentials)\n",
    "        self.ann_endpoint = f'{region}-aiplatform.googleapis.com'\n",
    "        self.ann_parent = f'https://{self.ann_endpoint}/v1alpha1/projects/{project_id}/locations/{region}'\n",
    "        self.project_id = project_id\n",
    "        self.project_number = project_number\n",
    "        self.region = region\n",
    "        \n",
    "    def wait_for_completion(self, operation_id, message, sleep_time):\n",
    "        \"\"\"Waits for a completion of a long running operation.\"\"\"\n",
    "        \n",
    "        api_url = f'{self.ann_parent}/operations/{operation_id}'\n",
    "\n",
    "        start_time = datetime.datetime.utcnow()\n",
    "        while True:\n",
    "            response = self.authed_session.get(api_url)\n",
    "            if response.status_code != 200:\n",
    "                raise RuntimeError(response.json())\n",
    "            if 'done' in response.json().keys():\n",
    "                logging.info('Operation completed!')\n",
    "                break\n",
    "            elapsed_time = datetime.datetime.utcnow() - start_time\n",
    "            logging.info('{}. Elapsed time since start: {}.'.format(\n",
    "                message, str(elapsed_time)))\n",
    "            time.sleep(sleep_time)\n",
    "    \n",
    "        return response.json()['response']\n",
    "\n",
    "\n",
    "class IndexClient(ANNClient):\n",
    "    \"\"\"Encapsulates a subset of control plane APIs \n",
    "    that manage ANN indexes.\"\"\"\n",
    "\n",
    "    def __init__(self, project_id, project_number, region):\n",
    "        super().__init__(project_id, project_number, region)\n",
    "\n",
    "    def create_index(self, display_name, description, metadata):\n",
    "        \"\"\"Creates an ANN Index.\"\"\"\n",
    "    \n",
    "        api_url = f'{self.ann_parent}/indexes'\n",
    "    \n",
    "        request_body = {\n",
    "            'display_name': display_name,\n",
    "            'description': description,\n",
    "            'metadata': metadata\n",
    "        }\n",
    "    \n",
    "        response = self.authed_session.post(api_url, data=json.dumps(request_body))\n",
    "        if response.status_code != 200:\n",
    "            raise RuntimeError(response.text)\n",
    "        operation_id = response.json()['name'].split('/')[-1]\n",
    "        \n",
    "        return operation_id\n",
    "\n",
    "    def list_indexes(self, display_name=None):\n",
    "        \"\"\"Lists all indexes with a given display name or\n",
    "        all indexes if the display_name is not provided.\"\"\"\n",
    "    \n",
    "        if display_name:\n",
    "            api_url = f'{self.ann_parent}/indexes?filter=display_name=\"{display_name}\"'\n",
    "        else:\n",
    "            api_url = f'{self.ann_parent}/indexes'\n",
    "\n",
    "        response = self.authed_session.get(api_url).json()\n",
    "\n",
    "        return response['indexes'] if response else []\n",
    "    \n",
    "    def delete_index(self, index_id):\n",
    "        \"\"\"Deletes an ANN index.\"\"\"\n",
    "        \n",
    "        api_url = f'{self.ann_parent}/indexes/{index_id}'\n",
    "        response = self.authed_session.delete(api_url)\n",
    "        if response.status_code != 200:\n",
    "            raise RuntimeError(response.text)\n",
    "\n",
    "\n",
    "class IndexDeploymentClient(ANNClient):\n",
    "    \"\"\"Encapsulates a subset of control plane APIs \n",
    "    that manage ANN endpoints and deployments.\"\"\"\n",
    "    \n",
    "    def __init__(self, project_id, project_number, region):\n",
    "        super().__init__(project_id, project_number, region)\n",
    "\n",
    "    def create_endpoint(self, display_name, vpc_name):\n",
    "        \"\"\"Creates an ANN endpoint.\"\"\"\n",
    "    \n",
    "        api_url = f'{self.ann_parent}/indexEndpoints'\n",
    "        network_name = f'projects/{self.project_number}/global/networks/{vpc_name}'\n",
    "\n",
    "        request_body = {\n",
    "            'display_name': display_name,\n",
    "            'network': network_name\n",
    "        }\n",
    "\n",
    "        response = self.authed_session.post(api_url, data=json.dumps(request_body))\n",
    "        if response.status_code != 200:\n",
    "            raise RuntimeError(response.text)\n",
    "        operation_id = response.json()['name'].split('/')[-1]\n",
    "    \n",
    "        return operation_id\n",
    "    \n",
    "    def list_endpoints(self, display_name=None):\n",
    "        \"\"\"Lists all ANN endpoints with a given display name or\n",
    "        all endpoints in the project if the display_name is not provided.\"\"\"\n",
    "        \n",
    "        if display_name:\n",
    "            api_url = f'{self.ann_parent}/indexEndpoints?filter=display_name=\"{display_name}\"'\n",
    "        else:\n",
    "            api_url = f'{self.ann_parent}/indexEndpoints'\n",
    "\n",
    "        response = self.authed_session.get(api_url).json()\n",
    " \n",
    "        return response['indexEndpoints'] if response else []\n",
    "    \n",
    "    def delete_endpoint(self, endpoint_id):\n",
    "        \"\"\"Deletes an ANN endpoint.\"\"\"\n",
    "        \n",
    "        api_url = f'{self.ann_parent}/indexEndpoints/{endpoint_id}'\n",
    "        \n",
    "        response = self.authed_session.delete(api_url)\n",
    "        if response.status_code != 200:\n",
    "            raise RuntimeError(response.text)\n",
    "        \n",
    "        return response.json()\n",
    "    \n",
    "    def create_deployment(self, display_name, deployment_id, endpoint_id, index_id):\n",
    "        \"\"\"Deploys an ANN index to an endpoint.\"\"\"\n",
    "    \n",
    "        api_url = f'{self.ann_parent}/indexEndpoints/{endpoint_id}:deployIndex'\n",
    "        index_name = f'projects/{self.project_number}/locations/{self.region}/indexes/{index_id}'\n",
    "\n",
    "        request_body = {\n",
    "            'deployed_index': {\n",
    "                'id': deployment_id,\n",
    "                'index': index_name,\n",
    "                'display_name': display_name\n",
    "            }\n",
    "        }\n",
    "\n",
    "        response = self.authed_session.post(api_url, data=json.dumps(request_body))\n",
    "        if response.status_code != 200:\n",
    "            raise RuntimeError(response.text)\n",
    "        operation_id = response.json()['name'].split('/')[-1]\n",
    "        \n",
    "        return operation_id\n",
    "    \n",
    "    def get_deployment_grpc_ip(self, endpoint_id, deployment_id):\n",
    "        \"\"\"Returns a private IP address for a gRPC interface to \n",
    "        an Index deployment.\"\"\"\n",
    "  \n",
    "        api_url = f'{self.ann_parent}/indexEndpoints/{endpoint_id}'\n",
    "\n",
    "        response = self.authed_session.get(api_url)\n",
    "        if response.status_code != 200:\n",
    "            raise RuntimeError(response.text)\n",
    "            \n",
    "        endpoint_ip = None\n",
    "        if 'deployedIndexes' in response.json().keys():\n",
    "            for deployment in response.json()['deployedIndexes']:\n",
    "                if deployment['id'] == deployment_id:\n",
    "                    endpoint_ip = deployment['privateEndpoints']['matchGrpcAddress']\n",
    "                    \n",
    "        return endpoint_ip\n",
    "\n",
    "    \n",
    "    def delete_deployment(self, endpoint_id, deployment_id):\n",
    "        \"\"\"Undeployes an index from an endpoint.\"\"\"\n",
    "        \n",
    "        api_url = f'{self.ann_parent}/indexEndpoints/{endpoint_id}:undeployIndex'\n",
    "        \n",
    "        request_body = {\n",
    "            'deployed_index_id': deployment_id\n",
    "        }\n",
    "    \n",
    "        response = self.authed_session.post(api_url, data=json.dumps(request_body))\n",
    "        if response.status_code != 200:\n",
    "            raise RuntimeError(response.text)\n",
    "        \n",
    "        return response\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Compute PMI component\n",
    "\n",
    "This component encapsulates a call to the BigQuery stored procedure that calculates item cooccurence. Refer to the preceeding notebooks for more details about item coocurrent calculations.\n",
    "\n",
    "The component tracks the output `item_cooc` table created by the stored procedure using the TFX (simple) Dataset artifact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile compute_pmi.py\n",
    "# Copyright 2020 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "\"\"\"BigQuery compute PMI component.\"\"\"\n",
    "\n",
    "import logging\n",
    "\n",
    "from google.cloud import bigquery\n",
    "\n",
    "import tfx\n",
    "import tensorflow as tf\n",
    "\n",
    "from tfx.dsl.component.experimental.decorators import component\n",
    "from tfx.dsl.component.experimental.annotations import InputArtifact, OutputArtifact, Parameter\n",
    "\n",
    "from tfx.types.experimental.simple_artifacts import Dataset as BQDataset\n",
    "\n",
    "\n",
    "@component\n",
    "def compute_pmi(\n",
    "    project_id: Parameter[str],\n",
    "    bq_dataset: Parameter[str],\n",
    "    min_item_frequency: Parameter[int],\n",
    "    max_group_size: Parameter[int],\n",
    "    item_cooc: OutputArtifact[BQDataset]):\n",
    "    \n",
    "    stored_proc = f'{bq_dataset}.sp_ComputePMI'\n",
    "    query = f'''\n",
    "        DECLARE min_item_frequency INT64;\n",
    "        DECLARE max_group_size INT64;\n",
    "\n",
    "        SET min_item_frequency = {min_item_frequency};\n",
    "        SET max_group_size = {max_group_size};\n",
    "\n",
    "        CALL {stored_proc}(min_item_frequency, max_group_size);\n",
    "    '''\n",
    "    result_table = 'item_cooc'\n",
    "\n",
    "    logging.info(f'Starting computing PMI...')\n",
    "  \n",
    "    client = bigquery.Client(project=project_id)\n",
    "    query_job = client.query(query)\n",
    "    query_job.result() # Wait for the job to complete\n",
    "  \n",
    "    logging.info(f'Items PMI computation completed. Output in {bq_dataset}.{result_table}.')\n",
    "  \n",
    "    # Write the location of the output table to metadata.  \n",
    "    item_cooc.set_string_custom_property('table_name',\n",
    "                                         f'{project_id}:{bq_dataset}.{result_table}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Train Item Matching Model component\n",
    "\n",
    "This component encapsulates a call to the BigQuery stored procedure that trains the BQML Matrix Factorization model. Refer to the preceeding notebooks for more details about model training.\n",
    "\n",
    "The component tracks the output `item_matching_model` BQML model created by the stored procedure using the TFX (simple) Model artifact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile train_item_matching.py\n",
    "# Copyright 2020 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "\"\"\"BigQuery compute PMI component.\"\"\"\n",
    "\n",
    "import logging\n",
    "\n",
    "from google.cloud import bigquery\n",
    "\n",
    "import tfx\n",
    "import tensorflow as tf\n",
    "\n",
    "from tfx.dsl.component.experimental.decorators import component\n",
    "from tfx.dsl.component.experimental.annotations import InputArtifact, OutputArtifact, Parameter\n",
    "\n",
    "from tfx.types.experimental.simple_artifacts import Dataset as BQDataset\n",
    "from tfx.types.standard_artifacts import Model as BQModel\n",
    "\n",
    "\n",
    "@component\n",
    "def train_item_matching_model(\n",
    "    project_id: Parameter[str],\n",
    "    bq_dataset: Parameter[str],\n",
    "    dimensions: Parameter[int],\n",
    "    item_cooc: InputArtifact[BQDataset],\n",
    "    bq_model: OutputArtifact[BQModel]):\n",
    "    \n",
    "    item_cooc_table = item_cooc.get_string_custom_property('table_name')\n",
    "    stored_proc = f'{bq_dataset}.sp_TrainItemMatchingModel'\n",
    "    query = f'''\n",
    "        DECLARE dimensions INT64 DEFAULT {dimensions};\n",
    "        CALL {stored_proc}(dimensions);\n",
    "    '''\n",
    "    model_name = 'item_matching_model'\n",
    "  \n",
    "    logging.info(f'Using item co-occurrence table: item_cooc_table')\n",
    "    logging.info(f'Starting training of the model...')\n",
    "    \n",
    "    client = bigquery.Client(project=project_id)\n",
    "    query_job = client.query(query)\n",
    "    query_job.result()\n",
    "  \n",
    "    logging.info(f'Model training completed. Output in {bq_dataset}.{model_name}.')\n",
    "  \n",
    "    # Write the location of the model to metadata. \n",
    "    bq_model.set_string_custom_property('model_name',\n",
    "                                         f'{project_id}:{bq_dataset}.{model_name}')\n",
    "   \n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Extract Embeddings component\n",
    "\n",
    "This component encapsulates a call to the BigQuery stored procedure that extracts embdeddings from the model to the staging table. Refer to the preceeding notebooks for more details about embeddings extraction.\n",
    "\n",
    "The component tracks the output `item_embeddings` table created by the stored procedure using the TFX (simple) Dataset artifact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile extract_embeddings.py\n",
    "# Copyright 2020 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "\"\"\"Extracts embeddings to a BQ table.\"\"\"\n",
    "\n",
    "import logging\n",
    "\n",
    "from google.cloud import bigquery\n",
    "\n",
    "import tfx\n",
    "import tensorflow as tf\n",
    "\n",
    "from tfx.dsl.component.experimental.decorators import component\n",
    "from tfx.dsl.component.experimental.annotations import InputArtifact, OutputArtifact, Parameter\n",
    "\n",
    "from tfx.types.experimental.simple_artifacts import Dataset as BQDataset \n",
    "from tfx.types.standard_artifacts import Model as BQModel\n",
    "\n",
    "\n",
    "@component\n",
    "def extract_embeddings(\n",
    "    project_id: Parameter[str],\n",
    "    bq_dataset: Parameter[str],\n",
    "    bq_model: InputArtifact[BQModel],\n",
    "    item_embeddings: OutputArtifact[BQDataset]):\n",
    "  \n",
    "    embedding_model_name = bq_model.get_string_custom_property('model_name')\n",
    "    stored_proc = f'{bq_dataset}.sp_ExractEmbeddings'\n",
    "    query = f'''\n",
    "        CALL {stored_proc}();\n",
    "    '''\n",
    "    embeddings_table = 'item_embeddings'\n",
    "\n",
    "    logging.info(f'Extracting item embeddings from: {embedding_model_name}')\n",
    "    \n",
    "    client = bigquery.Client(project=project_id)\n",
    "    query_job = client.query(query)\n",
    "    query_job.result() # Wait for the job to complete\n",
    "  \n",
    "    logging.info(f'Embeddings extraction completed. Output in {bq_dataset}.{embeddings_table}')\n",
    "  \n",
    "    # Write the location of the output table to metadata.\n",
    "    item_embeddings.set_string_custom_property('table_name', \n",
    "                                                f'{project_id}:{bq_dataset}.{embeddings_table}')\n",
    "    \n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Export Embeddings component\n",
    "\n",
    "This component encapsulates a BigQuery table extraction job that extracts the `item_embeddings` table to a GCS location as files in the JSONL format. The format of the extracted files is compatible with the ingestion schema for the ANN Service.\n",
    "\n",
    "The component tracks the output files location in the TFX (simple) Dataset artifact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile export_embeddings.py\n",
    "# Copyright 2020 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "\"\"\"Exports embeddings from a BQ table to a GCS location.\"\"\"\n",
    "\n",
    "import logging\n",
    "\n",
    "from google.cloud import bigquery\n",
    "\n",
    "import tfx\n",
    "import tensorflow as tf\n",
    "\n",
    "from tfx.dsl.component.experimental.decorators import component\n",
    "from tfx.dsl.component.experimental.annotations import InputArtifact, OutputArtifact, Parameter\n",
    "\n",
    "from tfx.types.experimental.simple_artifacts import Dataset \n",
    "\n",
    "BQDataset = Dataset\n",
    "\n",
    "@component\n",
    "def export_embeddings(\n",
    "    project_id: Parameter[str],\n",
    "    gcs_location: Parameter[str],\n",
    "    item_embeddings_bq: InputArtifact[BQDataset],\n",
    "    item_embeddings_gcs: OutputArtifact[Dataset]):\n",
    "    \n",
    "    filename_pattern = 'embedding-*.json'\n",
    "    gcs_location = gcs_location.rstrip('/')\n",
    "    destination_uri = f'{gcs_location}/{filename_pattern}'\n",
    "    \n",
    "    _, table_name = item_embeddings_bq.get_string_custom_property('table_name').split(':')\n",
    "  \n",
    "    logging.info(f'Exporting item embeddings from: {table_name}')\n",
    "  \n",
    "    bq_dataset, table_id = table_name.split('.')\n",
    "    client = bigquery.Client(project=project_id)\n",
    "    dataset_ref = bigquery.DatasetReference(project_id, bq_dataset)\n",
    "    table_ref = dataset_ref.table(table_id)\n",
    "    job_config = bigquery.job.ExtractJobConfig()\n",
    "    job_config.destination_format = bigquery.DestinationFormat.NEWLINE_DELIMITED_JSON\n",
    "\n",
    "    extract_job = client.extract_table(\n",
    "        table_ref,\n",
    "        destination_uris=destination_uri,\n",
    "        job_config=job_config\n",
    "    )  \n",
    "    extract_job.result() # Wait for resuls\n",
    "    \n",
    "    logging.info(f'Embeddings export completed. Output in {gcs_location}')\n",
    "  \n",
    "    # Write the location of the embeddings to metadata.\n",
    "    item_embeddings_gcs.uri = gcs_location\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create ANN index component\n",
    "\n",
    "This component encapsulats the calls to the ANN Service to create an ANN Index. \n",
    "\n",
    "The component tracks the created index int the TFX custom `ANNIndex` artifact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile create_index.py\n",
    "# Copyright 2020 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "\"\"\"Creates an ANN index.\"\"\"\n",
    "\n",
    "import logging\n",
    "\n",
    "import google.auth\n",
    "import numpy as np\n",
    "import tfx\n",
    "import tensorflow as tf\n",
    "\n",
    "from google.cloud import bigquery\n",
    "from tfx.dsl.component.experimental.decorators import component\n",
    "from tfx.dsl.component.experimental.annotations import InputArtifact, OutputArtifact, Parameter\n",
    "from tfx.types.experimental.simple_artifacts import Dataset \n",
    "\n",
    "from ann_service import IndexClient\n",
    "from ann_types import ANNIndex\n",
    "\n",
    "NUM_NEIGHBOURS = 10\n",
    "MAX_LEAVES_TO_SEARCH = 200\n",
    "METRIC = 'DOT_PRODUCT_DISTANCE'\n",
    "FEATURE_NORM_TYPE = 'UNIT_L2_NORM'\n",
    "CHILD_NODE_COUNT = 1000\n",
    "APPROXIMATE_NEIGHBORS_COUNT = 50\n",
    "\n",
    "@component\n",
    "def create_index(\n",
    "    project_id: Parameter[str],\n",
    "    project_number: Parameter[str],\n",
    "    region: Parameter[str],\n",
    "    display_name: Parameter[str],\n",
    "    dimensions: Parameter[int],\n",
    "    item_embeddings: InputArtifact[Dataset],\n",
    "    ann_index: OutputArtifact[ANNIndex]):\n",
    "    \n",
    "    index_client = IndexClient(project_id, project_number, region)\n",
    "    \n",
    "    logging.info('Creating index:')\n",
    "    logging.info(f'    Index display name: {display_name}')\n",
    "    logging.info(f'    Embeddings location: {item_embeddings.uri}')\n",
    "    \n",
    "    index_description = display_name\n",
    "    index_metadata = {\n",
    "        'contents_delta_uri': item_embeddings.uri,\n",
    "        'config': {\n",
    "            'dimensions': dimensions,\n",
    "            'approximate_neighbors_count': APPROXIMATE_NEIGHBORS_COUNT,\n",
    "            'distance_measure_type': METRIC,\n",
    "            'feature_norm_type': FEATURE_NORM_TYPE,\n",
    "            'tree_ah_config': {\n",
    "                'child_node_count': CHILD_NODE_COUNT,\n",
    "                'max_leaves_to_search': MAX_LEAVES_TO_SEARCH\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    operation_id = index_client.create_index(display_name, \n",
    "                                             index_description,\n",
    "                                             index_metadata)\n",
    "    response = index_client.wait_for_completion(operation_id, 'Waiting for ANN index', 45)\n",
    "    index_name = response['name']\n",
    "    \n",
    "    logging.info('Index {} created.'.format(index_name))\n",
    "  \n",
    "    # Write the index name to metadata.\n",
    "    ann_index.set_string_custom_property('index_name', \n",
    "                                         index_name)\n",
    "    ann_index.set_string_custom_property('index_display_name', \n",
    "                                         display_name)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deploy ANN index component\n",
    "\n",
    "This component deploys an ANN index to an ANN Endpoint. \n",
    "The componet tracks the deployed index in the TFX custom `DeployedANNIndex` artifact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile deploy_index.py\n",
    "# Copyright 2020 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "\"\"\"Deploys an ANN index.\"\"\"\n",
    "\n",
    "import logging\n",
    "\n",
    "import numpy as np\n",
    "import uuid\n",
    "import tfx\n",
    "import tensorflow as tf\n",
    "\n",
    "from google.cloud import bigquery\n",
    "from tfx.dsl.component.experimental.decorators import component\n",
    "from tfx.dsl.component.experimental.annotations import InputArtifact, OutputArtifact, Parameter\n",
    "from tfx.types.experimental.simple_artifacts import Dataset \n",
    "\n",
    "from ann_service import IndexDeploymentClient\n",
    "from ann_types import ANNIndex\n",
    "from ann_types import DeployedANNIndex\n",
    "\n",
    "\n",
    "@component\n",
    "def deploy_index(\n",
    "    project_id: Parameter[str],\n",
    "    project_number: Parameter[str],\n",
    "    region: Parameter[str],\n",
    "    vpc_name: Parameter[str],\n",
    "    deployed_index_id_prefix: Parameter[str],\n",
    "    ann_index: InputArtifact[ANNIndex],\n",
    "    deployed_ann_index: OutputArtifact[DeployedANNIndex]\n",
    "    ):\n",
    "    \n",
    "    deployment_client = IndexDeploymentClient(project_id, \n",
    "                                              project_number,\n",
    "                                              region)\n",
    "    \n",
    "    index_name = ann_index.get_string_custom_property('index_name')\n",
    "    index_display_name = ann_index.get_string_custom_property('index_display_name')\n",
    "    endpoint_display_name = f'Endpoint for {index_display_name}'\n",
    "    \n",
    "    logging.info(f'Creating endpoint: {endpoint_display_name}')\n",
    "    operation_id = deployment_client.create_endpoint(endpoint_display_name, vpc_name)\n",
    "    response = deployment_client.wait_for_completion(operation_id, 'Waiting for endpoint', 30)\n",
    "    endpoint_name = response['name']\n",
    "    logging.info(f'Endpoint created: {endpoint_name}')\n",
    "  \n",
    "    endpoint_id = endpoint_name.split('/')[-1]\n",
    "    index_id = index_name.split('/')[-1]\n",
    "    deployed_index_display_name = f'Deployed {index_display_name}'\n",
    "    deployed_index_id = deployed_index_id_prefix + str(uuid.uuid4())\n",
    "    \n",
    "    logging.info(f'Creating deployed index: {deployed_index_id}')\n",
    "    logging.info(f'                  from: {index_name}')\n",
    "    operation_id = deployment_client.create_deployment(\n",
    "        deployed_index_display_name, \n",
    "        deployed_index_id,\n",
    "        endpoint_id,\n",
    "        index_id)\n",
    "    response = deployment_client.wait_for_completion(operation_id, 'Waiting for deployment', 60)\n",
    "    logging.info('Index deployed!')\n",
    "  \n",
    "    deployed_index_ip = deployment_client.get_deployment_grpc_ip(\n",
    "        endpoint_id, deployed_index_id\n",
    "    )\n",
    "    # Write the deployed index properties to metadata.\n",
    "    deployed_ann_index.set_string_custom_property('endpoint_name', \n",
    "                                                  endpoint_name)\n",
    "    deployed_ann_index.set_string_custom_property('deployed_index_id', \n",
    "                                                  deployed_index_id)\n",
    "    deployed_ann_index.set_string_custom_property('index_name', \n",
    "                                                  index_name)\n",
    "    deployed_ann_index.set_string_custom_property('deployed_index_grpc_ip', \n",
    "                                                  deployed_index_ip)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a TFX pipeline\n",
    "\n",
    "The pipeline automates the process of preparing item embeddings (in BigQuery), training a Matrix Factorization model (in BQML), and creating and deploying an ANN Service index.\n",
    "\n",
    "The pipeline has a simple sequential flow. The pipeline accepts a set of runtime parameters that define GCP environment settings and embeddings and index assembly parameters.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# Only required for local run.\n",
    "from tfx.orchestration.metadata import sqlite_metadata_connection_config\n",
    "\n",
    "from tfx.orchestration.pipeline import Pipeline\n",
    "from tfx.orchestration.kubeflow.v2 import kubeflow_v2_dag_runner\n",
    "\n",
    "from compute_pmi import compute_pmi\n",
    "from export_embeddings import export_embeddings\n",
    "from extract_embeddings import extract_embeddings\n",
    "from train_item_matching import train_item_matching_model\n",
    "from create_index import create_index\n",
    "from deploy_index import deploy_index\n",
    "\n",
    "def ann_pipeline(\n",
    "    pipeline_name,\n",
    "    pipeline_root,\n",
    "    metadata_connection_config,\n",
    "    project_id,\n",
    "    project_number,\n",
    "    region,\n",
    "    vpc_name,\n",
    "    bq_dataset_name,\n",
    "    min_item_frequency,\n",
    "    max_group_size,\n",
    "    dimensions,\n",
    "    embeddings_gcs_location,\n",
    "    index_display_name,\n",
    "    deployed_index_id_prefix) -> Pipeline:\n",
    "    \"\"\"Implements the SCANN training pipeline.\"\"\"\n",
    " \n",
    "    pmi_computer = compute_pmi(\n",
    "        project_id=project_id,\n",
    "        bq_dataset=bq_dataset_name,\n",
    "        min_item_frequency=min_item_frequency,\n",
    "        max_group_size=max_group_size\n",
    "    )\n",
    "    \n",
    "    bqml_trainer = train_item_matching_model(\n",
    "        project_id=project_id,\n",
    "        bq_dataset=bq_dataset_name,\n",
    "        item_cooc=pmi_computer.outputs.item_cooc,\n",
    "        dimensions=dimensions,\n",
    "    )\n",
    "    \n",
    "    embeddings_extractor = extract_embeddings(\n",
    "        project_id=project_id,\n",
    "        bq_dataset=bq_dataset_name,\n",
    "        bq_model=bqml_trainer.outputs.bq_model\n",
    "    )\n",
    "    \n",
    "    embeddings_exporter = export_embeddings(\n",
    "        project_id=project_id,\n",
    "        gcs_location=embeddings_gcs_location,\n",
    "        item_embeddings_bq=embeddings_extractor.outputs.item_embeddings\n",
    "    )\n",
    "    \n",
    "    index_constructor = create_index(\n",
    "        project_id=project_id,\n",
    "        project_number=project_number,\n",
    "        region=region,\n",
    "        display_name=index_display_name,\n",
    "        dimensions=dimensions,\n",
    "        item_embeddings=embeddings_exporter.outputs.item_embeddings_gcs\n",
    "    )\n",
    "    \n",
    "    index_deployer = deploy_index(\n",
    "        project_id=project_id,\n",
    "        project_number=project_number,\n",
    "        region=region,\n",
    "        vpc_name=vpc_name,\n",
    "        deployed_index_id_prefix=deployed_index_id_prefix,\n",
    "        ann_index=index_constructor.outputs.ann_index\n",
    "    )\n",
    "\n",
    "    components = [\n",
    "        pmi_computer,\n",
    "        bqml_trainer,\n",
    "        embeddings_extractor,\n",
    "        embeddings_exporter,\n",
    "        index_constructor,\n",
    "        index_deployer\n",
    "    ]\n",
    "    \n",
    "    return Pipeline(\n",
    "        pipeline_name=pipeline_name,\n",
    "        pipeline_root=pipeline_root,\n",
    "        # Only needed for local runs.\n",
    "        metadata_connection_config=metadata_connection_config,\n",
    "        components=components)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the pipeline locally\n",
    "\n",
    "You will first run the pipeline locally using the Beam runner."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean the metadata and artifacts from the previous runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_root = f'/tmp/{PIPELINE_NAME}'\n",
    "local_mlmd_folder = '/tmp/mlmd'\n",
    "\n",
    "if tf.io.gfile.exists(pipeline_root):\n",
    "  print(\"Removing previous artifacts...\")\n",
    "  tf.io.gfile.rmtree(pipeline_root)\n",
    "if tf.io.gfile.exists(local_mlmd_folder):\n",
    "  print(\"Removing local mlmd SQLite...\")\n",
    "  tf.io.gfile.rmtree(local_mlmd_folder)\n",
    "print(\"Creating mlmd directory: \", local_mlmd_folder)\n",
    "tf.io.gfile.mkdir(local_mlmd_folder)\n",
    "print(\"Creating pipeline root folder: \", pipeline_root)\n",
    "tf.io.gfile.mkdir(pipeline_root)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set pipeline parameters and create the pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bq_dataset_name = 'song_embeddings'\n",
    "index_display_name = 'Song embeddings'\n",
    "deployed_index_id_prefix = 'deployed_song_embeddings_'\n",
    "min_item_frequency = 15\n",
    "max_group_size = 100\n",
    "dimensions = 50\n",
    "embeddings_gcs_location = f'gs://{BUCKET_NAME}/embeddings'\n",
    "\n",
    "metadata_connection_config = sqlite_metadata_connection_config(\n",
    "    os.path.join(local_mlmd_folder, 'metadata.sqlite'))\n",
    "\n",
    "pipeline = ann_pipeline(\n",
    "    pipeline_name=PIPELINE_NAME,\n",
    "    pipeline_root=pipeline_root,\n",
    "    metadata_connection_config=metadata_connection_config,\n",
    "    project_id=PROJECT_ID,\n",
    "    project_number=PROJECT_NUMBER,\n",
    "    region=REGION,\n",
    "    vpc_name=VPC_NAME,\n",
    "    bq_dataset_name=bq_dataset_name,\n",
    "    index_display_name=index_display_name,\n",
    "    deployed_index_id_prefix=deployed_index_id_prefix,\n",
    "    min_item_frequency=min_item_frequency,\n",
    "    max_group_size=max_group_size,\n",
    "    dimensions=dimensions,\n",
    "    embeddings_gcs_location=embeddings_gcs_location\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start the run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.getLogger().setLevel(logging.INFO)\n",
    "\n",
    "BeamDagRunner().run(pipeline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect produced metadata\n",
    "\n",
    "During the execution of the pipeline, the inputs and outputs of each component have been tracked in ML Metadata. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ml_metadata import metadata_store\n",
    "from ml_metadata.proto import metadata_store_pb2\n",
    "\n",
    "connection_config = metadata_store_pb2.ConnectionConfig()\n",
    "connection_config.sqlite.filename_uri = os.path.join(local_mlmd_folder, 'metadata.sqlite')\n",
    "connection_config.sqlite.connection_mode = 3 # READWRITE_OPENCREATE\n",
    "store = metadata_store.MetadataStore(connection_config)\n",
    "store.get_artifacts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NOTICE. The following code does not work with ANN Service Experimental. It will be finalized when the service moves to the Preview stage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the pipeline on AI Platform Pipelines\n",
    "\n",
    "You will now run the pipeline on AI Platform Pipelines (Unified)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Package custom components into a container\n",
    "\n",
    "The modules containing custom components must be first package as a docker container image, which is a derivative of the standard TFX image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a Dockerfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile Dockerfile\n",
    "FROM gcr.io/tfx-oss-public/tfx:0.25.0\n",
    "WORKDIR /pipeline\n",
    "COPY ./ ./\n",
    "ENV PYTHONPATH=\"/pipeline:${PYTHONPATH}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build and push the docker image to Container Registry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!gcloud builds submit --tag gcr.io/{PROJECT_ID}/caip-tfx-custom:{USER} ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create AI Platform Pipelines client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aiplatform.pipelines import client\n",
    "\n",
    "aipp_client = client.Client(\n",
    "    project_id=PROJECT_ID,\n",
    "    region=REGION,\n",
    "    api_key=API_KEY\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the the parameters for AIPP execution and create the pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_connection_config = None\n",
    "pipeline_root = PIPELINE_ROOT\n",
    "\n",
    "pipeline = ann_pipeline(\n",
    "    pipeline_name=PIPELINE_NAME,\n",
    "    pipeline_root=pipeline_root,\n",
    "    metadata_connection_config=metadata_connection_config,\n",
    "    project_id=PROJECT_ID,\n",
    "    project_number=PROJECT_NUMBER,\n",
    "    region=REGION,\n",
    "    vpc_name=VPC_NAME,\n",
    "    bq_dataset_name=bq_dataset_name,\n",
    "    index_display_name=index_display_name,\n",
    "    deployed_index_id_prefix=deployed_index_id_prefix,\n",
    "    min_item_frequency=min_item_frequency,\n",
    "    max_group_size=max_group_size,\n",
    "    dimensions=dimensions,\n",
    "    embeddings_gcs_location=embeddings_gcs_location\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compile the pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = kubeflow_v2_dag_runner.KubeflowV2DagRunnerConfig(\n",
    "    project_id=PROJECT_ID,\n",
    "    display_name=PIPELINE_NAME,\n",
    "    default_image='gcr.io/{}/caip-tfx-custom:{}'.format(PROJECT_ID, USER))\n",
    "runner = kubeflow_v2_dag_runner.KubeflowV2DagRunner(\n",
    "    config=config,\n",
    "    output_filename='pipeline.json')\n",
    "runner.compile(\n",
    "    pipeline,\n",
    "    write_out=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Submit the pipeline run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aipp_client.create_run_from_job_spec('pipeline.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License\n",
    "\n",
    "Copyright 2020 Google LLC\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n",
    "\n",
    "See the License for the specific language governing permissions and limitations under the License.\n",
    "\n",
    "**This is not an official Google product but sample code provided for an educational purpose**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "environment": {
   "name": "tf2-gpu.2-4.m61",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-4:m61"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: retail/recommendation-system/bqml-scann/ann_grpc/match_pb2.py
================================================
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler.  DO NOT EDIT!
# source: match.proto
"""Generated protocol buffer code."""
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)

_sym_db = _symbol_database.Default()




DESCRIPTOR = _descriptor.FileDescriptor(
  name='match.proto',
  package='google.cloud.aiplatform.container.v1alpha1',
  syntax='proto3',
  serialized_options=None,
  create_key=_descriptor._internal_create_key,
  serialized_pb=b'\n\x0bmatch.proto\x12*google.cloud.aiplatform.container.v1alpha1\"\xcb\x01\n\x0cMatchRequest\x12\x19\n\x11\x64\x65ployed_index_id\x18\x01 \x01(\t\x12\x11\n\tfloat_val\x18\x02 \x03(\x02\x12\x15\n\rnum_neighbors\x18\x03 \x01(\x05\x12H\n\trestricts\x18\x04 \x03(\x0b\x32\x35.google.cloud.aiplatform.container.v1alpha1.Namespace\x12,\n$per_crowding_attribute_num_neighbors\x18\x05 \x01(\x05\"\x8f\x01\n\rMatchResponse\x12T\n\x08neighbor\x18\x01 \x03(\x0b\x32\x42.google.cloud.aiplatform.container.v1alpha1.MatchResponse.Neighbor\x1a(\n\x08Neighbor\x12\n\n\x02id\x18\x01 \x01(\t\x12\x10\n\x08\x64istance\x18\x02 \x01(\x01\"\xa1\x02\n\x11\x42\x61tchMatchRequest\x12i\n\x08requests\x18\x01 \x03(\x0b\x32W.google.cloud.aiplatform.container.v1alpha1.BatchMatchRequest.BatchMatchRequestPerIndex\x1a\xa0\x01\n\x19\x42\x61tchMatchRequestPerIndex\x12\x19\n\x11\x64\x65ployed_index_id\x18\x01 \x01(\t\x12J\n\x08requests\x18\x02 \x03(\x0b\x32\x38.google.cloud.aiplatform.container.v1alpha1.MatchRequest\x12\x1c\n\x14low_level_batch_size\x18\x03 \x01(\x05\"\x8a\x02\n\x12\x42\x61tchMatchResponse\x12l\n\tresponses\x18\x01 \x03(\x0b\x32Y.google.cloud.aiplatform.container.v1alpha1.BatchMatchResponse.BatchMatchResponsePerIndex\x1a\x85\x01\n\x1a\x42\x61tchMatchResponsePerIndex\x12\x19\n\x11\x64\x65ployed_index_id\x18\x01 \x01(\t\x12L\n\tresponses\x18\x02 \x03(\x0b\x32\x39.google.cloud.aiplatform.container.v1alpha1.MatchResponse\"D\n\tNamespace\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x14\n\x0c\x61llow_tokens\x18\x02 \x03(\t\x12\x13\n\x0b\x64\x65ny_tokens\x18\x03 \x03(\t2\x9e\x02\n\x0cMatchService\x12~\n\x05Match\x12\x38.google.cloud.aiplatform.container.v1alpha1.MatchRequest\x1a\x39.google.cloud.aiplatform.container.v1alpha1.MatchResponse\"\x00\x12\x8d\x01\n\nBatchMatch\x12=.google.cloud.aiplatform.container.v1alpha1.BatchMatchRequest\x1a>.google.cloud.aiplatform.container.v1alpha1.BatchMatchResponse\"\x00\x62\x06proto3'
)




_MATCHREQUEST = _descriptor.Descriptor(
  name='MatchRequest',
  full_name='google.cloud.aiplatform.container.v1alpha1.MatchRequest',
  filename=None,
  file=DESCRIPTOR,
  containing_type=None,
  create_key=_descriptor._internal_create_key,
  fields=[
    _descriptor.FieldDescriptor(
      name='deployed_index_id', full_name='google.cloud.aiplatform.container.v1alpha1.MatchRequest.deployed_index_id', index=0,
      number=1, type=9, cpp_type=9, label=1,
      has_default_value=False, default_value=b"".decode('utf-8'),
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      serialized_options=None, file=DESCRIPTOR,  create_key=_descriptor._internal_create_key),
    _descriptor.FieldDescriptor(
      name='float_val', full_name='google.cloud.aiplatform.container.v1alpha1.MatchRequest.float_val', index=1,
      number=2, type=2, cpp_type=6, label=3,
      has_default_value=False, default_value=[],
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      serialized_options=None, file=DESCRIPTOR,  create_key=_descriptor._internal_create_key),
    _descriptor.FieldDescriptor(
      name='num_neighbors', full_name='google.cloud.aiplatform.container.v1alpha1.MatchRequest.num_neighbors', index=2,
      number=3, type=5, cpp_type=1, label=1,
      has_default_value=False, default_value=0,
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      serialized_options=None, file=DESCRIPTOR,  create_key=_descriptor._internal_create_key),
    _descriptor.FieldDescriptor(
      name='restricts', full_name='google.cloud.aiplatform.container.v1alpha1.MatchRequest.restricts', index=3,
      number=4, type=11, cpp_type=10, label=3,
      has_default_value=False, default_value=[],
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      serialized_options=None, file=DESCRIPTOR,  create_key=_descriptor._internal_create_key),
    _descriptor.FieldDescriptor(
      name='per_crowding_attribute_num_neighbors', full_name='google.cloud.aiplatform.container.v1alpha1.MatchRequest.per_crowding_attribute_num_neighbors', index=4,
      number=5, type=5, cpp_type=1, label=1,
      has_default_value=False, default_value=0,
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      serialized_options=None, file=DESCRIPTOR,  create_key=_descriptor._internal_create_key),
  ],
  extensions=[
  ],
  nested_types=[],
  enum_types=[
  ],
  serialized_options=None,
  is_extendable=False,
  syntax='proto3',
  extension_ranges=[],
  oneofs=[
  ],
  serialized_start=60,
  serialized_end=263,
)


_MATCHRESPONSE_NEIGHBOR = _descriptor.Descriptor(
  name='Neighbor',
  full_name='google.cloud.aiplatform.container.v1alpha1.MatchResponse.Neighbor',
  filename=None,
  file=DESCRIPTOR,
  containing_type=None,
  create_key=_descriptor._internal_create_key,
  fields=[
    _descriptor.FieldDescriptor(
      name='id', full_name='google.cloud.aiplatform.container.v1alpha1.MatchResponse.Neighbor.id', index=0,
      number=1, type=9, cpp_type=9, label=1,
      has_default_value=False, default_value=b"".decode('utf-8'),
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      serialized_options=None, file=DESCRIPTOR,  create_key=_descriptor._internal_create_key),
    _descriptor.FieldDescriptor(
      name='distance', full_name='google.cloud.aiplatform.container.v1alpha1.MatchResponse.Neighbor.distance', index=1,
      number=2, type=1, cpp_type=5, label=1,
      has_default_value=False, default_value=float(0),
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      serialized_options=None, file=DESCRIPTOR,  create_key=_descriptor._internal_create_key),
  ],
  extensions=[
  ],
  nested_types=[],
  enum_types=[
  ],
  serialized_options=None,
  is_extendable=False,
  syntax='proto3',
  extension_ranges=[],
  oneofs=[
  ],
  serialized_start=369,
  serialized_end=409,
)

_MATCHRESPONSE = _descriptor.Descriptor(
  name='MatchResponse',
  full_name='google.cloud.aiplatform.container.v1alpha1.MatchResponse',
  filename=None,
  file=DESCRIPTOR,
  containing_type=None,
  create_key=_descriptor._internal_create_key,
  fields=[
    _descriptor.FieldDescriptor(
      name='neighbor', full_name='google.cloud.aiplatform.container.v1alpha1.MatchResponse.neighbor', index=0,
      number=1, type=11, cpp_type=10, label=3,
      has_default_value=False, default_value=[],
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      serialized_options=None, file=DESCRIPTOR,  create_key=_descriptor._internal_create_key),
  ],
  extensions=[
  ],
  nested_types=[_MATCHRESPONSE_NEIGHBOR, ],
  enum_types=[
  ],
  serialized_options=None,
  is_extendable=False,
  syntax='proto3',
  extension_ranges=[],
  oneofs=[
  ],
  serialized_start=266,
  serialized_end=409,
)


_BATCHMATCHREQUEST_BATCHMATCHREQUESTPERINDEX = _descriptor.Descriptor(
  name='BatchMatchRequestPerIndex',
  full_name='google.cloud.aiplatform.container.v1alpha1.BatchMatchRequest.BatchMatchRequestPerIndex',
  filename=None,
  file=DESCRIPTOR,
  containing_type=None,
  create_key=_descriptor._internal_create_key,
  fields=[
    _descriptor.FieldDescriptor(
      name='deployed_index_id', full_name='google.cloud.aiplatform.container.v1alpha1.BatchMatchRequest.BatchMatchRequestPerIndex.deployed_index_id', index=0,
      number=1, type=9, cpp_type=9, label=1,
      has_default_value=False, default_value=b"".decode('utf-8'),
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      serialized_options=None, file=DESCRIPTOR,  create_key=_descriptor._internal_create_key),
    _descriptor.FieldDescriptor(
      name='requests', full_name='google.cloud.aiplatform.container.v1alpha1.BatchMatchRequest.BatchMatchRequestPerIndex.requests', index=1,
      number=2, type=11, cpp_type=10, label=3,
      has_default_value=False, default_value=[],
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      serialized_options=None, file=DESCRIPTOR,  create_key=_descriptor._internal_create_key),
    _descriptor.FieldDescriptor(
      name='low_level_batch_size', full_name='google.cloud.aiplatform.container.v1alpha1.BatchMatchRequest.BatchMatchRequestPerIndex.low_level_batch_size', index=2,
      number=3, type=5, cpp_type=1, label=1,
      has_default_value=False, default_value=0,
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      serialized_options=None, file=DESCRIPTOR,  create_key=_descriptor._internal_create_key),
  ],
  extensions=[
  ],
  nested_types=[],
  enum_types=[
  ],
  serialized_options=None,
  is_extendable=False,
  syntax='proto3',
  extension_ranges=[],
  oneofs=[
  ],
  serialized_start=541,
  serialized_end=701,
)

_BATCHMATCHREQUEST = _descriptor.Descriptor(
  name='BatchMatchRequest',
  full_name='google.cloud.aiplatform.container.v1alpha1.BatchMatchRequest',
  filename=None,
  file=DESCRIPTOR,
  containing_type=None,
  create_key=_descriptor._internal_create_key,
  fields=[
    _descriptor.FieldDescriptor(
      name='requests', full_name='google.cloud.aiplatform.container.v1alpha1.BatchMatchRequest.requests', index=0,
      number=1, type=11, cpp_type=10, label=3,
      has_default_value=False, default_value=[],
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      serialized_options=None, file=DESCRIPTOR,  create_key=_descriptor._internal_create_key),
  ],
  extensions=[
  ],
  nested_types=[_BATCHMATCHREQUEST_BATCHMATCHREQUESTPERINDEX, ],
  enum_types=[
  ],
  serialized_options=None,
  is_extendable=False,
  syntax='proto3',
  extension_ranges=[],
  oneofs=[
  ],
  serialized_start=412,
  serialized_end=701,
)


_BATCHMATCHRESPONSE_BATCHMATCHRESPONSEPERINDEX = _descriptor.Descriptor(
  name='BatchMatchResponsePerIndex',
  full_name='google.cloud.aiplatform.container.v1alpha1.BatchMatchResponse.BatchMatchResponsePerIndex',
  filename=None,
  file=DESCRIPTOR,
  containing_type=None,
  create_key=_descriptor._internal_create_key,
  fields=[
    _descriptor.FieldDescriptor(
      name='deployed_index_id', full_name='google.cloud.aiplatform.container.v1alpha1.BatchMatchResponse.BatchMatchResponsePerIndex.deployed_index_id', index=0,
      number=1, type=9, cpp_type=9, label=1,
      has_default_value=False, default_value=b"".decode('utf-8'),
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      serialized_options=None, file=DESCRIPTOR,  create_key=_descriptor._internal_create_key),
    _descriptor.FieldDescriptor(
      name='responses', full_name='google.cloud.aiplatform.container.v1alpha1.BatchMatchResponse.BatchMatchResponsePerIndex.responses', index=1,
      number=2, type=11, cpp_type=10, label=3,
      has_default_value=False, default_value=[],
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      serialized_options=None, file=DESCRIPTOR,  create_key=_descriptor._internal_create_key),
  ],
  extensions=[
  ],
  nested_types=[],
  enum_types=[
  ],
  serialized_options=None,
  is_extendable=False,
  syntax='proto3',
  extension_ranges=[],
  oneofs=[
  ],
  serialized_start=837,
  serialized_end=970,
)

_BATCHMATCHRESPONSE = _descriptor.Descriptor(
  name='BatchMatchResponse',
  full_name='google.cloud.aiplatform.container.v1alpha1.BatchMatchResponse',
  filename=None,
  file=DESCRIPTOR,
  containing_type=None,
  create_key=_descriptor._internal_create_key,
  fields=[
    _descriptor.FieldDescriptor(
      name='responses', full_name='google.cloud.aiplatform.container.v1alpha1.BatchMatchResponse.responses', index=0,
      number=1, type=11, cpp_type=10, label=3,
      has_default_value=False, default_value=[],
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      serialized_options=None, file=DESCRIPTOR,  create_key=_descriptor._internal_create_key),
  ],
  extensions=[
  ],
  nested_types=[_BATCHMATCHRESPONSE_BATCHMATCHRESPONSEPERINDEX, ],
  enum_types=[
  ],
  serialized_options=None,
  is_extendable=False,
  syntax='proto3',
  extension_ranges=[],
  oneofs=[
  ],
  serialized_start=704,
  serialized_end=970,
)


_NAMESPACE = _descriptor.Descriptor(
  name='Namespace',
  full_name='google.cloud.aiplatform.container.v1alpha1.Namespace',
  filename=None,
  file=DESCRIPTOR,
  containing_type=None,
  create_key=_descriptor._internal_create_key,
  fields=[
    _descriptor.FieldDescriptor(
      name='name', full_name='google.cloud.aiplatform.container.v1alpha1.Namespace.name', index=0,
      number=1, type=9, cpp_type=9, label=1,
      has_default_value=False, default_value=b"".decode('utf-8'),
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      serialized_options=None, file=DESCRIPTOR,  create_key=_descriptor._internal_create_key),
    _descriptor.FieldDescriptor(
      name='allow_tokens', full_name='google.cloud.aiplatform.container.v1alpha1.Namespace.allow_tokens', index=1,
      number=2, type=9, cpp_type=9, label=3,
      has_default_value=False, default_value=[],
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      serialized_options=None, file=DESCRIPTOR,  create_key=_descriptor._internal_create_key),
    _descriptor.FieldDescriptor(
      name='deny_tokens', full_name='google.cloud.aiplatform.container.v1alpha1.Namespace.deny_tokens', index=2,
      number=3, type=9, cpp_type=9, label=3,
      has_default_value=False, default_value=[],
      message_type=None, enum_type=None, containing_type=None,
      is_extension=False, extension_scope=None,
      serialized_options=None, file=DESCRIPTOR,  create_key=_descriptor._internal_create_key),
  ],
  extensions=[
  ],
  nested_types=[],
  enum_types=[
  ],
  serialized_options=None,
  is_extendable=False,
  syntax='proto3',
  extension_ranges=[],
  oneofs=[
  ],
  serialized_start=972,
  serialized_end=1040,
)

_MATCHREQUEST.fields_by_name['restricts'].message_type = _NAMESPACE
_MATCHRESPONSE_NEIGHBOR.containing_type = _MATCHRESPONSE
_MATCHRESPONSE.fields_by_name['neighbor'].message_type = _MATCHRESPONSE_NEIGHBOR
_BATCHMATCHREQUEST_BATCHMATCHREQUESTPERINDEX.fields_by_name['requests'].message_type = _MATCHREQUEST
_BATCHMATCHREQUEST_BATCHMATCHREQUESTPERINDEX.containing_type = _BATCHMATCHREQUEST
_BATCHMATCHREQUEST.fields_by_name['requests'].message_type = _BATCHMATCHREQUEST_BATCHMATCHREQUESTPERINDEX
_BATCHMATCHRESPONSE_BATCHMATCHRESPONSEPERINDEX.fields_by_name['responses'].message_type = _MATCHRESPONSE
_BATCHMATCHRESPONSE_BATCHMATCHRESPONSEPERINDEX.containing_type = _BATCHMATCHRESPONSE
_BATCHMATCHRESPONSE.fields_by_name['responses'].message_type = _BATCHMATCHRESPONSE_BATCHMATCHRESPONSEPERINDEX
DESCRIPTOR.message_types_by_name['MatchRequest'] = _MATCHREQUEST
DESCRIPTOR.message_types_by_name['MatchResponse'] = _MATCHRESPONSE
DESCRIPTOR.message_types_by_name['BatchMatchRequest'] = _BATCHMATCHREQUEST
DESCRIPTOR.message_types_by_name['BatchMatchResponse'] = _BATCHMATCHRESPONSE
DESCRIPTOR.message_types_by_name['Namespace'] = _NAMESPACE
_sym_db.RegisterFileDescriptor(DESCRIPTOR)

MatchRequest = _reflection.GeneratedProtocolMessageType('MatchRequest', (_message.Message,), {
  'DESCRIPTOR' : _MATCHREQUEST,
  '__module__' : 'match_pb2'
  # @@protoc_insertion_point(class_scope:google.cloud.aiplatform.container.v1alpha1.MatchRequest)
  })
_sym_db.RegisterMessage(MatchRequest)

MatchResponse = _reflection.GeneratedProtocolMessageType('MatchResponse', (_message.Message,), {

  'Neighbor' : _reflection.GeneratedProtocolMessageType('Neighbor', (_message.Message,), {
    'DESCRIPTOR' : _MATCHRESPONSE_NEIGHBOR,
    '__module__' : 'match_pb2'
    # @@protoc_insertion_point(class_scope:google.cloud.aiplatform.container.v1alpha1.MatchResponse.Neighbor)
    })
  ,
  'DESCRIPTOR' : _MATCHRESPONSE,
  '__module__' : 'match_pb2'
  # @@protoc_insertion_point(class_scope:google.cloud.aiplatform.container.v1alpha1.MatchResponse)
  })
_sym_db.RegisterMessage(MatchResponse)
_sym_db.RegisterMessage(MatchResponse.Neighbor)

BatchMatchRequest = _reflection.GeneratedProtocolMessageType('BatchMatchRequest', (_message.Message,), {

  'BatchMatchRequestPerIndex' : _reflection.GeneratedProtocolMessageType('BatchMatchRequestPerIndex', (_message.Message,), {
    'DESCRIPTOR' : _BATCHMATCHREQUEST_BATCHMATCHREQUESTPERINDEX,
    '__module__' : 'match_pb2'
    # @@protoc_insertion_point(class_scope:google.cloud.aiplatform.container.v1alpha1.BatchMatchRequest.BatchMatchRequestPerIndex)
    })
  ,
  'DESCRIPTOR' : _BATCHMATCHREQUEST,
  '__module__' : 'match_pb2'
  # @@protoc_insertion_point(class_scope:google.cloud.aiplatform.container.v1alpha1.BatchMatchRequest)
  })
_sym_db.RegisterMessage(BatchMatchRequest)
_sym_db.RegisterMessage(BatchMatchRequest.BatchMatchRequestPerIndex)

BatchMatchResponse = _reflection.GeneratedProtocolMessageType('BatchMatchResponse', (_message.Message,), {

  'BatchMatchResponsePerIndex' : _reflection.GeneratedProtocolMessageType('BatchMatchResponsePerIndex', (_message.Message,), {
    'DESCRIPTOR' : _BATCHMATCHRESPONSE_BATCHMATCHRESPONSEPERINDEX,
    '__module__' : 'match_pb2'
    # @@protoc_insertion_point(class_scope:google.cloud.aiplatform.container.v1alpha1.BatchMatchResponse.BatchMatchResponsePerIndex)
    })
  ,
  'DESCRIPTOR' : _BATCHMATCHRESPONSE,
  '__module__' : 'match_pb2'
  # @@protoc_insertion_point(class_scope:google.cloud.aiplatform.container.v1alpha1.BatchMatchResponse)
  })
_sym_db.RegisterMessage(BatchMatchResponse)
_sym_db.RegisterMessage(BatchMatchResponse.BatchMatchResponsePerIndex)

Namespace = _reflection.GeneratedProtocolMessageType('Namespace', (_message.Message,), {
  'DESCRIPTOR' : _NAMESPACE,
  '__module__' : 'match_pb2'
  # @@protoc_insertion_point(class_scope:google.cloud.aiplatform.container.v1alpha1.Namespace)
  })
_sym_db.RegisterMessage(Namespace)



_MATCHSERVICE = _descriptor.ServiceDescriptor(
  name='MatchService',
  full_name='google.cloud.aiplatform.container.v1alpha1.MatchService',
  file=DESCRIPTOR,
  index=0,
  serialized_options=None,
  create_key=_descriptor._internal_create_key,
  serialized_start=1043,
  serialized_end=1329,
  methods=[
  _descriptor.MethodDescriptor(
    name='Match',
    full_name='google.cloud.aiplatform.container.v1alpha1.MatchService.Match',
    index=0,
    containing_service=None,
    input_type=_MATCHREQUEST,
    output_type=_MATCHRESPONSE,
    serialized_options=None,
    create_key=_descriptor._internal_create_key,
  ),
  _descriptor.MethodDescriptor(
    name='BatchMatch',
    full_name='google.cloud.aiplatform.container.v1alpha1.MatchService.BatchMatch',
    index=1,
    containing_service=None,
    input_type=_BATCHMATCHREQUEST,
    output_type=_BATCHMATCHRESPONSE,
    serialized_options=None,
    create_key=_descriptor._internal_create_key,
  ),
])
_sym_db.RegisterServiceDescriptor(_MATCHSERVICE)

DESCRIPTOR.services_by_name['MatchService'] = _MATCHSERVICE

# @@protoc_insertion_point(module_scope)


================================================
FILE: retail/recommendation-system/bqml-scann/ann_grpc/match_pb2_grpc.py
================================================
# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
"""Client and server classes corresponding to protobuf-defined services."""
import grpc

import match_pb2 as match__pb2


class MatchServiceStub(object):
    """MatchService is a Google managed service for efficient vector similarity
    search at scale.
    """

    def __init__(self, channel):
        """Constructor.

        Args:
            channel: A grpc.Channel.
        """
        self.Match = channel.unary_unary(
                '/google.cloud.aiplatform.container.v1alpha1.MatchService/Match',
                request_serializer=match__pb2.MatchRequest.SerializeToString,
                response_deserializer=match__pb2.MatchResponse.FromString,
                )
        self.BatchMatch = channel.unary_unary(
                '/google.cloud.aiplatform.container.v1alpha1.MatchService/BatchMatch',
                request_serializer=match__pb2.BatchMatchRequest.SerializeToString,
                response_deserializer=match__pb2.BatchMatchResponse.FromString,
                )


class MatchServiceServicer(object):
    """MatchService is a Google managed service for efficient vector similarity
    search at scale.
    """

    def Match(self, request, context):
        """Returns the nearest neighbors for the query. If it is a sharded
        deployment, calls the other shards and aggregates the responses.
        """
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')

    def BatchMatch(self, request, context):
        """Returns the nearest neighbors for batch queries. If it is a sharded
        deployment, calls the other shards and aggregates the responses.
        """
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')


def add_MatchServiceServicer_to_server(servicer, server):
    rpc_method_handlers = {
            'Match': grpc.unary_unary_rpc_method_handler(
                    servicer.Match,
                    request_deserializer=match__pb2.MatchRequest.FromString,
                    response_serializer=match__pb2.MatchResponse.SerializeToString,
            ),
            'BatchMatch': grpc.unary_unary_rpc_method_handler(
                    servicer.BatchMatch,
                    request_deserializer=match__pb2.BatchMatchRequest.FromString,
                    response_serializer=match__pb2.BatchMatchResponse.SerializeToString,
            ),
    }
    generic_handler = grpc.method_handlers_generic_handler(
            'google.cloud.aiplatform.container.v1alpha1.MatchService', rpc_method_handlers)
    server.add_generic_rpc_handlers((generic_handler,))


 # This class is part of an EXPERIMENTAL API.
class MatchService(object):
    """MatchService is a Google managed service for efficient vector similarity
    search at scale.
    """

    @staticmethod
    def Match(request,
            target,
            options=(),
            channel_credentials=None,
            call_credentials=None,
            insecure=False,
            compression=None,
            wait_for_ready=None,
            timeout=None,
            metadata=None):
        return grpc.experimental.unary_unary(request, target, '/google.cloud.aiplatform.container.v1alpha1.MatchService/Match',
            match__pb2.MatchRequest.SerializeToString,
            match__pb2.MatchResponse.FromString,
            options, channel_credentials,
            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)

    @staticmethod
    def BatchMatch(request,
            target,
            options=(),
            channel_credentials=None,
            call_credentials=None,
            insecure=False,
            compression=None,
            wait_for_ready=None,
            timeout=None,
            metadata=None):
        return grpc.experimental.unary_unary(request, target, '/google.cloud.aiplatform.container.v1alpha1.MatchService/BatchMatch',
            match__pb2.BatchMatchRequest.SerializeToString,
            match__pb2.BatchMatchResponse.FromString,
            options, channel_credentials,
            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)


================================================
FILE: retail/recommendation-system/bqml-scann/ann_setup.md
================================================
## Setting up the ANN Service Experimental release

This document outlines the steps required to enable and configure the Experimental release of the AI Platform ANN service. 
This instructions will be updated when the service moves to the Preview and General Availability.

### Allow-listing the project

Contact your Google representative to allow-list your project and user id(s).

### Enabling the Cloud APIs required by the ANN Service

You need to enable the following APIs to use the ANN service:

* aiplatform.googleapis.com
* servicenetworking.googleapis.com
* compute.googleapis.com

### Configuring private IP access to the ANN Service

In the experimental release, ANN service is only accessible using private endpoints. Before using the service you need to have a [VPC network](https://cloud.google.com/vpc) configured with [private services access](https://cloud.google.com/vpc/docs/configure-private-services-access). You can use the `default` VPC or create a new one.

The below instructions are for a VPC that was created with auto subnets and regional dynamic routing mode (defaults). It is recommended that you execute the below commands from Cloud Shell, using the account with the `roles/compute.networkAdmin` permissions.

1. Set environment variables for your project ID, the name of your VPC network, and the name of your reserved range of addresses. The name of the reserved range can be an arbitrary name. It is for display only.

```
PROJECT_ID=<your-project-id>
gcloud config set project $PROJECT_ID
NETWORK_NAME=<your-VPC-network-name>
PEERING_RANGE_NAME=google-reserved-range

```

2. Reserve an IP range for Google services. The reserved range should be large enought to accommodate all peered services. The below command reserves a CIDR block with mask /16

```
gcloud compute addresses create $PEERING_RANGE_NAME \
  --global \
  --prefix-length=16 \
  --description="peering range for Google service: AI Platform Online Prediction" \
  --network=$NETWORK_NAME \
  --purpose=VPC_PEERING \
  --project=$PROJECT_ID

```

3. Create a private connection to establish a VPC Network Peering between your VPC network and the Google services network.

```
gcloud services vpc-peerings connect \
  --service=servicenetworking.googleapis.com \
  --network=$NETWORK_NAME \
  --ranges=$PEERING_RANGE_NAME \
  --project=$PROJECT_ID

```



================================================
FILE: retail/recommendation-system/bqml-scann/embeddings_exporter/__init__.py
================================================


================================================
FILE: retail/recommendation-system/bqml-scann/embeddings_exporter/pipeline.py
================================================
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import os
import apache_beam as beam

EMBEDDING_FILE_PREFIX = 'embeddings'

def get_query(dataset_name, table_name):
  query = f'''
    SELECT 
      item_Id,
      embedding
    FROM 
      `{dataset_name}.{table_name}`;
  '''
  return query


def to_csv(entry):
  item_Id = entry['item_Id']
  embedding = entry['embedding']
  csv_string = f'{item_Id},'
  csv_string += ','.join([str(value) for value in embedding])
  return csv_string


def run(bq_dataset_name, embeddings_table_name, output_dir, pipeline_args):

    pipeline_options = beam.options.pipeline_options.PipelineOptions(pipeline_args)
    project = pipeline_options.get_all_options()['project']
    with beam.Pipeline(options=pipeline_options) as pipeline:

      query = get_query(bq_dataset_name, embeddings_table_name)
      output_prefix = os.path.join(output_dir, EMBEDDING_FILE_PREFIX)
      
      _ = (
        pipeline
        | 'ReadFromBigQuery' >> beam.io.ReadFromBigQuery(
            project=project, query=query, use_standard_sql=True, flatten_results=False)
        | 'ConvertToCsv' >> beam.Map(to_csv)
        | 'WriteToCloudStorage' >> beam.io.WriteToText(
            file_path_prefix = output_prefix,
            file_name_suffix = ".csv")
      )

================================================
FILE: retail/recommendation-system/bqml-scann/embeddings_exporter/runner.py
================================================
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import argparse
import pipeline

SETUP_FILE_PATH = './setup.py'


def get_args(argv):

  args_parser = argparse.ArgumentParser()

  args_parser.add_argument('--bq_dataset_name',
                           help='BigQuery dataset name.',
                           required=True)

  args_parser.add_argument('--embeddings_table_name',
                           help='BigQuery table name where the embeddings are stored.',
                           required=True)

  args_parser.add_argument('--output_dir',
                           help='GCS location where the embedding CSV files will be stored.',
                           required=True)

  return args_parser.parse_known_args()


def main(argv=None):
  args, pipeline_args = get_args(argv)
  pipeline_args.append('--setup_file={}'.format(SETUP_FILE_PATH))

  pipeline.run(
    args.bq_dataset_name, 
    args.embeddings_table_name, 
    args.output_dir, 
    pipeline_args)


if __name__ == '__main__':
  main()


================================================
FILE: retail/recommendation-system/bqml-scann/embeddings_exporter/setup.py
================================================
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import setuptools

REQUIRED_PACKAGES = []

setuptools.setup(
    name='embedding_exporter',
    description='Export embeddings from BigQuery to Cloud Storage.',
    version='0.1',
    install_requires=REQUIRED_PACKAGES,
    py_modules=['pipeline'],
)



================================================
FILE: retail/recommendation-system/bqml-scann/embeddings_lookup/lookup_creator.py
================================================
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import tensorflow as tf
import numpy as np

VOCABULARY_FILE_NAME = 'vocabulary.txt'


class EmbeddingLookup(tf.keras.Model):

  def __init__(self, embedding_files_prefix, **kwargs):
    super(EmbeddingLookup, self).__init__(**kwargs)

    vocabulary = list()
    embeddings = list()

    # Read embeddings from csv files.
    print('Loading embeddings from files...')
    for embedding_file in tf.io.gfile.glob(embedding_files_prefix):
      print(f'Loading embeddings in {embedding_file} ...')
      with tf.io.gfile.GFile(embedding_file, 'r') as lines:
        for line in lines:
          try:
            line_parts = line.split(',')
            item = line_parts[0]
            embedding = np.array([float(v) for v in line_parts[1:]])
            vocabulary.append(item)
            embeddings.append(embedding)
          except: pass
    print('Embeddings loaded.')
    
    embedding_size = len(embeddings[0])
    oov_embedding = np.zeros((1, embedding_size))
    self.embeddings = np.append(np.array(embeddings), oov_embedding, axis=0)
    print(f'Embeddings: {self.embeddings.shape}')

    # Write vocabulary file.
    print('Writing vocabulary to file...')
    with open(VOCABULARY_FILE_NAME, 'w') as f:
      for item in vocabulary: 
        f.write(f'{item}\n')
    print('Vocabulary file written and will be added as a model asset.')

    self.vocabulary_file = tf.saved_model.Asset(VOCABULARY_FILE_NAME)
    initializer = tf.lookup.KeyValueTensorInitializer(
        keys=vocabulary, values=list(range(len(vocabulary))))
    self.token_to_id = tf.lookup.StaticHashTable(
        initializer, default_value=len(vocabulary))

  @tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
  def __call__(self, inputs):
    tokens = tf.strings.split(inputs, sep=None).to_sparse()
    ids = self.token_to_id.lookup(tokens) 
    embeddings = tf.nn.embedding_lookup_sparse(
        params=self.embeddings, 
        sp_ids=ids, 
        sp_weights=None, 
        combiner="mean"
    )
    return embeddings



def export_saved_model(embedding_files_path, model_output_dir):
  print('Instantiating embedding lookup model...')
  embedding_lookup_model = EmbeddingLookup(embedding_files_path)
  print('Model is Instantiated.')
  
  signatures = {
    'serving_default': embedding_lookup_model.__call__.get_concrete_function(),
  }

  print('Exporting embedding lookup model as a SavedModel...')
  tf.saved_model.save(embedding_lookup_model, model_output_dir, signatures=signatures)
  print('SavedModel is exported.')

================================================
FILE: retail/recommendation-system/bqml-scann/index_builder/builder/__init__.py
================================================


================================================
FILE: retail/recommendation-system/bqml-scann/index_builder/builder/indexer.py
================================================
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import scann
import tensorflow as tf
import numpy as np
import math
import pickle

METRIC = 'dot_product'
DIMENSIONS_PER_BLOCK = 2
ANISOTROPIC_QUANTIZATION_THRESHOLD = 0.2
NUM_NEIGHBOURS = 10
NUM_LEAVES_TO_SEARCH = 200
REORDER_NUM_NEIGHBOURS = 200
TOKENS_FILE_NAME = 'tokens'


def load_embeddings(embedding_files_pattern):
    
  embedding_list = list()
  tokens = list()
  embed_files = tf.io.gfile.glob(embedding_files_pattern)
  print(f'{len(embed_files)} embedding files are found.')

  for file_idx, embed_file in enumerate(embed_files):
    print(f'Loading embeddings in file {file_idx+1} of {len(embed_files)}...')
    with tf.io.gfile.GFile(embed_file, 'r') as file_reader:
      lines = file_reader.readlines()
      for line in lines:
        parts = line.split(',')
        item_Id = parts[0]
        embedding = parts[1:]
        embedding = np.array([float(v) for v in embedding])
        normalized_embedding = embedding / np.linalg.norm(embedding)
        embedding_list.append(normalized_embedding)
        tokens.append(item_Id)
        
    print(f'{len(embedding_list)} embeddings are loaded.')
    
  return tokens, np.array(embedding_list)
    
    
def build_index(embeddings, num_leaves):
  
  data_size = embeddings.shape[0] 
  if not num_leaves:
    num_leaves = int(math.sqrt(data_size))
    
  print('Start building the ScaNN index...')
  scann_builder = scann.scann_ops.builder(embeddings, NUM_NEIGHBOURS, METRIC)
  scann_builder = scann_builder.tree(
    num_leaves=num_leaves, 
    num_leaves_to_search=NUM_LEAVES_TO_SEARCH, 
    training_sample_size=data_size)
  scann_builder = scann_builder.score_ah(
    DIMENSIONS_PER_BLOCK, 
    anisotropic_quantization_threshold=ANISOTROPIC_QUANTIZATION_THRESHOLD)
  scann_builder = scann_builder.reorder(REORDER_NUM_NEIGHBOURS)
  scann_index = scann_builder.build()
  print('ScaNN index is built.')
  
  return scann_index


def save_index(index, tokens, output_dir):
  print('Saving index as a SavedModel...')
  module = index.serialize_to_module()
  tf.saved_model.save(
    module, output_dir, signatures=None, options=None
  )
  print(f'Index is saved to {output_dir}')
  
  print(f'Saving tokens file...')
  tokens_file_path = os.path.join(output_dir, TOKENS_FILE_NAME)
  with tf.io.gfile.GFile(tokens_file_path, 'wb') as handle:
    pickle.dump(tokens, handle, protocol=pickle.HIGHEST_PROTOCOL)
  print(f'Item file is saved to {tokens_file_path}.')
 

def build(embedding_files_pattern, output_dir, num_leaves=None):
  print("Indexer started...")
  tokens, embeddings = load_embeddings(embedding_files_pattern)
  index = build_index(embeddings, num_leaves)
  save_index(index, tokens, output_dir)
  print("Indexer finished.")
    
    
    

================================================
FILE: retail/recommendation-system/bqml-scann/index_builder/builder/task.py
================================================
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
from . import indexer

def get_args():

  args_parser = argparse.ArgumentParser()

  args_parser.add_argument(
    '--embedding-files-path',
    help='GCS or local paths to embedding files',
    required=True
  )

  args_parser.add_argument(
    '--output-dir',
    help='GCS or local paths to output index file',
    required=True
  )

  args_parser.add_argument(
    '--num-leaves',
    help='Number of trees to build in the index',
    default=250,
    type=int
  )

  args_parser.add_argument(
    '--job-dir',
    help='GCS or local paths to job package'
  )

  return args_parser.parse_args()


def main():
  args = get_args()
  indexer.build(
    embedding_files_pattern=args.embedding_files_path, 
    output_dir=args.output_dir,
    num_leaves=args.num_leaves
  )
    
if __name__ == '__main__':
    main()

================================================
FILE: retail/recommendation-system/bqml-scann/index_builder/config.yaml
================================================
trainingInput:
  scaleTier: CUSTOM
  masterType: n1-standard-8

================================================
FILE: retail/recommendation-system/bqml-scann/index_builder/setup.py
================================================
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from setuptools import find_packages
from setuptools import setup

REQUIRED_PACKAGES = ['scann==1.1.1']

setup(
    name='scann-index-buider',
    version='v1',
    install_requires=REQUIRED_PACKAGES,
    packages=find_packages(),
    include_package_data=True,
    description=''
)

================================================
FILE: retail/recommendation-system/bqml-scann/index_server/Dockerfile
================================================
FROM python:3.8-slim

COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . ./

ARG PORT
ENV PORT=$PORT

CMD exec gunicorn --bind :$PORT main:app  --workers=1 --threads 8 --timeout 1800

================================================
FILE: retail/recommendation-system/bqml-scann/index_server/cloudbuild.yaml
================================================
steps:

- name: 'gcr.io/cloud-builders/docker'
  args: ['build', '--tag', '${_IMAGE_URL}', '.', '--build-arg=PORT=${_PORT}']
  dir: 'index_server'

images: ['${_IMAGE_URL}']

================================================
FILE: retail/recommendation-system/bqml-scann/index_server/lookup.py
================================================
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import googleapiclient.discovery
from google.api_core.client_options import ClientOptions


class EmbeddingLookup(object):
    
  def __init__(self, project, region, model_name, version):
    api_endpoint = f'https://{region}-ml.googleapis.com'
    client_options = ClientOptions(api_endpoint=api_endpoint)
    self.service = googleapiclient.discovery.build(
      serviceName='ml', version='v1', client_options=client_options)
    self.name = f'projects/{project}/models/{model_name}/versions/{version}'
    print(f'Embedding lookup service {self.name} is initialized.')
    
  def lookup(self, instances):
    request_body = {'instances': instances}
    response = self.service.projects().predict(name=self.name, body=request_body).execute()

    if 'error' in response:
      raise RuntimeError(response['error'])

    return response['predictions']

================================================
FILE: retail/recommendation-system/bqml-scann/index_server/main.py
================================================
# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
from flask import Flask
from flask import request
from flask import jsonify

from lookup import EmbeddingLookup
from matching import ScaNNMatcher

PROJECT_ID = os.environ['PROJECT_ID']
REGION = os.environ['REGION']
EMBEDDNIG_LOOKUP_MODEL_NAME = os.environ['EMBEDDNIG_LOOKUP_MODEL_NAME']
EMBEDDNIG_LOOKUP_MODEL_VERSION = os.environ['EMBEDDNIG_LOOKUP_MODEL_VERSION']
INDEX_DIR = os.environ['INDEX_DIR']
PORT = os.environ['PORT']


scann_matcher = ScaNNMatcher(INDEX_DIR)
embedding_lookup = EmbeddingLookup(
    PROJECT_ID, REGION, EMBEDDNIG_LOOKUP_MODEL_NAME, EMBEDDNIG_LOOKUP_MODEL_VERSION)

app = Flask(__name__)


@app.route("/v1/models/<model>/versions/<version>", methods=["GET"])
def health(model, version):
  return jsonify({})


@app.route("/v1/models/<model>/versions/<version>:predict", methods=["POST"])
def predict(model, version):
  result = 'predictions'
  try:
    data = request.get_json()['instances'][0]
    query = data.get('query', None)
    show = data.get('show', 10)
    if not str(show).isdigit(): show = 10

    is_valid, error = validate_request(query, show)

    if not is_valid: 
      value = error
    else:
      vector = embedding_lookup.lookup([query])[0]
      value = scann_matcher.match(vector, int(show))

  except Exception as error:
    value = 'Unexpected error: {}'.format(error)
    result = 'error'

  response = jsonify({result: value})
  return response


def validate_request(query, show):
  is_valid = True
  error = ''

  if not query:
    is_valid = False
    error = 'You need to provide the item Id(s) in the query!'

  return is_valid, error


if __name__ == '__main__':
  app.run(host='0.0.0.0', port=PORT)

================================================
FILE: retail/recommendation-system/bqml-scann/index_server/matching.py
================================================
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import tensorflow as tf
import numpy as np
import scann
import pickle
import os

TOKENS_FILE_NAME = 'tokens'


class ScaNNMatcher(object):

  def __init__(self, index_dir):
    print('Loading ScaNN index...')
    scann_module = tf.saved_model.load(index_dir)
    self.scann_index = scann.scann_ops.searcher_from_module(scann_module)
    tokens_file_path = os.path.join(index_dir, TOKENS_FILE_NAME)
    with tf.io.gfile.GFile(tokens_file_path, 'rb') as handle:
      self.tokens = pickle.load(handle)
    print('ScaNN index is loadded.')

  def match(self, vector, num_matches=10):
    embedding = np.array(vector)
    query = embedding / np.linalg.norm(embedding)
    matche_indices, _ = self.scann_index.search(query, final_num_neighbors=num_matches)
    match_tokens = [self.tokens[match_idx] for match_idx in matche_indices.numpy()]
    return match_tokens


================================================
FILE: retail/recommendation-system/bqml-scann/index_server/requirements.txt
================================================
pip==20.2.4
Flask==1.1.2
gunicorn==20.0.4
google-api-python-client==1.12.5
scann==1.1.1

================================================
FILE: retail/recommendation-system/bqml-scann/perf_test.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the Retrieval Latency of Approximate vs Exact Matching "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROJECT_ID = 'ksalama-cloudml'\n",
    "BUCKET = 'ksalama-cloudml'\n",
    "INDEX_DIR = f'gs://{BUCKET}/bqml/scann_index'\n",
    "BQML_MODEL_DIR = f'gs://{BUCKET}/bqml/item_matching_model'\n",
    "LOOKUP_MODEL_DIR = f'gs://{BUCKET}/bqml/embedding_lookup_model'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "songs = {\n",
    "    '2114406': 'Metallica: Nothing Else Matters',\n",
    "    '2114402': 'Metallica: The Unforgiven',\n",
    "    '2120788': 'Limp Bizkit: My Way',\n",
    "    '2120786': 'Limp Bizkit: My Generation',\n",
    "    '1086322': 'Jacques Brel: Ne Me Quitte Pas',\n",
    "    '3129954': 'Édith Piaf: Non, Je Ne Regrette Rien',\n",
    "    '53448': 'France Gall: Ella, Elle l\\'a',\n",
    "    '887688': 'Enrique Iglesias: Tired Of Being Sorry',\n",
    "    '562487': 'Shakira: Hips Don\\'t Lie',\n",
    "    '833391': 'Ricky Martin: Livin\\' la Vida Loca',\n",
    "    '1098069': 'Snoop Dogg: Drop It Like It\\'s Hot',\n",
    "    '910683': '2Pac: California Love',\n",
    "    '1579481': 'Dr. Dre: The Next Episode',\n",
    "    '2675403': 'Eminem: Lose Yourself',\n",
    "    '2954929': 'Black Sabbath: Iron Man',\n",
    "    '625169': 'Black Sabbath: Paranoid',\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exact Matching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ExactMatcher(object):\n",
    "  def __init__(self, model_dir):\n",
    "    print(\"Loading exact matchg model...\")\n",
    "    self.model = tf.saved_model.load(model_dir)\n",
    "    print(\"Exact matchg model is loaded.\")\n",
    "  \n",
    "  def match(self, instances):\n",
    "    outputs = self.model.signatures['serving_default'](tf.constant(instances, tf.dtypes.int64))\n",
    "    return outputs['predicted_item2_Id'].numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exact_matcher = ExactMatcher(BQML_MODEL_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exact_matches = {}\n",
    "\n",
    "start_time = time.time()\n",
    "for i in range(100):\n",
    "  for song in songs:\n",
    "    matches = exact_matcher.match([int(song)])\n",
    "    exact_matches[song] = matches.tolist()[0]\n",
    "end_time = time.time()\n",
    "exact_elapsed_time = end_time - start_time\n",
    "\n",
    "print(f'Elapsed time: {round(exact_elapsed_time, 3)} seconds - average time: {exact_elapsed_time / (100 * len(songs))} seconds')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approximate Matching (ScaNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from index_server.matching import ScaNNMatcher\n",
    "scann_matcher = ScaNNMatcher(INDEX_DIR)\n",
    "embedding_lookup = tf.saved_model.load(LOOKUP_MODEL_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "approx_matches = dict()\n",
    "\n",
    "start_time = time.time()\n",
    "for i in range(100):\n",
    "  for song in songs:\n",
    "    vector = embedding_lookup([song]).numpy()[0]\n",
    "    matches = scann_matcher.match(vector, 50)\n",
    "    approx_matches[song] = matches\n",
    "end_time = time.time()\n",
    "scann_elapsed_time = end_time - start_time\n",
    "\n",
    "print(f'Elapsed time: {round(scann_elapsed_time, 3)} seconds - average time: {scann_elapsed_time / (100 * len(songs))} seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "speedup_percent = round(exact_elapsed_time / scann_elapsed_time, 1)\n",
    "print(f'ScaNN speedup: {speedup_percent}x')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License\n",
    "\n",
    "Copyright 2020 Google LLC\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n",
    "\n",
    "See the License for the specific language governing permissions and limitations under the License.\n",
    "\n",
    "**This is not an official Google product but sample code provided for an educational purpose**"
   ]
  }
 ],
 "metadata": {
  "environment": {
   "name": "tf2-2-3-gpu.2-3.m58",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/tf2-2-3-gpu.2-3:m58"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}


================================================
FILE: retail/recommendation-system/bqml-scann/requirements.txt
================================================
tensorflow==2.4.0
tfx==0.25.0
apache-beam[gcp]
google-cloud-bigquery 
pyarrow
google-auth 
google-api-python-client 
google-api-core
scann
kfp==1.1.2

================================================
FILE: retail/recommendation-system/bqml-scann/sql_scripts/sp_ComputePMI.sql
================================================
CREATE OR REPLACE PROCEDURE @DATASET_NAME.sp_ComputePMI(
  IN min_item_frequency INT64,
  IN max_group_size INT64
)

BEGIN

  DECLARE total INT64;

  # Get items with minimum frequency
  CREATE OR REPLACE TABLE @DATASET_NAME.valid_item_groups
  AS

  # Create valid item set
  WITH 
  valid_items AS (
    SELECT item_Id, COUNT(group_Id) AS item_frequency
    FROM @DATASET_NAME.vw_item_groups
    GROUP BY item_Id
    HAVING item_frequency >= min_item_frequency
  ),

  # Create valid group set
  valid_groups AS (
    SELECT group_Id, COUNT(item_Id) AS group_size
    FROM @DATASET_NAME.vw_item_groups
    WHERE item_Id IN (SELECT item_Id FROM valid_items)
    GROUP BY group_Id
    HAVING group_size BETWEEN 2 AND max_group_size
  )

  SELECT item_Id, group_Id
  FROM @DATASET_NAME.vw_item_groups
  WHERE item_Id IN (SELECT item_Id FROM valid_items)
  AND group_Id IN (SELECT group_Id FROM valid_groups);

  # Compute pairwise cooc
  CREATE OR REPLACE TABLE @DATASET_NAME.item_cooc
  AS
  SELECT item1_Id, item2_Id, SUM(cooc) AS cooc
  FROM
  (
    SELECT
      a.item_Id item1_Id,
      b.item_Id item2_Id,
      1 as cooc
    FROM @DATASET_NAME.valid_item_groups a
    JOIN @DATASET_NAME.valid_item_groups b
    ON a.group_Id = b.group_Id
    AND a.item_Id < b.item_Id
  )
  GROUP BY  item1_Id, item2_Id;

  ###################################
  
  # Compute item frequencies
  CREATE OR REPLACE TABLE @DATASET_NAME.item_frequency
  AS
  SELECT item_Id, COUNT(group_Id) AS frequency
  FROM @DATASET_NAME.valid_item_groups
  GROUP BY item_Id;

  ###################################
  
  # Compute total frequency |D|
  SET total = (
    SELECT SUM(frequency)  AS total
    FROM @DATASET_NAME.item_frequency
  );
  
  ###################################
  
  # Add mirror item-pair cooc and same item frequency as cooc
  CREATE OR REPLACE TABLE @DATASET_NAME.item_cooc
  AS
  SELECT item1_Id, item2_Id, cooc
  FROM @DATASET_NAME.item_cooc
  UNION ALL
  SELECT item2_Id as item1_Id, item1_Id AS item2_Id, cooc
  FROM @DATASET_NAME.item_cooc
  UNION ALL
  SELECT item_Id as item1_Id, item_Id AS item2_Id, frequency as cooc
  FROM @DATASET_NAME.item_frequency;

  ###################################
  
  # Compute PMI
  CREATE OR REPLACE TABLE @DATASET_NAME.item_cooc
  AS
  SELECT
    a.item1_Id,
    a.item2_Id,
    a.cooc,
    LOG(a.cooc, 2) - LOG(b.frequency, 2) - LOG(c.frequency, 2) + LOG(total, 2) AS pmi
  FROM @DATASET_NAME.item_cooc a
  JOIN @DATASET_NAME.item_frequency b
  ON a.item1_Id = b.item_Id
  JOIN @DATASET_NAME.item_frequency c
  ON a.item2_Id = c.item_Id; 
END

================================================
FILE: retail/recommendation-system/bqml-scann/sql_scripts/sp_ExractEmbeddings.sql
================================================
CREATE OR REPLACE PROCEDURE @DATASET_NAME.sp_ExractEmbeddings() 
BEGIN
  CREATE OR REPLACE TABLE  @DATASET_NAME.item_embeddings AS
    WITH 
    step1 AS
    (
        SELECT 
            feature AS item_Id,
            factor_weights,
            intercept AS bias,
        FROM
            ML.WEIGHTS(MODEL `@DATASET_NAME.item_matching_model`)
        WHERE feature != 'global__INTERCEPT__'
    ),

    step2 AS
    (
        SELECT 
            item_Id, 
            factor, 
            SUM(weight) AS weight,
            SUM(bias) AS bias
        FROM step1,
        UNNEST(step1.factor_weights) AS embedding
        GROUP BY 
        item_Id,
        factor 
    )

    SELECT 
        item_Id, 
        ARRAY_AGG(weight ORDER BY factor ASC) embedding,
        bias
    FROM step2
    GROUP BY item_Id, bias;
END

================================================
FILE: retail/recommendation-system/bqml-scann/sql_scripts/sp_TrainItemMatchingModel.sql
================================================
CREATE OR REPLACE PROCEDURE @DATASET_NAME.sp_TrainItemMatchingModel(
  IN dimensions INT64
)

BEGIN

  CREATE OR REPLACE MODEL @DATASET_NAME.item_matching_model
  OPTIONS(
    MODEL_TYPE='matrix_factorization', 
    FEEDBACK_TYPE='implicit',
    WALS_ALPHA=1,
    NUM_FACTORS=(dimensions),
    USER_COL='item1_Id', 
    ITEM_COL='item2_Id',
    RATING_COL='score',
    DATA_SPLIT_METHOD='no_split'
  )
  AS
  SELECT 
    item1_Id, 
    item2_Id, 
    cooc *  pmi AS score
  FROM @DATASET_NAME.item_cooc;

END

================================================
FILE: retail/recommendation-system/bqml-scann/tfx01_interactive.ipynb
================================================
{"nbformat":4,"nbformat_minor":0,"metadata":{"environment":{"name":"tf2-2-3-gpu.2-3.m59","type":"gcloud","uri":"gcr.io/deeplearning-platform-release/tf2-2-3-gpu.2-3:m59"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.8"},"colab":{"name":"tfx01_interactive.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true}},"cells":[{"cell_type":"markdown","metadata":{"id":"lIGxhEiFRBzM"},"source":["# Create an interactive TFX pipeline\n","\n","This notebook is the first of two notebooks that guide you through automating the [Real-time Item-to-item Recommendation with BigQuery ML Matrix Factorization and ScaNN](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/tree/master/retail/recommendation-system/bqml-scann) solution with a pipeline.\n","\n","Use this notebook to create and run a [TFX](https://www.tensorflow.org/tfx) pipeline that performs the following steps:\n","\n","1. Compute PMI on item co-occurrence data by using a [custom Python function](https://www.tensorflow.org/tfx/guide/custom_function_component) component.\n","2. Train a BigQuery ML matrix factorization model on the PMI data to learn item embeddings by using a custom Python function component.\n","3. Extract the embeddings from the model to a BigQuery table by using a custom Python function component.\n","4. Export the embeddings in [TFRecord](https://www.tensorflow.org/tutorials/load_data/tfrecord) format by using the standard [BigQueryExampleGen](https://www.tensorflow.org/tfx/api_docs/python/tfx/extensions/google_cloud_big_query/example_gen/component/BigQueryExampleGen) component.\n","5. Import the schema for the embeddings by using the standard [ImporterNode](https://www.tensorflow.org/tfx/api_docs/python/tfx/components/ImporterNode) component.\n","6. Validate the embeddings against the imported schema by using the standard [StatisticsGen](https://www.tensorflow.org/tfx/guide/statsgen) and [ExampleValidator](https://www.tensorflow.org/tfx/guide/exampleval) components. \n","7. Create an embedding lookup SavedModel by using the standard [Trainer](https://www.tensorflow.org/tfx/api_docs/python/tfx/components/Trainer) component.\n","8. Push the embedding lookup model to a model registry directory by using the standard [Pusher](https://www.tensorflow.org/tfx/guide/pusher) component.\n","9. Build the ScaNN index by using the standard Trainer component.\n","10. Evaluate and validate the ScaNN index latency and recall by implementing a [TFX custom component](https://www.tensorflow.org/tfx/guide/custom_component).\n","11. Push the ScaNN index to a model registry directory by using the standard Pusher component.\n","\n","The [tfx_pipeline](tfx_pipeline) directory contains the source code for the TFX pipeline implementation. \n","\n","Before starting this notebook, you must run the [00_prep_bq_procedures](00_prep_bq_procedures.ipynb) notebook to complete the solution prerequisites.\n","\n","After completing this notebook, run the [tfx02_deploy_run](tfx02_deploy_run.ipynb) notebook to deploy the pipeline."]},{"cell_type":"markdown","metadata":{"id":"BhAa_9hBRBzP"},"source":["## Setup\r\n","\r\n","Import the required libraries, configure the environment variables, and authenticate your GCP account."]},{"cell_type":"code","metadata":{"id":"suCbts8ZRBzQ"},"source":["%load_ext autoreload\n","%autoreload 2"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"fmfpkqv-RBzR"},"source":["!pip install -U -q tfx"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"HXqqJCHdRBzR"},"source":["### Import libraries"]},{"cell_type":"code","metadata":{"id":"1Bgf_d80RBzR"},"source":["import os\n","import numpy as np\n","import tfx\n","import tensorflow as tf\n","import tensorflow_data_validation as tfdv\n","from tensorflow_transform.tf_metadata import schema_utils\n","import logging\n","\n","logging.getLogger().setLevel(logging.INFO)\n","\n","print(\"Tensorflow Version:\", tf.__version__)\n","print(\"TFX Version:\", tfx.__version__)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"V1NPgGXqRBzS"},"source":["### Configure GCP environment settings\r\n","\r\n","Update the following variables to reflect the values for your GCP environment:\r\n","\r\n","+ `PROJECT_ID`: The ID of the Google Cloud project you are using to implement this solution.\r\n","+ `BUCKET`: The name of the Cloud Storage bucket you created to use with this solution. The `BUCKET` value should be just the bucket name, so `myBucket` rather than `gs://myBucket`."]},{"cell_type":"code","metadata":{"id":"9lAiZAzHRBzS"},"source":["PROJECT_ID = 'yourProject' # Change to your project.\n","BUCKET = 'yourBucket' # Change to the bucket you created.\n","BQ_DATASET_NAME = 'recommendations'\n","ARTIFACT_STORE = f'gs://{BUCKET}/tfx_artifact_store'\n","LOCAL_MLMD_SQLLITE = 'mlmd/mlmd.sqllite'\n","PIPELINE_NAME = 'tfx_bqml_scann'\n","EMBEDDING_LOOKUP_MODEL_NAME = 'embeddings_lookup'\n","SCANN_INDEX_MODEL_NAME = 'embeddings_scann'\n","\n","PIPELINE_ROOT = os.path.join(ARTIFACT_STORE, f'{PIPELINE_NAME}_interactive')\n","MODEL_REGISTRY_DIR = os.path.join(ARTIFACT_STORE, 'model_registry_interactive')\n","\n","!gcloud config set project $PROJECT_ID"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"UDcqiH6LRBzT"},"source":["### Authenticate your GCP account\n","This is required if you run the notebook in Colab. If you use an AI Platform notebook, you should already be authenticated."]},{"cell_type":"code","metadata":{"id":"_FqvtmZXRBzT"},"source":["try:\n","  from google.colab import auth\n","  auth.authenticate_user()\n","  print(\"Colab user is authenticated.\")\n","except: pass"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ovAIUTxmRBzU"},"source":["## Instantiate the interactive context\r\n","\r\n","Instantiate an [interactive context](https://www.tensorflow.org/tfx/api_docs/python/tfx/orchestration/experimental/interactive/interactive_context/InteractiveContext) so that you can execute the TFX pipeline components interactively in the notebook. The interactive context creates a local SQLite database in the `LOCAL_MLMD_SQLLITE` directory to use as its [ML Metadata (MLMD)](https://github.com/google/ml-metadata) store.\r\n"]},{"cell_type":"code","metadata":{"id":"fkvxex7bRBzU"},"source":["CLEAN_ARTIFACTS = True\n","if CLEAN_ARTIFACTS:\n","  if tf.io.gfile.exists(PIPELINE_ROOT):\n","    print(\"Removing previous artifacts...\")\n","    tf.io.gfile.rmtree(PIPELINE_ROOT)\n","  if tf.io.gfile.exists('mlmd'):\n","    print(\"Removing local mlmd SQLite...\")\n","    tf.io.gfile.rmtree('mlmd')\n","\n","if not tf.io.gfile.exists('mlmd'):\n","  print(\"Creating mlmd directory...\")\n","  tf.io.gfile.mkdir('mlmd')\n","    \n","print(f'Pipeline artifacts directory: {PIPELINE_ROOT}')\n","print(f'Model registry directory: {MODEL_REGISTRY_DIR}')\n","print(f'Local metadata SQLlit path: {LOCAL_MLMD_SQLLITE}')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"RvhwgAKERBzV"},"source":["import ml_metadata as mlmd\n","from ml_metadata.proto import metadata_store_pb2\n","from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext\n","\n","connection_config = metadata_store_pb2.ConnectionConfig()\n","connection_config.sqlite.filename_uri = LOCAL_MLMD_SQLLITE\n","connection_config.sqlite.connection_mode = 3 # READWRITE_OPENCREATE\n","mlmd_store = mlmd.metadata_store.MetadataStore(connection_config)\n","\n","context = InteractiveContext(\n","  pipeline_name=PIPELINE_NAME,\n","  pipeline_root=PIPELINE_ROOT,\n","  metadata_connection_config=connection_config\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"AxQtakfoRBzV"},"source":["## Executing the pipeline steps\n","The components that implement the pipeline steps are in the [tfx_pipeline/bq_components.py](tfx_pipeline/bq_components.py) module."]},{"cell_type":"code","metadata":{"id":"JNhuOk_ARBzW"},"source":["from tfx_pipeline import bq_components"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"D7mDZFagRBzW"},"source":["### Step 1: Compute PMI\r\n","\r\n","Run the `pmi_computer` step, which is an instance of the `compute_pmi` custom Python function component. This component executes the [sp_ComputePMI](sql_scripts/sp_ComputePMI.sql) stored procedure in BigQuery and returns the name of the resulting table as a custom property.\r\n"]},{"cell_type":"code","metadata":{"id":"61x6Bv-qRBzW"},"source":["pmi_computer = bq_components.compute_pmi(\n","  project_id=PROJECT_ID,\n","  bq_dataset=BQ_DATASET_NAME,\n","  min_item_frequency=15,\n","  max_group_size=100,\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"08R_Q3FdRBzX"},"source":["context.run(pmi_computer)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"QGa2o4XkRBzX"},"source":["pmi_computer.outputs.item_cooc.get()[0].get_string_custom_property('bq_result_table')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"eHPpKQ1FRBzX"},"source":["### Step 2: Train the BigQuery ML matrix factorization model\r\n","\r\n","Run the `bqml_trainer` step, which is an instance of the `train_item_matching_model` custom Python function component. This component executes the [sp_TrainItemMatchingModel](sql_scripts/sp_TrainItemMatchingModel.sql) stored procedure in BigQuery and returns the name of the resulting model as a custom property.\r\n"]},{"cell_type":"code","metadata":{"id":"7k5M_-U_RBzX"},"source":["bqml_trainer = bq_components.train_item_matching_model(\n","  project_id=PROJECT_ID,\n","  bq_dataset=BQ_DATASET_NAME,\n","  item_cooc=pmi_computer.outputs.item_cooc,\n","  dimensions=50,\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"wlYTLifDRBzY"},"source":["context.run(bqml_trainer)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"7m_hEloIRBzY"},"source":["bqml_trainer.outputs.bq_model.get()[0].get_string_custom_property('bq_model_name')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"LVxA1JXIRBzY"},"source":["### Step 3: Extract the trained embeddings\r\n","\r\n","Run the `embeddings_extractor` step, which is an instance of the `extract_embeddings` custom Python function component. This component executes the [sp_ExractEmbeddings](sql_scripts/sp_ExractEmbeddings.sql) stored procedure in BigQuery and returns the name of the resulting table as a custom property."]},{"cell_type":"code","metadata":{"id":"KXvjETyZRBzZ"},"source":["embeddings_extractor = bq_components.extract_embeddings(\n","  project_id=PROJECT_ID,\n","  bq_dataset=BQ_DATASET_NAME,\n","  bq_model=bqml_trainer.outputs.bq_model,\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZD8fJeqeRBzZ"},"source":["context.run(embeddings_extractor)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"9UiNteV4RBzZ"},"source":["embeddings_extractor.outputs.item_embeddings.get()[0].get_string_custom_property('bq_result_table')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"kDg5QqDgRBza"},"source":["### Step 4: Export the embeddings in TFRecord format\r\n","\r\n","Run the `embeddings_exporter` step, which is an instance of the `BigQueryExampleGen` standard component. This component uses a SQL query to read the embedding records from BigQuery and produces an [Examples](https://www.tensorflow.org/tfx/api_docs/python/tfx/types/standard_artifacts/Examples) artifact containing training and evaluation datasets as an output. It then exports these datasets in TFRecord format by using a Beam pipeline. This pipeline can be run using the [DirectRunner or DataflowRunner](https://beam.apache.org/documentation/runners/capability-matrix/). Note that in this interactive context, the embedding records to read is limited to 1000, and the runner of the Beam pipeline is set to DirectRunner.\r\n"]},{"cell_type":"code","metadata":{"id":"gYQFEObGRBza"},"source":["from tfx.proto import example_gen_pb2\n","from tfx.extensions.google_cloud_big_query.example_gen.component import BigQueryExampleGen\n","\n","query = f'''\n","  SELECT item_Id, embedding, bias,\n","  FROM {BQ_DATASET_NAME}.item_embeddings\n","  LIMIT 1000\n","'''\n","\n","output_config = example_gen_pb2.Output(\n","  split_config=example_gen_pb2.SplitConfig(splits=[\n","    example_gen_pb2.SplitConfig.Split(name='train', hash_buckets=1)])\n",")\n","\n","embeddings_exporter = BigQueryExampleGen(\n","  query=query,\n","  output_config=output_config\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"YO69fef-RBza"},"source":["beam_pipeline_args = [\n","  '--runner=DirectRunner',\n","  f'--project={PROJECT_ID}',\n","  f'--temp_location=gs://{BUCKET}/bqml_scann/beam/temp',\n","]\n","\n","context.run(embeddings_exporter, beam_pipeline_args=beam_pipeline_args)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"H04odbChRBzb"},"source":["### Step 5: Import the schema for the embeddings\r\n","\r\n","Run the `schema_importer` step, which is an instance of the `ImporterNode` standard component. This component reads the [schema.pbtxt](tfx_pipeline/schema/schema.pbtxt) file from the solution's `schema` directory, and produces a `Schema` artifact as an output. The schema is used to validate the embedding files exported from BigQuery, and to parse the embedding records in the TFRecord files when they are read in the training components.\r\n"]},{"cell_type":"code","metadata":{"id":"HXoDKq9CRBzb"},"source":["schema_importer = tfx.components.ImporterNode(\n","  source_uri='tfx_pipeline/schema',\n","  artifact_type=tfx.types.standard_artifacts.Schema,\n","  instance_name='SchemaImporter'\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"rvFNQKJARBzb"},"source":["context.run(schema_importer)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"9yCOqNYNRBzb"},"source":["context.show(schema_importer.outputs.result)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"N1SqWeOaRBzc"},"source":["#### Read a sample embedding from the exported TFRecord files using the schema"]},{"cell_type":"code","metadata":{"id":"djeml3uJRBzc"},"source":["schema_file = schema_importer.outputs.result.get()[0].uri + \"/schema.pbtxt\"\n","schema = tfdv.load_schema_text(schema_file)\n","feature_sepc = schema_utils.schema_as_feature_spec(schema).feature_spec"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"j2p2ua1pRBzc"},"source":["data_uri = embeddings_exporter.outputs.examples.get()[0].uri + \"/train/*\"\n","\n","def _gzip_reader_fn(filenames):\n","  return tf.data.TFRecordDataset(filenames, compression_type='GZIP')\n","\n","dataset = tf.data.experimental.make_batched_features_dataset(\n","  data_uri, \n","  batch_size=1, \n","  num_epochs=1,\n","  features=feature_sepc,\n","  reader=_gzip_reader_fn,\n","  shuffle=True\n",")\n","\n","counter = 0\n","for _ in dataset: counter +=1\n","print(f'Number of records: {counter}')\n","print('')\n","\n","for batch in dataset.take(1):\n","  print(f'item: {batch[\"item_Id\"].numpy()[0][0].decode()}')\n","  print(f'embedding vector: {batch[\"embedding\"].numpy()[0]}')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"iyE7IGJORBzd"},"source":["### Step 6: Validate the embeddings against the imported schema\r\n","\r\n","Runs the `stats_generator`, which is an instance of the `StatisticsGen` standard component. This component accepts the output `Examples` artifact from the `embeddings_exporter` step and computes descriptive statistics for these examples by using an Apache Beam pipeline. The component produces a `Statistics` artifact as an output."]},{"cell_type":"code","metadata":{"id":"wnvLUn5lRBze"},"source":["stats_generator = tfx.components.StatisticsGen(\n","  examples=embeddings_exporter.outputs.examples,\n",")\n","\n","context.run(stats_generator)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"CX5WVSVfGZ73"},"source":["Run the `stats_validator`, which is an instance of the `ExampleValidator` component. This component validates the output statistics against the schema. It accepts the `Statistics` artifact produced by the `stats_generator` step and the `Schema` artifact produced by the `schema_importer` step, and produces `Anomalies` artifacts as outputput if any anomalies are found."]},{"cell_type":"code","metadata":{"id":"0R7mtaPARBze"},"source":["stats_validator = tfx.components.ExampleValidator(\n","  statistics=stats_generator.outputs.statistics,\n","  schema=schema_importer.outputs.result,\n",")\n","\n","context.run(stats_validator)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ExKbz0L_RBze"},"source":["context.show(stats_validator.outputs.anomalies)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3BwvlXVjRBzf"},"source":["### Step 7: Create an embedding lookup SavedModel\r\n","\r\n","Runs the `embedding_lookup_creator` step, which is an instance of the `Trainer` standard component. This component accepts the `Schema` artifact from the `schema_importer` step and the`Examples` artifact from the `embeddings_exporter` step as inputs, executes the [lookup_creator.py](tfx_pipeline/lookup_creator.py) module, and produces an embedding lookup `Model` artifact as an output.\r\n"]},{"cell_type":"code","metadata":{"id":"YX_CUruaRBzf"},"source":["from tfx.components.base import executor_spec\n","from tfx.components.trainer import executor as trainer_executor\n","\n","_module_file = 'tfx_pipeline/lookup_creator.py'\n","\n","embedding_lookup_creator = tfx.components.Trainer(\n","  custom_executor_spec=executor_spec.ExecutorClassSpec(trainer_executor.GenericExecutor),\n","  module_file=_module_file,\n","  train_args={'splits': ['train'], 'num_steps': 0},\n","  eval_args={'splits': ['train'], 'num_steps': 0},\n","  schema=schema_importer.outputs.result,\n","  examples=embeddings_exporter.outputs.examples,\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"InEQYZFsRBzf"},"source":["context.run(embedding_lookup_creator)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"UOxSZZuiRBzg"},"source":["#### Validate the lookup model\r\n","\r\n","Use the [TFX InfraValidator](https://www.tensorflow.org/tfx/guide/infra_validator) to make sure the created model is mechanically fine and can be loaded successfully."]},{"cell_type":"code","metadata":{"id":"Ujlyqbt0RBzg","colab":{"base_uri":"https://localhost:8080/","height":398},"executionInfo":{"status":"error","timestamp":1611353374505,"user_tz":480,"elapsed":593,"user":{"displayName":"Christa Carpentiere","photoUrl":"","userId":"14715348382857067059"}},"outputId":"bd5664a7-5370-43e1-fcbe-0e9d545c45f1"},"source":["from tfx.proto import infra_validator_pb2\n","\n","serving_config = infra_validator_pb2.ServingSpec(\n","  tensorflow_serving=infra_validator_pb2.TensorFlowServing(\n","      tags=['latest']),\n","  local_docker=infra_validator_pb2.LocalDockerConfig(),\n",")\n","  \n","validation_config = infra_validator_pb2.ValidationSpec(\n","  max_loading_time_seconds=60,\n","  num_tries=3,\n",")\n","\n","infra_validator = tfx.components.InfraValidator(\n","  model=embedding_lookup_creator.outputs.model,\n","  serving_spec=serving_config,\n","  validation_spec=validation_config,\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"8yXOe5iwRBzh"},"source":["context.run(infra_validator)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"r4xXYpFkRBzh"},"source":["tf.io.gfile.listdir(infra_validator.outputs.blessing.get()[0].uri)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0dXDUaMkRBzh"},"source":["### Step 8: Push the embedding lookup model to the model registry\r\n","\r\n","Run the `embedding_lookup_pusher` step, which is an instance of the `Pusher` standard component. This component accepts the embedding lookup `Model` artifact from the `embedding_lookup_creator` step, and stores the SavedModel in the location specified by the `MODEL_REGISTRY_DIR` variable.\r\n"]},{"cell_type":"code","metadata":{"id":"KFoUKJpkRBzi"},"source":["embedding_lookup_pusher = tfx.components.Pusher(\n","  model=embedding_lookup_creator.outputs.model,\n","  infra_blessing=infra_validator.outputs.blessing,\n","  push_destination=tfx.proto.pusher_pb2.PushDestination(\n","    filesystem=tfx.proto.pusher_pb2.PushDestination.Filesystem(\n","      base_directory=os.path.join(MODEL_REGISTRY_DIR, EMBEDDING_LOOKUP_MODEL_NAME))\n","  )\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"pEd8xgejRBzi"},"source":["context.run(embedding_lookup_pusher)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"MKCWQoDYRBzi"},"source":["lookup_savedmodel_dir = embedding_lookup_pusher.outputs.pushed_model.get()[0].get_string_custom_property('pushed_destination')\n","!saved_model_cli show --dir {lookup_savedmodel_dir} --tag_set serve --signature_def serving_default"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"736qRzRhRBzj"},"source":["loaded_model = tf.saved_model.load(lookup_savedmodel_dir)\n","vocab = [token.strip() for token in tf.io.gfile.GFile(\n","  loaded_model.vocabulary_file.asset_path.numpy().decode(), 'r').readlines()]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"4ULGeFMeRBzj"},"source":["input_items = [vocab[0], ' '.join([vocab[1], vocab[2]]), 'abc123']\n","print(input_items)\n","output = loaded_model(input_items)\n","print(f'Embeddings retrieved: {len(output)}')\n","for idx, embedding in enumerate(output):\n","  print(f'{input_items[idx]}: {embedding[:5]}')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OnmChMRFRBzj"},"source":["### Step 9: Build the ScaNN index\r\n","\r\n","Run the `scann_indexer` step, which is an instance of the `Trainer` standard component. This component accepts the `Schema` artifact from the `schema_importer` step and the `Examples` artifact from the `embeddings_exporter` step as inputs, executes the [scann_indexer.py](tfx_pipeline/scann_indexer.py) module, and produces the ScaNN index `Model` artifact as an output.\r\n"]},{"cell_type":"code","metadata":{"id":"-5KIy2nYRBzj"},"source":["from tfx.components.base import executor_spec\n","from tfx.components.trainer import executor as trainer_executor\n","\n","_module_file = 'tfx_pipeline/scann_indexer.py'\n","\n","scann_indexer = tfx.components.Trainer(\n","  custom_executor_spec=executor_spec.ExecutorClassSpec(trainer_executor.GenericExecutor),\n","  module_file=_module_file,\n","  train_args={'splits': ['train'], 'num_steps': 0},\n","  eval_args={'splits': ['train'], 'num_steps': 0},\n","  schema=schema_importer.outputs.result,\n","  examples=embeddings_exporter.outputs.examples\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"1ZB1YQItRBzj"},"source":["context.run(scann_indexer)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"45g-mNioRBzk"},"source":["### Step 10: Evaluate and validate the ScaNN index\n","\n","Runs the `index_evaluator` step, which is an instance of the `IndexEvaluator` custom TFX component. This component accepts the `Examples` artifact from the `embeddings_exporter` step, the `Schema` artifact from the `schema_importer` step, and ScaNN index `Model` artifact from the `scann_indexer` step. The IndexEvaluator component completes the following tasks:\n","\n","+ Uses the schema to parse the embedding records. \n","+ Evaluates the matching latency of the index.\n","+ Compares the recall of the produced matches with respect to the exact matches.\n","+ Validates the latency and recall against the `max_latency` and `min_recall` input parameters.\n","\n","When it is finished, it produces a `ModelBlessing` artifact as output, which indicates whether the ScaNN index passed the validation criteria or not.\n","\n","The IndexEvaluator custom component is implemented in the [tfx_pipeline/scann_evaluator.py](tfx_pipeline/scann_evaluator.py) module."]},{"cell_type":"code","metadata":{"id":"kycmXtepRBzk"},"source":["from tfx_pipeline import scann_evaluator\n","\n","index_evaluator = scann_evaluator.IndexEvaluator(\n","  examples=embeddings_exporter.outputs.examples,\n","  model=scann_indexer.outputs.model,\n","  schema=schema_importer.outputs.result,\n","  min_recall=0.8,\n","  max_latency=0.01,\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"OO0XRx-hRBzk"},"source":["context.run(index_evaluator)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"rzpuQLhtRBzk"},"source":["### Step 11: Push the ScaNN index to the model registry\r\n","\r\n","Runs the `embedding_scann_pusher` step, which is an instance of the `Pusher` standard component. This component accepts the ScaNN index `Model` artifact from the `scann_indexer` step and the `ModelBlessing` artifact from the `index_evaluator` step, and stores the SavedModel in the location specified by the `MODEL_REGISTRY_DIR` variable.\r\n"]},{"cell_type":"code","metadata":{"id":"N16OZ93bRBzl"},"source":["embedding_scann_pusher = tfx.components.Pusher(\n","  model=scann_indexer.outputs.model,\n","  model_blessing=index_evaluator.outputs.blessing,\n","  push_destination=tfx.proto.pusher_pb2.PushDestination(\n","    filesystem=tfx.proto.pusher_pb2.PushDestination.Filesystem(\n","      base_directory=os.path.join(MODEL_REGISTRY_DIR, SCANN_INDEX_MODEL_NAME))\n","  )\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"jQ9oZEy6RBzl"},"source":["context.run(embedding_scann_pusher)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"9aWf8HdIRBzl"},"source":["from index_server.matching import ScaNNMatcher\n","scann_index_dir = embedding_scann_pusher.outputs.pushed_model.get()[0].get_string_custom_property('pushed_destination')\n","scann_matcher = ScaNNMatcher(scann_index_dir)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qeVARPQXRBzl"},"source":["vector = np.random.rand(50)\n","scann_matcher.match(vector, 5)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ej2mP1RmRBzl"},"source":["## Check the local MLMD store "]},{"cell_type":"code","metadata":{"id":"VUeGmnuZRBzm"},"source":["mlmd_store.get_artifacts()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"D8YzlDHeRBzm"},"source":["## View the model registry directory"]},{"cell_type":"code","metadata":{"id":"U69QVxbDRBzm"},"source":["!gcloud storage ls {MODEL_REGISTRY_DIR}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"dcA67n1lRBzm"},"source":["## License\n","\n","Copyright 2020 Google LLC\n","\n","Licensed under the Apache License, Version 2.0 (the \"License\");\n","you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0\n","\n","Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n","\n","See the License for the specific language governing permissions and limitations under the License.\n","\n","**This is not an official Google product but sample code provided for an educational purpose**"]}]}

================================================
FILE: retail/recommendation-system/bqml-scann/tfx02_deploy_run.ipynb
================================================
{"nbformat":4,"nbformat_minor":0,"metadata":{"environment":{"name":"tf2-2-3-gpu.2-3.m59","type":"gcloud","uri":"gcr.io/deeplearning-platform-release/tf2-2-3-gpu.2-3:m59"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.8"},"colab":{"name":"tfx02_deploy_run.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true}},"cells":[{"cell_type":"markdown","metadata":{"id":"Ocb1x0zeP7Fe"},"source":["# Compile and deploy the TFX pipeline to Kubeflow Pipelines\n","\n","This notebook is the second of two notebooks that guide you through automating the [Real-time Item-to-item Recommendation with BigQuery ML Matrix Factorization and ScaNN](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/tree/master/retail/recommendation-system/bqml-scann) solution with a pipeline.\n","\n","Use this notebook to compile the TFX pipeline to a Kubeflow Pipelines (KFP) package. This process creates an Argo YAML file in a .tar.gz package, and is accomplished through the following steps:\n","\n","1. Build a custom container image that includes the solution modules.\n","2. Compile the TFX Pipeline using the TFX command-line interface (CLI).\n","3. Deploy the compiled pipeline to KFP.\n","\n","The pipeline workflow is implemented in the [pipeline.py](tfx_pipeline/pipeline.py) module. The [runner.py](tfx_pipeline/runner.py) module reads the configuration settings from the [config.py](tfx_pipeline/config.py) module, defines the runtime parameters of the pipeline,  and creates a KFP format that is executable on AI Platform pipelines. \n","\n","Before starting this notebook, you must run the [tfx01_interactive](tfx01_interactive.ipynb) notebook to create the TFX pipeline.\n"]},{"cell_type":"markdown","metadata":{"id":"qdYregr9P7Fl"},"source":["## Install required libraries"]},{"cell_type":"code","metadata":{"id":"oFAdKJSdP7Fm"},"source":["%load_ext autoreload\n","%autoreload 2"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"KfrHaJmyP7Fm"},"source":["!pip install -q -U kfp"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C-Ibd8heP7Fn"},"source":["## Set environment variables\r\n","\r\n","Update the following variables to reflect the values for your GCP environment:\r\n","\r\n","+ `PROJECT_ID`: The ID of the Google Cloud project you are using to implement this solution.\r\n","+ `BUCKET`: The name of the Cloud Storage bucket you created to use with this solution. The `BUCKET` value should be just the bucket name, so `myBucket` rather than `gs://myBucket`.\r\n","+ `GKE_CLUSTER_NAME`: The name of the Kubernetes Engine cluster used by the AI Platform pipeline. You can find this by looking at the **Cluster** column of the `kubeflow-pipelines` pipeline instance on the AI Platform Pipelines page.\r\n","+ `GKE_CLUSTER_ZONE`: The zone of the Kubernetes Engine cluster used by the AI Platform pipeline. You can find this by looking at the **Zone** column of the `kubeflow-pipelines` pipeline instance on the AI Platform Pipelines page."]},{"cell_type":"code","metadata":{"id":"VHES7S4iP7Fn"},"source":["import os\n","\n","os.environ['PROJECT_ID'] = 'yourProject' # Set your project.\n","os.environ['BUCKET'] = 'yourBucket' # Set your bucket.\n","os.environ['GKE_CLUSTER_NAME'] = 'yourCluster' # Set your GKE cluster name.\n","os.environ['GKE_CLUSTER_ZONE'] = 'yourClusterZone' # Set your GKE cluster zone.\n","\n","os.environ['IMAGE_NAME'] = 'tfx-ml'\n","os.environ['TAG'] = 'tfx0.25.0'\n","os.environ['ML_IMAGE_URI']=f'gcr.io/{os.environ.get(\"PROJECT_ID\")}/{os.environ.get(\"IMAGE_NAME\")}:{os.environ.get(\"TAG\")}'\n","\n","os.environ['NAMESPACE'] = 'kubeflow-pipelines'\n","os.environ['ARTIFACT_STORE_URI'] = f'gs://{os.environ.get(\"BUCKET\")}/tfx_artifact_store'\n","os.environ['GCS_STAGING_PATH'] = f'{os.environ.get(\"ARTIFACT_STORE_URI\")}/staging'\n","\n","os.environ['RUNTIME_VERSION'] = '2.2'\n","os.environ['PYTHON_VERSION'] = '3.7'\n","os.environ['BEAM_RUNNER'] = 'DirectRunner'\n","os.environ['MODEL_REGISTRY_URI'] = f'{os.environ.get(\"ARTIFACT_STORE_URI\")}/model_registry'\n","\n","os.environ['PIPELINE_NAME'] = 'tfx_bqml_scann'"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"OQua8zUuP7Fo"},"source":["from tfx_pipeline import config\n","\n","for key, value in config.__dict__.items():\n","  if key.isupper(): print(f'{key}: {value}')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"jiOIUg-fP7Fo"},"source":["## Run the Pipeline locally by using the Beam runner"]},{"cell_type":"code","metadata":{"id":"zuheADbDP7Fp"},"source":["import kfp\n","import tfx\n","from tfx.orchestration.beam.beam_dag_runner import BeamDagRunner\n","from tfx_pipeline import pipeline as pipeline_module\n","import tensorflow as tf\n","import ml_metadata as mlmd\n","from ml_metadata.proto import metadata_store_pb2\n","import logging\n","\n","logging.getLogger().setLevel(logging.INFO)\n","\n","print(\"TFX Version:\", tfx.__version__)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"K5UbLvJ8P7Fp"},"source":["pipeline_root = f'{config.ARTIFACT_STORE_URI}/{config.PIPELINE_NAME}_beamrunner'\n","model_regisrty_uri = f'{config.MODEL_REGISTRY_URI}_beamrunner'\n","local_mlmd_sqllite = 'mlmd/mlmd.sqllite'\n","\n","print(f'Pipeline artifacts root: {pipeline_root}')    \n","print(f'Model registry location: {model_regisrty_uri}')  \n","\n","if tf.io.gfile.exists(pipeline_root):\n","  print(\"Removing previous artifacts...\")\n","  tf.io.gfile.rmtree(pipeline_root)\n","if tf.io.gfile.exists('mlmd'):\n","  print(\"Removing local mlmd SQLite...\")\n","  tf.io.gfile.rmtree('mlmd')\n","print(\"Creating mlmd directory...\")\n","tf.io.gfile.mkdir('mlmd')\n","\n","metadata_connection_config = metadata_store_pb2.ConnectionConfig()\n","metadata_connection_config.sqlite.filename_uri = local_mlmd_sqllite\n","metadata_connection_config.sqlite.connection_mode = 3\n","print(\"ML metadata store is ready.\")\n","\n","beam_pipeline_args = [\n","  f'--runner=DirectRunner',\n","  f'--project={config.PROJECT_ID}',\n","  f'--temp_location={config.ARTIFACT_STORE_URI}/beam/tmp'\n","]\n","\n","pipeline_module.SCHEMA_DIR = 'tfx_pipeline/schema'\n","pipeline_module.LOOKUP_CREATOR_MODULE = 'tfx_pipeline/lookup_creator.py'\n","pipeline_module.SCANN_INDEXER_MODULE = 'tfx_pipeline/scann_indexer.py'"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"BA3IbMZZP7Fq"},"source":["runner = BeamDagRunner()\n","\n","pipeline = pipeline_module.create_pipeline(\n","  pipeline_name=config.PIPELINE_NAME,\n","  pipeline_root=pipeline_root,\n","  project_id=config.PROJECT_ID,\n","  bq_dataset_name=config.BQ_DATASET_NAME,\n","  min_item_frequency=15,\n","  max_group_size=10,\n","  dimensions=50,\n","  num_leaves=500,\n","  eval_min_recall=0.8,\n","  eval_max_latency=0.001,\n","  ai_platform_training_args=None,\n","  beam_pipeline_args=beam_pipeline_args,\n","  model_regisrty_uri=model_regisrty_uri,\n","  metadata_connection_config=metadata_connection_config,\n","  enable_cache=True\n",")\n","\n","runner.run(pipeline)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"sDHQOTlzP7Fr"},"source":["## Build the container image\n","\n","The pipeline uses a custom container image, which is a derivative of the [tensorflow/tfx:0.25.0](https://hub.docker.com/r/tensorflow/tfx) image, as a runtime execution environment for the pipeline's components. The container image is defined in a [Dockerfile](tfx_pipeline/Dockerfile).\n","\n","The container image installs the required libraries and copies over the modules from the solution's [tfx_pipeline](tfx_pipeline) directory, where the custom components are implemented. The container image is also used by AI Platform Training for executing the training jobs. \n","\n","Build the container image using Cloud Build and then store it in Cloud Container Registry:\n","\n"]},{"cell_type":"code","metadata":{"scrolled":true,"id":"PW_sUGUtP7Fr"},"source":["!gcloud builds submit --tag $ML_IMAGE_URI tfx_pipeline"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"-_bmd8YmP7Fr"},"source":["## Compile the TFX pipeline using the TFX CLI\r\n","\r\n","Use the TFX CLI to compile the TFX pipeline to the KFP format, which allows the pipeline to be deployed and executed on AI Platform Pipelines. The output is a .tar.gz package containing an Argo definition of your pipeline.\r\n"]},{"cell_type":"code","metadata":{"id":"n5QGIAclP7Fs"},"source":["!rm ${PIPELINE_NAME}.tar.gz\n","!tfx pipeline compile \\\n","    --engine=kubeflow \\\n","    --pipeline_path=tfx_pipeline/runner.py "],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"qo6z0fQcP7Fs"},"source":["## Deploy the compiled pipeline to KFP\r\n","\r\n","Use the KFP CLI to deploy the pipeline to a hosted instance of KFP on AI Platform Pipelines:\r\n"]},{"cell_type":"code","metadata":{"id":"baYgjczHP7Fs"},"source":["%%bash\n","\n","gcloud container clusters get-credentials ${GKE_CLUSTER_NAME} --zone ${GKE_CLUSTER_ZONE}\n","export KFP_ENDPOINT=$(kubectl describe configmap inverse-proxy-config -n ${NAMESPACE} | grep \"googleusercontent.com\")\n","\n","kfp --namespace=${NAMESPACE} --endpoint=${KFP_ENDPOINT} \\\n","    pipeline upload \\\n","    --pipeline-name=${PIPELINE_NAME} \\\n","    ${PIPELINE_NAME}.tar.gz"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xuSbXIvdbYqL"},"source":["After deploying the pipeline, you can browse it by following these steps:\r\n","\r\n","1. Open the [AI Platform Pipelines page](https://pantheon.corp.google.com/ai-platform/pipelines/clusters).\r\n","1. For the `kubeflow-pipelines` instance, click **Open Pipelines Dashboard**.\r\n","1. Click **Pipelines** and confirm that `tfx_bqml_scann` appears on the list of pipelines."]},{"cell_type":"markdown","metadata":{"id":"YlNx68crP7Ft"},"source":["## Run the deployed pipeline\r\n","\r\n","Run the pipeline by using the KFP UI:\r\n","\r\n","1. Open the [AI Platform Pipelines page](https://pantheon.corp.google.com/ai-platform/pipelines/clusters).\r\n","1. For the `kubeflow-pipelines` instance, click **Open Pipelines Dashboard**.\r\n","1. Click **Experiments**.\r\n","1. Click **Create Run**.\r\n","1. For **Pipeline**, choose **tfx_bqml_scann** and then click **Use this pipeline**.\r\n","1. For **Pipeline Version**, choose **tfx_bqml_scann**.\r\n","1. For **Run name**, type `run of tfx_bqml_scann`.\r\n","1. For **Experiment**, choose **Default** and then click **Use this experiment**.\r\n","1. Click **Start**.\r\n","\r\n"]},{"cell_type":"markdown","metadata":{"id":"HmudthxFlnTm"},"source":["The pipelines dashboard displays a list of pipeline runs. In the list, click the name of your run to see a graph of the run displayed. While your run is still in progress, the graph changes as each step executes. Click any step to explore the run's inputs, outputs, logs, etc."]},{"cell_type":"markdown","metadata":{"id":"b9lcrRA_P7Fu"},"source":["## License\n","\n","Copyright 2020 Google LLC\n","\n","Licensed under the Apache License, Version 2.0 (the \"License\");\n","you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0\n","\n","Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n","\n","See the License for the specific language governing permissions and limitations under the License.\n","\n","**This is not an official Google product but sample code provided for an educational purpose**"]}]}

================================================
FILE: retail/recommendation-system/bqml-scann/tfx_pipeline/Dockerfile
================================================
FROM tensorflow/tfx:0.25.0

RUN pip install scann==1.1.1 google-cloud-bigquery==1.26.1 protobuf==3.13.0

WORKDIR /pipeline
COPY ./ ./
ENV PYTHONPATH="/pipeline:${PYTHONPATH}"

================================================
FILE: retail/recommendation-system/bqml-scann/tfx_pipeline/__init__.py
================================================


================================================
FILE: retail/recommendation-system/bqml-scann/tfx_pipeline/bq_components.py
================================================
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""BigQuery components."""

import os
import warnings
import logging

from google.cloud import bigquery

import tfx
import tensorflow as tf
from tfx.types.experimental.simple_artifacts import Dataset
from tfx.types.standard_artifacts import Artifact
from tfx.dsl.component.experimental.decorators import component
from tfx.dsl.component.experimental.annotations import InputArtifact, OutputArtifact, Parameter

from tfx.types.standard_artifacts import Model as BQModel


@component
def compute_pmi(
  project_id: Parameter[str],
  bq_dataset: Parameter[str],
  min_item_frequency: Parameter[int],
  max_group_size: Parameter[int],
  item_cooc: OutputArtifact[Dataset]):
  
  stored_proc = f'{bq_dataset}.sp_ComputePMI'
  query = f'''
      DECLARE min_item_frequency INT64;
      DECLARE max_group_size INT64;

      SET min_item_frequency = {min_item_frequency};
      SET max_group_size = {max_group_size};

      CALL {stored_proc}(min_item_frequency, max_group_size);
  '''
  result_table = 'item_cooc'

  logging.info(f'Starting computing PMI...')
  
  client = bigquery.Client(project=project_id)
  query_job = client.query(query)
  query_job.result() # Wait for the job to complete
  
  logging.info(f'Items PMI computation completed. Output in {bq_dataset}.{result_table}.')
  
  # Write the location of the output table to metadata.  
  item_cooc.set_string_custom_property('bq_dataset', bq_dataset)
  item_cooc.set_string_custom_property('bq_result_table', result_table)
    
    
@component
def train_item_matching_model(
  project_id: Parameter[str],
  bq_dataset: Parameter[str],
  dimensions: Parameter[int],
  item_cooc: InputArtifact[Dataset],
  bq_model: OutputArtifact[BQModel]):
    
  item_cooc_table = item_cooc.get_string_custom_property('bq_result_table')
  stored_proc = f'{bq_dataset}.sp_TrainItemMatchingModel'
  query = f'''
    DECLARE dimensions INT64 DEFAULT {dimensions};
    CALL {stored_proc}(dimensions);
  '''
  model_name = 'item_matching_model'
  
  logging.info(f'Using item co-occurrence table: {bq_dataset}.{item_cooc_table}')
  logging.info(f'Starting training of the model...')
    
  client = bigquery.Client(project=project_id)
  query_job = client.query(query)
  query_job.result()
  
  logging.info(f'Model training completed. Output in {bq_dataset}.{model_name}.')
  
  # Write the location of the model to metadata.  
  bq_model.set_string_custom_property('bq_dataset', bq_dataset)
  bq_model.set_string_custom_property('bq_model_name', model_name)
  
  
@component
def extract_embeddings(
  project_id: Parameter[str],
  bq_dataset: Parameter[str],
  bq_model: InputArtifact[BQModel],
  item_embeddings: OutputArtifact[Dataset]):
  
  embedding_model_name = bq_model.get_string_custom_property('bq_model_name')
  stored_proc = f'{bq_dataset}.sp_ExractEmbeddings'
  query = f'''
      CALL {stored_proc}();
  '''
  result_table = 'item_embeddings'

  logging.info(f'Extracting item embedding from: {bq_dataset}.{embedding_model_name}')
  logging.info(f'Starting exporting embeddings...')
  
  client = bigquery.Client(project=project_id)
  query_job = client.query(query)
  query_job.result() # Wait for the job to complete
  
  logging.info(f'Embeddings export completed. Output in {bq_dataset}.{result_table}')
  
  # Write the location of the output table to metadata.
  item_embeddings.set_string_custom_property('bq_dataset', bq_dataset)
  item_embeddings.set_string_custom_property('bq_result_table', result_table)

================================================
FILE: retail/recommendation-system/bqml-scann/tfx_pipeline/config.py
================================================
# Copyright 2020 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""The pipeline configurations."""

import os


PIPELINE_NAME=os.getenv('PIPELINE_NAME', 'bqml_scann_embedding_matching')
EMBEDDING_LOOKUP_MODEL_NAME=os.getenv('EMBEDDING_LOOKUP_MODEL_NAME', 'embeddings_lookup')
SCANN_INDEX_MODEL_NAME=os.getenv('SCANN_INDEX_MODEL_NAME', 'embeddings_scann')
PROJECT_ID=os.getenv('PROJECT_ID', 'tfx-cloudml')
REGION=os.getenv('REGION', 'europe-west1')
BQ_DATASET_NAME=os.getenv('BQ_DATASET_NAME', 'recommendations')
ARTIFACT_STORE_URI=os.getenv('ARTIFACT_STORE_URI', 'gs://<YOUR-BUCKET>')
RUNTIME_VERSION=os.getenv('RUNTIME_VERSION', '2.2')
PYTHON_VERSION=os.getenv('PYTHON_VERSION', '3.7')
USE_KFP_SA=os.getenv('USE_KFP_SA', 'False')
ML_IMAGE_URI=os.getenv('ML_IMAGE_URI', 'tensorflow/tfx:0.23.0')
BEAM_RUNNER=os.getenv('BEAM_RUNNER', 'DirectRunner')
MODEL_REGISTRY_URI=os.getenv('MODEL_REGISTRY_URI', 'gs://<YOUR-BUCKET>/model_registry')


================================================
FILE: retail/recommendation-system/bqml-scann/tfx_pipeline/item_matcher.py
================================================
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""ScaNN index matchers."""

import tensorflow as tf
import numpy as np
import scann
import pickle
import os
import logging

TOKENS_FILE_NAME = 'tokens'


class ScaNNMatcher(object):

  def __init__(self, index_dir):
    logging.info('Loading ScaNN index...')
    scann_module = tf.saved_model.load(index_dir)
    self.scann_index = scann.scann_ops.searcher_from_module(scann_module)
    tokens_file_path = os.path.join(index_dir, TOKENS_FILE_NAME)
    with tf.io.gfile.GFile(tokens_file_path, 'rb') as handle:
      self.tokens = pickle.load(handle)
    logging.info('ScaNN index is loaded.')

  def match(self, vector, num_matches=10):
    embedding = np.array(vector)
    query = embedding / np.linalg.norm(embedding)
    matche_indices, _ = self.scann_index.search(query, final_num_neighbors=num_matches)
    match_tokens = [self.tokens[match_idx] for match_idx in matche_indices.numpy()]
    return match_tokens


class ExactMatcher(object):
  
  def __init__(self, embeddings, tokens):
    logging.info('Loading Exact index...')
    self.embeddings = embeddings
    self.tokens = tokens
    logging.info('Embeddings and vocabulary are loaded.')

  def match(self, vector, num_matches=10):
    embedding = np.array(vector)
    query = embedding / np.linalg.norm(embedding)
    similarities = np.dot(self.embeddings, query.T) 
    matches = list(zip(self.tokens, list(similarities)))
    matches = sorted(
      matches, key=lambda kv: kv[1], reverse=True)[:num_matches]
    return [kv[0] for kv in matches]

================================================
FILE: retail/recommendation-system/bqml-scann/tfx_pipeline/lookup_creator.py
================================================
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Embedding lookup model."""


import tensorflow as tf
import tensorflow_data_validation as tfdv
from tensorflow_transform.tf_metadata import schema_utils
import numpy as np
import logging

VOCABULARY_FILE_NAME = 'vocabulary.txt'
class EmbeddingLookup(tf.keras.Model):

  def __init__(self, embedding_files_prefix, schema_file_path, **kwargs):
    super(EmbeddingLookup, self).__init__(**kwargs)
    
    vocabulary = list()
    embeddings = list()

    logging.info('Loading schema...')
    schema = tfdv.load_schema_text(schema_file_path)
    feature_sepc = schema_utils.schema_as_feature_spec(schema).feature_spec
    logging.info('Schema is loadded.')
    
    def _gzip_reader_fn(filenames):
      return tf.data.TFRecordDataset(filenames, compression_type='GZIP')
    
    dataset = tf.data.experimental.make_batched_features_dataset(
      embedding_files_prefix, 
      batch_size=1, 
      num_epochs=1,
      features=feature_sepc,
      reader=_gzip_reader_fn,
      shuffle=False
    )

    # Read embeddings from tfrecord files.
    logging.info('Loading embeddings from files ...')
    for tfrecord_batch in dataset:
      vocabulary.append(tfrecord_batch["item_Id"].numpy()[0][0].decode())
      embeddings.append(tfrecord_batch["embedding"].numpy()[0])
    logging.info('Embeddings loaded.')
    
    embedding_size = len(embeddings[0])
    oov_embedding = np.zeros((1, embedding_size))
    self.embeddings = np.append(np.array(embeddings), oov_embedding, axis=0)
    logging.info(f'Embeddings: {self.embeddings.shape}')

    # Write vocabualry file.
    logging.info('Writing vocabulary to file ...')
    with open(VOCABULARY_FILE_NAME, 'w') as f:
      for item in vocabulary: 
        f.write(f'{item}\n')
    logging.info('Vocabulary file written and will be added as a model asset.')

    self.vocabulary_file = tf.saved_model.Asset(VOCABULARY_FILE_NAME)
    initializer = tf.lookup.KeyValueTensorInitializer(
        keys=vocabulary, values=list(range(len(vocabulary))))
    self.token_to_id = tf.lookup.StaticHashTable(
        initializer, default_value=len(vocabulary))

  @tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
  def __call__(self, inputs):
    tokens = tf.strings.split(inputs, sep=None).to_sparse()
    ids = self.token_to_id.lookup(tokens) 
    embeddings = tf.nn.embedding_lookup_sparse(
        params=self.embeddings, 
        sp_ids=ids, 
        sp_weights=None, 
        combiner="mean"
    )
    return embeddings



# TFX will call this function
def run_fn(params):

  embedding_files_path = params.train_files
  model_output_dir = params.serving_model_dir
  schema_file_path = params.schema_file

  logging.info('Instantiating embedding lookup model...')
  embedding_lookup_model = EmbeddingLookup(embedding_files_path, schema_file_path)
  logging.info('Model is instantiated.')
  
  signatures = {
    'serving_default': embedding_lookup_model.__call__.get_concrete_function(),
  }

  logging.info('Exporting embedding lookup model as a SavedModel...')
  tf.saved_model.save(embedding_lookup_model, model_output_dir, signatures=signatures)
  logging.info('SavedModel is exported.')

================================================
FILE: retail/recommendation-system/bqml-scann/tfx_pipeline/pipeline.py
================================================
# Copyright 2020 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#            http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TFX pipeline DSL."""

import os
import sys
from typing import Dict, List, Text, Optional
from kfp import gcp
import tfx
from tfx.proto import example_gen_pb2, infra_validator_pb2
from tfx.orchestration import pipeline, data_types
from tfx.dsl.components.base import executor_spec
from tfx.components.trainer import executor as trainer_executor
from tfx.extensions.google_cloud_ai_platform.trainer import executor as ai_platform_trainer_executor
from tfx.extensions.google_cloud_big_query.example_gen.component import BigQueryExampleGen
from ml_metadata.proto import metadata_store_pb2

try:
  from . import bq_components
  from . import scann_evaluator
except:
  import bq_components
  import scann_evaluator


EMBEDDING_LOOKUP_MODEL_NAME = 'embeddings_lookup'
SCANN_INDEX_MODEL_NAME = 'embeddings_scann'
LOOKUP_CREATOR_MODULE = 'lookup_creator.py'
SCANN_INDEXER_MODULE = 'scann_indexer.py'
SCHEMA_DIR = 'schema'


def create_pipeline(pipeline_name: Text, 
                    pipeline_root: Text,
                    project_id: Text,
                    bq_dataset_name: Text,
                    min_item_frequency: data_types.RuntimeParameter,
                    max_group_size: data_types.RuntimeParameter,
                    dimensions: data_types.RuntimeParameter,
                    num_leaves: data_types.RuntimeParameter,
                    eval_min_recall: data_types.RuntimeParameter,
                    eval_max_latency: data_types.RuntimeParameter,
                    ai_platform_training_args: Dict[Text, Text],
                    beam_pipeline_args: List[Text],
                    model_regisrty_uri: Text,
                    metadata_connection_config: Optional[
                      metadata_store_pb2.ConnectionConfig] = None,
                    enable_cache: Optional[bool] = False) -> pipeline.Pipeline:
  """Implements the online news pipeline with TFX."""

  
  local_executor_spec = executor_spec.ExecutorClassSpec(
    trainer_executor.GenericExecutor)

  caip_executor_spec = executor_spec.ExecutorClassSpec(
    ai_platform_trainer_executor.GenericExecutor)
  
  # Compute the PMI.
  pmi_computer = bq_components.compute_pmi(
    project_id=project_id,
    bq_dataset=bq_dataset_name,
    min_item_frequency=min_item_frequency,
    max_group_size=max_group_size
  )
  
  # Train the BQML Matrix Factorization model.
  bqml_trainer = bq_components.train_item_matching_model(
    project_id=project_id,
    bq_dataset=bq_dataset_name,
    item_cooc=pmi_computer.outputs.item_cooc,
    dimensions=dimensions,
  )
  
  # Extract the embeddings from the BQML model to a table.
  embeddings_extractor = bq_components.extract_embeddings(
    project_id=project_id,
    bq_dataset=bq_dataset_name,
    bq_model=bqml_trainer.outputs.bq_model
  )
  
  # Export embeddings from BigQuery to Cloud Storage.
  embeddings_exporter = BigQueryExampleGen(
    query=f'''
      SELECT item_Id, embedding, bias,
      FROM {bq_dataset_name}.item_embeddings
    ''',
    output_config=example_gen_pb2.Output(
      split_config=example_gen_pb2.SplitConfig(splits=[
        example_gen_pb2.SplitConfig.Split(name='train', hash_buckets=1)]))
  )
  
  # Add dependency from embeddings_exporter to embeddings_extractor.
  embeddings_exporter.add_upstream_node(embeddings_extractor)
  
  # Import embeddings schema.
  schema_importer = tfx.components.ImporterNode(
    source_uri=SCHEMA_DIR,
    artifact_type=tfx.types.standard_artifacts.Schema,
    instance_name='ImportSchema',
  )
  
  # Generate stats for the embeddings for validation.
  stats_generator = tfx.components.StatisticsGen(
    examples=embeddings_exporter.outputs.examples,
  )

  # Validate the embeddings stats against the schema.
  stats_validator = tfx.components.ExampleValidator(
    statistics=stats_generator.outputs.statistics,
    schema=schema_importer.outputs.result,
  )

  # Create an embedding lookup SavedModel.
  embedding_lookup_creator = tfx.components.Trainer(
    custom_executor_spec=local_executor_spec,
    module_file=LOOKUP_CREATOR_MODULE,
    train_args={'splits': ['train'], 'num_steps': 0},
    eval_args={'splits': ['train'], 'num_steps': 0},
    schema=schema_importer.outputs.result,
    examples=embeddings_exporter.outputs.examples
  )
  embedding_lookup_creator.id = 'CreateEmbeddingLookup'
  
  # Add dependency from stats_validator to embedding_lookup_creator.
  embedding_lookup_creator.add_upstream_node(stats_validator)

  # Infra-validate the embedding lookup model.
  infra_validator = tfx.components.InfraValidator(
    model=embedding_lookup_creator.outputs.model,
    serving_spec=infra_validator_pb2.ServingSpec(
      tensorflow_serving=infra_validator_pb2.TensorFlowServing(
        tags=['latest']),
      local_docker=infra_validator_pb2.LocalDockerConfig(),
    ),
    validation_spec=infra_validator_pb2.ValidationSpec(
      max_loading_time_seconds=60,
      num_tries=3,
    )
  )

  # Push the embedding lookup model to model registry location.
  embedding_lookup_pusher = tfx.components.Pusher(
    model=embedding_lookup_creator.outputs.model,
    infra_blessing=infra_validator.outputs.blessing,
    push_destination=tfx.proto.pusher_pb2.PushDestination(
      filesystem=tfx.proto.pusher_pb2.PushDestination.Filesystem(
        base_directory=os.path.join(model_regisrty_uri, EMBEDDING_LOOKUP_MODEL_NAME))
    )
  )
  embedding_lookup_pusher.id = 'PushEmbeddingLookup'
  
  # Build the ScaNN index.
  scann_indexer = tfx.components.Trainer(
    custom_executor_spec=caip_executor_spec if ai_platform_training_args else local_executor_spec,
    module_file=SCANN_INDEXER_MODULE,
    train_args={'splits': ['train'], 'num_steps': num_leaves},
    eval_args={'splits': ['train'], 'num_steps': 0},
    schema=schema_importer.outputs.result,
    examples=embeddings_exporter.outputs.examples,
    custom_config={'ai_platform_training_args': ai_platform_training_args}
  )
  scann_indexer.id = 'BuildScaNNIndex'
  
  # Add dependency from stats_validator to scann_indexer.
  scann_indexer.add_upstream_node(stats_validator)
  
  # Evaluate and validate the ScaNN index.
  index_evaluator = scann_evaluator.IndexEvaluator(
    examples=embeddings_exporter.outputs.examples,
    schema=schema_importer.outputs.result,
    model=scann_indexer.outputs.model,
    min_recall=eval_min_recall,
    max_latency=eval_max_latency
  )
  
  # Push the ScaNN index to model registry location.
  scann_index_pusher = tfx.components.Pusher(
    model=scann_indexer.outputs.model,
    model_blessing=index_evaluator.outputs.blessing,
    push_destination=tfx.proto.pusher_pb2.PushDestination(
      filesystem=tfx.proto.pusher_pb2.PushDestination.Filesystem(
        base_directory=os.path.join(model_regisrty_uri, SCANN_INDEX_MODEL_NAME))
    )
  )
  scann_index_pusher.id = 'PushScaNNIndex'
  
  components=[
    pmi_computer,
    bqml_trainer,
    embeddings_extractor,
    embeddings_exporter,
    schema_importer,
    stats_generator,
    stats_validator,
    embedding_lookup_creator,
    infra_validator,
    embedding_lookup_pusher,
    scann_indexer,
    index_evaluator,
    scann_index_pusher
  ]
  
  print('The pipeline consists of the following components:')
  print([component.id for component in components])
  
  return pipeline.Pipeline(
    pipeline_name=pipeline_name,
    pipeline_root=pipeline_root,
    components=components,
    beam_pipeline_args=beam_pipeline_args,
    metadata_connection_config=metadata_connection_config,
    enable_cache=enable_cache
  )

================================================
FILE: retail/recommendation-system/bqml-scann/tfx_pipeline/runner.py
================================================
# Copyright 2020 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#            http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""KFP runner"""

import kfp
from kfp import gcp
from tfx.orchestration import data_types
from tfx.orchestration.kubeflow import kubeflow_dag_runner

from typing import Optional, Dict, List, Text

import config
import pipeline

if __name__ == '__main__':

  # Set the values for the compile time parameters.
    
  ai_platform_training_args = {
    'project': config.PROJECT_ID,
    'region': config.REGION,
    'masterConfig': {
      'imageUri': config.ML_IMAGE_URI
    }
  }
   
  beam_pipeline_args = [
    f'--runner={config.BEAM_RUNNER}',
    '--experiments=shuffle_mode=auto',
    f'--project={config.PROJECT_ID}',
    f'--temp_location={config.ARTIFACT_STORE_URI}/beam/tmp',
    f'--region={config.REGION}',
  ]
    
  
  # Set the default values for the pipeline runtime parameters.
  
  min_item_frequency = data_types.RuntimeParameter(
      name='min-item-frequency',
      default=15,
      ptype=int
  )

  max_group_size = data_types.RuntimeParameter(
      name='max_group_size',
      default=100,
      ptype=int
  )
  
  dimensions = data_types.RuntimeParameter(
      name='dimensions',
      default=50,
      ptype=int
  )
  
  num_leaves = data_types.RuntimeParameter(
      name='num-leaves',
      default=0,
      ptype=int
  )
  
  eval_min_recall = data_types.RuntimeParameter(
      name='eval-min-recall',
      default=0.8,
      ptype=float
  )
  
  eval_max_latency = data_types.RuntimeParameter(
      name='eval-max-latency',
      default=0.01,
      ptype=float
  )
    
  pipeline_root = f'{config.ARTIFACT_STORE_URI}/{config.PIPELINE_NAME}/{kfp.dsl.RUN_ID_PLACEHOLDER}'

  # Set KubeflowDagRunner settings
  metadata_config = kubeflow_dag_runner.get_default_kubeflow_metadata_config()

  runner_config = kubeflow_dag_runner.KubeflowDagRunnerConfig(
    kubeflow_metadata_config = metadata_config,
    pipeline_operator_funcs = kubeflow_dag_runner.get_default_pipeline_operator_funcs(
      config.USE_KFP_SA == 'True'),
    tfx_image=config.ML_IMAGE_URI
  )

  # Compile the pipeline
  kubeflow_dag_runner.KubeflowDagRunner(config=runner_config).run(
    pipeline.create_pipeline(
      pipeline_name=config.PIPELINE_NAME,
      pipeline_root=pipeline_root,
      project_id=config.PROJECT_ID,
      bq_dataset_name=config.BQ_DATASET_NAME,
      min_item_frequency=min_item_frequency,
      max_group_size=max_group_size,
      dimensions=dimensions,
      num_leaves=num_leaves,
      eval_min_recall=eval_min_recall,
      eval_max_latency=eval_max_latency,
      ai_platform_training_args=ai_platform_training_args,
      beam_pipeline_args=beam_pipeline_args,
      model_regisrty_uri=config.MODEL_REGISTRY_URI)
  )

================================================
FILE: retail/recommendation-system/bqml-scann/tfx_pipeline/scann_evaluator.py
================================================
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""ScaNN index evaluator custom component."""

import os
import time
from typing import Any, Dict, List, Optional, Text, Union
import logging
import json

import tfx
from tfx.types import standard_artifacts
from tfx.types.component_spec import ChannelParameter
from tfx.types.component_spec import ExecutionParameter
from tfx.dsl.components.base import base_executor
from tfx.dsl.components.base import base_component
from tfx.dsl.components.base import executor_spec
from tfx.types import artifact_utils
from tfx.utils import io_utils
from typing import Optional
from tfx import types

import tensorflow as tf
import numpy as np
import tensorflow_data_validation as tfdv
from tensorflow_transform.tf_metadata import schema_utils

try:
  from . import item_matcher
  from . import scann_indexer
except:
  import item_matcher
  import scann_indexer

  
QUERIES_SAMPLE_RATIO = 0.01
MAX_NUM_QUERIES = 10000
NUM_NEIGBHOURS = 20


class IndexEvaluatorSpec(tfx.types.ComponentSpec):
  
  INPUTS = {
    'examples': ChannelParameter(type=standard_artifacts.Examples),
    'schema': ChannelParameter(type=standard_artifacts.Schema),
    'model': ChannelParameter(type=standard_artifacts.Model),
  }
    
  OUTPUTS = {
    'evaluation': ChannelParameter(type=standard_artifacts.ModelEvaluation),
    'blessing': ChannelParameter(type=standard_artifacts.ModelBlessing),
  }
    
  PARAMETERS = {
    'min_recall': ExecutionParameter(type=float),
    'max_latency': ExecutionParameter(type=float),
  }


class ScaNNIndexEvaluatorExecutor(base_executor.BaseExecutor):
  
  def Do(self, 
         input_dict: Dict[Text, List[types.Artifact]],
         output_dict: Dict[Text, List[types.Artifact]],
         exec_properties: Dict[Text, Any]) -> None:
    
    if 'examples' not in input_dict:
      raise ValueError('Examples is missing from input dict.')
    if 'model' not in input_dict:
      raise ValueError('Model is missing from input dict.')
    if 'evaluation' not in output_dict:
      raise ValueError('Evaluation is missing from output dict.')
    if 'blessing' not in output_dict:
      raise ValueError('Blessing is missing from output dict.')
    
    valid = True
        
    self._log_startup(input_dict, output_dict, exec_properties)
    
    embedding_files_pattern = io_utils.all_files_pattern(
      artifact_utils.get_split_uri(input_dict['examples'], 'train'))
    
    schema_file_path = artifact_utils.get_single_instance(
      input_dict['schema']).uri + '/schema.pbtxt'
    
    vocabulary, embeddings = scann_indexer.load_embeddings(
      embedding_files_pattern, schema_file_path)
    
    num_embeddings = embeddings.shape[0]
    logging.info(f'{num_embeddings} embeddings are loaded.')
    num_queries = int(min(num_embeddings * QUERIES_SAMPLE_RATIO, MAX_NUM_QUERIES))
    logging.info(f'Sampling {num_queries} query embeddings for evaluation...')
    query_embedding_indices = np.random.choice(num_embeddings, num_queries)
    query_embeddings = np.take(embeddings, query_embedding_indices, axis=0)

    # Load Exact matcher
    exact_matcher = item_matcher.ExactMatcher(embeddings, vocabulary)
    exact_matches = []
    logging.info(f'Computing exact matches for the queries...')
    for query in query_embeddings:
      exact_matches.append(exact_matcher.match(query, NUM_NEIGBHOURS))
    logging.info(f'Exact matches are computed.')
    del num_embeddings, exact_matcher
    
    # Load ScaNN index matcher
    index_artifact = artifact_utils.get_single_instance(input_dict['model'])
    ann_matcher = item_matcher.ScaNNMatcher(index_artifact.uri + '/serving_model_dir')
    scann_matches = []
    logging.info(f'Computing ScaNN matches for the queries...')
    start_time = time.time()
    for query in query_embeddings:
      scann_matches.append(ann_matcher.match(query, NUM_NEIGBHOURS))
    end_time = time.time()
    logging.info(f'ScaNN matches are computed.')
    
    # Compute average latency
    elapsed_time = end_time - start_time
    current_latency = elapsed_time / num_queries

    # Compute recall
    current_recall = 0
    for exact, approx in zip(exact_matches, scann_matches):
      current_recall += len(set(exact).intersection(set(approx))) / NUM_NEIGBHOURS
    current_recall /= num_queries
    
    metrics = {
      'recall': current_recall,
      'latency': current_latency
    }
    
    min_recall = exec_properties['min_recall']
    max_latency = exec_properties['max_latency']
    
    logging.info(f'Average latency per query achieved {current_latency}. Maximum latency allowed: {max_latency}')
    logging.info(f'Recall acheived {current_recall}. Minimum recall allowed: {min_recall}')
    
    # Validate index latency and recall
    valid = (current_latency <= max_latency) and (current_recall >= min_recall)
    logging.info(f'Model is valid: {valid}')
    
    # Output the evaluation artifact.
    evaluation = artifact_utils.get_single_instance(output_dict['evaluation'])
    evaluation.set_string_custom_property('index_model_uri', index_artifact.uri)
    evaluation.set_int_custom_property('index_model_id', index_artifact.id)
    io_utils.write_string_file(
      os.path.join(evaluation.uri, 'metrics'), json.dumps(metrics))
    
    # Output the blessing artifact.
    blessing = artifact_utils.get_single_instance(output_dict['blessing'])
    blessing.set_string_custom_property('index_model_uri', index_artifact.uri)
    blessing.set_int_custom_property('index_model_id', index_artifact.id)

    if valid:
      io_utils.write_string_file(os.path.join(blessing.uri, 'BLESSED'), '')
      blessing.set_int_custom_property('blessed', 1)
    else:
      io_utils.write_string_file(os.path.join(blessing.uri, 'NOT_BLESSED'), '')
      blessing.set_int_custom_property('blessed', 0)


class IndexEvaluator(base_component.BaseComponent):
  
  SPEC_CLASS = IndexEvaluatorSpec
  EXECUTOR_SPEC = executor_spec.ExecutorClassSpec(ScaNNIndexEvaluatorExecutor)
    
  def __init__(self,
               examples: types.channel,
               schema: types.channel,
               model: types.channel,
               min_recall: float,
               max_latency: float,
               evaluation: Optional[types.Channel] = None,
               blessing: Optional[types.Channel] = None,
               instance_name=None):
    
    blessing = blessing or types.Channel(
      type=standard_artifacts.ModelBlessing,
      artifacts=[standard_artifacts.ModelBlessing()])
    
    evaluation = evaluation or types.Channel(
      type=standard_artifacts.ModelEvaluation,
      artifacts=[standard_artifacts.ModelEvaluation()])
    
    spec = IndexEvaluatorSpec(
      examples=examples, 
      schema=schema,
      model=model, 
      evaluation=evaluation,
      blessing=blessing, 
      min_recall=min_recall, 
      max_latency=max_latency
    )
        
    super().__init__(spec=spec, instance_name=instance_name)

================================================
FILE: retail/recommendation-system/bqml-scann/tfx_pipeline/scann_indexer.py
================================================
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""ScaNN index builder."""

import os
import sys
import scann
import tensorflow as tf
import tensorflow_data_validation as tfdv
from tensorflow_transform.tf_metadata import schema_utils
import numpy as np
import math
import pickle
import logging

METRIC = 'dot_product'
DIMENSIONS_PER_BLOCK = 2
ANISOTROPIC_QUANTIZATION_THRESHOLD = 0.2
NUM_NEIGHBOURS = 10
NUM_LEAVES_TO_SEARCH = 250
REORDER_NUM_NEIGHBOURS = 250
TOKENS_FILE_NAME = 'tokens'


def load_embeddings(embedding_files_pattern, schema_file_path):

  embeddings = list()
  vocabulary = list()
  
  logging.info('Loading schema...')
  schema = tfdv.load_schema_text(schema_file_path)
  feature_sepc = schema_utils.schema_as_feature_spec(schema).feature_spec
  logging.info('Schema is loaded.')

  def _gzip_reader_fn(filenames):
    return tf.data.TFRecordDataset(filenames, compression_type='GZIP')
    
  dataset = tf.data.experimental.make_batched_features_dataset(
    embedding_files_pattern, 
    batch_size=1, 
    num_epochs=1,
    features=feature_sepc,
    reader=_gzip_reader_fn,
    shuffle=False
  )

  # Read embeddings from tfrecord files.
  logging.info('Loading embeddings from files...')
  for tfrecord_batch in dataset:
    vocabulary.append(tfrecord_batch["item_Id"].numpy()[0][0].decode())
    embedding = tfrecord_batch["embedding"].numpy()[0]
    normalized_embedding = embedding / np.linalg.norm(embedding)
    embeddings.append(normalized_embedding)
  logging.info('Embeddings loaded.')
  embeddings = np.array(embeddings)
  
  return vocabulary, embeddings
    
    
def build_index(embeddings, num_leaves):
  
  data_size = embeddings.shape[0] 
  if not num_leaves:
    num_leaves = int(math.sqrt(data_size))
  logging.info(f'Indexing {data_size} embeddings with {num_leaves} leaves.')
    
  logging.info('Start building the ScaNN index...')
  scann_builder = scann.scann_ops.builder(embeddings, NUM_NEIGHBOURS, METRIC).tree(
    num_leaves=num_leaves, 
    num_leaves_to_search=NUM_LEAVES_TO_SEARCH, 
    training_sample_size=data_size).score_ah(
      DIMENSIONS_PER_BLOCK,
      anisotropic_quantization_threshold=ANISOTROPIC_QUANTIZATION_THRESHOLD).reorder(REORDER_NUM_NEIGHBOURS)
  scann_index = scann_builder.build()
  logging.info('ScaNN index is built.')
  
  return scann_index


def save_index(index, tokens, output_dir):
  logging.info('Saving index as a SavedModel...')
  module = index.serialize_to_module()
  tf.saved_model.save(
    module, output_dir, signatures=None, options=None
  )
  logging.info(f'Index is saved to {output_dir}')
  
  logging.info(f'Saving tokens file...')
  tokens_file_path = os.path.join(output_dir, TOKENS_FILE_NAME)
  with tf.io.gfile.GFile(tokens_file_path, 'wb') as handle:
    pickle.dump(tokens, handle, protocol=pickle.HIGHEST_PROTOCOL)
  logging.info(f'Item file is saved to {tokens_file_path}.')


  
# TFX will call this function
def run_fn(params):
  embedding_files_path = params.train_files
  output_dir = params.serving_model_dir
  num_leaves = params.train_steps
  schema_file_path = params.schema_file
  
  logging.info("Indexer started...")
  tokens, embeddings = load_embeddings(embedding_files_path, schema_file_path)
  index = build_index(embeddings, num_leaves)
  save_index(index, tokens, output_dir)
  logging.info("Indexer finished.")
    
    
    

================================================
FILE: retail/recommendation-system/bqml-scann/tfx_pipeline/schema/schema.pbtxt
================================================
feature {
  name: "item_Id"
  type: BYTES
  int_domain {
  }
  presence {
    min_fraction: 1.0
    min_count: 1
  }
  shape {
    dim {
      size: 1
    }
  }
}
feature {
  name: "embedding"
  type: FLOAT
  presence {
    min_fraction: 1.0
    min_count: 1
  }
  shape {
    dim {
      size: 50
    }
  }
}

feature {
  name: "bias"
  type: FLOAT
  presence {
    min_fraction: 1.0
    min_count: 1
  }
  shape {
    dim {
      size: 1
    }
  }
}


================================================
FILE: retail/time-series/bqml-demand-forecasting/README.md
================================================
# How to build a time series demand forecasting model using BigQuery ML

The goal of this repo is to provide an end-to-end solution for forecasting the demand of multiple retail products, using [this notebook](bqml_retail_demand_forecasting.ipynb) to walk through the steps. Learn how to use BigQuery ML to train a time series model on historical sales data of liquor products, and how to visualize the forecasted values in a dashboard. For an overview of the use case, see [Overview of a demand forecasting solution](https://cloud.google.com/architecture/demand-forecasting-overview).

After completing the notebook, you will know how to:

* Pre-process time series data into the correct format needed to create the model.
* Train the time series model in BigQuery ML.
* Evaluate the model.
* Make predictions about future demand using the model.
* Create a dashboard to visualize the forecasted demand using Data Studio.

This solution is intended for data engineers, data scientists, and data analysts
who build machine learning (ML) datasets and models to support business
decisions. It assumes that you have basic knowledge of the following:

* Machine learning concepts
* Python
* Standard SQL

## Dataset

This tutorial uses the public
[Iowa Liquor Sales](https://console.cloud.google.com/marketplace/product/iowa-department-of-commerce/iowa-liquor-sales)
dataset that is hosted on BigQuery. This dataset contains the
spirits purchase information of Iowa Class "E" liquor licensees from
January 1, 2012 until the present. For more information, see the
[official documentation by the State of Iowa](https://data.iowa.gov/Sales-Distribution/Iowa-Liquor-Sales/m3tr-qhgy).

## Using forecasting for inventory management

In the retail business, it is important to find a balance when it comes to
inventory: don't stock too much, but don't stock too little. For a large
business, this can mean making decisions about inventory levels for
potentially millions of products.

To inform inventory level decisions, you can take advantage of historical
data about items purchased by consumers over time. You can use this data about
past customer behavior to make predictions about likely future purchases, which
you can then use to make decisions about how much inventory to stock. In this
scenario, time series forecasting is the right tool to use.

Time series forecasting depends on the creation of machine learning (ML) models.
If you are a member of a data science team that supports inventory decisions,
this can mean not only producing large numbers of forecasts, but also procuring
and managing the infrastructure to handle model training and prediction. To
save time and effort, you can use BigQuery ML SQL statements to
train, evaluate and deploy models in BigQuery ML instead of
configuring a separate ML infrastructure.

## How time series modeling works in BigQuery ML

When you train a time series model with BigQuery ML, multiple
components are involved, including an
[Autoregressive integrated moving average (ARIMA)](https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average)
model. The BigQuery ML model creation pipeline uses the following
components, listed in the order that they are run:

1.  Pre-processing: Automatic cleaning adjustments to the input time
    series, which addresses issues like missing values, duplicated timestamps,
    spike anomalies, and accounting for abrupt level changes in the time series
    history.
1.  Holiday effects: Time series modeling in BigQuery ML can also account
    for holiday effects. By default, holiday effects modeling is disabled. But
    since this data is from the United States, and the data includes a minimum
    one year of daily data, you can also specify an optional `HOLIDAY_REGION`.
    With holiday effects enabled, spike and dip anomalies that appear during
    holidays will no longer be treated as anomalies. For more information, see
    [HOLIDAY_REGION](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-time-series#holiday_region).
1.  Seasonal and trend decomposition using the
    [Seasonal and Trend decomposition using Loess (STL)](https://otexts.com/fpp2/stl.html)
    algorithm. Seasonality extrapolation using the
    [double exponential smoothing (ETS)](https://en.wikipedia.org/wiki/Exponential_smoothing#Double_exponential_smoothing)
    algorithm.
1.  Trend modeling using the ARIMA model and the
    [auto.ARIMA](https://otexts.com/fpp2/arima-r.html)
    algorithm for automatic hyper-parameter tuning. In auto.ARIMA, dozens of
    candidate models are trained and evaluated in parallel. The best model comes
    with the lowest
    [Akaike information criterion (AIC)](https://wikipedia.org/wiki/Akaike_information_criterion).

You can use a single SQL statement to train the model to forecast a single
product or to forecast multiple products at the same time. For more
information, see
[The CREATE MODEL statement for time series models](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-time-series).

## Costs 

This tutorial uses billable components of Google Cloud Platform (GCP):

* AI Platform
* BigQuery
* BigQuery ML

Learn about [BigQuery pricing](https://cloud.google.com/bigquery/pricing), [BigQuery ML
pricing](https://cloud.google.com/bigquery-ml/pricing) and use the [Pricing
Calculator](https://cloud.google.com/products/calculator/)
to generate a cost estimate based on your projected usage.

## Set up the GCP environment

1. [If you don't want to use an existing project, create a new GCP project.](https://console.cloud.google.com/cloud-resource-manager) When you first create an account, you get a $300 free credit towards your compute/storage costs.
1. [If you created a new project, make sure that billing is enabled for it.](https://cloud.google.com/billing/docs/how-to/modify-project)
1. [Enable the AI Platform, AI Platform Notebooks, and Compute Engine APIs in the project you plan to use.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,notebooks.googleapis.com,compute_component)

## Run the notebook

1. Open the [bqml_retail_demand_forecasting.ipynb](bqml_retail_demand_forecasting.ipynb) notebook.
1. Click **Run on AI Platform Notebooks**.
2. In the GCP console, select the project you want to use to run this notebook.
3. If you have existing notebook instances in this project, select **Create a new notebook instance**.
4. For **Instance name**, type `demand-forecasting`.
5. Click **Create**.

## Clean up the GCP environment

Unless you plan to continue using the resources you created while using this notebook, you should delete them once you are done
to avoid incurring charges to your GCP account. You can either delete the project containing the resources, or
keep the project but delete just those resources.

Either way, you should remove the resources so you won't be billed for them in
the future. The following sections describe how to delete these resources.

### Delete the project

The easiest way to eliminate billing is to delete the project you created for
the solution.

1. In the Cloud Console, go to the [Manage resources page](https://console.cloud.google.com/cloud-resource-manager).
1. In the project list, select the project that you want to delete, and then click **Delete**.
1. In the dialog, type the project ID, and then click **Shut down** to delete the project.

### Delete the components

If you don't want to delete the project, delete the billable components of the solution.
These include:

1. The `demand-forecasting` AI Platform notebook instance.
2. The `bqmlforecast` BigQuery dataset.

## Disclaimer
This is not an officially supported Google product.

All files in this folder are under the Apache License, Version 2.0 unless noted otherwise.

## Questions? Feedback?
If you have any questions or feedback, please open up a [new issue](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/issues).


================================================
FILE: retail/time-series/bqml-demand-forecasting/bqml_retail_demand_forecasting.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ur8xi4C7S06n"
   },
   "outputs": [],
   "source": [
    "# Copyright 2020 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eHLV0D7Y5jtU"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <td>\n",
    "      <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/analytics-componentized-patterns/master/retail/time-series/bqml-demand-forecasting/bqml_retail_demand_forecasting.ipynb\">\n",
    "        <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI Workbench\">Run on Vertex AI Workbench</a>\n",
    "    </td>\n",
    "  <td>\n",
    "    <a href=\"https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/tree/master/retail/time-series/bqml-demand-forecasting/\">\n",
    "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
    "      View on GitHub\n",
    "    </a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tvgnzT1CKxrO"
   },
   "source": [
    "# Building a demand forecasting model by using BigQuery ML\n",
    "\n",
    "This notebook shows you how to train, deploy, and evaluate a time series model by using BigQuery ML. It provides an end-to-end solution for forecasting multiple products. Using the public [Iowa Liquor Sales data](https://console.cloud.google.com/marketplace/details/obfuscated-ga360-data/obfuscated-ga360-data?filter=solution-type:dataset) dataset, you use a single SQL query to create five time series models, where each model forecasts the retail sales of a single liquor product. \n",
    "\n",
    "By the end of this notebook, you will know how to:\n",
    "* Pre-process time series data into the correct format needed to create the model.\n",
    "* Train the time series model in BigQuery ML.\n",
    "* Evaluate the model.\n",
    "* Make predictions about future demand using the model.\n",
    "* Create a dashboard to visualize the forecasted demand using Data Studio."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EeWsN9G3ap9a"
   },
   "source": [
    "## Setup\n",
    "\n",
    "Install the required packages, configure the environment variables, and create a BigQuery dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i7EUnXsZhAGF"
   },
   "source": [
    "### PIP Install Packages and dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wyy5Lbnzg5fi",
    "outputId": "cf9a4f1f-5eae-4748-c3ab-8f989f9896a4",
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install google-cloud-bigquery google-cloud-bigquery-storage --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9luQpONrzPb6",
    "outputId": "8201a985-0654-471d-9199-06c2585a19cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'status': 'ok', 'restart': True}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Automatically restart kernel after installs\n",
    "import IPython\n",
    "app = IPython.Application.instance()\n",
    "app.kernel.do_shutdown(True)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BF1j6f9HApxa"
   },
   "source": [
    "### Configure GCP environment settings\n",
    "\n",
    "Update the following variables to reflect the values for your GCP environment:\n",
    "\n",
    "* PROJECT_ID: The ID of the Google Cloud project you are using to implement this solution.\n",
    "* REGION: The region to use for the BigQuery dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "dgWHxHS-sDkl"
   },
   "outputs": [],
   "source": [
    "PROJECT_ID = 'YOUR-PROJECT-ID' # Change to your project.\n",
    "REGION = 'US'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XoEqT2Y4DJmf"
   },
   "source": [
    "### Import libraries and define constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "pRUOFELefqf1"
   },
   "outputs": [],
   "source": [
    "from google.cloud import bigquery\n",
    "import pandas as pd\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IKeTQnDdap9n"
   },
   "source": [
    "### Import libraries for data manipulation and plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "T0rsmNE1ap9n"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "def plot_historical_and_forecast(input_timeseries, \n",
    "                                 timestamp_col_name, \n",
    "                                 data_col_name, \n",
    "                                 forecast_output=None, \n",
    "                                 actual=None, \n",
    "                                 title=None,\n",
    "                                 plotstartdate=None):\n",
    "\n",
    "    if plotstartdate:\n",
    "        input_timeseries[timestamp_col_name] = pd.to_datetime(input_timeseries[timestamp_col_name])\n",
    "        input_timeseries = input_timeseries[input_timeseries[timestamp_col_name] >= pd.to_datetime(plotstartdate)]\n",
    "        \n",
    "    input_timeseries = input_timeseries.sort_values(timestamp_col_name)    \n",
    "    \n",
    "    # Plot the input historical data\n",
    "    plt.figure(figsize=(20,6))\n",
    "    plt.plot(input_timeseries[timestamp_col_name], input_timeseries[data_col_name], label = 'Historical')\n",
    "    plt.xlabel(timestamp_col_name)\n",
    "    plt.ylabel(data_col_name)\n",
    "\n",
    "    if forecast_output is not None:\n",
    "        forecast_output = forecast_output.sort_values('forecast_timestamp')\n",
    "        forecast_output['forecast_timestamp'] = pd.to_datetime(forecast_output['forecast_timestamp'])\n",
    "        x_data = forecast_output['forecast_timestamp']\n",
    "        y_data = forecast_output['forecast_value']\n",
    "        confidence_level = forecast_output['confidence_level'].iloc[0] * 100\n",
    "        low_CI = forecast_output['confidence_interval_lower_bound']\n",
    "        upper_CI = forecast_output['confidence_interval_upper_bound']\n",
    "        # Plot the forecast data\n",
    "        plt.plot(x_data, y_data, alpha = 1, label = 'Forecast', linestyle='--')\n",
    "        # Shade the confidence interval\n",
    "        plt.fill_between(x_data, low_CI, upper_CI, color = '#539caf', alpha = 0.4, \n",
    "                         label = f'{confidence_level} confidence interval')\n",
    "\n",
    "    # Plot actual data\n",
    "    if actual is not None:\n",
    "        actual = actual.sort_values(timestamp_col_name)\n",
    "        plt.plot(actual[timestamp_col_name], actual[data_col_name], label = 'Actual', linestyle='--')   \n",
    "\n",
    "    # Display title, legend\n",
    "    plt.title(f'{title}', fontsize= 16)\n",
    "    plt.legend(loc = 'upper center', prop={'size': 16})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "znIaF2QGsDkr"
   },
   "source": [
    "### Create a BigQuery dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H1waFXGXsDkr"
   },
   "source": [
    "Create a dataset in your project called `bqmlforecast`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "pIBm-lrVsDks"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BigQuery error in mk operation: Dataset 'polong-bigquery:bqmlforecast' already\n",
      "exists.\n"
     ]
    }
   ],
   "source": [
    "!bq mk --location=$REGION --dataset $PROJECT_ID:bqmlforecast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QB7wXYDpsDkv"
   },
   "source": [
    "## Prepare the training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MsxSG7yfsDkv"
   },
   "source": [
    "You train the time series models on a dataset containing transactional data. Each row represents a transaction on a single product, as identified by the `item_description` value, and contains details such as the number of bottles sold and the sales amount in dollars. In following steps, you use the number of bottles sold value to forecast product demand."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z3G-0P1vsDkv"
   },
   "source": [
    "_Note_: Jupyter runs cells starting with %%bigquery as SQL queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 197
    },
    "id": "cQlwqRKvap9u",
    "outputId": "9a97df3a-62c7-4e8e-8fef-01d4f8ff0d2f"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "76955776e0364743ada675300af2a22e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Query is running:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "48d4268da153486c98539a874c63f054",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoice_and_item_number</th>\n",
       "      <th>date</th>\n",
       "      <th>store_number</th>\n",
       "      <th>item_description</th>\n",
       "      <th>bottles_sold</th>\n",
       "      <th>sale_dollars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>INV-22973000026</td>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>5239</td>\n",
       "      <td>JAGERMEISTER LIQUEUR MINI MEISTERS</td>\n",
       "      <td>1</td>\n",
       "      <td>7.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>INV-26366100059</td>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>3993</td>\n",
       "      <td>JAGERMEISTER LIQUEUR MINI MEISTERS</td>\n",
       "      <td>1</td>\n",
       "      <td>7.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INV-38829900045</td>\n",
       "      <td>2021-07-30</td>\n",
       "      <td>4551</td>\n",
       "      <td>JAGERMEISTER LIQUEUR MINI MEISTERS</td>\n",
       "      <td>1</td>\n",
       "      <td>7.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>INV-24506900005</td>\n",
       "      <td>2020-01-13</td>\n",
       "      <td>5211</td>\n",
       "      <td>JAGERMEISTER LIQUEUR MINI MEISTERS</td>\n",
       "      <td>1</td>\n",
       "      <td>7.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>INV-14129500095</td>\n",
       "      <td>2018-08-29</td>\n",
       "      <td>2536</td>\n",
       "      <td>JAGERMEISTER MINI MEISTERS</td>\n",
       "      <td>1</td>\n",
       "      <td>7.400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  invoice_and_item_number        date store_number  \\\n",
       "0         INV-22973000026  2019-11-01         5239   \n",
       "1         INV-26366100059  2020-04-07         3993   \n",
       "2         INV-38829900045  2021-07-30         4551   \n",
       "3         INV-24506900005  2020-01-13         5211   \n",
       "4         INV-14129500095  2018-08-29         2536   \n",
       "\n",
       "                     item_description  bottles_sold  sale_dollars  \n",
       "0  JAGERMEISTER LIQUEUR MINI MEISTERS             1         7.400  \n",
       "1  JAGERMEISTER LIQUEUR MINI MEISTERS             1         7.400  \n",
       "2  JAGERMEISTER LIQUEUR MINI MEISTERS             1         7.400  \n",
       "3  JAGERMEISTER LIQUEUR MINI MEISTERS             1         7.400  \n",
       "4          JAGERMEISTER MINI MEISTERS             1         7.400  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery --project $PROJECT_ID\n",
    "\n",
    "SELECT \n",
    "    invoice_and_item_number,\n",
    "    date,\n",
    "    store_number,\n",
    "    item_description,\n",
    "    bottles_sold,\n",
    "    sale_dollars\n",
    "FROM\n",
    "  `bigquery-public-data.iowa_liquor_sales.sales` \n",
    "LIMIT \n",
    "  5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YiKvVQUAap9w"
   },
   "source": [
    "### [Optional] Transform your data to the expected schema"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1mVjF5WYap9x"
   },
   "source": [
    "If you use the sample dataset, you can skip this step.\n",
    "\n",
    "If you want to use your own data, import your sales data to BigQuery, and then adapt the following SQL query to create a view with the appropriate schema. Update `[YOUR_PROJECT].[YOUR_DATASET].[YOUR_SOURCE_TABLE]` to reflect the project, dataset, and table containing your sales data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "Kueioxtrap9x"
   },
   "outputs": [],
   "source": [
    "# %%bigquery --project $PROJECT_ID\n",
    "\n",
    "# CREATE OR REPLACE VIEW bqmlforecast.training_data AS (\n",
    "# SELECT\n",
    "#   date,\n",
    "#   item_name,\n",
    "#   total_amount_sold\n",
    "# FROM\n",
    "#   `[YOUR_PROJECT].[YOUR_DATASET].[YOUR_SOURCE_TABLE]`\n",
    "# );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PFq6aLFBap9z"
   },
   "source": [
    "### Set the start and end dates for the training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pWbaGiY-ap90"
   },
   "source": [
    "You can adjust the `TRAININGDATA_STARTDATE` and `TRAININGDATA_ENDDATE` parameters to specify the start/end dates of your training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "M2cyW1P8ap90",
    "outputId": "7253e1fd-6164-438b-a30b-53c49e61d6b2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'TRAININGDATA_STARTDATE': '2016-01-01', 'TRAININGDATA_ENDDATE': '2017-06-01'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARIMA_PARAMS = {\n",
    "    'TRAININGDATA_STARTDATE': '2016-01-01',\n",
    "    'TRAININGDATA_ENDDATE': '2017-06-01',\n",
    "}\n",
    "ARIMA_PARAMS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zmz_Wh7yap93"
   },
   "source": [
    "### Write the training data to a table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T1B5NDEfVL2C"
   },
   "source": [
    "If you review the training data, you can observe that some days have no transactions for a given product. To keep you from having to do additional pre-processing, BigQuery ML automatically handles the following situations:\n",
    "\n",
    "* Missing values: Values are imputed using local [linear interpolation](https://en.wikipedia.org/wiki/Linear_interpolation).\n",
    "* Duplicated timestamps: Values are averaged across duplicated timestamps.\n",
    "* Spike and dip anomalies: Values are standardized using local [z-scores](https://en.wikipedia.org/wiki/Standard_score)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "id": "7-bBYO-Hap93",
    "outputId": "59fd1987-a01b-4e3a-9719-456f790bedf3"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "177b496de7b445fb88718c04bae19801",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Query is running:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d72395034af943aa9bd47f20cc72d8cf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>item_name</th>\n",
       "      <th>total_amount_sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>BLACK VELVET</td>\n",
       "      <td>5014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>BLACK VELVET</td>\n",
       "      <td>5193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>BLACK VELVET</td>\n",
       "      <td>4422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>BLACK VELVET</td>\n",
       "      <td>3760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-11</td>\n",
       "      <td>BLACK VELVET</td>\n",
       "      <td>4492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-01-12</td>\n",
       "      <td>BLACK VELVET</td>\n",
       "      <td>4945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>BLACK VELVET</td>\n",
       "      <td>4302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-01-14</td>\n",
       "      <td>BLACK VELVET</td>\n",
       "      <td>3394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>BLACK VELVET</td>\n",
       "      <td>2318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-01-19</td>\n",
       "      <td>BLACK VELVET</td>\n",
       "      <td>4714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date     item_name  total_amount_sold\n",
       "0  2016-01-04  BLACK VELVET               5014\n",
       "1  2016-01-05  BLACK VELVET               5193\n",
       "2  2016-01-06  BLACK VELVET               4422\n",
       "3  2016-01-07  BLACK VELVET               3760\n",
       "4  2016-01-11  BLACK VELVET               4492\n",
       "5  2016-01-12  BLACK VELVET               4945\n",
       "6  2016-01-13  BLACK VELVET               4302\n",
       "7  2016-01-14  BLACK VELVET               3394\n",
       "8  2016-01-15  BLACK VELVET               2318\n",
       "9  2016-01-19  BLACK VELVET               4714"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery --params $ARIMA_PARAMS  --project $PROJECT_ID \n",
    "\n",
    "CREATE OR REPLACE TABLE bqmlforecast.training_data AS (\n",
    "    WITH topsellingitems AS(\n",
    "         SELECT \n",
    "            item_description,\n",
    "            count(item_description) cnt_transactions\n",
    "        FROM\n",
    "            `bigquery-public-data.iowa_liquor_sales.sales` \n",
    "        GROUP BY \n",
    "            item_description\n",
    "        ORDER BY cnt_transactions DESC\n",
    "        LIMIT 5 #Top N\n",
    "    )\n",
    "    SELECT \n",
    "        date,\n",
    "        item_description AS item_name,\n",
    "        SUM(bottles_sold) AS total_amount_sold\n",
    "    FROM\n",
    "        `bigquery-public-data.iowa_liquor_sales.sales` \n",
    "    GROUP BY\n",
    "        date, item_name\n",
    "    HAVING \n",
    "        date BETWEEN @TRAININGDATA_STARTDATE AND @TRAININGDATA_ENDDATE\n",
    "        AND item_description IN (SELECT item_description FROM topsellingitems)\n",
    "    );\n",
    "\n",
    "SELECT \n",
    "    date,\n",
    "    item_name,\n",
    "    total_amount_sold\n",
    "FROM \n",
    "    bqmlforecast.training_data \n",
    "ORDER BY item_name, date\n",
    "LIMIT 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "liuS8qCnap96"
   },
   "source": [
    "### Plot the sales histories of the target liquor products"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tQxRDGLAap97"
   },
   "source": [
    "Save the training data to the `dfhistorical` Pandas dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "xwVPZP3fap97"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7441ec04c6184951b08d2a69bbc61f57",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Query is running:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b62ff5bccb9f4a7c8c315b501361c223",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%bigquery dfhistorical --project $PROJECT_ID \n",
    "\n",
    "SELECT \n",
    "    * \n",
    "FROM \n",
    "    bqmlforecast.training_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JiRjcr-Uap99"
   },
   "source": [
    "Using the training data, plot the sales histories of the target liquor products:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "hCvY292Xap9-",
    "outputId": "3b464686-cc5a-4ad7-f860-860ffd919f17"
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "itemslist = list(dfhistorical.item_name.unique())\n",
    "\n",
    "for item in itemslist:\n",
    "    \n",
    "    datah = dfhistorical[dfhistorical.item_name==item]\n",
    "    plot_historical_and_forecast(input_timeseries = datah, \n",
    "                                 timestamp_col_name = \"date\", \n",
    "                                 data_col_name = \"total_amount_sold\", \n",
    "                                 forecast_output = None, \n",
    "                                 actual = None,\n",
    "                                 title = item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ft6ioX_kap-A"
   },
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BqGEpOzvW2pl"
   },
   "source": [
    "Because you are training the model on multiple products in a single model creation statement, you must specify the `item_name` column for the [TIME_SERIES_ID_COL](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-time-series#time_series_id_col) parameter. If you were only forecasting a single item, then you would not need to specify `TIME_SERIES_ID_COL`. For more information, see the [BigQuery ML time series model creation documentation](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-time-series#create_model_syntax).\n",
    "\n",
    "You can also account for holiday effects when doing time series modeling in BigQuery ML. By default, holiday effects modeling is disabled. But since this data is from the United States, and the data includes a minimum one year of daily data, you can also specify an optional `HOLIDAY_REGION`. With holiday effects enabled, spikes and dips that appear during holidays will no longer be treated as anomalies. A full list of the holiday regions can be found in the [HOLIDAY_REGION documentation](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-time-series#holiday_region).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 31
    },
    "id": "CibqJ8yVL6kS",
    "outputId": "6d8e6996-557a-4fd7-d8b1-22e8f3c9b075"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
      },
      "text/plain": [
       "Query is running:   0%|          |"
      ]
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     "metadata": {},
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    },
    {
     "data": {
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       "  </tbody>\n",
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       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery --project $PROJECT_ID \n",
    "\n",
    "CREATE OR REPLACE MODEL bqmlforecast.arima_model\n",
    "\n",
    "OPTIONS(\n",
    "  MODEL_TYPE='ARIMA',\n",
    "  TIME_SERIES_TIMESTAMP_COL='date', \n",
    "  TIME_SERIES_DATA_COL='total_amount_sold',\n",
    "  TIME_SERIES_ID_COL='item_name',\n",
    "  HOLIDAY_REGION='US'\n",
    ") AS\n",
    "\n",
    "SELECT \n",
    "    date,\n",
    "    item_name,\n",
    "    total_amount_sold\n",
    "FROM\n",
    "  bqmlforecast.training_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BUkZYuZH-rn2"
   },
   "source": [
    "### Evaluate the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7vEV-65m-0QJ"
   },
   "source": [
    "Use the [ML.EVALUATE](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate) function to see the evaluation metrics of all the created models. \n",
    "\n",
    "The `non_seasonal_`{`p`,`d`,`q`} and `has_drift` columns define the time series model. The `log_likelihood`, `AIC`, and `variance` columns are relevant to the model fitting process. The fitting process determines the best model by using the [auto.ARIMA algorithm](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-time-series#auto_arima), one for each time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 214
    },
    "id": "NAlyHOOjBxRV",
    "outputId": "c40ebf67-a621-46b0-c9db-e14e907b1cd5"
   },
   "outputs": [
    {
     "data": {
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       "model_id": "bf5f53dca12c43ebb2f793559eabc2d8",
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       "version_minor": 0
      },
      "text/plain": [
       "Query is running:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          |"
      ]
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     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_name</th>\n",
       "      <th>non_seasonal_p</th>\n",
       "      <th>non_seasonal_d</th>\n",
       "      <th>non_seasonal_q</th>\n",
       "      <th>has_drift</th>\n",
       "      <th>log_likelihood</th>\n",
       "      <th>AIC</th>\n",
       "      <th>variance</th>\n",
       "      <th>seasonal_periods</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BLACK VELVET</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>-4132.346</td>\n",
       "      <td>8276.691</td>\n",
       "      <td>562466.115</td>\n",
       "      <td>[WEEKLY]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FIREBALL CINNAMON WHISKEY</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>-3683.019</td>\n",
       "      <td>7376.037</td>\n",
       "      <td>97168.305</td>\n",
       "      <td>[WEEKLY]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FIVE O'CLOCK VODKA</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>-3491.474</td>\n",
       "      <td>6996.948</td>\n",
       "      <td>8757.842</td>\n",
       "      <td>[WEEKLY]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HAWKEYE VODKA</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>-3785.002</td>\n",
       "      <td>7580.004</td>\n",
       "      <td>145425.326</td>\n",
       "      <td>[WEEKLY]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TITOS HANDMADE VODKA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>-3807.245</td>\n",
       "      <td>7626.489</td>\n",
       "      <td>157343.325</td>\n",
       "      <td>[WEEKLY]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   item_name  non_seasonal_p  non_seasonal_d  non_seasonal_q  \\\n",
       "0               BLACK VELVET               3               1               2   \n",
       "1  FIREBALL CINNAMON WHISKEY               2               1               1   \n",
       "2         FIVE O'CLOCK VODKA               4               0               1   \n",
       "3              HAWKEYE VODKA               2               1               2   \n",
       "4       TITOS HANDMADE VODKA               0               1               5   \n",
       "\n",
       "   has_drift  log_likelihood      AIC   variance seasonal_periods  \n",
       "0      False       -4132.346 8276.691 562466.115         [WEEKLY]  \n",
       "1       True       -3683.019 7376.037  97168.305         [WEEKLY]  \n",
       "2      False       -3491.474 6996.948   8757.842         [WEEKLY]  \n",
       "3      False       -3785.002 7580.004 145425.326         [WEEKLY]  \n",
       "4      False       -3807.245 7626.489 157343.325         [WEEKLY]  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery --project $PROJECT_ID \n",
    "\n",
    "SELECT\n",
    "  *\n",
    "FROM\n",
    "  ML.EVALUATE(MODEL bqmlforecast.arima_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZXY99nSmCnsB"
   },
   "source": [
    "You can see that five models were trained, one for each of the products in the `item_name` column. Each model has its own `p,d,q` hyperparameters, and the detected seasonality for these five models is `WEEKLY`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0mDSGlrDap-C"
   },
   "source": [
    "## Make predictions using the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QZrj49Nwap-D"
   },
   "source": [
    "Make predictions using [ML.FORECAST](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-forecast), which forecasts the next _n_ values, as specified in the `horizon` parameter. You can also optionally change the `confidence_level` parameter to change the percentage of future values that fall in the prediction interval. You save the prediction data to the `dfforecast` dataframe so that you can plot it in a following step.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "tTwgjaITYOx8"
   },
   "outputs": [
    {
     "data": {
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       "model_id": "d9f7d4f0a1b34ce28146c92b32e848c2",
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       "version_minor": 0
      },
      "text/plain": [
       "Query is running:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%bigquery dfforecast --project $PROJECT_ID \n",
    "\n",
    "DECLARE HORIZON STRING DEFAULT \"30\"; #number of values to forecast\n",
    "DECLARE CONFIDENCE_LEVEL STRING DEFAULT \"0.90\";\n",
    "\n",
    "EXECUTE IMMEDIATE format(\"\"\"\n",
    "    SELECT\n",
    "      *\n",
    "    FROM \n",
    "      ML.FORECAST(MODEL bqmlforecast.arima_model, \n",
    "                  STRUCT(%s AS horizon, \n",
    "                         %s AS confidence_level)\n",
    "                 )\n",
    "    \"\"\",HORIZON,CONFIDENCE_LEVEL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "id": "YaHX0NWXap-G",
    "outputId": "5bd2bc73-4362-458d-c191-17b07c2dc912"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_name</th>\n",
       "      <th>forecast_timestamp</th>\n",
       "      <th>forecast_value</th>\n",
       "      <th>standard_error</th>\n",
       "      <th>confidence_level</th>\n",
       "      <th>prediction_interval_lower_bound</th>\n",
       "      <th>prediction_interval_upper_bound</th>\n",
       "      <th>confidence_interval_lower_bound</th>\n",
       "      <th>confidence_interval_upper_bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BLACK VELVET</td>\n",
       "      <td>2017-06-02 00:00:00+00:00</td>\n",
       "      <td>1404.775</td>\n",
       "      <td>749.977</td>\n",
       "      <td>0.900</td>\n",
       "      <td>172.509</td>\n",
       "      <td>2637.042</td>\n",
       "      <td>172.509</td>\n",
       "      <td>2637.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BLACK VELVET</td>\n",
       "      <td>2017-06-03 00:00:00+00:00</td>\n",
       "      <td>2013.170</td>\n",
       "      <td>971.562</td>\n",
       "      <td>0.900</td>\n",
       "      <td>416.824</td>\n",
       "      <td>3609.516</td>\n",
       "      <td>416.824</td>\n",
       "      <td>3609.516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BLACK VELVET</td>\n",
       "      <td>2017-06-04 00:00:00+00:00</td>\n",
       "      <td>3213.622</td>\n",
       "      <td>1177.028</td>\n",
       "      <td>0.900</td>\n",
       "      <td>1279.681</td>\n",
       "      <td>5147.562</td>\n",
       "      <td>1279.681</td>\n",
       "      <td>5147.562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BLACK VELVET</td>\n",
       "      <td>2017-06-05 00:00:00+00:00</td>\n",
       "      <td>3573.866</td>\n",
       "      <td>1281.806</td>\n",
       "      <td>0.900</td>\n",
       "      <td>1467.767</td>\n",
       "      <td>5679.964</td>\n",
       "      <td>1467.767</td>\n",
       "      <td>5679.964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BLACK VELVET</td>\n",
       "      <td>2017-06-06 00:00:00+00:00</td>\n",
       "      <td>2257.318</td>\n",
       "      <td>1391.810</td>\n",
       "      <td>0.900</td>\n",
       "      <td>-29.524</td>\n",
       "      <td>4544.161</td>\n",
       "      <td>-29.524</td>\n",
       "      <td>4544.161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      item_name        forecast_timestamp  forecast_value  standard_error  \\\n",
       "0  BLACK VELVET 2017-06-02 00:00:00+00:00        1404.775         749.977   \n",
       "1  BLACK VELVET 2017-06-03 00:00:00+00:00        2013.170         971.562   \n",
       "2  BLACK VELVET 2017-06-04 00:00:00+00:00        3213.622        1177.028   \n",
       "3  BLACK VELVET 2017-06-05 00:00:00+00:00        3573.866        1281.806   \n",
       "4  BLACK VELVET 2017-06-06 00:00:00+00:00        2257.318        1391.810   \n",
       "\n",
       "   confidence_level  prediction_interval_lower_bound  \\\n",
       "0             0.900                          172.509   \n",
       "1             0.900                          416.824   \n",
       "2             0.900                         1279.681   \n",
       "3             0.900                         1467.767   \n",
       "4             0.900                          -29.524   \n",
       "\n",
       "   prediction_interval_upper_bound  confidence_interval_lower_bound  \\\n",
       "0                         2637.042                          172.509   \n",
       "1                         3609.516                          416.824   \n",
       "2                         5147.562                         1279.681   \n",
       "3                         5679.964                         1467.767   \n",
       "4                         4544.161                          -29.524   \n",
       "\n",
       "   confidence_interval_upper_bound  \n",
       "0                         2637.042  \n",
       "1                         3609.516  \n",
       "2                         5147.562  \n",
       "3                         5679.964  \n",
       "4                         4544.161  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfforecast.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N11NZ1d3ap-I"
   },
   "source": [
    "Since `horizon` is set to 30, the result is 30 x (number of items), with one row per forecasted value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J5nKk-XQap-I",
    "outputId": "2246f362-c0a7-4bb8-b432-88e066276f7c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 150\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of rows: {dfforecast.shape[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XBSHZwbzed7W"
   },
   "source": [
    "#### Inspect the model coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3QLmtPK19VLE"
   },
   "source": [
    "You can view the coefficients of each of the time series models using [ML.ARIMA_COEFFICIENTS](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-arima-coefficients).\n",
    "\n",
    "For each of the models, the `ar_coefficients` value shows the model coefficients of the autoregressive (AR) part of the time series model. Similarly, the `ma_coefficients` value shows the model coefficients of the moving-average (MA) part of the model. Both of these values are arrays, and their lengths are equal to `non_seasonal_p` and `non_seasonal_q`, respectively. The `intercept_or_drift` value is the constant term in the time series model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 197
    },
    "id": "fzxhgxY6-EHs",
    "outputId": "389a2380-c920-447a-f1bd-447b0592a36e"
   },
   "outputs": [
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Query is running:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "Downloading:   0%|          |"
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_name</th>\n",
       "      <th>ar_coefficients</th>\n",
       "      <th>ma_coefficients</th>\n",
       "      <th>intercept_or_drift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BLACK VELVET</td>\n",
       "      <td>[-0.16549655445723319, -0.7477843111888047, -0...</td>\n",
       "      <td>[-0.010971872002911037, 0.7809755718652553]</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FIREBALL CINNAMON WHISKEY</td>\n",
       "      <td>[0.5099256868357989, 0.17365890632425174]</td>\n",
       "      <td>[-0.9999915981003452]</td>\n",
       "      <td>-2.764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FIVE O'CLOCK VODKA</td>\n",
       "      <td>[-0.3596844148293946, 0.6178849330746929, 0.15...</td>\n",
       "      <td>[0.8837349172639454]</td>\n",
       "      <td>2129.972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HAWKEYE VODKA</td>\n",
       "      <td>[-0.20942045212532412, 0.6765204517126348]</td>\n",
       "      <td>[-0.08520133108699894, -0.8556243195314104]</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TITOS HANDMADE VODKA</td>\n",
       "      <td>[]</td>\n",
       "      <td>[-0.2597486052415876, 0.1442453510061992, -0.5...</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   item_name  \\\n",
       "0               BLACK VELVET   \n",
       "1  FIREBALL CINNAMON WHISKEY   \n",
       "2         FIVE O'CLOCK VODKA   \n",
       "3              HAWKEYE VODKA   \n",
       "4       TITOS HANDMADE VODKA   \n",
       "\n",
       "                                     ar_coefficients  \\\n",
       "0  [-0.16549655445723319, -0.7477843111888047, -0...   \n",
       "1          [0.5099256868357989, 0.17365890632425174]   \n",
       "2  [-0.3596844148293946, 0.6178849330746929, 0.15...   \n",
       "3         [-0.20942045212532412, 0.6765204517126348]   \n",
       "4                                                 []   \n",
       "\n",
       "                                     ma_coefficients  intercept_or_drift  \n",
       "0        [-0.010971872002911037, 0.7809755718652553]               0.000  \n",
       "1                              [-0.9999915981003452]              -2.764  \n",
       "2                               [0.8837349172639454]            2129.972  \n",
       "3        [-0.08520133108699894, -0.8556243195314104]               0.000  \n",
       "4  [-0.2597486052415876, 0.1442453510061992, -0.5...               0.000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery --project $PROJECT_ID \n",
    "\n",
    "SELECT\n",
    "  *\n",
    "FROM \n",
    "  ML.ARIMA_COEFFICIENTS(MODEL bqmlforecast.arima_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MPdfnofgap-K"
   },
   "source": [
    "#### Plot the forecasted predictions against the historical data\n",
    "\n",
    "Plot the forecasted predictions, using the `dfhistorical` dataframe that contains the historical data that you used for training and the `dfforecast` dataframe that contains the prediction data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "PBKzdLi-ap-L",
    "outputId": "08b2165f-7f12-4144-87aa-c4c0363a020f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NN9/c4HFuuukmAN5+Mm63u0nzJyIiIiKKpuAuRSMiIiIiooShZBRcfvnlaNWqVYNjb7rpJjz//PNYsmQJiouL0a5dOwDe3iQZGRkwmUwA6pYlU4waNQovvfQSKioqADQ9EAMARqMRixYtwuTJk/HNN99g/PjxWLZsGYYOHeo3bv369QH7qCg6d+6MJ598ssn3X5933nlHLcsWyPjx43HDDTc0+bg333wz/v73v6t9UW688UYYDIZ6x7du3Rrr16/HlVdeiW3btqFv374YNGgQunfvDpvNhk2bNqG0tBQGgwEvvPCCGpAJpKysTP0dSpIEs9mMgwcP4tdff4UkSUhLS8Ps2bMxY8aMJp1TfY/hgQMHYDKZ0L59e4wfP77BY+Tn56Ndu3YoLi7GV199hcmTJzdpDkRERERE0cJADBERERFRC2I2m7Fw4UIAjWcgAEDfvn3Rr18/7Nq1C++//75ajkqr1WLkyJFYsmQJAGD06NEB9x85ciQEQYAsy8jIyMCAAQOaNW+DwYDPP/8c1157LRYvXoz8/HwsXboUw4cPV8ccPHgQBw8erPcY5513XkgDMUr/kvpkZWU1KxCTm5uLiRMn4uuvvwZQkyHTkA4dOmDLli349NNPsWDBAmzduhU//fQTkpKS0KVLF9x0002488470bVr1waPY7Va1XJoer0e6enpaNeuHa699lqMGTMG119/PTIyMpp8TkDgx9ButwMA/vSnPzXYbwYAdDod/vjHP+Lll1/Gu+++y0AMEREREcUNQZZlOdqTICIiIiIiIiIiIiIiSkTsEUNERERERERERERERBQmDMQQERERERERERERERGFCQMxREREREREREREREREYcJADBERERERERERERERUZgwEENERERERERERERERBQmDMQQERERERERERERERGFiS7aE4gXkiShoKAA6enpEAQh2tMhIiIiIiIiIiIiIqIokmUZZrMZeXl50Gjqz3thICZIBQUF6NSpU7SnQUREREREREREREREMeT48ePo2LFjvdsZiAlSeno6AO8vNCMjI8qzISIiIiIiIiIiIiKiaDKZTOjUqZMaP6gPAzFBUsqRZWRkMBBDREREREREREREREQA0Gg7k/qLlhEREREREREREREREdFpYSCGiIiIiIiIiIiIiIgoTBiIISIiIiIiIiIiIiIiChMGYoiIiIiIiIiIiIiIiMKEgRgiIiIiIiIiIiIiIqIwYSCGiIiIiIiIiIiIiIgoTBiIISIiIiIiIiIiIiIiChNdtCdARERERCTLMtxuNyRJivZUiIgSikajgV6vhyAI0Z4KERERUYvFQAwRERERRY3L5UJJSQlsNhtEUYz2dIiIEpJWq0VKSgpycnJgMBiiPR0iIiKiFoeBGCIiIiKKCpvNhuPHj0Or1aJVq1ZITk6GVqvlVdtERCEiyzJEUYTdbkd1dTWOHDmCjh07IiUlJdpTIyIiImpRoh6IOXnyJB566CEsXboUNpsNPXr0wLx58zBo0CAA3jeOjz32GN5++21UVVVh+PDhePPNN3HmmWeqx6ioqMBdd92Fr7/+GhqNBpMnT8Yrr7yCtLQ0dczu3bsxY8YMbN26FW3btsVdd92FBx98MOLnS0REREReZWVl0Ov16NKlC7RabbSnQ0SUsNLS0tC6dWscPXoUZWVl6Ny5c7SnRERERNSiaKJ555WVlRg+fDj0ej2WLl2KPXv24IUXXkCrVq3UMc8++yxeffVVzJkzB1u2bEFqairy8/PhcDjUMVOmTMGvv/6K5cuX45tvvsEPP/yA22+/Xd1uMpkwfvx4dOnSBdu3b8dzzz2Hxx9/HHPnzo3o+RIRERGRl8fjgdVqRevWrRmEISKKAK1Wi9atW8NqtcLj8UR7OkREREQtiiDLshytO3/44YexYcMGrFu3LuB2WZaRl5eH++67D/fffz8AoLq6Gu3atcP8+fNx/fXXY+/evejTpw+2bt2qZtEsW7YMEydOxIkTJ5CXl4c333wTf/vb31BUVKTWw3344Yfx5ZdfYt++fQHv2+l0wul0qj+bTCZ06tQJ1dXVyMjICOWvgYiIiKjFcTgcOHz4MLp27Yrk5ORoT4eIqEWw2+04cuQIunXrhqSkpGhPh4iIiCjumUwmZGZmNho3iGpGzFdffYVBgwbhmmuuQU5ODvr374+3335b3X748GEUFRXhoosuUm/LzMzEBRdcgE2bNgEANm3ahKysLDUIAwAXXXQRNBoNtmzZoo658MIL/ZoS5ufnY//+/aisrAw4t9mzZyMzM1P96tSpU0jPnYiIiIjAfjBERBHE51wiIiKi6IhqIObQoUNqv5fvvvsOd9xxB/7617/i/fffBwAUFRUBANq1a+e3X7t27dRtRUVFyMnJ8duu0+nQunVrvzGBjlH7Pk41a9YsVFdXq1/Hjx8/zbMlIiIiIiIiIiIiIqKWRhfNO5ckCYMGDcIzzzwDAOjfvz9++eUXzJkzBzfffHM0pwaj0Qij0RjVORARERERERERERERUXyLakZMbm4u+vTp43db7969cezYMQBA+/btAQDFxcV+Y4qLi9Vt7du3R0lJid92j8eDiooKvzGBjlH7PoiIiIiIiIioYQ63iB3HKiFJUWs3S0RERBR3ohqIGT58OPbv3+9324EDB9ClSxcAQLdu3dC+fXusXLlS3W4ymbBlyxYMHToUADB06FBUVVVh+/bt6phVq1ZBkiRccMEF6pgffvgBbrdbHbN8+XL07NkTrVq1Ctv5ERERERGdjq5du0IQBMyfP7/BcaNHj4YgCHj88cfV29asWQNBEDB69OiwzjHclHNbs2ZNVO5/6tSpQT0GRC3FSysOYNIbG/H17oJoT4WIiIgobkQ1EHPvvfdi8+bNeOaZZ/D777/j448/xty5czFjxgwA3kaC99xzD5566il89dVX+Pnnn3HTTTchLy8PV155JQBvBs2ECRPw5z//GT/++CM2bNiAO++8E9dffz3y8vIAADfccAMMBgNuvfVW/Prrr/jkk0/wyiuvYObMmdE6dSIiIiKiuDB//nwIgoCpU6dGeypEFAOOldsAAAVVjijPhIiIiCh+RLVHzODBg7Fo0SLMmjULTz75JLp164aXX34ZU6ZMUcc8+OCDsFqtuP3221FVVYURI0Zg2bJlSEpKUsd89NFHuPPOOzFu3DhoNBpMnjwZr776qro9MzMT33//PWbMmIGBAwciOzsb//jHP3D77bdH9HyJiIiIiCLl/PPPx969e5GSkhLtqZyWDz74ADabDZ07d472VIgIgNUlAgDcohTlmRARERHFj6gGYgDgsssuw2WXXVbvdkEQ8OSTT+LJJ5+sd0zr1q3x8ccfN3g/5557LtatW9fseRIRERERxZOUlBT06tUr2tM4bQzAEMUWm9MDAPAwEENEREQUtKiWJiMiIiIiovBoqEfM9u3bcd1116Fjx44wGAzIyMjAGWecgcmTJ2Px4sXquK5du2LatGkAgPfffx+CIKhfpx7XZrPhX//6FwYMGID09HSkpKTg7LPPxt///ndUVlbWmcORI0cgCAK6du0KURTx4osvon///khLS4MgCOq4xnrErFq1Ctdccw06duwIo9GItm3bYvDgwXjsscdQXl6ujnO73fjwww8xZcoU9OrVCxkZGUhOTkbPnj3x17/+FQUF7HdBFAybLyPGJcpRngkRERFR/Ih6RgwREREREUXOypUrcckll8DtduO8887D0KFDIYoiTp48iSVLlkAURVxxxRUAgKuvvhqbN2/Ghg0b0L17d4wYMUI9Tu1sm4qKCowbNw67du1CRkYGxo4dC71ej7Vr1+Lpp5/Gxx9/jFWrVqFr16515iPLMiZNmoRly5Zh5MiR6N27N3799degzuWvf/0rXnvtNQBAv379MHLkSFRXV2P//v148sknMWbMGDVgVFxcjBtvvBGZmZno3bs3zj33XFitVuzatQuvvfYaFixYgI0bN6JHjx7N/M0StQw2FzNiiIiIiJqKgRgiIiIiijmyLMPuFqM9jWZL1mv9sjpiydNPP+2XHVJbdXU19u7dq/78/PPPY/78+diwYQNGjBiB+fPnBzzmX/7yF+zatQsXXHABlixZgjZt2gAALBYLrr32WixduhRTpkzBhg0b6ux77NgxSJKEn3/+GWeddVbQ5/Haa6/htddeQ5s2bbBw4UKMGTPGb/uPP/6I3Nxc9efMzEwsXrwYEyZMgMFgUG93u9147LHHMHv2bNx9991YsmRJ0HMgaonYI4aIiIio6RiIISIiIqKYY3eL6POP76I9jWbb82Q+Ugyhe6s9bdo0tUTY6SouLgYATJw4sc62zMxMDBkypEnHO3bsGBYuXAhBEDB37lw1CAMAaWlpePvtt9GjRw9s3LgRGzduxLBhw+oc45lnnmlSEMbj8eCf//wnAGDu3Ll1gjAAcP755/v9nJ6ejssvv7zOOL1ej2eeeQbvv/8+li1bBrPZjPT09KDnQtTSKD1i3BJLkxEREREFi4EYIiIiIqIYN3z48AZLZi1btkwNsDTm/PPPx549ezBlyhQ88sgjGDJkCHS65n8s+OGHHyBJEgYMGIBzzz23zvYOHTogPz8fixcvxurVqwMGYiZPntyk+9y+fTtKS0uRnZ2Nq666qkn7/vTTT1i5ciUOHz4Mq9UKSfJe1e/xeCBJEn7//Xf079+/ScckailkWYbNl63o9jAjhoiIiChYDMQQERERUcxJ1mux58n8aE+j2ZL12pAe77bbbsPUqVPr3T569OigAzGzZ8/G7t27sXTpUixduhTJyckYMGAARo8ejSlTpqB3795NmtvJkycBAN26dat3TPfu3f3G1paTk4OUlJQm3efRo0cBAD179gy6BJzVasWNN96IRYsWNTjOZDI1aS5ELYnDLUH2JcKwNBkRERFR8BiIISIiIqKYIwhCSEt7UY327dtj27ZtWLt2LVasWIENGzZgy5Yt2LBhA5555hnMnj0bDz30UMTmk5ycHJH7mTVrFhYtWoRevXrhX//6FwYPHozs7Gy1X8ywYcOwadMmyDLLLRHVx+ryqN+zNBkRERFR8PjploiIiIiohREEAaNHj8bo0aMBAA6HA/Pnz8eMGTPwyCOP4Oqrr1azWBrToUMHAMChQ4fqHaNsU8aers6dOwMADhw4AFmWg8qK+fTTTwEAn3zyScASar/99ltI5kaUyOwuUf2epcmIiIiIgqeJ9gSIiIiIiCi6kpKSMH36dJx77rmQJAm7d+9WtykZIx6PJ+C+F154ITQaDXbt2oWffvqpzvbCwkIsW7YMADBmzJiQzHfQoEHIzs5GaWkpvvzyy6D2qaioAAB06dKlzrbvvvsOZWVlIZkbUSKrnRHjYUYMERERUdAYiCEiIiIiakGef/55HDt2rM7t+/btU7NCagcrOnbsCADYs2dPwON17twZ11xzDWRZxv/93/+hvLxc3Wa1WnH77bfD4XBg2LBhGDZsWEjOQafT4W9/+xsA4Pbbb8cPP/xQZ8zWrVtx4sQJ9Wel981rr73mN27//v2YPn16SOZFlOiszloZMewRQ0RERBQ0liYjIiIiImpBnnrqKTzwwAPo1asXevfujeTkZBQUFGD9+vXweDy46aabMGDAAHX8kCFDkJeXh507d2LAgAHo27cv9Ho9evbsiQceeAAA8Prrr2Pfvn3YsmULunfvjjFjxkCn02Ht2rUoLS1Ft27d8NFHH4X0PO6++27s378fc+bMwahRo9C/f3/07NkTJpMJ+/btw6FDh7B69Wo1kPTYY4/h6quvxqOPPopPP/0UZ599NkpKSrBu3TqMHDkSeXl52LhxY0jnSJRobLV7xDAQQ0RERBQ0ZsQQEREREbUgr7/+OqZNm6YGSj7//HMcPnwYF198MRYtWoT58+f7jTcYDPjuu+9w+eWX48SJE/jwww/x7rvvYsmSJeqYNm3aYOPGjZg9eza6deuG77//Ht988w2ys7PxyCOPYPv27ejatWtIz0MQBLz55ptYunQprrjiChQUFODzzz/H1q1bkZ2djSeeeMKvF8ykSZOwdu1ajBs3DoWFhfjqq69QUlKCxx9/HEuXLoVerw/p/IgSka12jxiRpcmIiIiIgiXIssx3T0EwmUzIzMxEdXU1MjIyoj0dIiIiorjmcDhw+PBhdOvWDUlJSdGeDhFRi3C6z72Ldp7AvZ94e0Gd1zETi+8cEeopEhEREcWVYOMGzIghIiIiIiIiokbV7hHjYkYMERERUdAYiCEiIiIiIiKiRtXuEeNhjxgiIiKioDEQQ0RERERERESN8u8Rw0AMERERUbAYiCEiIiIiIiKiRvkHYliajIiIiChYDMQQERERERERUaOszprSZMyIISIiIgoeAzFERERERERE1Cg7S5MRERERNQsDMURERERERETUKKurJiPGw9JkREREREFjIIaIiIiIiIiIGlW7R4yLGTFEREREQWMghoiIiIiIiIgaVbtHjEdiRgwRERFRsBiIISIiIiIiIqJG1c6IESUZEoMxREREREFhIIaIiIiIiIiIGlU7EAMAbonlyYiIiIiCwUAMERERERERETXK5vL4/ewWmRFDREREFAwGYoiIiIiIiIioUXUyYjzMiCEiIiIKBgMxRERERERERNQgSZJZmoyIiIiomRiIISIiIiKKUV27doUgCA1+vfzyy9GeJhG1AHa3WOc2liYjIiIiCo4u2hMgIiIiIqKGDR8+HD169Ai4rU+fPhGeTfyaP38+pk2bhptvvhnz58+P9nSI4orV1x9GEIBkvRY2lwiPyIwYIiIiomAwEENEREREFONuu+02TJ06NdrTIKIWzO4rS5ai10Kv08DmEuFmIIaIiIgoKAzEEBEREREREVGDrE5fIMaog+yrSMbSZERERETBYY8YIiIiIqIEceLECdx1110488wzkZSUhMzMTAwfPhxvvfUWRLFuf4f58+dDEARMnToVFRUVuOeee9C9e3cYjUaMHj3ab+zKlSsxadIk5ObmwmAwICcnB1dddRU2bdpU73xsNhtefvlljBgxAq1atYLRaESXLl3whz/8AR9//LHf2KNHj+Lf//43xo4di86dO8NoNCIrKwsjRozAW2+9BamepuDbt2/Hddddh44dO8JgMCAjIwNnnHEGJk+ejMWLF6vjunbtimnTpgEA3n//fb8+O6eeKxHVZfOVJks1aGHQCgDAjBgiIiKiIDEjhoiIiIgoAWzduhUTJkxARUUFOnfujCuvvBLV1dVYs2YNNm7ciEWLFuGrr76CwWCos29ZWRkGDRqEqqoqjBw5EgMHDvQbd//99+OFF16ARqPBoEGDMHLkSBw7dgyLFy/G119/jbffflsNciiOHz+OCRMmYM+ePUhJScHw4cPRpk0bnDx5EuvWrcPPP/+MG264QR3/3//+F48++ii6deuGs846C8OHD0dhYSE2bdqEDRs24Pvvv8dnn30GQRDUfVauXIlLLrkEbrcb5513HoYOHQpRFHHy5EksWbIEoijiiiuuAABcffXV2Lx5MzZs2IDu3btjxIgR6nF69eoVsseBKFHZfKXJkg06SLI3KMOMGCIiIqLgMBBDRERERLHLZa1/m6AF9ElBjtUA+uRmjrUBqG+xUQAMKf5ja/8cIU6nE9dccw0qKiowffp0vPrqq9Dr9QCAQ4cOYdy4cfjuu+/wxBNP4Omnn66z/5IlSzBu3Dh88cUXyMjI8Nv29ttv44UXXkCPHj3w+eef49xzz1W3/fDDD7jsssswffp0jBgxAmeeeSYAQJIkTJo0CXv27MH48ePx4Ycfom3btup+DocDq1at8ruf/Px8XHnllTjnnHP8bi8oKMDEiRPxxRdf4LPPPsM111yjbnv66afhdrvx4YcfYsqUKX77VVdXY+/everPzz//PObPn48NGzZgxIgRmD9/fjC/WiLyqZ0R4/J4gzLMiCEiIiIKDgMxRERERBS7nsmrf9uZ44EpC2t+fq4H4LYFHttlBDBtSc3PL/cFbOWBx+b1B25fU/Pz6xcA1ccCj23bC5ixpebnt8f4/xwi06ZNq5NxAgCjRo3CmjVrsHDhQhw9ehR5eXl4+eWX1SAMAJxxxhl4/vnncfXVV+O1117Do48+iqSkJL/j6PV6zJ07t04QRpIkPP744wCABQsW+AVhAODCCy/Eo48+igcffBBvvfUWnn/+eQDA119/jW3btiE3Nxeff/450tLS/PZLSkrCxIkT/W4bPHhwwHPPy8vDs88+i/z8fCxcuNAvEFNcXAwAdY4FAJmZmRgyZEjAYxJR09XuEWNxeoMyHmbEEBEREQWFgRgiIiIiohg3fPhw9OjRo87tSkmtNWvWAACuv/56GI3GOuMmTZqEVq1aobKyEtu3b8fw4cP9tvfv3x9nnHFGnf127tyJgoICdO/eHQMHDgw4N6W/ysaNG9Xbli1bBgC44YYb6gRhGuJ0OvH9999j69atKCkpgdPphCzLMJvNAID9+/f7jT///POxZ88eTJkyBY888giGDBkCnY4fcYjCoXZGjF7rbTfLjBgiIiKi4PBTChERERHFrkcK6t8maP1/fuD3BsZq/H++5+fgx87YggZLk9X259X1H/c03HbbbZg6dWq920+ePAkA6NatW8DtgiCgW7duqKysVMfW1rVr14D7HTp0CABw8OBBv94sgZSWlqrfHz16FEDTeq9s3rwZ1113HY4dqyf7CIDJZPL7efbs2di9ezeWLl2KpUuXIjk5GQMGDMDo0aMxZcoU9O7dO+j7J6KG1fSI0UKv9T4fuBiIISIiIgoKAzFEREREFLsMqTEwtgk9X6LQHyYUkpOTA94uSd5F1vbt2yM/P7/BY2RnZzf7/m02G6688koUFxdj2rRpuOOOO9CjRw9kZGRAq9XiwIED6NmzJ2TZPyDWvn17bNu2DWvXrsWKFSuwYcMGbNmyBRs2bMAzzzyD2bNn46GHHmr2vIiohtUXiEk16KDzZcSwNBkRERFRcBiIISIiIiKKcx06dABQk8ESyOHDh/3GBqNTp04AgDZt2jSpuX3nzp0BAPv27Qtq/A8//IDi4mIMGDAA7733Xp3tv/32W737CoKA0aNHqyXSHA4H5s+fjxkzZuCRRx7B1Vdfje7duwc9dyIKzObrC5Ni1MLA0mRERERETaJpfAgREREREcUyJQjxySefwOFw1Nm+aNEiVFZWIj09vd5eL4EMHjwY2dnZ2LNnD3799deg95swYQIA4H//+x+sVmuj4ysqKgDUBHBO9eGHHwZ930lJSZg+fTrOPfdcSJKE3bt3q9sMBgMAwOPxBH08IvJSMmJS9DrofKXJGIghIiIiCg4DMUREREREce6aa65B586dUVBQgJkzZ/oFGg4fPoz77rsPAHDXXXchKSkp6OPq9Xo89thjkGUZV111FdavX19njCiKWLVqFTZv3qzedvnll6N///4oKCjANddcg/Lycr99HA4Hli5dqv6s9HJZuXIl9uzZ4zd27ty5+OSTTwLO7/nnnw/YU2bfvn1qFk2XLl3U2zt27AgAde6DiBpnd3mfV1KNWujVjBiWJiMiIiIKBkuTERERERHFOaPRiM8++wwTJkzAm2++iW+//RZDhgyB2WzGqlWr4HA4kJ+fj8cee6zJx77zzjtx7NgxPPfccxg5ciTOPvts9OjRA8nJySgqKsKuXbtQVVWFN998E0OGDAEAaDQaLFq0CPn5+Vi6dCk6d+6MESNGoE2bNjh58iR++uknZGVl4ciRIwCA/v3744orrsDixYvRv39/jB49Gq1bt8auXbuwf/9+PPLII3j66afrzO2pp57CAw88gF69eqF3795ITk5GQUEB1q9fD4/Hg5tuugkDBgxQxw8ZMgR5eXnYuXMnBgwYgL59+0Kv16Nnz5544IEHmvfLJ2oh1IwYgw56X0aMR2JGDBEREVEwmBFDRERERJQABg8ejF27dmHGjBnQarVYtGgR1q1bh/79++PNN9/EN998o5bmaqpnn30WGzZswJQpU2CxWLBs2TIsWbIEBQUFGD16NN555x1cd911fvt06dIF27Ztw7///W+cffbZ2LRpE7744gscPXoUo0aNwr///W+/8QsXLsRzzz2Hnj17Yv369fj+++/RuXNnfPfdd7jtttsCzuv111/HtGnToNPpsHbtWnz++ec4fPgwLr74YixatKhOXxuDwYDvvvsOl19+OU6cOIEPP/wQ7777LpYsWdKs3wtRS2ILkBHj8jAQQ0RERBQMQZZl5hIHwWQyITMzE9XV1cjIyIj2dIiIiIjimsPhwOHDh9GtW7cmlcoiIqLmO53n3qve2ICdx6ow98aB+O7XYny+4wQevqQXpo/qHqbZEhEREcW+YOMGzIghIiIiIiIiogbZnN7SZKlGHQw6b2kyNzNiiIiIiILCQAwRERERERERNcjqK02WYtBCp/EuJbglFtggIiIiCgYDMURERERERETUIJvLmxGTYtCpPWLcIjNiiIiIiILBQAwRERERERERNchWKyNGz9JkRERERE3CQAwRERERERER1UuUZDjc3qBLqlEHva80mYelyYiIiIiCwkAMEREREREREdVLyYYBfBkxvtJkLpYmIyIiIgoKAzFEREREREREVC+7rz+MRgCMOg10Wm9pMg8DMURERERBYSCGiIiIiIiIiOpl9QViUg06CIIAgy8jxi2yNBkRERFRMBiIISIiIiIiIqJ6WZ3e0mQpRi0AqBkxbmbEEBEREQWFgRgiIiIiIiIiqpfNlxGTYtABgNojhoEYIiIiouAwEENERERERERE9bK5fBkxBm9GjF7tEcPSZERERETBYCCGiIiIiIiIiOplq9UjBqjJiHExI4aIiIgoKAzEEBEREREREVG9Tu0Rw9JkRERERE2ji/YEiIiIiIjqY3d74BbFaE+jSfRaLZL1fJtNRInD7lZ6xLA0GREREVFz8BMiEREREcUku9uD5fsOw+J0R3sqTZJm1OPiXt0YjCGihGF1KoEY/9JkzIghIiIiCg5LkxERERFRTHKLIixONwxaLdKMhrj4Mmi1sDjdIc3iOX78OO688050794dRqMR2dnZyM/Px5IlSxrdd8WKFZg4cSKys7ORnJyMXr164W9/+xssFkuz5/P7779j6tSp6NixI4xGIzp27IipU6fi0KFDzT5mLJNlGc899xzOOeccJCcnQxAECII3G2DNmjUQBAGjR49u8nFrH6clO3LkCARBQNeuXaM9lbgzf/58CIKAqVOnhv2+bC5vabJUX0aMTg3EMCOGiIiIKBi8TI+IiIiIYppBF1+lvlwhDMJs3boVEyZMQEVFBXJzc3HJJZegvLwcq1evxvfff49//OMfeOKJJwLu+9JLL2HmzJkQBAEjR45Eu3btsG7dOjzzzDP4/PPPsX79emRnZzdpPhs2bMD48eNhs9lw9tlnY8SIEfjll1/w/vvv47PPPsOKFSswZMiQUJx6zHjzzTfx4IMPIjMzE5dccgkyMjKiPSVqxOjRo7F27VqsXr26WUEyqkvJiElWM2K8QURmxBAREREFJ34+0RIRERERtSAOhwOTJ09GRUUFrrvuOsybNw/JyckAvAGaSy65BE8++SRGjBiBiy++2G/fnTt34r777oNWq8XXX3+NSy65BABgs9lw+eWXY+XKlZg+fTo+++yzoOdjs9lw7bXXwmazYdasWXjmmWfUbY888ghmz56Na6+9Fvv371fnmQg+/fRTAMDChQvr/J7PP/987N27FykpKdGYWkLo0KED9u7dC71eH+2pUAPsbv+MGKU0mUdiRgwRERFRMFiajIiIiIgoBi1atAjHjx9HVlYW5syZ4xfcGDx4MP7xj38AAJ588sk6+86ePRuyLGPatGlqEAYAUlJS8O6770Kj0eDzzz/Hvn37gp7P/PnzUVBQgLPOOgtPPfWU37annnoKZ511Fo4fP44PPvigqaca044dOwYAOPPMM+tsS0lJQa9evdC5c+dITyth6PV69OrVC927d4/2VKgBao8Yo3+PGJeHGTFEREREwWAghoiIiIgoBm3duhUAMHDgQGRlZdXZftFFFwHwlgsrKipSb3e5XGr/mBtuuKHOfl26dMHw4cMBeIM9wVLGXn/99dBo/D9GaDQaXHfddQCAL774IuhjKk6ePIkHHngAffv2RXp6OlJTU3HWWWdh6tSp2LhxY53xJ06cwF133YUzzzwTSUlJyMzMxPDhw/HWW29BDFAarnYvDavVilmzZqFHjx4wGo1o3749br75Zpw8edJvn9GjR0MQBBw+fBgA0K1bN7Wvy+OPPw6g8R4xmzZtwiWXXIKsrCykpaVh0KBBeO+99xr9fdjtdrzwwgsYMmQIsrKykJSUhJ49e+LBBx9EeXl5SM6vtqb+/ps6v4Y01COmdh+dzz//HCNGjEBGRgZSU1MxfPhwfPvtt37jlcdj7dq1AIAxY8aoxxAEAfPnz/cbX1lZicceewz9+vVDeno6UlJS0LdvXzz11FOw2Wx15vP444+rj/+xY8dw6623olOnTtDr9Zg6dSreeustCIKACRMm1Hu+5eXlMBqNMBgMKC0tVW9fsWIF7rrrLvTr1w/Z2dlq/6XrrrtOfS6IJqVHTIrSI0bjfVw8EgMxRERERMFgaTIiIiIiohhksVgAAG3atAm4XenvIssyduzYgYkTJwIADhw4oC4iDxo0KOC+gwYNwrp167Bz586g56OMbeiYtccFa+XKlbj66qtRVVWFnJwcjBs3DgaDAUeOHMHHH38MABg2bJg6vnbfnM6dO+PKK69EdXU11qxZg40bN2LRokX46quvYDAY6txXdXU1hg0bhmPHjmHkyJE455xzsGnTJnzwwQdYu3YtfvrpJ2RmZgIAJkyYgK5du+Kzzz6D1WrF5MmTkZaWBgDo169fo+e1cOFC/PGPf4QoijjnnHPQt29fHD9+HLfddht+/fXXevcrKCjAhAkT8PPPP6N169YYPHgw0tPTsWPHDjz33HNYuHAh1qxZgy5dupzW+TX3938682uuxx57DP/85z8xbNgwTJw4Efv27cPGjRtx2WWX4fPPP8dVV10FAGrQadmyZSguLkZ+fj7at2+vHqdHjx7q93v27MGECRNw/Phx5ObmYsSIEdDr9fjxxx/x6KOP4vPPP8eaNWvq/L4A4LfffkP//v1hMBgwfPhwyLKM7OxsXH/99bj33nuxfPlynDx5Eh06dKiz78cffwyXy4VJkyahbdu26u3Tp0/H8ePHcfbZZ2P48OHQ6XTYt28fPv30U3zxxRdYsGABJk+eHLLfaVOpGTG+QIxB5w3GukWWJiMiIiIKBgMxREREREQxKCcnBwBw6NChgNtr365kbdT+PisrC+np6QH37dSpU539GmI2m9VMh/rKcCnHLC0thdVqRWpqaqPHPX78OCZPnozq6mo8/PDDeOKJJ/wCKCUlJThw4ID6s9PpxDXXXIOKigpMnz4dr776qtpb5NChQxg3bhy+++47PPHEE3j66afr3N+XX36J/Px8rFu3DhkZGQC8WRFjx47Frl278MYbb2DWrFkAgIcffhiAN8vCarXi+eefD5i1EUhRURFuvfVWiKKIF198Effee6+6beXKlbjssssC7ifLMq699lr8/PPPuPXWW/HSSy+pj6HH48HDDz+MF154AdOmTcOqVatO6/yApv/+T3d+zfXqq69i06ZNuOCCC9TbHn/8cTzxxBN4+OGH1UBMr169MH/+fIwePRrFxcV4+OGHA2Yr2e12XH755Th+/Dj+/ve/49FHH1XP22az4bbbbsP//vc/3HvvvQEzmD7++GP86U9/wjvvvAOj0ei3bdKkSfjoo4/w3//+V/0bqm3evHkAgGnTpvnd/vzzz2PUqFFo1aqV3+1ffvklrrnmGvzf//0fJk6cGLX+Sza3NxCTavAuISgZMW6RGTFEREREwWBpMiIiIiKiGDR27FgAwPbt2wNmmcyZM0f93mQyqd+bzWYAaDAQomR21N6vIcoxGzqucsymHPfFF19EdXU1/vCHP2D27Nl1slhycnIwYsQI9eeFCxfi6NGjyMvLw8svv+zX4P2MM87A888/DwB47bXX4HA46txfamoq5s2bpwYpAKBVq1bqgvmKFSuCmndj3n33XZjNZgwZMsQvCAMA48aNw//93/8F3O+7777Dhg0b0K9fP8yZM8cvkKbT6fDss8/inHPOwerVq/HLL7/U2b+p59fU3//pzq+5nnzySb8gDADMmjULmZmZOHDgAI4fP96k473//vs4ePAgLrvsMvzzn//0O++UlBTMnTsXOTk5+O9//4vKyso6+7du3Rr/+c9/6gRhAOCWW24BgDpl0ADgp59+ws6dO9G+ffs65cuuvPLKOkEY5fZrrrkG5eXlWL16dZPOM5RsTl9pMqM3I0bpEcNADBEREVFwGIghIiIiIopBY8eOxYUXXghZlnH55Zfj66+/RnV1NQ4dOoT7778fH3zwgRqIOLVnS7xYtmwZAOD2228PavyaNWsAePvUBFoEnzRpElq1agWz2Yzt27fX2T5o0CDk5ubWub13794A0GAflaZQ5jllypSA22+++eaAtyu9fSZPngydrm7xAo1GgwsvvBAAAvZuaer5NfX3f7rza64//OEPdW4zGo0444wzADT9cVPOQ+lrdCqln4/H4wnYn+Wiiy4KWLIM8Pal6dq1K/bv349Nmzb5bVOyYW666aaAv7+CggK8/fbbuO+++3Dbbbdh6tSpmDp1qlrKbv/+/cGfZIjZXEppMu+8WZqMiIiIqGlYmoyIiIiIKEYtXLgQkyZNwoYNG3D55Zf7bbvnnnuwfv16bNu2Da1bt1ZvV7IUrFZrvcdV+s/UzpxoSO3Mh/qOqxyzKcc9evQoAG9JqWAoC+7dunULuF0QBHTr1g2VlZUBF+frK6umzDdQFk1znDhxosF51ne7Um7u0UcfxaOPPtrgfdRu9K5o6vk19fd/uvNrrlA/bsp53HjjjbjxxhsbHBvoPBoqUScIAqZOnYrHH38c8+bNw9ChQwEAbrcbH330EYC6ZckAqOX03G53vccONtMsHGwub0ZMqq9HjFKaTJRkSJIMje9nIiIiIgqMgRgiIiIiohiVk5ODdevWYcWKFVi1ahXKy8vRrl07XHHFFRg0aBDy8vIAAH379lX3URaJq6qqYDabA/aJUUo5BdvzJD09Ha1bt0ZFRQWOHTuG8847r95jZmdnB9UfJhpiPXNIkrxlnkaMGIHu3bs3OPbss8+uc1u4z+9059dcoT4v5TwmTJiAdu3aNTi2S5cudW5rrE/L1KlT8cQTT+DTTz/FK6+8guTkZHz99dcoKyvDkCFD6gS+vvjiCzz++ONIS0vDf/7zH4wdOxZ5eXlITk6GIAh45JFHMHv2bMhy9LJPrEpGjNG7hKDX1TwmbkmCUaONyryIiIiI4gUDMUREREREMUwQBFx88cW4+OKL/W4/ePAgCgsL0aZNGwwYMEC9vWfPnkhJSYHNZsO2bdswZsyYOsfctm0bAPjt15gBAwZgxYoV2LZtW8BSUc05ZufOnbF//37s27cPPXr0aHR8hw4dANRkNARy+PBhv7HR0KFDB+zbtw9HjhwJuL2+2zt16gQAuOKKK3D//feHaXY1mvr7j/T8wqVTp07Yt28fbr31Vlx99dUhP36XLl0wduxYrFy5El988QWmTJmi9oxResjU9umnnwIAnn766YBl4n777beQz7Ep3KIEl8cbvErR+3rE1AqOeUQZRq4sEBERETUoti8JIyIiIiKigJTG9Lfffrtfs3GDwYBLL70UAPDxxx/X2e/o0aNq/46rrroq6PtTxi5YsEDNKFBIkoRPPvkEgLdPS7CUhuVvv/12UONHjx4NAPjkk08ClqNatGgRKisrkZ6ejoEDBwY9j1AbNWoUAKilqE71wQcfBLz9kksuAeAtSReJ7Iem/v4jPb/mUv4/eDyegNuV81ACIOGgBFzmz5+P4uJiLF26FMnJyQH70lRUVAAInH1TUlKC5cuXh22ewVD6wwBAitEXiNHWlCJzi1KdfYiIiIjIHwMxREREREQxas+ePXX6Qng8HjzzzDN466230KNHD/ztb3+rs9/DDz8MQRAwb948tSE7ANhsNtx6660QRRGTJ0+uUyLpxx9/RK9evQL2DJk6dSry8vJw4MCBOv1BHn30URw4cAAdO3bETTfdFPT5zZw5E+np6fjqq6/w97//vU5/jJKSEqxfv179+ZprrkHnzp1RUFCAmTNn+i20Hz58GPfddx8A4K677kJSUlLQ8wi1W2+9FWlpadi0aRNeffVVv21r1qzBnDlzAu53xRVXYPDgwfjxxx8xbdq0gP1JKisrMWfOnHqDDE3R1N9/pOfXXB07dgQAtcn9qW6//XZ06dIFCxcuxEMPPQSz2VxnTFFRUdABqkAmTZqErKwsrFq1Ck8//TQ8Hg8mT54csH9S7969AQBz586Fy+VSb6+ursbNN9+M6urqZs8jFOy+QIxOI8Cg9S4haDW1AzGxG5QjIiIiihVMICYiIiKimObyiI0PihGhnuvcuXPx1ltvYeDAgejQoQOcTic2b96M4uJi9OjRA8uXLw/Yj2XAgAF44YUXMHPmTEycOBGjRo1S+80UFhaiZ8+eAYMBNpsN+/fvDziXlJQUfPrppxg/fjyeeeYZfPXVVzjnnHPwyy+/4JdffkFqaioWLlzYaP+M2jp37ozPPvsMV199NZ5++mm88847GDp0KPR6PY4ePYqdO3fihhtuwIgRIwAARqMRn332GSZMmIA333wT3377LYYMGQKz2YxVq1bB4XAgPz8fjz32WNBzCIe8vDy8/fbb+NOf/oS7774b77zzDs455xycPHkS69atwz333IOXXnqpzn4ajQZffvklLr30Urz//vv47LPPcN5556Fz585wuVw4dOgQfv75Z4iiiKlTp0KnO72Pc039/Ud6fs01efJkzJs3Dw8++CBWrFiBnJwcCIKAW265BcOGDUNqaiqWLFmCyy67DM8++yzmzp2Lc889Fx07doTNZsOBAwewd+9e5OTk4M9//nOz5pCUlITrr78ec+bMwWuvvQYgcFkyALjnnnvwwQcf4Ntvv8UZZ5yBIUOGwO12Y+3atUhJScEtt9yC9957r9m/j9NldXmDaikGLQTBG4ARBG9QxiVKzIghIiIiCgIzYoiIiIgoJum1WqQZ9XCJIixOV1x8uUQRaUY99NrQNK6eOHEi8vPzcezYMSxevBirV69G586d8eyzz2L37t3o2rVrvfvee++9WL58OfLz87F7924sXrwYaWlpmDVrFrZu3Yrs7Owmz2f48OH46aefcNNNN6GiogKff/45KioqcNNNN+Gnn37CkCFDmnzM8ePH45dffsHdd9+NrKwsLFu2DEuXLkVVVRVuvPFGTJ8+3W/84MGDsWvXLsyYMQNarRaLFi3CunXr0L9/f7z55pv45ptv/Eq1Rcv111+PNWvWID8/H0ePHsXixYthNpsxZ84cvPjii/Xul5eXh82bN2POnDk4//zzsX//fnz22WdqZsr06dPx3XffhSzjp6m//0jPrzkuvfRSvP322zjnnHOwatUqvPfee3j33Xdx4MABdczZZ5+N3bt349lnn0Xv3r2xe/duLFy4EFu2bEFqairuv/9+LFq06LTmUTvw0rVrV7W03qm6deuGnTt3YsqUKdBqtfjmm2/w008/4Y9//CN27typ9uaJFpvTG2BOMfgH1nS+8mQeZsQQERERNUqQY7m4bwwxmUzIzMxEdXV1wHRyIiIiIgqew+HA4cOH0a1btwYXbO1uD9xi/GTEAN4AUrKeiedEFHuCfe6tbcuhclw3dzPOaJuKVfeNVm8/74nvUW13Y8XMUeiRkxamGRMRERHFtmDjBlHNiHn88cchCILfV+161A6HAzNmzECbNm2QlpaGyZMno7i42O8Yx44dw6WXXoqUlBTk5OTggQceqFOPeM2aNRgwYACMRiN69OiB+fPnR+L0iIiIiOg0Jet1yEgyxtUXgzBElEhsvh4xqadkxOh9/WI8EkuTERERETUm6qXJzj77bBQWFqpftZtB3nvvvfj666+xcOFCrF27FgUFBZg0aZK6XRRFXHrppXC5XNi4cSPef/99zJ8/H//4xz/UMYcPH8all16KMWPGYNeuXbjnnntw22234bvvvovoeRIRERERERHFG6VHTLLBv+Si3leazO1hkQ0iIiKixkT9cj2dTof27dvXub26uhrvvvsuPv74Y4wdOxYAMG/ePPTu3RubN2/GkCFD8P3332PPnj1YsWIF2rVrh379+uGf//wnHnroITz++OMwGAyYM2cOunXrhhdeeAEA0Lt3b6xfvx4vvfQS8vPzI3quRERERERERPFE6RGTWicQ472u0yUyI4aIiIioMVHPiPntt9+Ql5eHM844A1OmTMGxY8cAANu3b4fb7cZFF12kju3Vqxc6d+6MTZs2AQA2bdqEvn37ol27duqY/Px8mEwm/Prrr+qY2sdQxijHqI/T6YTJZPL7IiIiIiIiImpJbL6MmBSj/3WcOl9GjIeBGCIiIqJGRTUQc8EFF2D+/PlYtmwZ3nzzTRw+fBgjR46E2WxGUVERDAYDsrKy/PZp164dioqKAABFRUV+QRhlu7KtoTEmkwl2u73euc2ePRuZmZnqV6dOnU73dImIiIiIiIjiitUVOCPG4MuIcYssTUZERETUmKiWJrvkkkvU788991xccMEF6NKlCz799FMkJydHcWbArFmzMHPmTPVnk8nEYAwRERERERG1KGpGjCFwRoxbYkYMERERUWOiXpqstqysLJx11ln4/fff0b59e7hcLlRVVfmNKS4uVnvKtG/fHsXFxXW2K9saGpORkdFgsMdoNCIjI8Pvi4iIiIhCS5Z5JTURUaQ05znX5suISamnR4zbw0AMERERUWNiKhBjsVhw8OBB5ObmYuDAgdDr9Vi5cqW6ff/+/Th27BiGDh0KABg6dCh+/vlnlJSUqGOWL1+OjIwM9OnTRx1T+xjKGOUYRERERBR5Go33bagoilGeCRFRy6E85yrPwcGwOX2lyU7pEaP3HcMjMaBORERE1JioBmLuv/9+rF27FkeOHMHGjRtx1VVXQavV4o9//CMyMzNx6623YubMmVi9ejW2b9+OadOmYejQoRgyZAgAYPz48ejTpw9uvPFG/PTTT/juu+/w97//HTNmzIDRaAQATJ8+HYcOHcKDDz6Iffv24Y033sCnn36Ke++9N5qnTkRERNSi6fV66PV6WCyWaE+FiKjFMJvN6vNvsKy+0mTJ+lMyYnS+0mQiM2KIiIiIGhPVHjEnTpzAH//4R5SXl6Nt27YYMWIENm/ejLZt2wIAXnrpJWg0GkyePBlOpxP5+fl444031P21Wi2++eYb3HHHHRg6dChSU1Nx880348knn1THdOvWDUuWLMG9996LV155BR07dsQ777yD/Pz8iJ8vEREREXkJgoD09HRUVVUhMzMz6v0BiYgSnd1uh8lkQlZWFgRBCHo/pTRZqtE/EKPzZcS4RWbEEBERETUmqoGYBQsWNLg9KSkJr7/+Ol5//fV6x3Tp0gXffvttg8cZPXo0du7c2aw5EhEREVF4ZGdnw26349ixY8jIyEB6ejq0Wm2TFgiJiKh+sixDFEWYzWaYTCYYjUZkZ2c36Rg2X0ZMiuGU0mRKjxhmxBARERE1KqqBGCIiIiJqubRaLTp16oSysjKYzWZUVVVFe0pERAlJr9cjKysL2dnZ0Gq1je9QS30ZMQaWJiMiIiIKGgMxRERERBQ1Wq0W7dq1Q05ODtxuNySJC3pERKGk0Wig1+ubnW1odSo9YvyXD1iajIiIiCh4DMQQERERUdQJggCDwRDtaRAR0Snqy4hhaTIiIqL44pEkaAQBGpaCjgpNtCdARERERERERLFJCcTU7RHjXcTxMBBDREQUF7YcKcSR8upoT6PFYiCGiIiIiIiIiAKyubylyVIMgTNiXCxNRkREFPNcHhGlZiscHk+0p9JiMRBDRERERERERHW4PJLaAyb1lIwYHTNiiIiI4obJ4YTV5YZH4gUU0cJADBERERERERHVYfeVJQOA5FMyYgzsEUNERAmo3GqHyyM2PjDOmBwubyAmAc8tXjAQQ0RERERERER1WH1lyQxaDQw6/+UDJSPGzdJkRESUIDyShK1HC1FoskR7KiFXZXfA6fHAKTIQEy0MxBARERERERFRHUp/mFOzYYCaHjHMiCEiokRRbXfC4nQl5GtbidkGSUZCZvvECwZiiIiIiIiIiKgOq9O7WJPaQCDGw4wYIqIWRZJl7Cksg93ljvZUQs4biHHDIyVWIMbu9qDa4USSTseMmChiIIaIiIiIiIiI6rD5esSkGHV1tunV0mSJtVhFREQNMztcOFRehSq7M9pTCblKmwM2V+IFYkx2JxxuEalGPTNiooiBGCIiIiIiIiKqQylNltJARoyLgRgiohYlUbNGZFlGkdkKjywlXLZntcMJjyTBoNXCJUqQ5cQ6v3jBQAwRERERERFFXInZgZ3HKqM9DWqAVcmICRCI0bE0GRFRi1Rps8PqSrw+KjaXG2aHC1pBA5foifZ0QqrS5oBGEKARBEiyBJGBmKhgIIaIiIiIiIgi7s6PduKqNzbiYKkl2lOheth9GTGphrqlyQwsTUZE1CCTI/FKdwFAkdkGtyglXEZMld0Ju9uDVENile+SZRmlFhuSdFpoNQJESYaHr91RwUAMERERERERRdxvJWYAQGGVI8ozofpYnfX3iNFpvMsJbolX1RIRnara7sTGQydRnWB9VOxuD6rsDkiSnJCBGFmWodNq4EygQIzV5YbF6UayXu/LiEm8xy5eMBBDREREREREEeUWJVTa3ACQUIsdiUbtEaMP0CNG5wvEeLiYQ0R0qgqbHRaXCy4xsV7jquwO2F0e6HRaeBIsEF9qsUGn1UArCHAl0GubyeGC3e1Gkl7rDcQkYBAtXtS9rIWIiIiIiIgojCqsLvX7RFrsSDRqjxhjgECMxluajIs5RER1lVnsCdnQvtruhCTL0AkC3Al0IYVbFFFmtSFFr4coSwkVQFMeM61GA0mWIckyxAQLosULZsQQERERERFRRJWaa0q1OBmIiVl2XyAmUI8Yvda7nOASuZhDRFSbKMkoNFkSshdHqcUGrUYDrUZIqIzWKrsTdpcHyXodtIIGHklKmGBFudWulhPVCOwRE00MxBAREREREVFElVpqB2ISZyEn0Vid3tJkyYa6GTE6rS8jhos5RER+TA4nzA4XREmCO4GeIz2ShFKzDcl6HTSCJuGyRlyiBIMuscp3iZKMUosNyXo9ALBHTJQxEENEREREREQRVVYrI4alyWKXTc2IqRuIMfgyYhJpkZGIKBQqbA443B7oNJqEWvCutjthdXmQYtBBKwhwejzRnlLIVNjs8FXchEYjQEyQYIXF6YLN5UaS3pvZKgjek3QnwLnFIwZiiIiIiIiIKKLKLDU9YliaLHbZXN5FthRjgNJkOu9ygoelyYiI/JRZbNAkYB+tKrsTbtEDg1YLjSZxGtpLsowik9Uva0SUpITI+Ky2O+H0iDDq/C+oSIRzi0cMxBAREREREVFEsUdMfLA20CNG51tkdHExh4hIJUoSCqotSNHrIQPwJEifEQCotDoAQYAgCNAIAtyiCFmO//OzOt2wON1I9mWNaBOofJfJ4X2/pfFlwigS4dziEQMxREREREREFFFlFgZi4oGaEROgNJmepcmIiOqosjthdbmRYtBDAOBOkD5osiyjyGxBss4XrFDLd8V/IKbK7oDD41HLdyVSabJSi019va4tEc4tHjEQQ0RERERERBHlH4hJjEWqRGRzeh+bhgIxLE1GRFSj0uaA0yMiSa+DVqOBI0H6qFhcbpidLiQbajV9T5CG9lV2JyDXZI1olXOL8wsN3KKEcptDDTApZABiAgTQ4hEDMURERERERBRRfqXJ3PG90JHIbEppskA9YrQsTUZEdKpSi01d0NcIAlwJcrFBlc0Bh9vjV74rUbJGSsxW6LU1FxwIguArKxff52ZyOP0eM0Ui/V3GGwZiiIiIKCyKTQ7M+GgHdhyrjPZUiIgoxtTOiOFCfuyy+kqTJTMjhoioUR5JQmG1Bam+rBGtRoBLTIwF7yq7E3KtrBGNxtdHJc5fw50eDypsDqSc0gtNQPz39zE5nHB5RBi0/q/hGkGAM0EyteINAzFEREQUFnN/OIQlPxfinXWHoj0VIiKKIW5RQqXNrf7MjJjYJMtyTUaMIVBGDHvEEBHVVmVzqP1hAGXBOzECMcUm/6wRjaBJiNJk1XYn7K66WSMA4j7IVG13QoY3w6c2b4Awvs8tXjEQQ0RERGGxen8JAOBklSPKMyEiolhSYXX5/czFgNjk9EhqDfkUY92MGJ2vNJlHkiHL8X3VMBFRKFTYHHCJIow673OmVhDgESVIcf4c6fR4UHlK1ohWI0CUpbgPxFTZnXBLkl+QSRHv51ZisSFJV/e8EilAGG8YiCEiIqKQO15hw6FSKwCgsMoe5dkQEVEsqd0fBgCcbi4GxCK7q+ZxSdHXX5oMANwsT0ZEhBKzDVqh5rlRqxEgJkDT9yq7Eza3B8l6vXqbRhAgy/GfNVJudajl1mqL9x4xTo8H1TYnkgJk+mgFAe4EKZkXbxiIISIiopBb48uGAYBSixMuT/y+iSUiotAqtZwSiOFrRExS+sMYdBrotHWXDgy1bovnxSoiolBwiyJKzFa1PwzgLd+VCA3tq2xOiJLkF4BXxPO5SbKMErMVKQGCFfHe0L7a7oTdE7jkmkYjwJ0AmVrxiIEYIiIiCrk1+0vV72UZKDGzPBkREXmVnZIRw2B9bKrpD1M3GwaoKU0GAG4PF3OIqGWrtDlgqdUfBqjV0D6OgxUAUGa1BcwagRDfDe3NDhcsTheSaz1mCo0gwBXHWSMmhwseUQxYck0j+P4u4zybKR4xEENEREQh5XCL2HiwHEDN1bKF1QzEEBGRV5nF2yMmPcl7lSbrlMcmJRBTuydAbTpNzaIc+/wQUUtXYXPAI0ow1OrJoRXivzSZKEkoMdv8AkyqOC9NVmV3wOkRA/ZR0Wriu49Kpc0BIVDwDN6/S0mK/wBhPGIghoiIiEJq65EK2N0ictKN6Nc5CwBQwD4xRETko/SI6ZCVDIClyWKVzektTZZST0aMIAjQ+7JiuJhDRC1didkKrcZ/mTURMmJMDhdsLnfAEldAfD//V9kckIGAAYt4Lk0myzJKLFYk6QI/ZhpNYpTMi0cMxBAREVFIKWXJRp3VFnmZSQCYEUNERDXKfD1iOrbyBmJYmiw2WZWMGGPghRwAar8AliYjopbM5RFRYrH59YcBlIwYCe44zxpxeEQYA2SNAIjrpu9FZlvAbBjA+9jFa2kym9sDs8ONpHqCZ0ppMjGOy8rFKwZiiIiIKKTW7C8BAIzumYNc39XORQzEEBGRjxKIYUZMbLO5vBkx9fWIAWrKk7l5VS0RtWCVNgdsTk+dUo6CIEBGfGeNeEtcBc4aiefyXXa3B1V2R72ZPvHc0N5kd8Lh9tR7bmppsjgOEMYrBmKIiIgoZI5X2HCw1AqtRsCIM7PVjBiWJiMiIoUSiMlTAzHxuYiT6BrrEQMABp0vI4aLOUTUglXYHHBLUsDG6ALiu6F9kamBEleCJm7Ld1XbnbC7PUjWB+h9A1/WSJwGK6odToiyXKdUnkKjESDKUlwHCOMVAzFEREQUMmsOeMuSDezcCpnJeuRmehfZWJqMiIgUao8YliaLadZGesQAgM63yOMR43eRkYjodBWbrdDXs+gNxG9De6vTBZPD1WBmRbxeTFFld0AUZei0gR83rSDEbR+VCqsd2gAZTAqNIECW4ztTK14xEENEREQhs9ZXlmxUz7YAgPZqjxhmxBARkTdzotLmBsDSZLFOyYhJNdYfiNHrvAs9rjhdZCQiOl1OjwelZhtSDIEzK4D4XfCuUrNG6i/fFa99VMotdmg1DQQrNBpIcRiIkWQZpRZ7vY9ZbfF2bomAgRgiIiIKCadHxMaD5QCA0b5AjFJ2psziiturpYiIKHQqrC4A3rrySrCegZjYFExpMj0zYoiohauwOWB1uZHaQCAmXss3VtmdkBsqcSUIcHkkyHHWR0WUJJRYGg6eaQQBYhyWJrM4XbC63EhqLBAjxHfJvHjFQAwRERGFxNbDlbC5ROSkG9EnNwMA0CpFD6OvfnxxtTOa0yMiohiglCVrnWpAks6baRGPCx0tgc3VeGkyvZY9YoioZau0OiDK9Ze4EgQBLtET4VmFRqnZWu95Ad6LKiRZirsFfZPDBZvL3WDWiFYQ4jIjptruhNPjaTwQI8dvybx4xkAMERERhcQapSzZWW0h+GrSCoKgZsUUsDwZEVGLV2rxBmKy04ww6ms+jrK0VeyxOoPIiPGVJmMghohaqkKTFQZt/QFrrSDEZUN7l0dEmdWOlHqa2QO+rJE4DFZU2R1weEQYdfU/bpo4CzLJsoxquxPFZhtk2fvYNCbeHrdE0HjBOCIiIqIgrDlQCgAY3TPH7/b2GUk4XGZlnxgiIkKZLyOmbboRhlpX2bo8ElIM0ZoVBaJkxDTUI0anUTJi4mOhiogolOxuD8qtdqQ2ELDWaOKzoX213Qmb24M2Kcn1jtEq5bviaEHf5RFRarFBEKBePBiI2tA+hi80cIsSKqx2lFptOFlpQbnNDrvbgwxjcG+o3HHa3yeeMRBDREREp+1EpQ2/l1ig1QgYcWa237bcLG8PgIIqRzSmRkREMaTM4u0Rk51mgE6rgVbjXcRhn5jYE0yPGANLkxFRC1bp6w+Tm5Fa7xitr49KvKlyOOEWRRgazRqJ/fKisiyjwubAySozDpZVodLuQIbR2Oh+AmIva8QtijhZZUGx2YqCajPMDjfckgSjTos0owHZqckNBpgUGkHge68oYCCGiIiITtua/d5smAGds5CZ7J++npfpvYqqqJqBGCKilq7MV5qsbZp3AcSo08DmEuF0czEg1gTTI0anZWkyImq5Kqx2SLJUbzN7ANBqNHB64qtHjN3tQYnZCo3QcEcLjaCJ6T4qTo8HJ6ssOFJRjcJqC+xuD9KMBuSmpzXY+0YhI/YCMb+VVGLL0QLIANIMBmSnJUPfQGm8+mg18du7KJ4xEENERESnTQnEnFqWDKjJiGFpMiIiKq1VmgyoCcS4WB4j5tT0iKl/gUevZWkyImqZ3KKIgmoLjLqGl1Y1ggCPLEOUGg7YRIvLI8LkcMLkcKHK7kSpxYpquwtWlwtphoZLXClZrbEWrDA7XfitpBJHyqtQ7XBCIwjISk5CTnr9mUv1ibVsn3KbHRpBg7zMtNM6jjcjhu+9Io2BGCIiIjotTo+IjQfLAACjzmpbZ7uSEcPSZEREpGTEZPsyYgw676KUgxkxMcfu9i7QpBrrXzbQ+zJiYm2hiogoXDyShGMVJuwtLkdRtQVtG1nc12gEeEQJnhgLxBwur8LB0ipU2R2wuz2+fiECjDodkvVatEtPbTTTQiPEZmmyXwvLsPtkCbKSk5Cbkdbs37sMwCPHzoUGkiyj3GJHkr7pGTCn0ggC3CxNFnEMxBAREdFp2XakEjaXiLbpRpydl1Fne/tMZsQQEZHXqYEYo6/2POuUxx6r01uyJLmBBR89e8QQUQshSjJOVpmxt7gcBdVm6DRa5Gamq8+D9dEKAly+YEUDce2I21dUgZPVZmQlJ6F1SnKDvWAaEmt9VGRZRpnFhnSjAa1Skk7rWAIAdwxljdhcbjg8IpL0p/+HpNVomI0cBTH0FEBEROFUbXOjwuZCt+ymp+MSNWTN/hIA3myYQI0BlYyYSpsbdpeI5AZKnBARUWIrs7gAANnp3nInRl9GTDw2Mk50NlfjGTE6liYjogQnyTIKqy3YW1yOE1VmCBCQk5YadOBCIwgQJQnuGApWOD0eWJwutE5JQnpS403rG+ORYuc1wO72wOp0hyhYEVvlu8xOFxxuD7KST/8x0wgCxBgumZeo+JsmImohps7/EeNeWMOG6RRyNf1h6pYlA4CMZJ1aX77IxL8/IqKWyi1KqLB6AzFtTylNFksLHeS9otjq8mbEpDbYI8Z7AQYzYogoEZVZbPjht+NYeeAoTlSZkZ2ajLzMtCZlj2g13gXvWCrfZXa44PB4Gu1vE6yYOjenCw6PGJJz0wixlTVicbohyXJIAicajQApBvv7JDoGYoiIWoh9hWZIMnCi0hbtqVACOVllx28lFmgEYGSPwIEYQRCQq5Qnq2J5MiKilkoJwmg1Alql+GfEsDRZbHF6JChl8RvKZDX4MmJi6WpoIqJQkGUZW44U4veySmQlJ6FDZnqzFvc1grLgHTvPk2anCy5RarSsWrBiaTHf7HDBI4Xm3LSCAKfHE4JZhYbZ4fTWSwsBbYz290l0DMQQEbUAVqdHbbiq/EsUCkpZsgGdWyEzRV/vuFxfebICZmQREbVYpWZvf5jWqQZoNN6VBANLk8UkpT8MAKQYGipN5n0c+fgRUaKxudwwO13ITk1G8mmUuVIb2sdSsMLphgAELCvdHO4YyhoxOVyhilVAoxFi6vWtzGqHURuaMt9KybxYChC2BAzEEBG1AEpjXACwu2LnTRLFv8bKkimYEUNERMr7key0mtrmRl95F2bExBalP0ySXgOtpv4lLb2aEcPHj4gSi8nXj+N0e40owY5YyjyosNhClg2jEYSYeg0vs9rU9xanSyMIcIsiZDn6wQq3KMJkd4asnFwsBghbAgZiiIhaAL9ADDNiKERcHgkbfy8DAIzumdPg2Nwsb0ZMIXvEEBG1WEpGTNv02oEY9oiJRTX9YRpe8FEW8txi9BepiIhCyexwQQxRPw4AcMfIgrcoSaiwOUK2oK/VCHCJsVG+y+UJbbBC7e8TA1kjFqcbDo+IJH1ogkyx2LuoJWAghoioBSg1u9TvmRFDobL9aCWsLhHZaUb0yc1ocGweM2KIiFq8Mov3/Uh2mkG9jaXJYpOSEdNQfxgA0LM0GRElqGq7M2QlriDETkaMuqAfsqwRTcxcTGFxukIarKjp7xP9x87scMHlEWEIYWmyWDm3loSBGCKiFoAZMRQOewtNAICBXbLUWv/1aa8EYhKkR4zDLeLlFQfw84nqaE+FiChuKO9H2rI0WcyzOb3vFxvLiNFpWJqMiBJTqcV22mXJVHLsPE9anC443CKMITo3rUaAK0YCMSZnaIMVWkHJiIn+Y2dxei9mCVVfH0EQICN2/i5bCgZiiIhagNqBGFscZsQcLbfi9dW/o8ScGIv4ieK3EgsA4Myc9EbH5vlKkxUkSEbMqn0leHnFb/j3sn3RngoRUdwI2CNG7ytN5uZCQCxRSpOlGBtezFIymtye6JdtISIKFbvLDbPTFbpADGKnob3Z6QIgQxOiBX1vH5XYeA23OEIbrNBofH1UYuD8Ku2OBnu2NYcAxETZtZaEgRgiohagdiDGEYcZMf/8Zg+e+24/xr/0AxbvOhkTzfIIOOgLxPTISWt0bK4vI8bk8MDqjI0awqdDCSgVVidGYImIKBIC9Ygx+HqMuGJkgYq8lFK2KY2UJtP5FoVipfcBEVEomJ0uON0ikkLYGD1WMj8rbc6QBSoA77mJshQTmRUVNruaqRkKGkETEw3tZVlGudUest43tcVCkKklYSCGiKgFKIvjHjF2l4h1v3kbwlfZ3Lh7wS5M/3C7uphD0fNbiRlAcIGY9CQ90o3eN46JUJ5M+fursLoaGUlERApmxMQPNSOmkdJkel8gzS3yIhkiShwmhwtuSVKf406Xt3xXbFyMVm61haw/DOBr+i5FP2tElGSUWx0hzWJSzy3KgRib2wObyxPSx00R7XNraRiIISJqAfxKk8VZRszGg2VweiTkZSbhvovPgl4r4LtfizH+pbX46qcCZsdESbnFiUqbG4IAdG/beCAGAHKzlD4x8Z9FogRiKm3uqH/oICKKF2UWb/A6O92g3qb0iHHxuTSm1PSIaXjRR6/1XlXN10IiSiQmhxOhLAKljZGG9naXG1aXO2T9YQBf0/cYyBqxulywuz3q+4pQUM8tyq9xFqcLTo8Y8owY9oiJvKAfwUmTJgV90C+++KJZkyEiovAorV2aLM4yYlbuKwEAjOvdDneNOxMX9WmH+z79CXsKTfjr/3Zi6c+F+OeV5/hdXUvh97uvLFnHVslIbmSRRtE+MxkHii0orEqAjJha/6cqbW6/MjtERFSXW5TULMK2tTNidMyIiUU1PWKCzYjh40dEiaPUEtoyUBqNEBMlOJWSa+lGQ+ODg6TVCJBiIGvE7HDB6fGgdUpySI/r7aMS/XPzSBJ0IcrQUmgEAe4YKZnXUgT9CGZmZqpfGRkZWLlyJbZt26Zu3759O1auXInMzMywTJSIiJqvrFYZL3scZcTIsozVvkDM2N45AIDeuRlYfOdw3HPRmdBpBCz9pQjjX/oBS3YXRnOqLc5vSn+YILNhACDP1yemIIEyYgCg3MoyeUREjVGCMFqNgFYptTNifIGYGLhSmGqoPWL0jWXEsDQZESUWp8cDk92JpEae/5pCIwhweaSoV3MwO92+kmuhPTcxBrJGzE4XJBkhb2gPRL+hvcXpQgjb+qg0ggCnGBsl81qKoMO78+bNU79/6KGHcO2112LOnDnQ+v7ziqKIv/zlL8jIyAj9LImIqNnsLhHWWlkwtjjKiNlbaEZhtQPJei2GntFGvV2v1eCei87Cxb7smH1FZsz4eAfysoahf+dWUZxxy6FkxATTH0aRm+m9OqkoAXrE1C73V25hnxgiosYoAezWqQZoai2SKIEYliaLLcFmxOh8pcmYEUNEicLscMER4swKrUaAJEvwSLJa0jEazA5nyBf0NYLGlxET3WBFtd0JTTiiFYh++c1yqx2GEAbPFN4AYfysDyWCZuU0vffee7j//vvVIAwAaLVazJw5E++9917IJkdERKev9oIxEF8ZMav2FQMAhvfIDnhF0tl5mfjqzhHo1T4dQGI0gY8XSiDmzJz0oPdResQUxPnj5BEllFtrgi+1vyciosCU9yOnlhI1sDRZTAq2R4yBpcmIKMGYHC64RQmGEPca8Ta0j+5n8XAs6HuDTNEvTVZqsYWlmT0Q3dJkHklCld0Z0t43Cm2MlMxrSZoViPF4PNi3b1+d2/ft2weJTX6IiGJK6SmBGEccBWJq+sPk1DvGoNOo/Tni6dzinRKI6d6kjBhvIKawKr5Lk1VYXahdVaDcwtJkRESNUTJiTu2ppSwsOOOsRrlHlLC30BT1MjPhomRQpzQSiNGxNBkRJRiTI/Tv7bVK+a4oZo24RQmVYVrQB6KbNWJ3uWF1uZGkD20ze4U7isEKi9MFh9uDpBD2LFJoBIGlYSOsWY/itGnTcOutt+LgwYM4//zzAQBbtmzBv/71L0ybNi2kEyQiotNTuz8MUFPzO9aVWZzYdbwKADCmZ/2BGKBmEcfBq2kjwuxwo8jkzWppTmmyeM9cOjW4WcGMGCKiRpX5yjhmp/k3CFZLk8VZIObVVb/j1ZW/4ZXr++GKfh2iPZ2QU0uTGRpeMtCzNBkRJZgyiz2k2TAAoImBrBGL0wWn24OMJGPjg5shmudmdrrgdItIN4b+3LzBimg+bm44PWLI/yYBb4DQI0qQZDlsZd3IX7MCMc8//zzat2+PF154AYWF3ubIubm5eOCBB3DfffeFdIJERHR6ai98lFlccdMjZs3+UsgycHZeBtr7MinqY9Sz0W8kKdkwOelGZCbrg94vz1eazOL0wOxwIz0p+H1jSekpwc0y9oghImqUUpqsbX2lyeLsNfz7X4sAAD8erkjIQIzyfjHV2PDCj96XEeNhRgwRJQC3KKLK4Qh59oFW6aMSxaC1xemC0yOGLyMmioEYk8MFtySpr0mh5C3fFb2G9maH97NmOAIlGo03U0uUJGjC0IOG6mrWM4tGo8GDDz6IBx98ECaTCQCQkZER0okREVFoKAsfHVuloMziipvyXUp/mHG9Gs6GAaDWgmVGTGQogZimZMMA3qtqM5P1qLa7UVjtSJhADEuTERE1rr4eMfFYmqza5sb+YjMA4Gi5LcqzCQ+rM9iMGPaIIaLEYXZ4Py9nJYc2s8K74C1FN2vEt6AvJGBDe5PDhXDlc2gETVQvFqmyO8OWraLxZcR4RAl6BmIi4rRDhRkZGQzCEBHFMGXho3PrFACAPQ4CMS6PhB8OlAEAxvZu1+j4JGbERJQSiDmziYEYoKZPTEEc94lRSpMpDYxZmoyIqHH19YgxxGFpsu3HKtReYUfKrdGdTJgo7xcb7xHjK03GXrFElABMThdcHjHkDe01ggBZjm7QutLugFYTngV9GdHNiCmz2sJSugvwZcREcZ2h3GoLWxZTLPQuammCzojp379/0FHTHTt2NHtCREQUWkogplNrb38Ou1uELMthuxImFLYeqYDF6UF2mgHndshsdDx7xERWczNiAG8gZl+ROa77xCiLiT3bp2PHsSqUMxBDRNSo+jNilIsp4uc1fOuRSvX7gio7XB5JDSglCqtTCcQ0vGRgUDJiPFzEIaL4F86sEQHRC1bIsoxyqx3GMDR8B7wL+o4oBSvcogiT3RmWZvaAN4gWrQCa3e2B1eWGUR+mc4uB3kUtTdCP5JVXXhnGaRARUbiUmb1vJju18mbEyLJ3sSNJH7uppyv3lgAAxvTMgSaIq3aYERNZv5cqgZj0Ju+bm+UNCBbGcUaM0hOmZ/sMbyCGpcmIiBql9qxLN/jdHo993rYerlC/l2TgRKUNZ7Rt+sUJsczm8pYmC7pHDBdxiCgBlFvt0IWhzwgQ3awRm8sNm8ujlvQONU0Us0bMDhccntCXk1NohJqycjpNZC+6sDi8fX3SjYbGBzeDRtAwEBNhQQdiHnvssXDOg4iIwqQmIyZFvc3uEmM6EKP2h+ndeH8YgBkxkeRwizhW4a2H35yMmDxfabL4zojxzr1Xe28gyuTwJOTV0EREoeIWJVTavIGYtqdkxCgZFfGSEeNwi9h9ohoA1L5nR8sTKxAjSbJamiw5yNJk8VRajogoEFGSUGG1hy2zAoheHxWz0w2H24PMMAUrtEIUAzFhKien0GoEuHx9VCIdiDE7XWHt36LVCBAlBmIi6bT+grZv344PP/wQH374IXbu3BmqORERNVuF1YUfDpRCYo1LldLPol2GUV3ssMVwn5hDpRYcKbdBrxUw4sy2Qe2jZsTE8HklikOlVsiyd/EpO63pV+bkZvoyYuI6EOP9P9UjJ02ts8w+MURE9auwuiDL3g/8rVJOzYjxLi7ESyBm94lquEQJ2WlGDD2jDYDE6xPj8IhqD5zUIEuTsb48EcU7i9MNu9uDpDCVgQKit+BtdrogyXLYAgkaQYBTjF4gRkZ4yskB3nOLVtaIxRnez5jquUWxd1FL06xnl5KSElx//fVYs2YNsrKyAABVVVUYM2YMFixYgLZtg1s4IyIKtUcX/4Iluwsxf9pgjO4ZXDZFInO4RZgd3tIS2WlGJBu0cNkl2F2xG7BYtc9blmzIGW2QZgzuZSoe68vHK6Us2Zk5ac16s5vry4gpqI7f0mRKIKZdhhGtUgwoszhRbnWive/ciIjIn/K82TrVUKfkqPIa7vJIMd/DDvD2sQOAwV1boUubVADA0XJbNKcUckp/GABIbiSDWsmIiWYDaiKiUDA5nHCKIrLDVL5LEDRRyx402Z3eJjVhotVoopYRU2F1hDVTRasRIEUpa6Tcag9bNowimr2LWqJm/aXeddddMJvN+PXXX1FRUYGKigr88ssvMJlM+Otf/xrqORIRBe2gr4m40ky8pVOaiOu1AjKT9eqHaUcMZ44o/WHG9go+kJYUB+eVKH4vNgNoXlkyoHaPGAdkOf6unnW4RZh8wc22aUlqVlC5hRkxRET1UcqkZqfVLYlSu6yjKw4W87epgZjW6NLGW/Y10TJilP4wKQZto736lB4xblGOy9f1liaeejERRZrZ4QJkb5ZAOHizRjxhOXZjyqx2GMO4oO/toyJDjPCCviTLKLfakRy2LKaac4t01ogoSai0OdQy7OHErNbIaVYgZtmyZXjjjTfQu3dv9bY+ffrg9ddfx9KlS0M2OSKiplKuuCw2xW/Zo1Aq8/0+2qQaIQiCWufbFqMZMdV2t3qlaXMCMcyICT8lI6bZgRhf1ojdLcJkj84HkdOhBDcNWg0yknVoneoLxFid0ZwWEVFMK/MFq9um1w3EGGsFYmL9dVyUZGw7WgnAPxCTaBkxyvvElEb6wwCAvtZVyFzIiW1bDpXjnMe+wzvrDkV7KkQxqcLmgDbMmRXRCIa6PCJMDieMYex9o4lS1ojVV04unMEKjaCcW2Rf4yxONxweEUnhDsQI0etd1BI16xlGkiTo9fo6t+v1ekhMZyKiKHGLkrpIWmTioihQ6wrUdO9isZIRY4/RzJF1v5XCI8nokZOmlvsIhrKIw4yY8Put+PQCMUl6rRq8iMfyZEqwNzvNAEEQ0MZ3dTczYoiI6lf7ufNUSo8RIPYbvh8oNsPs8CDVoEXv3HR09b1XOV5hS6hFjJqMmMYX7fS6mivHWZ4stq3YWwy3KKsXPRFRDUmWUWaxqRf4hYNGiE4gxux0weEWw3pu2ij1GjE7XXB6POENMikZMRFe77Yo5xbGbB8AgIyIZzK1ZM0KxIwdOxZ33303CgoK1NtOnjyJe++9F+PGjQvZ5IiImqL2QmhxHDcCDyUlENPWt1isZMTEao+YVb6yZOOakA0D1CpNxnILYeURJbX8SnMDMQDQPsObFVMYx4EY5aruNmpGDAMxRET1OfX9SG2CIKjlyWI9I0ZZwB7QpRV0Wg3aZyTBoNPAI8koqEqc955Kj5hgMmJq1+V3i8yIiWV7Ck0AYjczniiarE43bG4PksK4oK8VhKj0UTE7XXCJIgzhLk0mSRHPGjE5nJBkb7ZRuAiC4O2jEoUgkxzGUnkKGewRE0nNCsT85z//gclkQteuXdG9e3d0794d3bp1g8lkwmuvvdbsyfzrX/+CIAi455571NscDgdmzJiBNm3aIC0tDZMnT0ZxcbHffseOHcOll16KlJQU5OTk4IEHHoDH41/uZM2aNRgwYACMRiN69OiB+fPnN3ueRBSblAVSACg2J86H4dOhlAJRarLXZMTEXkkoUZKxen/T+8MANRkxTjffQITT0Qob3KKMFIMWeZnJzT5OXpY3EBOPi1b1BWIqmBFDRFSvhnrEALVfx2N7gXjrkZqyZIC3FEuX1onXJ6Z2j5jG6LXMiIkHsixjb6G3z1+sXpBFFE0mhzP8mRUaAR5JghjpElcOFwR4AwrhotX4MmIivKBfbXeGPVChiPS5mRzOsD5mCq0g8ILWCGrWM0ynTp2wY8cOrFixAvv27QMA9O7dGxdddFGzJ7J161a89dZbOPfcc/1uv/fee7FkyRIsXLgQmZmZuPPOOzFp0iRs2LABACCKIi699FK0b98eGzduRGFhIW666Sbo9Xo888wzAIDDhw/j0ksvxfTp0/HRRx9h5cqVuO2225Cbm4v8/Pxmz5mIYktJreBLUbW3EXgkXrhimVoKJP3UjJjY+6C863glKm1uZCTpMLBLqybta2RGTEQoZcm6t01rtHlvQ3J9QZx4zohRFhPV0mTsEUNEVC81IyZAjxgAMOq0MMMDVwwv5MuyjK2HvRkxSiAGALq0ScVvJRYcLbcCaBul2YWWkjGRamx8uUAQBOg0AjySDA8zYmJWscmJCl/2LjNiiOpSsg/CmVmhFQSIvj4qWk34G7ArKmz2sPa+AZSMmMiXJiu12MLfQ8Un0oGYcosjrL1vFBpNdDK1Wqpmh3oFQcDFF1+Miy++GABQVVXV7ElYLBZMmTIFb7/9Np566in19urqarz77rv4+OOPMXbsWADAvHnz0Lt3b2zevBlDhgzB999/jz179mDFihVo164d+vXrh3/+85946KGH8Pjjj8NgMGDOnDno1q0bXnjhBQDeoNH69evx0ksvMRBDlEBqZ8Q4PRJMdg8yU+r2s2pJTr0CNZZ7xKz0lSUb3TMHOm3T3igm6ZkREwkHS0+vP4wiN0spTRZ/GTGnLiYq/W7KmBFDRFSvU4PYp4qHzNYTlXYUmRzQawX065Sl3t61jZIRY4vSzELP6gq+NBkA6LUaeCSRGTExbK+vLBkQm58DiKKt0uYIe2aFRtDAJXvgkSQYEZnggSTLKLc6kBzmPiMaIfIZMXaXG1aXO/w9VHwiGWRyejywOF0RCTJFq2ReS9WskOi///1vfPLJJ+rP1157Ldq0aYMOHTrgp59+avLxZsyYgUsvvbRORs327dvhdrv9bu/Vqxc6d+6MTZs2AQA2bdqEvn37ol27duqY/Px8mEwm/Prrr+qYU4+dn5+vHiMQp9MJk8nk90VEsa3E7H9FepEp/hZ5Q60mEONdLFYCMbHY1H7VPl9/mN5NK0sGQL1SJBbPK5H8VuwtaXG6gRilrFlhApQmU/5vVbBHDBFRvdRSqemGgNuNcdAjRukPc06HTDXDGAC6ZKcCgC8jJjHYnN7SZKmG4Ba3lPJkDMTErj21AjFK6Tki8pJl2ZtZEeYFfa3G10clgs+VVqcbdrcn7JkVgiAAAuCOYCDG7HTB6RbD2tdHEek+KmaHC44wl8pTaAQBLpHrKJHSrEDMnDlz0KlTJwDA8uXLsXz5cixduhSXXHIJHnjggSYda8GCBdixYwdmz55dZ1tRUREMBgOysrL8bm/Xrh2KiorUMbWDMMp2ZVtDY0wmE+z2wGVRZs+ejczMTPVLOV8iil2lpwRiihmIURc+lOa4ysJBrH0AO1llx74iMzQCMOqsppf1UDNiYngBJxH8HqKMmPaZSkZMHJYmO6XhtFqazMLSZEREgXhECZU2/551pzL4AjGuGH4dP7U/jCIRM2IsvkBMchMyYgDAzdJkMcs/EMMFN6LabC43bC5P2LMPopE1Yna6wt77RiHIkc0aMTvdcEuS+hoUTlpBgDOCWSMWpxtuMULnptEwIyaCmvU/saioSA1MfPPNN7j22msxfvx4dO3aFRdccEHQxzl+/DjuvvtuLF++HElJSc2ZStjMmjULM2fOVH82mUwMxhDFuNo9YgBmxAC1MmJivEfMV7sKAACDurRGVkrgq2Ubolzh4/RI7A0UJpIk42CJ92rfM0OVEROHvZxOzYhRSpNZXSIcbhFJ+sjVeyYiigcV1pq6+63qeY2vyYiJ3YUAJSOmbiDGmxFzrNwGUZLD2l8gUo76gkodW6UENb4mEBNb7y+pxt6CWqXJGIgh8mNyuuBwe5CVHPhigVDRagSIcmT7qJgd4e99U1tks0acEBCZCwAi3UfF7PRewBKJz8kaQYBHliFKUth7CVEzM2JatWqF48ePAwCWLVumlv2SZRliE9KZtm/fjpKSEgwYMAA6nQ46nQ5r167Fq6++Cp1Oh3bt2sHlctXpP1NcXIz27dsDANq3b4/i4uI625VtDY3JyMhAcnJywLkZjUZkZGT4fRFRbFMWSDOSvDHmkhYeiHGLEqpsbgCx3SNm57FKvLT8AADgiv55zTqGkhEDMCsmXE5W2WF3izBoNejcOriFmfq0y/T+PTo9Eip9f6Px4tS+SxlJOrUkSznLkxER1aGUjm2daqh3Iaj2BRWxqMLqwu8l3qzQQV1a+W3LzUyCXivAJUoJcxGQ0hOue9vUoMbrWJosptlcHhyuVTrPI8l8rIhqMTtcEGU5Ig3tJSmyGTHVjgi+LgmRDcSUWewwRCDTB/BlxESwfFeFzQ5dhIIiGkGAHOG/y5asWY/qpEmTcMMNN+Diiy9GeXk5LrnkEgDAzp070aNHj6CPM27cOPz888/YtWuX+jVo0CBMmTJF/V6v12PlypXqPvv378exY8cwdOhQAMDQoUPx888/o6SkRB2zfPlyZGRkoE+fPuqY2sdQxijHIKLEoHzQP6dDJgBmxJT7ypJpNQKykvUAagViYqQ0WanZiTs+3AGXKCH/7Ha44fzOzTpO7Zq3sdzoN54pZcm6ZqdAd5op0kadVg1kFFTFT3kyq9OjlvNQMmIEQUCbVJYnIyKqz6kB7EBivTTZNl82zJk5aWiV6p/Vo9Nq0MmXOZIIfWIkSVYDMcGWIjWwNFlM219khiwDrVL06m0sT0ZUo8ruQCTyRZTshkj2USm3OMLe+0YhyYAnQq8DblFClcMRkf4wgK+PSoQyYiRZRoU1co+bRsnUkvgaHgnNWkl56aWXcOedd6JPnz5Yvnw50tK8b9AKCwvxl7/8JejjpKen45xzzvH7Sk1NRZs2bXDOOecgMzMTt956K2bOnInVq1dj+/btmDZtGoYOHYohQ4YAAMaPH48+ffrgxhtvxE8//YTvvvsOf//73zFjxgwYjd43+9OnT8ehQ4fw4IMPYt++fXjjjTfw6aef4t57723O6RNRDJJlWc2I6dvRG4gpNrXsRVFl4aNNqgEa3xWoammyGMiIcYsSZny8A0UmB7q3TcXz15zX7NRbvVaAcpGtI4bLmsSz34u9izJn5qSH5Hi5ap+Y+AmYKs8xKQYtUo01b4yV8mTMiCEiqkvtV5defyAm1kuTbTvq7Q8z6JSyZIoubZRATPz3iSmotsPhlqDXCkFnwCoZMZEst0PBU/rD9O2YBZ3vDTPLkxHVKLPYI7boDUTuudLh9sDsdIW9943CmzUSmQs+LU5XRMtCR7KPitXlhsPj8bvYNJy0ggBRkvgaHiHNeqbR6/W4//7769x+amDj0ksvxTvvvIPc3NzmzQ7eoI9Go8HkyZPhdDqRn5+PN954Q92u1WrxzTff4I477sDQoUORmpqKm2++GU8++aQ6plu3bliyZAnuvfdevPLKK+jYsSPeeecd5OfnN3teRBRbzE6PWs7inDwlEBM/C7zhUBrgCtSa0mTRf5F95tu9+PFwBdKMOsy9aRDSk/SN71QPQRCQpNfC5hKZERMmSkmW7qfZH0aRm5mEn09Wo7A6fjJilP9Tpy4mtknzBWIsDMQQUV0nKm1Y91sZrh3UKSH6hzRVTUZM/T3gjPrYzoj58bA3I+b8bq0Cbu/SJhVAKY4kQEaM8nrftU1q0BmwSo8YFxdxYtJeXyCmT24Gdh6thNnpgS1GsuOJos3ucnuDFZEMxEQoI8bsdMHh8aBNSuCWDKGm0QhwReiCT7PDBZdHhEEbmWBFJPuoWBzeIFOr5Mj0UtcIAiSZpckiJazPND/88APs9qYtsKxZs8bv56SkJLz++ut4/fXX692nS5cu+Pbbbxs87ujRo7Fz584mzYWI4keJL/slPUmnXpXY0gMxZb6r97NrLRqrGTFR/vC1aOcJzNtwBADw4rXnoXvb01/cN+o0sLlEZsSEiVKa7MwQBWLysrwfCAqq4uf/qZIR0/aU8jptfBkxFdaWnYVHRIE9u2w/vvqpAG5Rwk1Du0Z7OhFX33NnbUppq1jsEWN3ifjlZDUAYFCXwBkxXZWMmLL4z4g5WOoNJjXlvZkSsIlUSRpqmj0F3kBM79x0JBu0vkAM3y8TAd5ghdMtIt1Y/2tUKAkR7KNidrjgESUYIpoRE5nnlnKbd605Es3sAW+QySNK8EQgEFNqsUUk4KOoKU0We+/BElFkHlUiojBTP+SnG9E+I0m9rSWnVyqlQGpfgRoLpcl+LajGrC9+BgDcNbYHxp/dPiTHVdKSmRETerIs47diM4Dg68U3RilNVhRHGTH19Tlok6b0iGFGDBHVdbzSuzi/fE9xlGcSHcH0iFHKb8RiIGbn8Up4JBm5mUno2CrwVcVdsr1N7RMpI6Ypr/cGX2kyNoCPPZIkY1+R9z3c2XkZSImBzwJEscTkcMEtSWpmX7hFso+KyRHZi8Qi1UfFLYo4Ul6NVEPzK2o0lZo1EubXObco4XCEz00rCJCk8J8beTEQQ0QJocTsvao+J92INmlGaDUCJLkmGNESKQsfbQOVJovSVXCVVhf+77/b4XBLGN2zLe656KyQHVupL8+MmNArtThhcnigEYBuvsWm05WrZMTEYY+Y+kqTteTnGyKqX4Wvf9SWQxWwOlteOaCyeso61qaUJovFQMy2IzX9Yeq78rZL65oeMbIc31khB0uVUqTBv94rC5huNvqNOUcrbLC5RBh1GnRtk4pkg7coCjNiiLxMDiciWTQ0Un1UXB4Rh8urkRLJBX2NJiIB+WKzDdV2JzKSIpPFBNTqoxLm17kSsxWVdgcykyN3boIgQAYg8jU8IhiIIaKEULNAmgStRlCDDy25PFmgK1CjGYgRJRl/XbATJyrt6NImBa9c1z+ktfKVjBgHr/ALOeXq2M6tU0LWEFHJiImrHjH1BWJYmoyIGlDhC9K6RAkbfi+L8mwir8ysZOgGU5os9l7Dtx7x9YfpGrg/DAB0bJUCjeDNMlBeK+LVQSUjpm160PsopcncMRhIa+mU/jC92qdDp9XUZMSwRwwRREnCiSozkiPYH0ajEeCMwOfVIpMVVXZnRBf0NYIAjyRBDHOJqxNVZkiQg+5jFgqR6qNyrNIESZahj1DvG4WAyJXMa+kYiCGihKB86M3xLZC2y/D+W8RADLLTa0qTRbMcwfPf78e638qQrNfirRsHIjMltFfnKBkxLE0Wes0pU9KYmtJkDkhxcvVN/YEYX2kyKzNiiMif0yPCXCsLZvX+0iYfo9rmVl/T41FpgPcjp1IzYmLsNdwjSthxtCYjpj4GnQYdfGXLjpTHb5+YSqtLfS07o23wGTFKaTIu4sSemv4wGQCi+1mA6FSyLOPF7/djye7CqFxMV2FzoNruRHpS/a9PoaYVhIhcdHCs0gRAhi5CfUYAb5BJksIbrLC7PTheaUJGhHr6KLQaAWKYy3dZXW4crzSH9dwyy3ah17anoHNW19nG1/DIYCCGiBLCqQuk7Xx9YkpaciAmwBWoSjZDpD98bT1SgTfXHAQA/Pvqc9GrfUbI78OoZMTE4NW08a4mEBP81bGNaZeRBEEA3KIcNwGM0gDl/gCgta80GXvEENGpKq1uv5/X7C9pUukqUZIxec5GjHl+DcwOd+M7xBiPKKHS1nhGjNIjxhVj9cn3FpphdYlIT9KhZ7uGXwO7ton/PjFKWbK8zCSkGoO/QlxZ6HNFqO8BBU/JiOmT533vrWTHszQZxYKCagdeXfU77l6wE9Go6lhqtsElijDqIpgRE4E+KmaHCyeqzMhMSgrr/ZxKI/iavofxtbzIZIHZ4UKaMXLBMyAyGTEF1RaYHeENDJ6/4mZ0+n0huu6bX2cbe8RERrMCMT/88AM8nrqprB6PBz/88IP68yOPPILWreu/coiIKFRK6mTE+K62b8mBmEClyQxK+S4polkIG38vBwBM7Nsel5+XF5b7UIJMsXY1bSL4rTj0GTF6rUb9/xov5cnKzMpV3f6LidlqRowz7nsDEFFolftKFmYm65Gk16Cw2oG9heag99/wexl+L7HA7PCgMI56aikqrC7IsvdK0lYp9S8sGGI0q1UpSzaoSytoGimn2qWN0icm/gMx3Zv4eq/3PX5cxIk9ewrryYhhIIZigJJx2CcvQ/2cGimyLONopSmiQRjA20fFJYb3/9/JajOsThfSjJHrDwNEpo/K8UoTBEEIaYnzU6WYj6LdsWWoHR1UesS5wxSIkWUZR8qroNdpoamnH93pEqSadXyDo/zUGTAjJkKaFYgZM2YMKioq6txeXV2NMWPGqD/PmjULWVlZzZ4cEVGwTs2Iae8re1Rsit9SHqfDI0qoCHAFakqtN7iRzBxRPtif1zErbPehlCZjRkzo/V4a+kAMALTP9JZxKaiK/cVFWZZrMmJOLU3my4hxuCVeYUpEfip8GX/tM5IwvHs2AGD1/pKg91+086T6fTw+vyjPm61TDQ0umqjlRWPsNVwJxAzu1vjFhTUZMfFbmkzJgO3etomBGN9jG4kmzRS8SqtLDeD2au/N6FIWu+Px+YQSz3ZfIGZA5/p7cIVLld2JCqsdGVHIrBBlKWyL3pIs43B5NYx6nRo8CIfU6t/RpnCD323hzhoxO10oqLaGve/N8CWX49yND6FN0Ub/DUL4LjiosDlQYrYhKyl855ZqOqx+70xu67dNEDQxl5WcqJoViJFlOeB/6PLycqSmBl9LlogoVJQP+jnpSb5/vS9gxS00I6bC5r0CVSN4Fz8USbqaQEwkr4RTr7Bs4gf7pmBGTHhU29xqoDPUgZg8X8A0HjJiqu1uuH0lV7LT/D+wpRi06iJiRZyUWSOiyFCeE1qnGjCmVw4AYPW+4AIxVqcHy34pUn+2xWFzbeX1o6GyZECt0mQx1OxdlmVsPeJdJBzcQH8YRRdfICa+M2K8c29yRoyvYbKbpcliilKWrHPrFKQnea+MV5qSMxBDsWDnMV8gpkvkAzGlFhscHg+S9JHOiAlvr5FSiw1lFhuywhmskGUMWzoZA9b+BSnmo+rNWk14S5MVVltgdbmRZghfpo/WXfMaXjtwAQCQw9dH5WSVGXa3B8lhPDeNVPM5Vefxv2hEIwhwBqh8RaHXpGecSZMmAfCmZE2dOhXGWg2ERFHE7t27MWzYsNDOkIioES6PpC501M2IaZmBGGXh49QrUDUaAUadBk6PFLE+MZIk41AzP9g3BTNiwuP3Um8JndzMJKQ1oV58MJT/p/FQQlD5P5WZrFcXDBWCICA7zYiTVXaUWZzo1DolGlMkohik9I5qnVYTiNlxrBKVVhdapTZ8Fe53vxb5vVbHYymhMov/+7P6qKXJYigQc6TchjKLEwadBud2zGx0fFelNFmZrd4LF2Od2hOuiRfO6LTMiIlFSlmyPrk1vRlrSpNxwY2iy+EW8WuB9290QOesiN//iUoz9Bpt+J6rZRnnbJoFnceCXSNfA3z3E+6skROVZrhFKawl15Itx9Xvte6aBX2NIECSwnNusizjaEU19FpNWF9fM8t/Vr8Xdcl1tofj3NyihMPlVWHve2NqfTZ2XvgftC7ahMqcgX7bNILA1/AIadL/zMxM7xtQWZaRnp6O5OSaP0qDwYAhQ4bgz3/+c2hnSETUCKX+uk4jICvZewWB2iMmDuuph4Ky8BHoCtQUg9YbiInQgk6hyQG7W4ReK6BTq7pvZkIlSe8LxDAjJqTURZkwBNGUv8+KOGhyX6r2XAr8BrlNmgEnq+zMiCEiP8pzQptUAzpkJaNnu3TsLzbjh99KcUW/Dg3uW7ssGYCIXUDRFPuKTHhzzUF0b5uGYd3b4NyOWWpQBajdr67hxYVYLE2mlCXr1zGrTgA+kE6tUyAIgNnpQYXVhTaNZAHFGodbxPFK74Ja95ymVbmoyYjhe7BYcmp/GIClySh27D5RDY8ko12GER2ywvcZMRCz04USiy2sTdFTzEeRe2wpAMBoL4EzpR0Ab9aIJMlheb50ejw4UlEd9gX9rLJdAICq7H4wt+6t3h7OPiqVvtJdmWEs3QUAydaa9146t8VvmwxADEP/m2KzFZU2J3LSw19hqixvJMryRta5XcuMmIhpUiBm3rx5AICuXbvi/vvvZxkyIooJJaaavg1KI1UlEGNyeGB3iRFv/hdtZQ2UAknWa1EJd8QWdA76FvK7tkmFTtusiphBUcquxdIiTiL4rTh8gZg2vqvBy+MgeHFqH6pTKSUAy+MgqEREkVNeqzQZAIzplYP9xWas3lfSYCCm2OTAht/LAADd26biYKk1JhdOX/j+AJbvKQYAvLjce7HH4K6tMbR7Gwzr3ka9IKZtI0EJJXgTS6XJlAsRzunQeDYM4C2RmpuRhIJqB46U2+IuEHO4zApZBjKSdI0+XqfS+zJiPCxNFlP2Fnqzmvvk1c2IscVgYJdaltr9YSKdQVhqtsHqcqNVSlLY7qN18Rb1e53bCqVzrbdHTHiyRgqqLai2O5GXGb4qFACQVboLgDcQU0eY+qgUmaywuz1hD1ac7D4ZSbYitD+6FC6jf1lSjSDAFYa1hmMVJsiQ1YsawkGQ3NC5rXAbswJu12iEmHoPlsialav22GOPhXoeRETNFmiBNCNJh2S9Fna3iGKTA12zW1bguKErUJPUkgQRCsREoD8MABh9GTHsERNav5eGMRDjW+gptzgbGRl9Nc8zgT+wtUn1nkuZNfbPhYgip8L3nKAEnsf2ysGctQex9kApREmut4H9V7sKIMnAwC6t0C7DiIOl1pgrTeZwi1j/mzdYNOqstvj5ZDUqrC6sPVCKtQdK/cY23iMm9kqTnaz09i/r0IRs3i5tUlFQ7cDRcisGRqHnwelQ36/lpDV5UVRZPGKj39jh8kj4vcQbiOmdm67enhLhzwFE9dnh6w8TjefKgmoLNIIATRgDQK2LN6vf186s0Agab4+YMARijlaYoBEEaDXhW9AHgKyynQDqCcSEoY+KKMk4XFGFFH34+qfUdrDvDBzsO6PO7eHoo2J1unC8yoSMMGf6ZJT/gvNXTkV163Pw08iXIIhuONJqLghSSpPFa2nVeNKs/53FxcW48cYbkZeXB51OB61W6/dFRBRJJb4F0pxagRhBENAuw/tzS+wTUxOICZwRA0TuSriaD/bhDYYxIyY8lCuCz8xJb2Rk07XxBQrL4iCLRClNVt9Vwsq5xEOZNSKKnAo1I8b73DGgcxYyknSotLmx63hlvft94StLdlX/DjHbXHvToXLY3SLaZyRh/rTB2Pa3i7D07pF49LI+uKh3O6TX6it2ZruGg/lG9TU8dhbyT1b5AjFNKJnTNdvXJ6bc1sjI2NPc/jBATSCGGTGx4/cSC9yijIwknd/fcLLB+/+SgRiKJlmWscOXEdO/c2QDMXa3B4XVFqSHsXyXIHnQungrAKCw8yVw+MqSAb7SZGFoaF9ld6Cg2oKs5PAu6Ouc1UgzHQIA9N30EDrt/6jOmFAHYsqsNlRYHchIDm/JtcZoNaHPiCkwWWFxusJeTi6z4hcA3v4+Fy6+GAPWTvfb7v27lOAJQ+k18tesjJipU6fi2LFjePTRR5Gbm8toGRFFVX0lg9plJOFIuS0uGoGHmtojJkAZJeVKOEekMmJKrACAM7IjkxHDHjGhY3N5cMJ3RXBYesT4FibL4yCLpLHSZPFUZo2IIufU0mQ6rQYXntUW3+wuxKp9JRjYpXWdffYVmbC30ASDVoPLzs3F/iLvVe2x1lx71d4SAMDY3jkQBAGC4O1F0Ts3A7eO6AZRkvFrQTWq7W6M6JHd4LFisTRZQTMCMV3aeC86OVpuDcucwulgqXfO3Zvxeq+UJmOPmNhRuz9M7fWalAhfkEUUyLEKG8qtLhi0GpzTIaPxHUKo1GyD2eVCXkb4PpvKggY7Rr+B1sVbcaTXVEDjf8G6gNAHKwqqLLC53MhODW+/nWTrSTiNrWF0VkArOmF0lNcZE+qgfGG1BS5RhFHXrCXsoOUdWoROv32Ck2dciRNnXl9nu0YQ4Azh65wsyzhcVgWDVhvW7CwA+H/23jtMkrO+Fj5Vnbune3ryzM7OzOasTYqrYAkhIYQkkIQN2L4YA8aGCwaEbQwXTBDG4EQy6foaC/xxbV+MyUIISSjvahU2aHOaDZNTT+dQ8fuj6q3qnunc9VbVzPZ5nn1AMz0Vuqvf8Du/c05o7hgAINJ7HXovPQonX7hOYRkGnCRBkCSqFmlN1EnEPPfcc3j22Wexc+dOgy+niSaaaKJ2TCdU//EFlkEkJ6apiCmEV92AmZYRk2d1QRPkvrLNjaVhOD+rLNDa/C6tiGgkiIoky0tIcwL8brqL60agkZslAqeJzdrsErBZa6KJJswDUcR05I0dt27qxi9encCTJ2fwF3dsWvQ3Pz6gqGFes6kLYb9bz3SwUQe7LMt44oSSDfPaTd1FX+NgGWxfGa7qeLo1mT3uMSeImuJ6Rbj6DIFVHYoi5sJlqojhm4oY2+CESsTk58MA+dZk9iJ2m7i8QPJhtvWHNEWkWZhMJAEZdO27GBbxju2Id2wv8XsYqjwQJQnDc1H43S7qjfKJ9i145t7fYNMrX8DA2f+3KNCeYRhwonHjCy9KOD8XQ8BNXw3TNn0AofkTwDCw5ti/IN6+CYd+6+va7x0GZ8TMpTKYTqbR6qOXVURAFDGRHkLEFH5uDoahZpnXRCHqGnkGBgYgy81FVhNNNGEPlOpU720lRMzlVxgl70mxorFmTWZCQSee5bVCxpouutZkdvSXX+ogapjBdj+V4/vdDnhVJZPdQ+6rVcREmoqYJppoQoUgSoimeQAoILNv3tAFhlE61kmYPYEoyfjJIWJLthIA4HOb20BRDU5OJjAey8LrYnFDBbVLNSBzgV0UMVMxZcz3ONmaGhGWqiJGkmQMN5AJ59SIGHt8fk0Ax8d1RUw+vDYkdpuoDE6QqASgWwWSD7PbZFsyThAxOp+gbgNF4ElPoXX2ELzJ0cJfyMYG2k8l0phNZdBKOWdEA8Mgo+aLOPlEwa+MJiumEynEMjnqGSoAEJ49BEDJvvFkZ+DJzBb8nmUZCJIE0SASbSyaQE4QNQtaWnDlovAnRwAAcz3XAAAcYg6MyGuvYSlZ5jWxGHURMV/5ylfwsY99DBcuXDD4cppoookmakep7AaSGXNZW5MVUcRo1mQmFHSGVZuL7qAHIS/dcL2mIsZ41BNUXAsYhtFD7m2uJKlIxKikp90JpSaaaMI8zKskDMMAYZ8+B3a0eLBDVYo8eWq64G9eGJ7DVDyHVp8Lr9nUBUBvoLBTpgNRw9ywtlObfxuB22GvjJj8fJhauotJ48J8mkcszVd4tX0wFs0gJ0hwO1isrGPOdzetyWwFWZZxYlJVxPSVUsTYZzxpojymE1lc+deP4d3fe3nZNES/cjEKALhyyFwiZjaVQTxLN4+DFTLY/OKD6Ln0K6w+/h1c8/g70D/844LXyDDWmmwkGocoyXDTVhfJsvIPgOBS8kMXKitYljFU3ToaS0CCTN0uy5Wdgz95CQAw13c9gMX35mAYSAapRnhRxPBcDAE33RoJAIQiii1ZKrgKWX+f9nOnoN8fy6hETFMRQx11Pclvfetb8dRTT2Ht2rUIBoNob28v+NdEE000YSamVcVLd6i4Imb6MiNiRElGJFW6aOwzcQN2TrW5WFuHzUWtsGPQ71IHUcTU4o9fKzqXAIFR6TsF6N3ukRS3bDbJTTTRRGMgCrlWn0tTDBDcqtp5PXmykIj5kWpLdtf2Pm1es6M12RPqdb92c0+FV1YHkvNmlzmc5MOsqHH+C3ic2jxxMbJ0VDFnVTXM6s7Aome1Gjib1mS2wkQsi2iah5NlsL6ncA3uVzuv7TSeNFEe+87NIZEV8PTpGTx+YrryH9gcyZyAUypRuNtkImYqkYIk0y3qt80cwMrh/8b6Q18B71aI0IWqEQYwTDWS5nhcmoubooYJRY7gt356Gza/+CAEl6IAXZg1oihijJnLs7yAkUgcQRMUTOHZVwEAyda1yKhkxSKSiWEhyjJEA8iKybh5Sp/WuSMAgFjHNoB1QHAqa5v8z45lWMNIpibKoy7901e+8hWDL6OJJppooj7IslxSEUMyYi43Rcx8mgNRyxaz0/CaGNKp58PQtSUD9CKO2YoYTpDwd786idds6jbEnsVOGIsqHvcr2+hYkwH6MzqXsq8iZi6VgyQDLANNwbMQ5OecKCGRE6grwJpoogn7g4xrxebi12zsxpceO43nzs4iJ4jwOB3IcCJ+dXQCAHD/rn7ttT41P8su4dqzyRwOjUQB6IRSoyD2oqKk2GLUQwYYCZ2Iqd23fVWHHzOJHC7MpavOyLEaWuNMnes1V9OazFYg+TDrulsW5W/kWx1KkgyWpZsn0UTjIDZzAPAPj57Cazd1L+nP7dWRKCRZafQi+3UzIEoSRubj1G2g2qf2AwAiPddCcKuqEW6BssJA1ch4LIlEjkN/OGjI8cohPHMInuwsPNlZCC6F5C2miOFEY+5tMp5CPMthRSv9ps5WYkvWsROidm8JRQGkKmNZloGoBto3ipH5uClKHwCIdu7A6NrfRkS1JRNcLXAKmQKC0KFakxllu9ZEadQ1Ar3jHe8w+jqaaKKJJupCPCNoHReLMmJCekaMLMvUg+vsAmLx1OZ3FZ3YzbQk0IgYExQxXosUMU+fnsG/PHcee8/N4ZcfusnUc9NGvjULLegh9/ZVxMwmlGtrD7jhKLHx9bkdCLgdSHEi5pJck4hpookmNEVMRxEiZuuKELqCHswkcnjxfAQ3re/Cr49PIsWJGGz3F9i12C1c+8mT05BlJWSZqI8bhdupr1c4OxAxsfoUMYCSE/PShXlcnF06ihiyXltX53rNpVqTNTtp7YFS+TCAPp4AQFYQ4XfTLUo30TiOT+hEzKmpBH7+6jjetLO/zF/YG69cVPNhTFbDRFJZxDI5tPnpkj/tky8o5+u9Dg5BaWpzLVDEsAxrCFkhyzIuRGJwsCxYE2odWoZK105w3nYkQ6uRbil8Fh0Mq+WolNo3VYuRaBwMw8DB0l8ThGcPA1BIC0IysZIAVsxBcirPjIMxJkclkeMwEk2YlukT6d2DSO8e7b8nVt0Dh5gF71o8RzQzYuijrln30qVLZX8/ODhY18U00UQTTdSKmaSidgl5nYs8ygkxwwlKWG5bDWGrSxmkaFwsHwbQvebNUI6cUzNiTLEms0gRQwJ5L86llh3hRzsjBlga2SpEdVfqO0XQ3uJGKpJBJJXD6k76KrAmmmjC3iBETDFFDMsyeM3GLvzg5VH85uQ0blrfpdmS3burv2Auye9gtwN+o9qS3brJGFsyAHDnES85XoLf4iXbWFRZX9ZDxKzqUFSkF+bShl4TTZzVFDH1EjGqIkZodtLaAaXyYQB9HwAoTVlNIsbekGUZx1Ri7Y6tPXj02BS+9NhpvOGKPlM66WngwCWFiLlyMGzqeaeTaVWBSu+Zd2XnEIqeAqCEordNvwKgeNZITmi8uSKSzmIqnkLYZ0JBX5YLwuwTbZux7w0/WfQyltFVIw62/syaRI7DeDSJVq85C4J0cBDe9BRinTsguAKQwYCBDCefBKcSMUqgfWNNB4Ik4eDIFBJZDgNti8doM3B2x4dK/q7ZUEEfdY1Aq1atKltoEg2SoTXRRBNNVIKeD7O4s8XrcqDN78J8msdUInv5EDEVisaaNRllRQwvShpJUe/GvhYQRUyWN3fxQHJUUpyI+TRftOC2FJHmBC1omiYR06laetnZmmwmUT4fhqAj4MFIJGNrdU8TTTRhHgjB3F7C0vA1G7vxg5dH8dSpGbzvliyePTMDALhvV2F3qd+kebsa5AQRz5xWrvO2zcbYkgFKxoiTZSBIMjgbdGOON6AIHepQiHiyBloKaLRxRiNimgUcW+DEhNJ9X0wRw7IMvC4WWV5CmhPRYeJ1TcQyiGcEbOylb6FkN+QEEaIk10x8TcVziKQ4OFgGX7h/O165+DQuzqXxXy+P4veuXXoN0JIk48ClKABzFTGSLONSxAxbshcBAInwRvDejvL2XQ26OEiyjFNTEWQFEd1B+kp8X3IUnuwcJNaFePvWkq9TrMmUrBEP6iNiJFnGiclZJHIcBk0iK45f+2DBf8c6tgGMA4ysr70URYwEoU77LlmWcWR8Bmdm5tEbCpiiYvIlLsHFxZAIb4TsqFynaBIx9FEXhX7w4EEcOHBA+7d//358+9vfxoYNG/Bf//VfRl9jE000YTIiKQ6TsaWRq1IqH4ZAy4lZIvdjBDQipkTRmGwAaHfWjkTS4EUZPpcDfSb4/+pBv+YWqoh9F6Dc83IBUcMEvU6qNltEEUM6x+2IaomYziVwL0000YR5KGdNBgA3ru+Ey8Hg/GwK//TEWUgysHMgvEhR5zPRUrQSXjwfQYoT0RX0YNuKVkOPTezJciY3VCyELMt5GTH1KGKUz2+pKGIiKU57Vtd01afmdKrWZM2MGOuRygm4oJKAm/uKEx5m7QUW4m3//ALu/qdntb3K5YTf/tY+3PS3TyKR5Wv6u+MTMQDA2q4A2gNufOA16wAAX3vijOkuAEZgeDaFWIaH18UWJQppIZbJIZLOUA9971DzYeZ6rgUACG6ViOEKrckcDANeFCHJ9asIL8zFcGYmgs4AvYa5fBA1TLxtCyRH6T2RgyhiGpgPjk/M4tjELDpb/Ja5Tbx0+/fx0m3fQ86vq38ZhoEs12/fNTwXw5GxGbT5vFSVWflYee6HuPax/4GNB/9e+xkrZOHOzMDJxRe9vknE0EddRMyOHTsK/l111VV4z3veg3/4h3/A1772NaOvsYllirlkDnf/07N46PnzVl9KE3mQJBlv/tZevO7LTy+JYqKuiClPxJDXXQ7QbZSKLzR9bnMsvEh35ZqugCmBkkTpY3YBhyhiAGBkfmkUXarBqAn5MMASyYipQPgSEDXU3GVYYGiiiSYWo5w1GQAEvS5cvaodAPD/vXARAHD/7sW+/yTTwQ6KmCdOqLZkG40Pi/Y4rWmoWIhYhtfe6746MnAGVWuy2WQOyZw9cn3KgeTD9Id9ddtUEWs5QWxak1mNk5MJyDLQE/Joa6yF8FmgsoukOFycU5q0zkwlK//BMkKaE3BkLIa5FKflo1SLY2OFNnO/e+0g+sM+TMaz+L46bywlHFDvf/vKsKnWatOJNDKCAC9lRYw3NQFAyYcBgGygH+e2vhcXNr+r4HUsy0CUZYh1Fr3j2RwOjU7ByTrgd5uTS5mfD0Nw7aNvxY0/uwOezLT2M5bkqNR5b8OzURwcnULQ40HApHtzZ2aAGkixeu5tKpHCy5cm4HKwCJpktwYArXNHAQCx9m3azza98gXc/NPbsPLMDwpeK6M5j5sBQ0e+jRs34qWXXjLykE0sY+w/H8HRsTj++8Co1ZfSRB5OTMZxfjaFeFbA/uE5qy+nIiopYnqJIiZ+GSliqsyIod1ZSzb2ZuTDAIBXLeBwogSpTrlwPRjNI19GIpkyr1xaIIqYlRRtyQC9U9zO5EXV1mRLgFRqookmzAOxXOwo0RgBKPZkBE6Wwd3bVyx6jY90r1tMxMiyjCdOTgEAXmugLRmBpohp0K6lUZAGi84Wz6L8wWrQ6nNp5NtSsCdrNB8GUJ5dALawlbvcQYLdy6kNfBq5ax5RSJ4zoHDtfDlgPKrvQw+qtlzVgnyeW1UFosfpwIduWw8A+MaTZ2tW2FgNLR/GRFsyQAl9dzsc1NUVB17zv/HMG3+N+e6rAACctwPDV7wPo+vfUvA6B8NAkuS6VISiJOHg6BTmM1l0tZijhgGAVGgVoh1XYL7rSu1n3tQEfOnJAsWPQyWZ6iErJuJJvHRxAg6WRasZuTcAIMu47tG34eYf34xA7GxVf1LrvSVyHF68MIEML6CzxV/PVdYFRhIQihwDAMQ7dCJGU2rxhUotlmGQE+3fQLLUURcRE4/HC/7FYjGcPHkSn/zkJ7F+/Xqjr7GJZYqomj1gh+6+JnTsO6eTL/vPRyy8kupQqUDaoyplpi4nIqYCOWVWRsy5aXOJGE9escSsIk4swyOR1Rcrl5aTNZlJihhCGEZSnKkEWi2oPiOmaU3WRBNN6KikiAGA12zSCY1bNnYXfS3JiOHExqw+GsXZ6SRGIhm4nSxuXN9p+PE9atab1USMng9Tv63qkKqKubgE7MnIem1dA+s1l7OpiLELTkwUKiiKwW+B3SFp0AIK1eSXAyZi+v0SIqJaECJmywr987x/Vz/WdAUwn+bxneeWlrsIUQTtHjSPiElkOcwm6duSEeT8PWWtuwCAZdm6yYozM/M4NxNFTzBgqm3XpY1vx0u3fx+z/TdrPyuWgcOqJFOt65X5dBYvnB9HVhDQZSJZ4U2Nw5OdhZNPIRNYqf1808t/jZt+eht6Lzy86G9quTdOEPHSxQlMJVLoC5lTFyEIxM/DIWYhOANIBVdpPxec6ucmFDaLOBgGvMWq5MsBdREx4XAYbW1t2r/29nZs2bIF+/btw7e+9S2jr7GJZYpYRiVics0vup2QT8S8uASImOmEQrCUtCZTLSUuRyKmM1h8sUlsJ+hbk5EOy/r8xmsFUcQA5tmaLOzoW04dfkQR009ZEdMWUCTngiQjbtOuvkrKOwLS9U664Jtowm44M5W4rOZDqxFJKWNaOSJmbVdAy4R5cxFbMkDvXgfMz3TIx+OqLdn1azvqtrAqB7tYkzWSD0Og58QsAUWMAes1F6t8ds2MGOtxfLwKRYwF1mTnpi9fImY8L0/y0Ei06sajeJbXyNx8Ys3pYPFnt28EAPzLs+eXTANQLMPjjPoc7BoMm3be6WQaKY6nb3MlFx///PHzaJ09DFbQnwMlR6V2smIulcHhsWkE3C7TMkbKoRgRwzCMYnFVA8mU4ni8cGEMkXTWdLIiPHsYAJBo2wTJqTdgOLkkvJkZuHMLydPqCTRJlnFobArnZ6PoC7WANTnvJjR3BAAQb98CsPpaUs8uKrSJZFnG8jXY5YC6vrlPPvlkwX+zLIuuri6sW7cOThsMBk0sDUQzyoIhZaIkuonyEESpgHw5MRlHLMOj1WeON2c90DrVW4p3LfYECRFz+RRGNSKmkjUZxWKOLMtaRoxZihing1Wk0JKMrEk5MWQjSc47siwVMXQ7kjxOB4JeJxJZAbNJDmG/eZ651aJ6RYzy+7klYE3GixIcDGNKflMT9sBYNIO7vvYcBjv8ePwjN1f+gyYagiTJmE8rYwEZG4qBYRh88/d34+hYDK/f1lv0NR4nC4ZR7MsznIig15p12W+ILdkm423JAN2ajLNaERNTyMpGiBhNETNr/3UBaZxpTBGjzCVNIsZaiJKMU5OK1Uy+gmIhrFDEnJ1pWpMBQCIr4NxMEut7ghX/7uSE8lmuaPWibQGhf+e2XmxdEcKx8Ti+/fQ5/K83bDb2oingoKoGWtXhL7lPNRq8KOHMdAROlqWrHpFE3PiLNyDVug7Hrn0QnLdD+9XVT7wT7tw89r3+h0iGFQchlq09R4UXRRwYmUKK47GytfLzYyS8yVHwnnaIrsJ9oeBSrmOhxRUDpcmuGnCCiP0XxjEWTWJlOGiqygcAWucOAQCinTsLfi66lOaEfJIJABiGrdqG8/R0BMcn5tDR4ofbWbvVaaNojaj5MHm2ZAAgONV7EwrvzcEwFddgsixjNJpAX2sLnKx5OU/LCXW9azfffHPBv5tuugmbNm1qkjBN1IS4qojJcCLkGoKxmqCHo+NxJHICQl4nhjr8kGXglYv2VsVMqwXSUoqY3tbLKyNGkmStCFySiHErQz/NLri5FIdYhgfDQOv0NQNEFUNb7UNAiJgr+hXf5rFoBqJN7bVqhVmKGEB/Vu2YE5MTRE3BWWnTSLre52zemZgTRLz2H5/G7/zvfc359zLCM6dnwIkSLs6lmp+7CYhneW0+IMq/UtjcF8LvXDVQsvjAMIxmT2aVpe98Xsj0rZt7qJzDY5OMmDEDFTEXI/ZWxGR5UVvLNJYRQxQxzbHFSlyYSyHDi/C6WO0ZLAaiaDNTYXc5W5PlK2KA6nNijo/HABQn1ViWwZ/foahivrf3AiZj9t/rHlDv20xbsvNzUYzFktStrkLRk/ClJ9E6ewi8u7XgdxpZkZ+jwjCQZKlqsgIATkzO4VIkhl6TLckAYOv+T+OWH92I7pHHC35eSlkBVGffJUoyDoxOYVhVjDgsKOwTRUy0c0fBz3lXmRwVoXIz+Vg0gQMjU/C7XfTVWCUQmlOImHj7AiLGTZ7JxYoYTiw/L8yns3h1fAbRtP3HHLui7qf83Llz+NM//VPcdtttuO222/DBD34Q586dM/LamljmIIUtQZKbwY42AbElu3ZNB/asUbo47JwTkxNELWuolGUQIWhmk7nLoksvluG1BV2pcGCfCZsvYj+wss1XV9BtvSDnMquIQzr6rl7VBifLgBflZWH7wwkSplTbP9oZMYCerWJHAoMQmy4HU1EduBTybgAls+BSJI1XLs5rZHYTyx971TmeF2XLC92XA8h4FvQ4teyTRkDmbquImKdOT0OSgU29QWrzAnmfLFfEXEYZMcMzKcgyEPa7tLm4Hrgd9rEmm03mcOPf/gZ/88sTVl+K6SBqmI29ITjKKF6J3aFZ40k+4QcomSl2eFbMwoRKkpDs0mpzYo6Nl8/7uWVDF65e1YacIOGffnPGgCuliwMkH2bIHCImw/E4NjELn8sJl4Nugb998gUAwHz31ZDZwgZ13r1YNcIwDGS5+qyRyXgKRydm0erzwuUwV1nBSDxaI0fByiJSodUFv9OUFXwRIqaC2keWZRydmMHxiVl0W6QYcfBpBKOnAQCxBUSMTqAtICsYBlwF+640x+PFixMQRAlt/vrXEo3i1JUfw+mdH0G0a1fBz0t9bizDQJCkso2lM8kMkjn77dmXEuoajR599FFs2bIFL774IrZv347t27dj//792Lp1Kx577DGjr7GJZQpSQAfMlUU3URp7z80CULy/r1ndDsDeOTGzeQXSsL94gbQz4IGDVRY6szbstjca5B5D3tKFH2JNxgnlJ9lGYLYtGYHHIkXMYEdA65xdDvZkE7EMZFl5PztLEHpGQstWseF3lNiSdbZ4Ktp4ka530cZ5NwAwnWfVeHQsZuGVNGEWZFnGPnWOB2Dr53O5gHj2txs0hmpWQrw1lr4kH+Y2SmoYQLcms5ooNDIjZiKWNW1NUg+0fJiuloY6rJ0O5W8FGyhi9p2bw+h8Bt/dewGJy2ysOzOlfJ4bKqibdGsyc8YTQvi1+lxwO1lIMpaEgsMokDHlDVf0AahBETOhEjErWov+nmEY/MUdmwAA/++lEVy0cSaVKMk4NBIFYJ4i5tR0BJFUFh0B+k1l7VP7AQCRnmsX/a5Yjor2uyqsybK8gFdGJsGJIlp95li65SM4fxIOMQveHVpExGQDK5AMrV5kWVZNRsx0Mo0j4zNo9Xngs0gx0jp3BIwsIePvQ85fuL7R1D4LPjdHFaqRyXgK8+ksekLmOYMUQ7RrNy5uekeBVR6gfG7jq96I6YHbCn7uYBhIUnnLvLFoopkj0yDqImI+9rGP4YEHHsD+/fvxpS99CV/60pewf/9+fPjDH8Zf/uVfGn2NTSxTEEUMAKSaRIzl4AQJL19QulT2rO3A1asUIubIaAxpm+b46PkwnpKbR5Zl0K3mOlwOC34SKt5ZJsvC56If+nsub2NvJsxXxCgbq5VtPgy2KwvQS8uAiMm3JTND+t5BrMlsqIipNh8G0PNuAJ0otiNmkvpYeHQsbuGVNGEWTk8lC57JRNae8/pyAlHTtTegMsiH3+QO9nzwooRnTs0AAG7dTCcfBsi3JrNuX8AJkqYUbISICftdCKnzgZ3XBUTB3Eg+DACt25wTJcutDydiyhqGEyT85uS0pddiNs5MKx3363vKf55mK2II4beuuwUrSePSZZITI8syxtVn8u7tChFzejpRsSGCEyScnlI+z61l8n6uWd2Omzd0QZBk/OOvTxt01cbj9FQCyZyAgNuBjb30802imSxOTUUQ8rmpB6SzQhbhmYMAgLne6xb9XihhcQVUqRoZn8FELIneoDVF/fDsIQCqdRdTWEI+t/0D2PeGn2B03VsKfl6NamQ6kUaOFxDymk8uEWT9PRje8kcYW3v/ot+VItAUa7Ly9zYRS4JhGOrPXr1Ih1bh2HWfw/C29xb8nGVYiGWyi5I5DtPJy2Pspom6iJgTJ07g3e9+96Kfv+td78Lx48cbvqgmLg/kK2LSuWZBwGocHo0iw4voCLixoTuIlW0+rGj1QpDkqrt2zMa0agFVqUDaE1LkoFNx+3XbG43ZCvkwAOB16UM/LTWaVUSM23RFjLIQGWjzYaCdbCyXvu/1qGbLQr+DDAA6iTWZDckLQm6Wsj9ciHx7MruiQBEz3lTEXA54/uxswX83iRj6IGNAI3ZP+fBZEK5N8NKFCBI5AR0BN3auDFM7j8dlvTXZVDwLWVbWE418dgzDYEhVxVyYtW+XuqaI6W6swOfOs/2xOisvPxj9V0cnLbwS83F2Wic8yoE0ZaVNWi/nE34ke/ByyYmZT/PI8sqYtq2/FSvbfJBl4NWR8uuvs9NJ8KKMoNeJlRXyGv/ijo1gGOBnh8fx8gV7ulkQO7adg+GytnlGQJZlHJ+YQ5Lj0GpCkT88ewgOiUPW1410cNWi35eyuAIq2zlOJlI4MRVBR8BnSX4KkJ+hsqvCK3VUIiskWcbIfBxelzVKGIJ0aBXObf9TnN/6x4t+x3k7kWxdi0xgRcHPHQwDQZQglWg6yPICxuNJBD30XSXKoe/8z9F78RG4stWPCSzLQJQkiCWImOlEumlLZgDq+iZ3dXXh0KFDi35+6NAhdHfT65JqYnkhnqeIscrvugkdJB/mujUdYFkGDMNo9mR2zYnRCqTB8r6bxI93OWR3VMJsonLRmGEYbQNGi7DQiRhzO3e8lO8rH7EMrxUz+8N+rGxTFDGjNu58rRZjeUofM6ArYuxHls7mWZNVg/aAfW3WCPJzYY41rckuC5B8GILLza7HCkTU8cwoRQyZt80M1yZ4QrUle82m7ooWjY2AFPOttCYby2tEaFQRSqw9oxn7ft/OVVm4rwRiTQYoOVRWIj8Y/alTM5eNBbYgShhWSb/13eUVB36Tid18wk9bL18mRAx5HjtbPPA4HZotV6WcGM2WrC9UcSza1t+Kt109AAD41E+PWU6GFsMraj7MlSbYkk0mUhiei6LD7zdF2d8+peTDRHquA4qcT7e4KlTEVGPfNR5NghdFtFhV1JdlhGcOAQCinTur/rNK9l2xTA7z6SyCHmuJmHKI9F6HfXf+CCeu+XTBz1mWgSjLJcmKmaRCVrRYfG9rjn4bV+z7mJaBUwBZBitk4M7MAJL+OTkYBlKZ7KLJeBIy7De+LDXURcS85z3vwR//8R/jb//2b/Hss8/i2WefxRe/+EX8yZ/8Cd7znvcYfY1NLEMIooREngomZVPrq3L4xpNn8X+eGbb6MgwDyYfZs1b3j7xmtfL/Xzw/V/RvrEa1lkG9miLmMiBiiDVZBU96mpYE+YGcaxvc2NcKj4n+8kQN09nihs/twIBqTbYcrBbGTFbEkEKlHe28dMK3OiKGdFDP2lkRk0fEjMeytiaNmmgcgihh/7Ayjwc9ilVSUxFDH8RqsT1gTDeuldZkxN7ptZvoNtx5VMVujreOiNHzYRoP1/Wr3ze7Kv9FSdYK940qmPODsPkqMg9oYiLPijjDi3j69OVhTzYynwEnSPC62IrrN59bfTZN2oPnE34rNUXM0l8vV4NxbU2tjCm7BsMAgIMViJhjqmJ5Sxlbsnz8+es2IuR14vhEHP/50qU6r5YeiMPGriG6RIwoSTg2MQteFBEwqRCeCq3FXM+1mF1xQ9Hfz/Vej3Nb/wSR3j0FP69k38WLEi7NxxGwKD8FALypMXiyM5BYJ+LtWxf9vn1yP6575M24Ym9hREUlRcxMMo0sL8Drchp+zdXCnZlB5/izcOZqa0rTclRKkhUpyIBlCiYAcOXm4U+NAgDi7VuKvuY1/30Dbv7pbfDk9FofyzKQSliT5QQB47EkWtzWKn2WA+p66v/qr/4KwWAQ//iP/4iPf/zjAIAVK1bgM5/5DD74wQ8aeoFNLE/EFxQA0rml1ak0Gcvi7x89BQC4d1d/1QU6uyLLizigLo4KiRhFEXPwUhQ5QSwZ/m4VSDGxu8L7360SMZOXFRFT/j2h2Vl7flYP5DTKkqVamKmIGdVyVBQCZkDdWI5Eln6HX35GjBnoaLGviqSWjBhAv5eIDUklgplE4Vh4bDyO39rQZdHVNEEbR8fjSOQEhLxO7B5qw1OnZpqKGBNgvDUZKZyau2Y+N5PE+dkUXA4GN1EeJ0gzRaUQXJrQiJjWxue/gEqe2TULc0wt3LudrKZSqBeufEWMhYomQP8Mb1zXiefOzuKRo5N4/bY+S6/JDJxR80TWdbdUVK75XeYRu6Ik43we4ZdU9/2XiyKGEIN96phCFDEHR6KQZbmkYuP4uKKI2bqitarzdLR48JHbN+AzPz+Of3j0FO66og9hvz0KppEUpz0DuwfoEjGX5uMYnU+gq8U8V4aJ1fdgYvU9JX8/13cD5voWkzQKWVGaDJ1NpRHL5tDd0tj43AhEVwtO7v4Y3Nk5SM7FDQqMxCMYOwuZLSwtOyqQTOOxJJwOhymKpVLoGn8WW176LCLdV+GVW79T9d+xLAtRFiAUUZ7xooiR+bjlZEVo7hgAIBVcBcFdhMxlGIiuAFguDgefAtQlD8swkGSp6L3NJjNI5nh0BHyYzyz/uhpN1EXRMQyDBx54AKOjo4jFYojFYhgdHcWHPvQhS79ITSwdxBZI9M3ypzUK5/O8ng+PRK27EINw4OI8OEFCT8iDNZ36omVtVwAdATdygoQjo/azr6lVETN9OWXEVHhPaHrND8+QzVbA9DmB5N+Yo4gptO8aVBUxk/GsaRk1tKArYsxZ+Hdq1mT2Iy9qJmIC9rVZI5hecE/NnJjlDaJ4vW5NB1p9SldlUxFDHxFNEWPMZpwUTjMmq8ifOT0DQHl+Wjx0O1dJzpuVihht/jOgESGgvl8pmypizs4ohfs1nYGGMxsYhoFTPUaxAo5ZyPKitpZ4142rAAC/OTFdMVh5OYDYf62rQt1EFHZmrFfH5jPI5RF+ZN08dpkQMeMxorJT7ntzXwhuJ4tomi+oKeRDyTjRrcmqxf+4bggbe4KYT/P40mNF7IgswgHVlmxddwta/fTUHTlBxLGJWThY1nYNpMXgYMuTFVPxFERJhsth3b3wnjBGNvwuzm3/QNHfa7ZrC/JvWJaBIBXPUUlxPKYSKcttyVpnDwIonX3j4JPY88v7cNNPbwcj6vVLhaworhqZTWYQz3LWWcmpaI0cBQDEOraVfI3gXGyZxzIM5BLWZFOJFCRZpp7xdDmgYa1UMBhEMFjeg7SJJhZiERFj0w1KKVyK6IumgyPlZcVLAcQ7/vq1nQWFc7vnxFSriOltbSpiFkJXxBj/3dPzYcy1JQOgLbrNUcQolgpkQ9kecGsb27Fo9ZvLRJbH91+4iJOTceMvsg5IkoyJmMmKGLVQGU3zFUMrzUbN1mRE3WNDUolgRiWlb1G724+N2+PZa4IO9p4lc3wHgl6lMLxQmdyE8ZhTGyPaK1iFVgutgcJkov/0lDKn7xwIUz8XmcOtzYhR1oorDLDmDLjtTcScm1YbZwyykSU5MZyFnx9RH/jdDtyyoRs9IQ8SOQHPn5217JrMwln1u7q+p3JthqZF8UKQfQEh/Mi6eSKWsd2ajwbGtTFF2Y+6nSyu6FdULsSRYiFG5zNIZAW4HExN+U1OB4tPv1GxIfr+CxdxYsIe6zuSh0M7H2Z4NoqpRApdLebsXwCgfXIfXNnyNu6skEUgNoxA9EzBzx0MU3K+EyXFlsxvoXVXNRBcpJhfSMSUs++aSaaRyvEIWExWhGcPAwBinTuK/l5yeNASH4Y3Mw2noN8fW+beJlWyIt+u0wqE5hQiJt5ehogpQaIxWJxdJEoSRubj8Ftok7ecUNfTMTc3h/e///3YsmULOjs70d7eXvCviSYqIZouLFDZVbJfChfndE/bQ8tAEbNP9Y7fs6Zj0e8IEfOiDYmY2So71XtCyu8vi4yYKt8TjYjhjN8AaUSMyfkwgLmKGD3QXlGNMAyDAfX/j0Sq973+xpPn8MmfHMXrv/Is3vq/9+GRIxMlPWfNwHQiB15Uul16TLJdDPvdIM018zYjMMh3qhK5SUC63+1oswYoKjiS0XarmvdwbKypiFmuyAkiXrqgzN83rOtE0EsUMU1rMtow2prMqoyYYVJE7aJv86JZk1lYyB83MCNNU8TYdJ9zdrp6BUU1IIUnK4vrE1FdfcCyDO7Y2gsAeOTIpGXXZBbO5OWwVIJfJQlpKOMXgjxnpEGrq8UDj5OFJAMT0eW/N8t/Jgl2V8iJIQ0yG3qCmlKwWly/thN3XdEHSQY+/bNjkIsoEszGK6oiZvdQmNo5kjkOxyZnEXC7qWdzuLJzGDj1f3HNr38PVz71Xtzyk1vRNvViyde3zr2K6x+5D9v3frTg52yZQPu5VAbRTA5Br3UW+A4+if5z/41ArHQusuBSiF8nnwTynjWWYSGWUI1MJVLqa6xTVrhy8wgkLgIAoh3bi75GZl0QHQqBmk9WOFgGoiyVJCt8VpNnslylIkZZ1zmFQmWejMVEzFwqg1iWQ9Bi8my5oK4n5O1vfzvOnj2Ld7/73ejp6WnakTVRMxYqYsy2WWgUF/OKrIdHYhClpSvRS+UEzV4tPx+GgBAxr1ychyBKcFrM7hPIsly1ZVCPak2WyApIc4K2+VhukGVZtyar0IFLs7PWDoqYnIkZMSvzVCMD7T6cmkpgpAa7hfxN2P7zEew/H0Ffqxf/47ohvO3qAXRUSQAYhbGoMr71hrymfd8dLIP2gBuzSQ5zKU7LdbIaqZygFdCqVcRoNms2zYgh46bHyeI6lXy/MJdGPMsj5G12OS03HLgYRU6Q0BX0YF13i6aIsaM12Zd+fQo/OzyOH7x3D7qD9hgD6oUsyxoR02aQR7/eQGEyEaNa56zppD+nEyLGKhspWZb1jBhDiBhCntnv+wboVlZGNc4QIsZKazKiSO5T1fCv39aLf9t3EY+dmAIvSpZ3KdOCJMna+rs6IsZ8RQx5zhiGQX+bD8MzKYzOpzHYYV3+hRkYX/BMAsCuwTYA50sqYuqxJcvH/7prM544OYUXz0fwi1cncM+OFXUdxwiIkoxXVYvz3RQVMSen5hBNZzHYVt97VgmskEHX2FPou/AwOib3gpWV747EODG98taS9lZAPlmRKPi5g2EhSBJESVpEHk0n0uBFyVKLtfDsYWx56UGkWwbw/N2/KPoaoohhZQGsmIXkVObOUqHvvChibD5hvXWXqoZJhtZA8JTOYRJcQTjEbIHiR7PvKkZWZHJo85unyCoGb2oc7tw8JNaJRHhjydfpipjE4t8tuLfppPI8up0O8Bbm+C0X1FWNfPbZZ/Hcc89hx47iEq4mmqiEhUSMXTvFSuFSniImmRNwbiaJDVXIwO2Ily5EIEgyVrb5MNC+eCG8qTeEoNeJRFbAiYkErlhZXWAgbcQyPDi1465SgbTF44Tf7UCaEzEVz2F15/IkYuJZQXtPqrcmM/a7J0mybnVhQvfsQhBFTNaUjBhlHBjII2KIOma0SkVMvgf0Q394NV65OI//ePESJmJZ/P2jp/DVx8/g7h19eOf1q0377hGCySxbMoKOgEchYmxEYBCrP5/LoYUuVwKxJovYTNlDMJ1Quk+7Qx60BdzoD/swFs3g+HhcI2aaWD7Yp+bDXL+2AwzD2FYRI4gSHnr+gmYhdN+ulVZfUkNI5vT5uMNgazIzFTHxLK+Rt2YqYqyyJotleO39zS+a1gtiTUbCye0EWZYpKGLsY022Qg1Gv2ZVOzoCbsylOOwfjuDG9Z2WXRtNjMcySHMiXA4GQ0X2cwvhdZlHEuqKGH0MWdnmV4mY6huX0pyAP/jOi7hiZSs+fc9Ww6+TBgRRwpQ6hvYXKGIUQuLUZBypnKCp5wiOq9l9W1fURyr0h334n7esw5ceO42/+eUJvHZzt2WNiJciaWR4EV4XizWUmvTmUhmcnp5Hu99LrUl88NT/xfoj/6T9d6x9GyZW3Y3JwTvAe8u7AhGywrWg4M0ySo6KsICIkWQZF+dj8Dmtq1kwkoD2yRcAANHOnSVfJzr9kBkWjCzByafAqUSMg2EgFrHvmktlkcjxptrHFcO6I98EUNqWjIB3t8CTnVlkvQYsVn9OJ9PgLCbPACAbWIFn3vhrBOIXIDtKr0G1jJiiihi9oUKWZYzMJ+BdArlLSwV1tYRs2rQJmczlEa7WBB3E0gsVMfbboJTDxTllsCKqg0MlulmWAvad073ji8HBMrh6FcmJKe9/aiZIPkyrz1VxsmMYBr1qh/1kbPlK4EnROOhxahusUtAUMQZvwCbjWWR4ZSNYjNijDXLftBUxsQyvZSzkd82Sex6Zr46IIR7QbgeLG9d34s/v2Ii9H78VX3rLDuxY2QpOlPCjA2O45+vP4bkz5vibF1P6mAE9W8U+ll75qrtqN3bEmiyS5iBa2BVcCnq2ljImbutXNvlHm/ZkyxLPL5jjQzZVxBwdj2uWeZfmlv4egxCxXhdrWPGLHMdMImZ4Rlnvdgc9GolHE26LrcmImqKzxV1xHVUNNEWMDTNi5lIcYhkeDGMcyWYHRcxCRZPTweJ1W3sAAI8cnbDsumiD2JKt6WypSs3sNzFzqphSh6wxR6tcLwOKavzli/N46PkLNWUxWonpRE4NW2cKmuR6W73oa/VCkoHDo9FFf3dctSbbsqL+Jqw//q01WNnmw0Qsi28+ea7u4zSKU5MK+bC+O0jNQWQinkSG46naeE0O3Yl0YCWGt7wHz7/hp3jxdf8XIxt+tyIJAwCCW2nYdYhZMJJeB3MQ1ciCgv58Oov5dA5Br7mqkWDkOK54/i9w3SNvxq0/vA6rTv0bgPJEDBgGqeAQUsFVYCW9Ca2UImY6kYIginA5zC3qL1R9ZAKKSizSc23ZvyuVgQMUqkYkWcZIJAGvBeSZMxdD98jj2PTy57Hm6LcBhkHO34NIb/l7i3bvxviqe5AKri74OQOAz1MmRzM5RFKZpi2ZgaiLiPnmN7+JT3ziE3j66acxNzeHeDxe8K+JJiqBKGLIXGzXEMtiiKY5rQB71xV9AICDI8X9XZcCtHyYEkQMoNuTEZ95O2BGKyZWt+DqvgxyYrQsiyreE52IMbbYQTZbQx0BS+wfSDdtlqdbxCH5MB0Bd0GRbVAlYi5VqYjRPKB7W7T3y+N04P7dK/HTD9yIn7z/BmzqVRbvp6YWy4ZpgGxuVxpgy1ILiAXbrA0VMZWs/vLRrtoQyfLiPDQ7QCOX1Pd7m7rJJ89iE8sHyTzr0evXKl3gdrUm23tOJ5qrJbIX4rvPn8e/7btg0BU1hjktH8a4opBeODXvszMzHwbIsxe1iIjRQ7WNmf9Il3vShvucc2rhfmWbzxDSCbBHRsy42nDVF9YVTa/fpuzXHj02ZcsGCSNwroZ8GEAfT3hRpvp5RVIc5tMq4ddZjIipnlA5OaGvg396aMy4i6SIiZhyfz0hL9gFJARRxRxc0NA5n+K053hzX/2OG16XA3919xYAwD8/M6w1kpqN0+r+haZ7yGwiDRflbv1sSz+ev/sXOLf9A0iHVtX0t4JLn0OdvP45sAwDUZIWkddT8RRyvAgv5ayRFcM/Qe8F3XLMIWTQO/JrBGNnwUo8RIcX811XYnrgtrLH2feGn2DvXT9FNqBb4DkYBpJceG+SLGM0moDPZY4VMiNy6B55HDue/TBu/skt8CVGtN9d2PyHOHzDP2Jy8PVlj1EtERNNZxFJZ8whz2QZbVP7sf7Ql3Hto2/DLT++GTue/zMMnP0B+s7/vOrDjK57C45d99eYW3Fjwc8dC7KLZpJpZHmB+vN4OaGudzIcDiMej+PWW28t+Lksy2AYBmLTM66JCoiqREx30IvJeNb04NFGcFG1JesOerBnbSe+t+/iogXUUkEsw2td0HvWlJbqEyLmxfMR7XtuNarNhyEgiphlTcRUmQ8D0LMmO1fEfsBMaIoYyv7ypINvoWpkoF3575FIdRtLYj1QygN650AYuwbDODmZMK2jdswyazL7hdzXOs4ASvdt2O9CNM1jLsWZnvFTCfnWZACwrV8hYpqKmOUHYj060K5bj9rVmoyoc4Hqiex8RFIcPvPz42AZ4C1XDRhWWK4XEXU+Jgo5I6A3UJiviKFlJ7MQHosVMZqaotWY+c8KFVO1oJH9Q6zJrCRiSDB6vg3UnjUdCHmdmE3m8MrFeW1fs5xwZqo2IsaXZ7ea5kS0+ug0TxFbsv6wr+CcA8TKtwYi5tSk3jDy4wNjeN/Na22xJy2HsTLk7q7BMB4+MlGQFQno+TBDHf6GlYiv29KDm9Z34tkzs/jcL07gX95xVUPHqweEiNnYS2ce4UUJs+kslYD0QOwsNr38Bcz3XI3hbe8F6nzeSOi7Q8zCySXAe8IA8hQxeQV9WZZxcT4OjwnrmL7zP0c6OIhkeCOS4fVIhtfj9M6PIBVag2RotUKsMPWNDQzJUcmbD2KZHObTWarKJYK26Zex/bmPwM3p+5uOyb0YDb5VuZZyKp88ZFoGkAxNQXQUXvNC+67pZBpZXkR3kD5Z0XvxEVzxwscLfpYMrUGk51pF4SPLdT+rAMAyLLi8WspYNAmnw2H78XYpoa6n5Pd///fhcrnw7//+7+jp6Wl+IE3UDKKI6QsTIsZ+nWKlcFEtEAx1+LFrMAxAWWAU83e1O148H4EkA2s6A+gt44W9bUUrfC4H5tM8zk4nsd4GeThaMbHKAmkPsSZbxkTMjPqeVMqHAfJDf4397p2bIfkw5hRtFsIsRYxu31Vov0Y2lop1WeXwc7LZ2lrGeoAUcszK0hrTihjmWsvpRIx9VCT1EDGAci/RNI/ZZM52+WHT8UI14VbVmuzcTBJpTrDMQ7wJ47H3rKIyuWGt3mhhR0VMThALFLfVZmzl44La6SvJivrAciImZTwRY2a4NsHwLLE7Mqe5wq1lxFhDXCy0tWoUxJrMjsp/0lAyaKCNrJMlihhrVCeyLBcNRnc7Wdy2pQc/OjCGR45OLE8iZlq1f+qpbv3tdrBwsEqGQ4YT0eqj06FOlPIL9wX1WJOdnNQVMWemkzg2HteaSeyKCY3cXbzP3pWniMlvdNRsyUo0adUChmHw6Xu24PVfeRaPn5jCU6emccvG7oaPWwsIEUOrfhDP5pDhBLT5jS/ut86+ivaZlxWvJry3oWPx7iAcmcWh7wtzVIgNVIi2DZQsIxg9hfaZlzGy/m0AAMEdwsVN7zD0NPkk00wyjawgoMtJf4+5YvgncHMx5LxdGF91FyZW34NU67qaj3Pyqk8U/flC+67R+bi2hqGNUOQoAGC+cydG170FkZ5rwPm6aj+QLIMVs2AlQbPPAxQ1E1mHpXIcphMp023yljvqelKOHj2Khx56CG9961txyy234Oabby7410QTlUCIGNJxZlaBcSEW+nFWg0vqZn+wPYCekO7v+uro0usmJlYg5WzJAGUDs3soDEDx57UDai2QEiKGFCGXI3RFTA3WZEYrYkpsuMyCxzRFTPEclYDHqRXeRqooJh7TPKBLb7YCWvGNfiFHlmXrFDHqc2urjJgksfGqLbSZ3AspxtoJCzNiuoNedAc9kGTgxIQ59ndNmIO95xZbj+qKGPsUhg9diiLLS9pYNxHP1jyG51uupG0QjK5bkxmoiKGkZC0HoohZW2WXfaOw2ppsTCNiahvzSyGgNVLY5/tGQJS7RMlrBFxqEaqe/ZURiGcFbU+5kEy7U7Un+9XRSUjLzJ5MlmUtI2Z9d3XFboZh4DdhTDk7XYqIUQqxE/FsVQo4XpS0PcaOgTAA4CcH7W9PVo7c3boiBJeDwVyKK1CCHlPV8lvL7A1qwbruIP7w+lUAgAd/cdxUxSEnSNo8spESERPL5MCJItwUMkdaI8cAAPH2bQ0fa2T97+Lc1veC87RpP2MZBuICRcxUIoWsQN+WzJuegItPQGKdSIXWNHSsdYe+jOse+W30XHq08BdMIREzFk3CyZqjrAhGTwEAjl/9SZzd+UBdJEw55Nt3xbM5zJiYoXJ611/gmTf+Gq/e8A+YXHVXfSQMgN6LD+O1P7wO25//84Kfs3n3NpPMIMXxCLjNsZO7XFAXEXPVVVdhZGSk8gubaKIEYmlVEaN2h1ihiPnEj49g9+ceqznsj1iTDXUoC0iiijmk+rAvJewrUqQphWtWKa950SZEzMJiYiVcDooYPc+iBkWMwcoRjYgxqWizEOYpYopbkwHAQFt19mSRFIcJzQO69GbLryrtUiYUF+fTvLYh7yujkqOBDtVSz04ZMY0oYgB7qXsIit0T6Sglm/8mlj7mU5ymuLu+iCKGEyVkTSzol8Pz6lrkNZu64XM5IMu6RWK1IGszwB5F74hKKNOwJjNLESNKsmZftdZA+6py8LjsYU3Wb3BGTJaXLCMnSkFfxxjXmexirbUmI59fe8C9SBV30/pOBNwOTMSyRcPRlzJmEjkksgJYBljVWf3n6TOh0YfsCxZapnW2uOFxspBlPUelHIZnUuBFGUGPE++/ZS0A4KeHx22f+aNnFi0eU7wuh6aIz7c5J3N3uSatWvHB29ajs8WN4ZkUvrv3vGHHrYTzsykIkvK50dpXRDNZMACV4n5IJWJi7VsbPtaFLe/G8BXvQy7Qq/2MYRhFWZFHVozMx+F2sNTJiuC8QlSkQmshOxorsnsz0wjGzsCTnir8RZ41WYrjMZ1MmUJWMCKHQGwYAJAIb6JyDpZhtaah6UQaGY6H3yyygmGQ8/fUTcAQiGr+jUMozI9iGQacIEGWZUzEk2AY5WdNGIe6iJg//dM/xYc+9CF897vfxSuvvIJXX3214F8TTVQCUcT0akSM+cWAR49NIp4V8PzZ2covzkO+NRmgZDgAWOTvanfMJXOaxPu6NVUQMQtyYqxGzRkxrcrrlndGjErEBKvIiHEbb02WyPKYUhVHZgX7LgTZeGdNU8Qs3vCSLIZKihhiPbCqw4+WMraGZipiSPGzK+gx3dqHZBvZSxFTfe5SPgipZKe8G4LpYkSMutm/HHJiBFHC7/2fF/DxHx2x+lKo4oXhOcgysKGnpeCzbnE7Ndtou6hi9qnq3BvWdWo2SSONEDE2sIEiipj2GseOciC2gWZlxIxHM+AECW4na5pC0u0g1mRWFfJL5znUA2JNBgBpmxCfBOQ7NmAkEaN+fpxF1mSkoF+s4Ot1OfCaTYol06+OTpp6XbRB1DCrOgKaqqwa+E3InTpbIjuSYZg8e7LK4/1JNR9mY28Qt2zsRpvfhZlEruZ9vNnQyd3iJARp6Dyg1hGyvKjZPG/pM852LeR14aOvVwrSX338DKZN2g+fUm3JNvQGqRELU4k0vE7j1SOsmENL9AwAIG4AEVMOhKyIZXKYSWYQ9NDPUCGKkUR4Q8PHqibQfiaZRirHI+ChT1a4c/OIdW5HJrACOX9PQ8fqufQo9vzyPmx85W8Kfu5gGC1HZTyWAMvQJ8+MhuBUxuWFnxvLMJAgIcMLGIsl0OJu2pIZjbqImLe+9a04ceIE3vWud+Hqq6/Gzp07sWvXLu1/m2iiEqIZZYNKOs7MtpGIpjmt6/r0ZG1WLJfmCj2Vdw4o8tJDI1FbEBTV4oVhRdmysSdYlYJi12AYLgeDyXi26iBymihWTCwHopyZjueW1OdUCy6oz2Y1HUd+CtZkRHreHfRUzEahBUIe5KxUxGiFxApEzASxHii/0TIzI2YsqlyzUd3AtaAjoNp52UhFMlunIqY9QGzW7HMvgLLRI0RXd0i/p62qIuboWLzo3y0nnJtJYe+5OfzHi5cQTdvr8zESz6vkRr4aBlDsBlrcJCeGN/26FiLNCVon8A1rOzWbpEs15sTkW5NZZXebjwgFazJ/HilvxjqGdLKv6vDDwZpTXCCKGCsyYnhRwpSatWcU8eR2sHCq750dLPMIsryoNTQVW8fUC6dDuVer1D/lgtEB3Z7skaOTy2ovcEYtdi9UnVQCWTPTaojMcKLmPFHs2kgzUzU5MaR5cFNfEG4ni7u3rwBgf3syonzvay3+TO7Oy4kBgFOTCYiSjI6AGz0hY4vxv717JXYMhJHiRHzxVycNPXYpkDoLrbzEDMcjns3BSyHfsCV6CqwsgPO0KaH1DcKZiyEQG4Y7s5g8JGTFdDKNDG9OXmMLIWLaNjZ8rFJEDMMw4NT5YCqeAkxSVuT8PXj5tQ/hubt/2VBoPQCwQhYt8WH4k6OFP1ftu9Icj4l4CiGTMlTaJ1/Ajmc/jP6zP2z4WIK7+OdG8sMm4ykksjxamvkwhqMuIub8+fOL/g0PD2v/20QT5ZATRM02iMh0zbaRIJtLQO/UqAZZXtSsrYY6FAb5iv5WOFgG04mctthaCtg3XF0+DIHX5cCOlWEAwP7zc7Quq2rMaNZktWXEcKKE+bT1xSejEUvzWtcZ+ZzKgWy+jOyCszofBsizJqNYxIlleMTVTvJixRrSXVpJEVNNPgygd9SmTejyHrUoHwbQVSQpTjSt47scZFmu25pMU/fYiFQCFGJIlgGW0YkvQLcmOz2VsCwk2yzM5qmUDi5BS9FqQfJhri8yxxN7MjsoYl66MA9BktEf9mGg3acR2aM1EzH6680YKyuBEDHtAeMKaUTJKsnQChs0QZor1phkSwZYmxEzGctClpVcRKMINIZhNHuypA2eSwIy17d4nAj7jWucIYomq6zJygWjA8AtG7vgcbK4FElr9k/LAWdL2H9Vgp+y3eHwbBKyDIT9rqI2jbUoYk6pBf2Nvcqa+d5d/QCAXx2btMTivBpkOFGbC0qRg0QRc2IijgwnFtiSGd1dz7IMPvtGRdnxowNjeOUifTcPUmfZ2ENnHollOWR4AT4KeSqhyHEAqhrGgM9i/atfxfWP3IeV5xYX0Al5PTqfgJM1R1kRnD8NAEiEjSRiCutqLMMgJwjgRRFjUQuUFQa8jxpZwS1WjXCChOlEGmmOR8CkfJjw7CF0jz2J8Ozhho8lOIsTMSzDQJJljMUSkGUZTrYu2qCJMqjrHR0aGir7r4kmyoHYkjGMXkTPcKKpnUnnpvXOydM1EDGkQzPodaJN3bj43A5s6lW6PPL9Xe2OYiG+lZBvT2YlsryoPUfVFkjzN9aTS4gwqxYHRpTF9OrOgBYUXg4+Cl1whIixypYMMEcRQ+y7OgLuoh1LpKO7krXO8SqJGHMVMarlmgWKmBaPE26VSLODPVk8I2jFzmpUg/nQ1D02U8QQYqmjxVPQ4b6i1Ys2vwuCJOP05GJbgeUE8h4AwEETihBWYDKWxfBMCiwDXFvEejSoKhbtQMTs1ZQ7HWAYRlMb16KISWT5AvWZHRQxhIQ1NCMmzy7SDLLaijmdzAFWEDFjefkwRhbBzLQXrRb5ql4j75UoYnjLrMnKK2ICHidu2ah46i8ne7IzU8p3dX2NxW7N7pCn82wSi611XS1FnzNdEVOFNZlKUJA99+7BMIY6/EhzIn59bKrcn1oGYpUXcDsQ8hYnCvrDPnQHPRAkGUfGYvreoEx2ZCPYORDGW65aCQD4zM+OQaKcsXM6z5qMBmKZHCSJTqGYFTlwnjZD8mEAgHcp78FCskKGQl4nchymEikEzVAfyDLG1t6PycE7DCZiipEVImZTGcRzHFpMsCUDlIwYo1CKZHKwDCRZwmhU+blZGSotUUKgrW/4WBrJJGQASV9XsgwDSZIxm8yYl3tzmaGhEev48eP41a9+hZ/97GcF/5poohxiqhqh1edCi7ooESTZlO4+gnxFzFQ8V7U9Cem4HOrwFywoSTfLoZGlUdSZiitFGoYBrltdBxFzwVoihnQ0ux0sWn3VTw7dqiqGWE8sJxxQC4pE4l4JpLPWyLBmQnDaQRFDs6u/nC0ZoCtiRufTJQnmDCdq49DWCpstTRFjQhHHSkUMwzC2CrmfSSrjRMjrrDkvhxRfZ21AKOVjWh37FioJGYbRVDFHxyvnxMQyPD7906N42eK5oB5cDooYQm5c0d9adI7UFTHWq0P3EeXOOmUtoikKq7CqIchXwwD2KHjTsCZzOVi41EK3GdmKRBFj5pxO5nASEmsmSJbDihJZDvXCjoqYkTI5d43AZbEihpBpxYLRCfLtyZYLiCJ+fXdtxW4fZUXMuenySnldEVN+vI9leC30fqNa0GcYBvfuVFQxP7KpPVl+5lQpwpNhmIKcmGPqGqxSk1Yj+Is7NiHoceLIWAw/eHmE2nnSnKA1VWykZE0WSWeoqUcubfoDPH3vkzi/5Y8MOZ5AiJgFygoHwyAniphJpJHieATMKHwzDC5seTeOXP93EDyNZxFpZMXCe1Ptu2YSaYiSBJfDhPxRWcLNP7kV1z/8RnjSjZO0YhmSSZRlTCaSaDFJDQMAQTW3KGkEgebUx2anoDeqO1jl3pImkmeXG+oiYoaHh7Fjxw5s27YNd911F+69917ce++9uO+++3DfffcZfY1NLDMQJUOrzwV/XnHLTO/kfCIGAE5PVdcBTDzIh9oLuwNJTsxSUcSQwsfWFSG01mBJcOVQG1hGKXpYqSrJtwuqZfHVq3rtTi1DRQyRl1+1qkoihliTGUnEEGuyGq0RjIQZipjRCgWMFWEfWAbI8hJmSoS1n5pKQJIVpQUhCEtBU8SYMEYStY8VGTFAXsi9DQiMC7PK5rGe0Ga7WpNNx0tbOpKsoqNjlYmYf3riDL637yK+/uRZYy/QBOQrYg5dilLvBrUCz58litfOor+3izVZLM1rz9ueNcq1Dnaoipi5+okYM8bKcshwoja3trcYuzmnoWYtheFZ8xUxhIgBzLFfy4dGxJTIcqgXfpWIsVNGTKWGknpBrMkEyxQx5YPRAeDWzd1wORicnU7i7HRtOaF2RCTFaYrAWr+rWl4kpfGkkmWaZkVZQRFDbMn6w76CDMr7VHuy587MaI0mdsJ4rDIxCOhNdC9fmNeycLZSJGK6gh586Dalm/7vHj2l1WaMxtlpxZqus8VdlVtDrZBkGdOJNBVbMg0MA9lhzDyu53EsVlbkBAGj0QQcDGOassJI8J4wct4OCO5Cwo1lGOR4ESPROHwucwr6vuQYXHwC3tQEOG97w8crp/YRJRnJLG8aEePg0/AlFfLUCEWM7HBhauB2jK+6p+DnRBHDiSI8Tvp5RZcj6iJiPvShD2H16tWYnp6G3+/HsWPH8Mwzz+Cqq67CU089ZfAlNrHcQCb7sM8Fp4PVbAjSBhaEK4F0DgXVzVG1OTGkq4MUCgh2DoQBAEfGYpZ1gdUCrQO1RJGmFIJel9ahY6UqZrrO3AaSEzMVt77IayQEUcIhtbP7yqHaFDFGFXMEUcKFOdI9a501mRkZMToRU3xj5XayWihnqZyYWjreAioRY0aXt2ZNZnCXbLUgll6zNiAwjjbQlUg2nLEMb6s5YVrL1lpcpNrWr9zn0fHyvvmxNI//ePESACC6BPO28snRRE7AmenlZcUmyzL2qYqYG9YVV7wSa7K4xYqY/efnIMlK8bBXzXQg42o8K2gK6kq4GEkV/HfKYuVBRFVZuxyMts40CpqVEGUiJpkTtLXSGhMVMe48IsZse7JKQe/1gliTmZ2HWQ6jEWWuJ4Vwo0Csycwm0QBAkmStSaxUMDoAhLwu3LhO2f/818ujJV+3VED2tCvbfDUHfNMmYjRFTHfxfQEZ7yfjWXBlvu+nJgttyQhWdQawazAMSQZ+dmjciEs2FOPRysQgAOxSiZhnTs8gzYnwulisppzN9Y7rV2FddwsiKQ5ffuw0lXMQAm0DJTVMIsshxfF0iBjJ+O+EpohZVNBnkczxmIybZEsGIBg5Dl/iEiAbM1bP9d2AZ+79DV698UsFP3cwDARJQjSdM42sCEZPAQBSresgs42TPzwhYoR0wXOhBNpLkEzMUAnEz4KBjJy3E7wBJBMAvHrDP+DYdX8Nwa3vd1mGQYbnmyQMRdT1xOzbtw8PPvggOjs7wbIsWJbFjTfeiC984Qv44Ac/aPQ1NrHMQAo3IdUuQ/NONmnjnBNEjVC5bUsPAOD0ZHVEjGZNtmDjsqYzgJDXiZwgaYsOO4MU7a9ZVfsAfs0qpbDz4vk5Iy+pJtQboE2ImMm4/bqmGsHJyQTSnIig14l1VRZMSFetUdZkI/MZ8KIMr4s1vJu0FhBFDC/KECl1uo9FK3eSkt+NRIp3+dXiAe336KQZze79ZE7QiHIrrMmAPEWMDYiYY+pntG1F7ZL9sM8FEsEyb6OcmHJjJ7nPExPxsuTR9/df1DI4rC5414OFJN/BS0vDUrRaXJxLYzyWhdvB4qqh4nO8XRQxe7WmEJ0w8rudWiZTtfZkF1X1GvnOWV3wjuTlwxhtmaIVTik3L51Xbck6W9w1WcA2CqKoAOgqW4thPEpHEUqsyaxWauWDliLGZaEiZjaZAy/KcLBMUdVnPkjQ+/9+Zhiff/g4tfWiGTijqnrW16FGJwVsGs2QoiRjeLa8vWFHwA2vi4Us69+/Yjih7q03FskZuV/9LH9yyH72ZBPRysQgoNiIOllGIzA39YYKcvxowOVg8el7tgAA/r8XLlKpX2j5MJSImHg2h6wgwEuBiBk69f/hpp/ejqETDxl2zFLKCgfLQBAl1QbKHLJi88t/jRsfvgfdo09QPQ+rWlxlBYGucikPwfmTAIBEW+PWXYBCoGV9XUiG1sAh6jUklmGRyHHmWMmpILZkRqhhyoFhGHQHA+jwW1fTWe6oi4gRRRHBoDKgdnZ2Ynxc6UAYGhrCqVOnjLu6JpYl8q3JAL27zwybBUApUkiyEgxNOqIaVcSwLIMdqipmKRR1SBdsd6h2mbCWE3N+KStilhcR80pePgxb5cKdFHN4UTakY590va3pbKn6GmjA68rvpqUzplSyJgP0LtPSihilyF+N9UAgr8ORZvGN2JK1+lxoMbiLu1qQAuxcCUs3M3FMtUyqxx6CZRktJ2bORkSMlhFTZOwfbPcj6HGCE6RF9p0EWV7EQ8+f1/57KRIxhIwin+uBJTBn14LnVTXMrsGwpnxcCKKIsZqIKaXOHWhXNn6XSoyfC0EUMaTYZ7UFFLFWbA8Yb8XiMyn4nYwBayh3ZS8EwzB6ToxV1mS0FDE2Gi9JRszAMsqIIYrenqAHTkf5Escbd6zAh1Vrpv/z7Hm8+3svWa4QrBdEEVPK/qscaCpixuYz4AQJbidbcr3MMIz2u3L2ZKfKEDF3bV8BJ8vg6FgcZ6rcz5sFYk1WaUzxuR3YnNeYRTMfJh83re/CHVt7IEoyPvvzY4bncp1Srd+LfW5GIJbJQZbphKSHIsfgzUyDMUgxAuiKGBdX+JyyjJKjwjDmBL4zkoAWLWdkA9VzORgGWV6AizUhG0ZFMKoSMQZkqACA7HDj2Tc9jn1v+DFEl67uczlYdLcEEPYbmytXDk4uDtHhNfZzk2WwQhaMWDgHhryV59Im6kdd7+y2bdtw+PBhAMC1116Lv/u7v8Pzzz+PBx98EGvWrDH0AptYfogSazI/IWLMlezrMukWbWFweipRcfEhSrLWQTbUsVhiTWTFSyH8l2wGa5WwA9ACBU9PJS3rIJvR7HVqK3L0tqoZMcuUiKnWlgxAQfi4EaoYO+TDAIDHmX9fdAoB1QTalwucFiUZJ1WbhWo2W14XC8aETm+i9LEqHwbQg60jFpMXkRSnBcPWuyHWiBgbqHsIpsuMnSzLaPd6dKy4PdkPXxnFbJLTFHUJiwqLz5yewbeeOldX0WBWJflet6UXwNLJdqsWe6uwHtUVMdYVHmcSOa0J5ro1hRZqgxWI7IUgamXy/FquiFHHLzKeGQkt341y89LwTHlLIZog9mQ5Ey2LZVnOI2KMLapoihibWJOlcoL2jK5sN1oRoyxWeMl8ImaC2JJVsYZhGAYfvm0DvvF7u+F1sXjq1Azu+8bzOD+bqvi3dgMhYtZ3117spknsnp1Rxvc1nYGy6g6iyhotoYCUZVkjYjYXUZG3B9y4ZWM3AODHB+2litFzpyqPKbvV/TVANx9mIT551xZ4nCz2npvDI0cnDT32acrWZDPJdIGK0ki0Ro4CAOLt2ww7ZiawApc2/B5G17654OcOlkE8m0PQY3zzRjH4ExfhkDgITh/SLQOGHJMVMrjqiXfiul/9DhhR3/c4WBbxLIcWkyzXACA4rwgD4m2bqJ+r1ecxNdPn4uZ34jdv3ovhre817Ji7nn4fXvvDa9Ez8phhx2yiMuoauT75yU9CUhdYDz74IM6fP4+bbroJv/zlL/G1r33N0AtsYvkhvkgRQ6zJzNlwaQXjrgDWdbeAZRS7tPwA32IYjyrWS24Hi94i4dq7VEXMoSVAxBD1UcBTe3cCIdAAxcrICsyoXd21KmJILkKTiFGyVMi6wYiCTv73yko4WEYrBNBQxMSzvG7fVWazTzq6i1mTnZ9NIstL8LsdWFWE1F0IhmE0VQxNa5OxKggm2iDZKrMWEzEkw2d1Z0BTD9QKkndDuuPtAN2arHhRYKtqT0YC1PMhSjL+z7PDAIA/vGEVAKWgZ3QHZTX46A9fxd/+6iSOFLnOchAlWVNb3a5ak56ZTlILqjUbaU7A3rPl82EAIGQDa7IXhhXCaHNfSCMtCQiRXY0iJsuLWgGWFOnMUliXAilyL7wvI2B0vlspnFML0mYrYgC9ocLMjJh4RtAsFw1XxHjMVf5Xwmie+jVU5/xWCqR7lhfMnxfqUTTdtb0PP3zv9ehr9eLcTAr3fuN5PK+OoUsFZ1TVwbqeeqzJ6I0n56ZVpWKFBi2diCmuiBmdzyCZE+ByMFjdWXzNfJ9qT/bTQ+NULXxrgSzL2txUzTNJGjqB6myLjcJAux9/cvNaAMDnHz5hGMkfy/CaFfiGOp7NSuBFEXPpLJUAeFc2Al9KcfyJt2827Li5QC9O7f5LXNz8hwU/9zidkCSgxWOOxRUhKpKtGwDGGCJLYt1omzmAYPQ0nLyu+PG6nOhrbUGLSfZdrmwE3sw0APpqH8vAOiC6jFOzSg5lT7jQMq8Juqjrm3fHHXfg/vvvBwCsW7cOJ0+exOzsLKanp3HrrbdqrxsdHdUImyaaIChpTWZS5xvpHFrb1QKvSy+EVrInIwWBle2+op09xJpseCZVdcCsFeAECYK6SK1HEeNxOrTuE+uImNKB0+VAwoBnk5ytArQbwWQsi7FoBiyjP4PVgGEY+ElnrSGKmPI+0GaCFHFoKGIIWdEecGvFlWIgHd3FConElmxTb7BqD2hCmtK0Nhml5I9fC/SMGGvJC6IIacQewk55N4BSFCiniAGAbf3K/RIiKh+PHJ3Axbk02vwuvOuG1QAASaanPCuFWFrf3I+VsTIphvk0B0kGGEYpDAypNqNLoYGiGnz2Z8cxn+bR1+rF9pXhkq/TrMly1q1ViuXDEGiKmCo+X6KaCXqcWkHPqrUJwRxFIkZrXqK8Zh5W5/Q1FjRXaNZkJhIxxNZKyaww1kKFNFJY/VwS0MqHAay1JhtX8ziqUR/kY1t/K376gRuwazCMWIbHH/zri/i3fRcsaTKoFYmsPh/azZosf79dDro1WXHinahh1nUHtedrIV67uRtBjxNj0QxevGCddXY+YhleI7h6q3gmSTOd28FiU695RAwAvO/mtegP+zAWzeDbT58z5JjEJm5Fq7fuhqZyiGc5ZDg6+TCh+RMAgFRwqCDAnBY8TgdWdbTCYVLge4saZm9UhgoAgHVAcCrrBdeCgn7QY3xeXsnLEDmMrX4TpvtvKbARaxRbXvwM9jxyP9qm9ht2TLuAWOY5hSYRYyYM+7a3t7cv+oJt2bIFFy5cMOoUTSwTRNPKBjXsUzaopMCYNmmDsrBgTOSylULqiPXFUHtxBro94MYqUtQZjRpxqVSQLz/3l/CPr4QWtZs2aVE3bb0ZMe1+t6aWmK6ggFoqIPkGm3pDNed6+AwK/ZVlueoNlxkgOTFGWK4thJ4PU76AQTJiJmKZRQWJ41o+TPUh8AETsrTGqrw3muiwiZ0XISK21fAZLYR2LzZRxMQzglbYLDV2butX7vfYeLygq1SWZW1z/gd7VqEj4NYUdWYXF8/m5dfUqm4ktmRtfjecDlZTsi6FbLdK+Pnhcfy/l0fAMMCX3rJTs3cqhqANFDH7zpVW7lTK2MrHhTk9u09XHlhsTZakZ01GGmgyFO9RkmScn1UzYiyY0wkRY6YihqgpaChCzd7nVAL5XhmdDwMALrW5RLDEmqz+jJ/uoBf/8Z7rcP+ufoiSjE/99Bg+8ZOjtm/aImvvnpCnLnUTTYUdUcpXIogqKWKIle+mMjkjXpcDb7iiDwDwE5vYkxFisLOlOnJ3oN2PL791B77+e7tK5rvRgs/twCfuUpQf3376XNW2oOVAGlw3UMyH4UWxwJLaKITmiC3ZVsOP7c7Mwh8/vyiPw0wECRFjsGJEcCnfdSuVFblAL45f+yAO3/RVQ4/rTY2jJXYOnsycocetBR3jz2HPI/dj3WFjHagEt/q5cU0ixkxQpV2XQidJE+aDKGJCqiLGRyx3TJDsy7KctzBUWPINeTkx5UDCYIvlwxDsXAJFHfI+u51syc6iSiAFfyv85SVJ1opptWbEsCyDHtVWbrhEGPVSQz22ZARegywJIikOsQwPhkFJ2wAzQdPWpNpO0q4WD9xOFpIMTEQLi8XHJ2pXW/iJIoZqRowdFDG6nZeVawiiWiIKkXpAgrqtzrshmFYtHUNeZ8miwJrOALwuFmlOxPk53St/77k5HB2Lw+ti8Y7rV4Fl8+3yzC0ukpw3AJiqkVDXrNnU52y3Om4eWOI5MSORNP7Xj44AAD7wmnXYU0Rlkg9NEWMRETMWzeDCXBoOlsHVq9oX/Z5YO47NZypm0V1Un9NVHQGdsDbJ6rYUNEVMCz1rsgxHr0A8Hssgy0twORgMWEDMuy1QxGih2q00iBiiiLGXNRkVRYz22VlnTdZXoyKGwOty4B/fsgMfv3MTGAb49/2X8P0XLhp5iYbjTAP5MABdV4pqLYsHNEVMKSJG2Z9XCny/b7diT/bwkQkqjVi1Qn8eq/+e3bdrJV63tZfWJZXFndt6sWdNB3KChM8/fKLh45F8mI2U8mGiGXo2462RYwCAmIH5MATX//Je3PDLe+FLWUcYBqOnARisiMHyLugLqrrGSpIpNH9CIYPSxmY5ESWTo6mIMRXm6N+aaCIPC63JAtqmkn5BYDKeRZoT4WQZjVAhC4RTU+UHn0uk67KEIgbQ/V3tbHNCOvICDXTbaN20FnT3xTI8eFHZ4HXUUeQgNiiPHjN2ErMKjRAxRBGVbZCIISqz/rDP9C6uYvCYoogp30nKsoxW5BjJs1uQZVkr8tcSxuk3ocBoi4wYtYOcF2XELSoSJ7K8Fthbi2ppIUiell3yR2aqUBI6HayWs5GfE0PUMG+7elCzWyJd3ktREdMZVO5h14A6Z1+at42vfK3gRQkf/M+DSOQEXDnUhg+9dn3Fv9EVMdY8myTHZvvK1qKWJX2tPjhZBpwoVfyMNbVyh1+b06wORY+oKrh2PwVFDGmg4OndI7ElG+oIaJkfZkJXxJhXTCXzn9H5MECenZzFzyUBWZMMlNnP1AvS4GWFIma8hjyOUmAYBn9y81r88U1rAOhEh11BFDH12JIBxu0DFmIumcN8WmnQqpQzRdbKU4ls0e88cawop4gBgGtWtaM/7EMiK+A3J6frvHLjQBRa9RKDZoNhGHzmjVvhYBn86tgknjvTWFaSpoihRMRMxdPwOo23JQOAeNtmRDuuQKzzCsOPratG4oYfuyrIEk5e+XEMb3kPkq3rDD20ZnHFl29upgl//AIYyfi1rf65WXdvLSqBZnT2jeBWP7dlSKDZGU0ipgnTQYpSpEjlN1ERQ4IDBzv82mZhY68ysJ6ZSpQtxORv9kuBKGIOjURtqwgj73M9+TAERBFjhTUZsRQL+111yZHv2r4CAPCro5MQbG45UAlZXtQslOohYnwGZcToXW/W25IBgNcGihhA7/LLl/hPxXOIpDg4WKamzUmAcoExy4vad8tKRYzX5dDGF6tyYk5M6L7WjWQ82MH+KR/TVWZrETs2QhgeHYvh2TOzcLAM3n3jau11epe3yUTMdP1EzEJFzKa+ILwuFvGsgOHZpbkB+fJjp3HwUhRBrxNffdvOqgrnRJEcz1jzbO4rkw8DAA6W0QjhYjlb+bgY0ddmZOywWhEToZgR46OY6UBAFMNrLFK40lS1lsKYFvRufNHUKvVgKVBVxKj2v2ZbeuUEURvfjSDTulX1vF3m71JolIjRrMkMJnZradBqD7jhczkgy7qdF0FOEDGsNsZUyk1hWQZv2qns8X5+eLzeSzcMY9HGiUGzsbE3iLdfNwQA+MzPj9X9PZZlWSPQKimZ6kGa4xHP5eBtoJZRDsNXvA8v3f59xDu2G35sy4veDIvpgdtxbvsHIDmNJeOttiZjhQyuf+Q+3PrDPXBljc2K0kkm6/YKQY2IqdxwVQtKZfs0QRdNIqYJUyHL8iJFjNYpZsIG5ey0sijILxgPdQTgdihWLGQjthCyLGvFgHJEzOa+ENxOFtE0r/mW2w3kfa43HwawtsA4UyFsuhKuX9uBsN+F2SSHF8/bI9CxXhwZi4EXZXQFPXVtqI2yJjtno3wYwCxFTOX3Ww+c1scCQpyt62qpKRDYrxUY6XznJtROUq+LpVI8rAVayL1Fll5ECbKlATUMoNs/WaXsWQhiTdYdKj92Ejs28j4QNczd2/sKOqhJ0dvs4mIhEVMbWTerZnd0qkSMy8FqofYHLkYNuT4z8fzZWXxL/Xy+eP/2iko9AjKHc6JkuoWLLMvYqxExnSVfN1hlTgyxJhvqCBRYOFrZDEPGrnpUu5VAM9OBgBQ+rciHAfQ53FRrMorWnIS0NqPhrBpoGTEUFTFEuW4WpmLKXOBxsmjzNx4MHtSyMO2haC2FM+q+dn2Dihijid1aciMZhsnLiSkc789OJyFKMsJ+F3oqrF0A4Lo1CrlPVM1WQs8sWhqKGIIHbtuA9oAbZ6eT+Ld99VnzzSY5TRFVL0lYDrFsDhlOgM9Fh4ihCavJCprIeduR83ZABlP5xRTQEjsLRpYguFrAe2pvUC0H/XOzZmxhhQz8yUsAjM/2SQcHMd1/C6IUiMcmSoMqEcMw1nwJm7AvMryoLc41RYyH/qaSgHTo5C8MXQ4Wa1T/WtK9sRCRFIdkTgDDlLckcjtZbFPthg6N2DMnRlPE1Bjsng9SYEzmzN+gkGJiOXudcnA5WLxe9d/9+asThl2XFdBsyQbb6hpvtc5aoxQx3dbnwwC0FTHVWZMBes7BpYhO8B4frz0fBshXxNAZJzVbsrDP8rlbC7lPWkPEGJEPA1hv/7QQ0/HqSGxix3Z0LIaLcyn88ogyTv7Jb60teB3p8jZTEZPlxQJis2ZrsgSxJtPfg12DYQDAQZvO2aUwl8zhgf93CLIM/O41A7hre1/Vf9vidoJ8zc1uqDg/m8JkPAu3gy2r5FxZRFG4ELwoaWPXUIdfeyYlGcjy1iheOUHS3lOSE2UkiDUZXUUMIWKsmdPdDvOtycYpdq8TG0c7KGJiGV5rDqBBOjlZaxQx+Rl3Rqxhgh57KVqLIcOJ2pp0fZ32T361kG30HlzPY62uCK8TMYUNkScn9JyRaj5X0khkh2y+8Sg9u0OaaPW78NE7lOyQrzx2WmuArAUkd3dVR6CmprNqEcvkIMkynKzxpUxvagKsQK+ZlhT0XRZZXHWNPYWO8efg5Iy3Rjt+7efwzL2/wcSaNxl+7GoQnD8FAEiENwIG72WttiZriQ+DkSVwnjZw3tJNTPVgvucaHL7pq7iw9Y8MPW4T5UGViLGrNVMT1iGaVgpSLgej2SJp4aqmEDHFgwOJbJb4mS4Esb7oDXkrLih2qp7zB20a/ks8qhvJiLHSmmymSnudcrhbsyebWNL2ZI3kwwB53tANEzGLCU4rQUsRE8/ymqKvmgJGMWuy4xO158MAeRkxlKzJxqLEcs34Dtla0aGqFeZS1liTEdXStgYVMSGvtfZPCzGTrJwRAyh+3i4Hg3hWwKd/dgySDNy8oWsRedjiJYoY84qlwzMpyLKi3AKUIlkt3wntPWjR34PdarbbUlLEyLKMP/+vw5hO5LCuuwWfuntrTX/Psgxa3NYQhUQNs3soXHY9pSsKiyuVAaXQJUgyPE4WPUGvtq4ErMuJmU8rBUCWAcK+xjvzF4L2XABUH7JNC2QON8uajBclTCVoEjH2sSYjioOOgFu7LiPhVvN9BJMVMVoeh0HqA9JwZmci5txMErKskA/1Kpm9buXzyvCioXWbWhQxgL72XKiIIfvySvkwBESFGElxltehCLnb17q0iBgA+J2rBnBFfysSOQF//+jJmv+eNLZu6KGzL4ykMmApNY1tefHTuPW/b0DPpUepHJ+32OJq3eGvYvcz70d49rAl56eJYFQhYuJtmww/Nu9pQ9bXpREyZoPkwyTCGwwnmZqwBlSJmOPHj2NoaIjmKZpYYsi3JSOdLT4TQyxLdeiQrIbTpYgY1fpisAoZP+muPTQSrfMq6YIUzRrKiCGd3hZsKqerCJyuhOvWtKM94MZ8mse+4TmjLs1UyLKMAyoRs7tOIsYIa7L8DnW7EDGaIsZgIoZ0XrdXWcAgth+jBdZkqiKmr0ZFjNZRS1kRQ8EzvlZ0tliniMnyohbOu3XZKmLKF6rcTlZrTnjq1AwA4L03r130Oiusyc6qc/iWvpBGJE/XYE82U0YRc3o6gbhNPqtK+NfnL+DJUzNwO1l8/fd2VfTgLwarLEb3VWFLBujrrXIZMSS7b7DdD5ZlwLJMnt2tNTZQZNxq87vBssZvlo1SspZCmhM0q8pKIdu0QDJizLImm4xlIcvK2NdBwZozv+HM6uIwzXwYAFqHOmdykxN5ZlcYVPQm+xyzM9BqQaP5MIC+F5QNVhEapYg5oTYvbapyzUwIKUGSLW2CESUZk6pi18rcxXrhYBl85o1Kg8cPXh6tuaZB6ikb61RqlYMky5hJZujYkskyQpHjYGQJ6eCg8ccHILhVZQVnvrKCFbLwJxS7uUTbRtPPTxtEEZOkQMRMrL4Hz77pcZy86hOGH7sayIwTidZ1iLdtpnYORrReSXg5oeoR7P7776/6oD/60Y8AAAMDA7VfURPLGkQR05rXJaiFWFJWxCSyvOYnv9D3miwUSlmTkc3+qo7K3YE7B8IAFAuiLC9SkeQ2Ak0R41maGTGEFGtkYet0sHj9tl78+/5LePjVCdy0vsuoyzMNF+fSmEtxcDvYui2UfAZYnFyYUzrUQ16nVkC3Gl5K3bS1FjCIImY2ySHNCRAkPWuqVmsy2l3QoxT98WtFh2rnM5c0XxFzajIBUZLREXCjN9RYZy1RxOQECZwgaZ3CVkHLiKmCxN62ohVHx5QCyI6BMK5b077oNWQOMZOQzy88zad5zeZqVZWh4rNFFDHdQS9WtvkwOp/BqyMx3LjeWMm/0Tg6FsMXHzkBAPiruzZXDDAuhaDXBcSyps7jkiRrzQ/Xr+0o+1pi7VjOmiw/H4bA73YizYmWKWKIJQ6trC0j5u1yILZk7QE32izKC9Otycwp5msWQq1eKuQZGSsFSUZOkCzdF5Dv00oK+TCA4ngAwHS1ObEm6zNoDUP2OXYm5xvNhwFQoCJMc0JdpP5CZPIyV6tV1emKmEIiptbAd4/TgaDHiUROwFwqh1YD8oLqwUwiB1GS4WSZhhoHrcSVQ224f3c/fnRgDJ/+2TH8+H3XVz0+EiXThio/t1qQyHJIcTxa3BQUp8lLcPEJiA4Pkq3rDD8+AMx3XwMwDsQ6zc/jaImfAyuL4DxtyHmNr310jj+DVSceQqzjCpzZ+RHDj18WkoiWGFGNLD+SaWL1PZhYfQ+VY3sy07jx528AIOOJ33m5qbgxCVVXBVpbW6v+10QTpZCviCHwm6SIIfZJXUFPwfkBfYE3PJMq6mt8iXRddlTeuKxs86GzxQ1BkjWLGzvBCEVM0EJrshMTtcnUS+HuKxQ//V8dmzTdy9oIEFuyK1a2at2jtcIIa7Jz06otWXeL5dkiBOT9MNqajChbqiViWv0uhNTN/Oh8BidUNUx/2Iewv7YCl1kZMbS6ZGsBKWDOWuDxfVQds7f2tzb8PJOOWsAeqhiiJuyuIvB2a7++lnvfzWuKvhdW2O3kd9mS4N5qc2JESdaK5J3Bwu+fZk92yf45MZ/48RHwoozbt/Tgf1xXv+rcCsXWqakEIikOfrcD21eGy76WKGKmE7mSpMMFdW02lLc2a/GYs6YsBWKpSIuI0dfMlIgYNeR6TZXkJg1o1mSUVD8LMR6jm+WQv942w4a5HGgrYlwqicabbU2mNZMYZE3m0RUxVquYSsEIRYyDZeBRm0SMejaHZxXLtDa/S7OarQRdEaMT75EUp61balFWEHuyOQtzYggR1RPywkGB3DULH3v9JrR4nDg8EsV/Hxit6m9kWcbpSXqKmHg2h6wgwENBEROaOwZAKeTLLB0Sb3rgNpza/ZeYXfFbVI5fDi0UM1QAwJWLoW3mgKZMMRP+5AicQgaiw4tUsOnIVAsEpx+sxIOVBLBSUxVjFqoewR566CGa19HEZYJ4WSKG7ubknOZXu3hz2R/2we92IM2JuDiXwrruwoUDyYgZqoKIYRgGOwfa8PiJKRy8FMWVQ4s7ia1EmjcgI8YiyX4sw2uL23q7gAmuWd2OzhY3ZpMcnj87i1s2dhtxiabhlUuN5cMAeidcI9+9Yc1L3h62ZIAZipjqO0kH2v04Nh7Hpbm0ZuFWqxoGAPxqUSBN6Ts3ZidFjGZNZr4ihqhAas3wKQYHy6DF40QyJyCeFaouSNBAlhc15UNXS+VC1XWr28EywPruIG7f0lv0NSRjxFQiJq/wRGz+qrUmi6Q4SLKy92xfQITuGgzjZ4fHcdDmREyGE/HqmEIWfvaNWxsiC61QtpJ8mGtWt1dUiLX6XFpn8+h8umgYta5W1sdkv/ZcWlPwJmRfByWFqI82EaPO6WssyocBoBWGcyY1yZAsB1pEjINVcjEzvIhUTqBG0lUDUugeoJQHpxMx5jY4GZ3HQTJiZFlpgGmhkKfTKIiN6vruxordfrcDOUEyrHmpntxIYuU7Fc8hJ4jwOB04OanM8YPt/pryjNoDblyYS1tib0swoZG7xhCDVqE75MUHX7sOf/PLk/jbX53EHdt6NbV3KYxFM0hxIlwOpmq1ci2IZXKQZVDJiAlFFCIm3l5b7t5SAclQSYQ3UDm+4Cb5N+bbrokuP85e8QE4hDTAGq869aQmsX3fXwKyjJdu/zfDj18OjCRABkPlvgBAdPohgwEDGU4+Cc6xNFV8Sw3W+mQ0cdkhmlEWRfnd4AGtwEiZiClTMGZZRtvkn55aHJ5GNvtD7dUtKOycE0PeZ38Dm4oWDwmxNLfLm0jU+8O+huXmTgeLO7cpqpiHX51o+NrMhpYPM1g/EeM1wGu+3PfKKnhcdBQx9ahGSLFjZD5ddz4MQNfCUZRkTKr+6vbIiCHWZOZvoo+riphtK4xR99olJ4Zko7idLEK+ymP/+p4gfvGnN+Hf33NtyW5Oswl5UZK1bv11XUH0qNZx1SpiyHvQEXDD6Shc/pJx9OBI1Lbdz0BhOHNfa2MFHlJoNNN6Z9+5WQDAnjXlbckApamFFOdG5ovbkxWzJtPztJanNRkhmmhlxJAi6kILXzNBSLqcgZkV5UAaEWgRMUDec2mRUouAekaMak1mOhFjcOHb62K1uc8K9X8lcIKk7U3XNxiI7s/LMDIC9Sh12vwurTGTkGpkz1erA0I7sbdNmd/MQzBuwphiFv7w+tVY0xnAbJLD1x4/U/H1Z6b0faHLYXypcTqZ1uwrjUYochQAXSKGkXi4MzPwpsyvPQRJ4DulfBgSZO/kF9fSaCPn68b5re/B2R0fonMClkV49hBaI0cUht5EtE++gFv/+3pcsfcv6ZyAYSG4lHW0FZ/d5Yq6R7Ef/vCHeMtb3oLrrrsOu3fvLvjXRBOlUMyazOc2Z3NSKThwo7qQXZgTk8oJmq98NdZkgJ4Tc2TMhtZkXOOKGK24aHKhQwttNMhz9q7tChHz6LFJ00JhjUA8y2v+u7uHwnUfx+8ygoghnW/Wdc8uhNdJSRETrc2aDMjPOcjg+Hj9ags/RbudqXgWguplXSnI3QxYZSvBixJOqOO/EYoYwNo8rXzk58NUq6LYsiJUVsVjtjXZSCQNTpDgcbLob/NpWTeTVRIxZB7vLHJPm/tC8DhZRNO8RvbYEWR9sqGncStIs59NQZSwfzgCALhhXXU5PGT8JPaw+ZDyMreGiiliLLKAmtOIGDodhaRYSS8jxvrmCmIvanZGzEqKRVO/BQrChZBlWcuIGaCWEWO+NVkiy2vjmFGKGIZhbNNIUQwX5lIQJRlBr7Oq3LdyMFplV0+DFsMw2tqaPKMn67SiJnmVEQsVMUYrtKyE28niU/dsAQB8d+8FnJ0ur3bQ8mEo2JLxoohIOgOfy3jbMEYSEJo/CYAuEdM+9SJu/ult2PksJcKgFGQZLSoRk6SUocJbSMTQBrk3RpYU1Y2JCMZOwyFWt9epF1aSaJcr6iJivva1r+Gd73wnenp6cPDgQVxzzTXo6OjA8PAw7rzzTqOvsYllBELEhPKIGNLpneFEqp2olaTSGzRFTOECg2z0w37XomyZUiDe9eR+7QRjFDHWZMQQmfrmOhQFxXD1qnZ0BT2IZwU8f3bWkGOagUOXopBlRa7fSOGcbL6ydW6+ZFnWN1wNeFQbDVqKGNJJ2h+uzZoMUDyzSbBqPdZkZJykoRzUQ27t4WXdoRYw59McRMm8Ys65mSQ4QULQ49TyKRoFUR1YXcgh9l2NFmzy0aIRMeYUvEmX7ZquFjhYBr2qIqRaazKiiCkWnOt2srhCzcU5eClqwNXSAVmfGOG7rj+b5szjR8ZiSOQEtPpcVc/hg5oiJrPod1OJLHKCBCfLFFgqBizOiCHFvw5KihhfnpJVMnh8lGUZ50lGjIXNFUQRY1aDjBnd6wGTx8tiiKZ5jaCkZUNKOtUFExUxE6qit9XnqsnCqhLIHGd201k1IKqDdQbkM2rkLm/MfWpW4N21jSHE9pestU+S+a5GK2qiRrQyI2bc4Mwiq3HLxm7ctrkHgiTjsz8/XrZeczqvYcRoxDI5ZDgBXENeRQAA74NJREFUXgr5MIwk4Oz2P8XY6jciFVpl+PEJBJd19l0v3/odHL3mQWr3pxfzzW9o6hp7Cr7kKDW1iuTwQWKUsdJssqIlqijRaFnKAYBIFDFck4gxC3URMd/85jfxz//8z/inf/onuN1ufPSjH8Vjjz2GD37wg4jF7KcAaMI+iKaVYlQ4PyNG3TQLkgyO0sKdFyVcmNVDxYtho9pxc2oBEaPbklVfmPNTLJo2CkMVMSYTMcdJd1SfMV02DpbBG7Yp+Qc/f3XckGOagVcuNp4PAwDeBjNiJuNZpDkRTpYxrHBtBIi/fNZAW5NEltfGr1rsu4g12b5zc+BFGa0+V10FED9F5eCYRjDZo3Ovze8Cwyhr6fm0eRtpkg+zZUUIrEGEVEgdK+MZaws5M8nSJES9CJhcpDq7QNWqWZMlGlfEAMBudTw9YOOcGK3T1ABVqNnd3iQf5ro17VUTvmReIQ0x+SBrs/42X4HVXMAmGTG0rMlIthsAZAVj79Euc7qWEWPw/RWDLMvaHEgzz4Gsua1UxBCLv66gR1v/GQ1iTcaZqIihZS1nNlldC0hjz3oDmqCMyIskWGghWguIImZ0Pg1J0gPfa93z2YGIIeTgclDEEPzV3ZvhdrB49swsfn18quTraCpiYlkOvChqqkkjITm9uLTxf+D4tZ8DGHrpDRpZwZlMxDAMkm2bMLHmTZBZ4xVFgE4yOcQsGMm8BjR3ZhY7n/0Qbnj4HrC0lCMMY5lqhGT7JMPrqZ1D++yEJhFjFuoaZS5duoTrr78eAODz+ZBIKAPJ29/+dvzHf/yHcVfXxLJDMWsyf95mgBZxcSmShiDJ8Lkc6AsV32iRDtMLs6mCTvpLEWVBOdhRfWcPKZpyomQ7yyuy0CZkUT0gm5MML5rW9SbmLcqNUsQAwF3bVwAAHjs2Zcqm3wiQQuHuBomYRr3mz00Tf34/FR/gekEKDEZ+nmSj3+Z31RTaShQxxGJlS1+oru5FmllaJLy3FqUPTTgdLNrUHDEzc2KOqlaSWw3KhwGsyeEoBl0RY1yhscXkLA6iiCE2iD1BPSOmGjWtTsQUL5DvUi1F7ayIIV3QRhQ4QiY3VOxTiZjr11ZnSwYAK4kipigRszgfBsgbKy1SxJBcAmqKmPw1s8H2ZMOqcnyw3do5nRTZzFg/xzOCphIxRRFjkWUeoCsNBihmwZHnRpBMVMSoNlArGszNWghCVtsxI4bMh+u7G58LjLQmG51XLETdqoVoLdCJmAwuRdLI8CI8Tharath/A3qzRaSZEWMohjoCeM9vrQYAfO4Xx4u6DoiSjDPqs7nRIBvxfETTWQDWK/cbAU8C7YWU6VkjtCG6AhAdHuQ87XAIi5XMtECIilRwCJKT3nfOCiKGETn44xcAAAlKlnIAMN+5E9P9t4B3N1ZbaqJ61LXK7u3tRSSi+DwPDg7ihRdeAACcP3/e1iGnTVgPQsSE84LWnQ5WsyFI0wof1SxNAiU7nbuCHoT9Lkiy7m8LNKaIAej5eNcLUjTzN6CIIdYfyvHMub+Lc6m6F+XlcNVQG3pCHiRyAp49bX97MlGStULhlYONTZa+Bi286vGBNgM0FDGjERJwWxtZsTBPpt7skfygX6PnWUIy1bpppglSxJxLmreRJhk+2/qNI3rtmBFjFMzOiFkYANytWoBmeakqxVE5azJAJ7ZPTcaRtKEVTSLLa9/VDQYU37Ru7xx9kjAniHjpAsmH6aj67wbziJiF496FEmszsrax6jPUFDElCL9GwbIMvC5ljjN6fUnmdCttyQDdmsyMjBjyneoIuKmpRADrLfMAndCsdR1TC1yqIoY3sQltIkZJEeMxVzVYCxbOh43AyNwpbQzpDNRsdatbk6VxclJXVdR6HE0RY1FGTJYXNTUOTZWdFXj/a9ahN+TF6HwG//zM8KLfX5xLgRMkeF2s5ghgJKYTaSq2ZADQNfokArGzgES3riFYlDUycPo/MHD63+FJl1YzNQqZdeI3v/MinrnvSQhu4/ZSlRBUs31oZd8QiBYQMYH4MFhZBO8OIefrpnaeszsfwOGbvopodzPv3SzURcTceuut+NnPfgYAeOc734kHHngAt99+O9761rfivvvuM/QCm1heKKaIAXTJfprSxpnkw5RbsDIMUzQnhlhiDHZUv6BwO1ltM5I2yHPXKJCOp3wypVZ4nA5to2xGEQeAtijf2Fv7orwcWJbBG67oAwA8fGTCsOPSwqnJBJI5AQG3o+FuI59bJUDr3HzZMR8GoKOIIaqRhcRKNdeSX/yuJx8G0O12JNlYggnQu2RpBhXXCrKRnjXJWkKSZBwbVxQx2/qNV8RYT8SoipiQ8RkxZhS8ZVnWGirIPO51ObS1RDX2ZLNqUaaUNVlPyIv+sA+SDLw6EjXgqo3FaVUN0xvyotXfuK2EmSThgYtR5AQJXUFPTcQ9sUtMcaJGcBBcIkTMgrUZTfVgJYiSjKi6zqVlTQbk2d9SUsRY3VxhpjWZWZ3rZA63kuTVFDHt9BUxvIn5bvk5d0bCLo0UCyGIkvZdNYKI8bmMG0+IUr6efUG+IoZkgtazz7HamozYkvndjqqzZZcK/G4n/tddmwEA33zqrPbdIzidZ0tmlMUvQZrjEc/mtOfVSLBiDtuf/3Nc/8ib4U1PGn78fEgOLyRGuQcz7cmGTv4bNh34W/gTl0w7p1kgiph42yaq58n5upD1dYGRzWs0CEZPAwASrRuABvPAmrAX6iJi/vmf/xmf+MQnAADvf//78a//+q/YvHkzHnzwQXzrW9+q+jjf+ta3sH37doRCIYRCIezZswePPPKI9vtsNov3v//96OjoQEtLC9785jdjaqqQxb106RLuuusu+P1+dHd34y/+4i8gCIULpqeeegq7d++Gx+PBunXr8N3vfree227CAJQiYmhtKgmq7dwn9mSnJhtTxAD6PVkZzFkMpBuvEWsyIL9TzJwNyokJZVG+ucbQxmpw93aFiHns+JThAe9G4xXVlmzXYFvDhBQhLOq2JrucFDGErKhDNTKQN3bUa3uVb0djdE4M2UjVc2+0QIrlZiliLkbSSHGK4m5Np3Hd4CGfmhFjcUdtJTVIPSBETE6QqFtUziRySOQEsAywOu/z6Q3p9mTVHAMo/x7sHAwDAA7akogxLh8GMJck3HdOUZtev7ajJmtGr8uhfcYj84UFnwslrMlo5mlVwnya05xGiL0iDfganLtLwS6KGDKHm2FNRvIMVhk47heDlQQhwcg8fUUMyYjhRck0lwzdmszYNUwLIWJsppB85eI8OFGCz+UwJNtPV8Q0fp+aUqeOfQF5LqcTObw6qjTGbKpjviPrx/kUB8lEQpBgghCDrd66rIjtjnu29+Ga1e3I8hL+5uETBb8j9RMa+TDxLIcML1BRxLRET4GVBXCeNmQDKww/fgEYBgKxJ+PNIWKcXBy+tJKFm2ijqxqxAkQRQ9O6CwAO3vwNPPumxzG74iaq58kH5+3AdP9rEOm91pwTmkgyXe6oi4gZHR2Fw6EXhd72trfha1/7Gj7wgQ9gcrJ6FnnlypX44he/iFdeeQUvv/wybr31VrzpTW/CsWPHAAAPPPAAfv7zn+O//uu/8PTTT2N8fBz333+/9veiKOKuu+4Cx3HYu3cvvve97+G73/0uPvWpT2mvOX/+PO666y685jWvwaFDh/DhD38Yf/RHf4RHH320nltvogFIkqwTMf6FRAzdjbPuLV9+YUgKHKTgwYuSVqRcuNmvBE3lY6ENQTEQYijQKBHjNbe778REfaGN1WDXQBv6Wr1I5gQ8fXrG8OMbiQMXFSLmygbzYQCdjMvWq4ghnW8WF20Wwtug5Vox6ERM7QUM4sfudrJ1F7hYltHGSSMLObIsax3BtrImazHXWoLkw2zuCxUEfzcKvdhtcUYMUcQYmBETyMtKot1wQObwwXZ/QUgrUfhMxSsTdnpGTGkiZrdq90jGWTtBI2IMUiDq3d70n829Wj5M9bZkBKR7/1JeTowsy5oiZtVCRQzlxp5ymFc7sFt9LqoZK35K60vSZb/G4uYKM63JDqmk646Vxikhi8FqyzwgPyOGHhHjVp97WVYUYmZgnJY1mU3m73wIooRP/0ypk9yzo88Q1YFGxBiwZm5EKd/md2l75+fPKuT9pjqa79oCyucmSLIlTTDjqiJmOeXD5INhGHzmnq1gGcVJYu853dabrFM2UiBiREmCKMtwssbPra1zyncq3r7VFNXB+Oo34tKG39OsrmiDqCoy/j7qlmHrD30ZVz3xLrRNvUT1PAQOPq2pfJLLkGSa67sBh2/6Cs5v/WOq5xk6+T3c+oOrsfmlv6Z6niZ01DWSrV69GjMzi4uVkUgEq1evrvo499xzD97whjdg/fr12LBhAz7/+c+jpaUFL7zwAmKxGL7zne/gS1/6Em699VZceeWVeOihh7B3714tk+bXv/41jh8/ju9///vYuXMn7rzzTnzuc5/DN77xDXCcshn69re/jdWrV+Mf//EfsXnzZnzgAx/Ab//2b+PLX/5yPbfeRANI5AStU3CxIsb4AiOBLMt5C8PyRVBdEaMsJMajGYiSDI+Trdlb3++xnyJGkmRtoe1vwJoM0DvFzAqx1BQxfcYvIArsyV61tz3ZKwYSMaSrNs2LNXcuJnMCJtUudKuLNgtBiBgju2lHo/VZkwF6zsGm3mBDxTlNZWdg8W0uxSHLS2AYoM/gbtJG0BFQFTEmha0eVW3J6s3wKQWzA9GLQZRkTVlkZEaMKy/fjbZF5dmZ4n74PVUqYgRRQiRd3poMAHblKWLslnlovCKGqLXoPpupnKAVu69f21nz3w/k5cQQRFKc1qU+0F7cmsys7KJ8ECucDoq2ZIAerm1kRkyGE7WCtpGqwHpAyFbaRIwsy9qzuXMgTPVcmiLGouYsWZbrtlitBfmNDIIJRIwkyZoVVF+rsdZkmv2mjazJvrv3Ak5OJhD2u/CxOzcbckyfRuw2Np7IsqzN1fU0aDEMozU7ke9+PdZkHqdDm9+ssCfT7A5ttKY2GltWhPD71w4BAD77s+OaKvqUwesUsxCeOQAAiLVvNeV8Z3Z+BKd2/yWygT5TzteiWneZoYZpiZ5G28wr1C3etPPFzoCBjJy3C5y39mafJhRIrBMOiTNNpdVEnUSMLMtFpZbJZBJeb32LIFEU8Z//+Z9IpVLYs2cPXnnlFfA8j9tuu017zaZNmzA4OIh9+/YBAPbt24crrrgCPT092mvuuOMOxONxTVWzb9++gmOQ15BjlEIul0M8Hi/410RjiKtqGK+LLehoBfKsySjYQs0kc0hkFUuTSiHvG3qUIs9YNINEltdsyQbb/TV3HdlREZPf7dSoIoZsUMzoNornhRTTsCYDdHuyx0/Y155sOpHFpUgaDKNb6DQCQsSIkgxerG3DPKxutrqCHtt5IOvWZPZQxFy7pgMMA7xmY2MhezTCfsfU++oOerSiuh1AFDGzJilijo8rc7yR+TCAPTzm51I5SLLS5Gd0bkWLSQ0Hmqp1ERFDFDHliZhISrGMYiu8B1tXhOB2sIikOG3+twuI5YdRnaak25sTJKpZHC9diECQZAy0+xaRJtWAdO/nEzEX1f/f1+pdFLDu9xhTVKwHJMeGZj4MkNdEYeA9np9NQZaVRina118JHpc51mST8SxmEjk4WKZu29BqQfYEVjVnzSb1pguanfokHxMAOMqWlYBSaOcE5b56DSZi7NBIkY/xaAZfekzpbP9fd2427HvqN4jYjaQ4RNM8GAZY01lfg1Y+SdjZ4q7bTpWQ4QuzxczARIxOZpHd8Gev24Cw34VTUwl8/4WLyAkizs8qqkoaihhacHIJdI0/DQCY6b/F2ouhhOC8QsQkwxuon0vQAu3NKeing0N49fq/w9ntH6B+rr7zP8PVj78Dq45/h/q5AIAVsvCkpwATGsMEJ/nckhVe2YRRqKkS+5GPfASA0rHwV3/1V/D79Q2VKIrYv38/du7cWdMFHDlyBHv27EE2m0VLSwt+/OMfY8uWLTh06BDcbjfC4XDB63t6ejT7s8nJyQIShvye/K7ca+LxODKZDHy+4ovRL3zhC/jsZz9b0700UR7RtFKwD/sWLxy1AiOFDkZinzTQ7l+0YV+IsN+NnpAHU/EczkwncVHzIK+9eODT7NbsU9QnnfQMoxBijYAUccywWSAKpRWtxoQUF8POgTD6wz6MRTN46tQ0Xr/NnC6VWnDgYhSAssANeRt/H8gzCigkXS2F+HMNdL3RBvmeG9VNm8jy2vhVj33XDes6cfjTr9NyleoFjdwpQnAa4TNuJDpbzNtEy7KsWZNtM7gYR76nVmbETKu2XR0Bj6G2a4Ayd0dS9OeBUr7z1WbEzKiKoPaAp2y2lsfpwLb+EA5ciuLApXnquRHVYi6Z06zV1vcYo0BsyRuPElkBnpbGVLKloNmSraldDQPoikKSbwGg7Nos4LZeEUObyDCqcJqP4Vl9Trc614DYW9EkCAHgsKqG2dgTLFgP0YCm1LKoOYt8f3pDXqpNF6482yChxgafekCK3t1Bj+F2gC0mWzBXwmd+dgxpTsTVq9rw21euNOy4PoPsHMk83R/21f19yidi6lHDELQH3LgwlzYtZzAfY9HlbU1GEPa78eev24hP/uQovvTYaazvCUKUZIS8Tq1JZimge/QJOMQckqHVSLQZozKrBFbMwcklIDnc1K3CgLzAd8oZKkA+EWNOQZ/3hDE1eIcp53Jn5xCePYR0y4Ap5wvPHsSVT70XsfZtePF1/5fquQQ3+dxSVM/ThI6aViwHDx7EwYMHIcsyjhw5ov33wYMHcfLkSezYsQPf/e53a7qAjRs34tChQ9i/fz/e97734R3veAeOHz9e0zFo4OMf/zhisZj2b2RkxOpLWvLQ8mGKdM/7NMsd4zddtQaKk4C505OJPEVM7cUYzafcJgt4QLd+C7idDW+0gyZK9okt2SYKtmQEDMPgLlUV83Ob2pMduKTYku02wJYMUDoXSVGy1oKOng9jL1sywHhFDCEr2vyuguJlLQh5XQ1/52io7Igipp+iZ3w96FDto8zYRI/HsphP83CyDDb0Gvs8mxmIXgozCeNtyQhaPMr90S56a0TMAkVMt0bElH9OyHtQTXftLjUn5uClaK2XSQ2np5T7H2j3aYRso3CwjDae0Xw+iX/89evqs4wYVMmW/IwYsjYbKrI2o505WA4RVcFHFH20oKnIDbxHu+TDAHqjEG1rssNqIPgOyrZkgLWWeYA5+TCAYvVL1pW8CYqYcYpF76DHPhkxjx2fwq+PT8HJMvjre68wJBuGIN+muBGcU8eQhfN0LchXnW/sqX/Pp60hrVDEXAbWZAS/e80gtvSFEM8K+OgPXwWgEGhWk/m1oGvsSQDAxNDdpuTDAMCml/8aN//0tVh59r+on4uRBATiwwCWJxFjJsy+Ny3bJ7CC+rlETRHTtCYzCzXt5p58Uhmo3vnOd+KrX/0qQqHGi6Jutxvr1q0DAFx55ZV46aWX8NWvfhVvfetbwXEcotFogSpmamoKvb29AIDe3l68+OKLBcebmprSfkf+l/ws/zWhUKikGgYAPB4PPJ6lw+YvBUQzaohpEUVDQOvuM36DolmaVNm5v7EniGfPzOLUVELbuKzqrH3j4vcY02FkJEhhwm9A55+ZnWInJpRJYXMfXanzXVf04Z+fGcZvTkwjy4sVFVRmgxBS2w2yT2IYBj6XA8mcUHNIZ60Ep5kwWhEzGqnflsxI0MidsqsihthKzJlgTXZMVcOs7wkuss1sFPmB6KVsXWljOqEUqropdCi2eIjdDr15IJ7lMa0SKYutyapTxBCLu84qCuS7B9vwHZzHq6PROq6WDs5M0wnADXqdSOYEaoXGaJrDMdX2b8+a+ogYUjgej2YhiBKcDlYnYoqszbQsDgssoCJqplWb36SMGN64QjexG11jA5UrGYdpW5Md1vJh6NqSAflEjDV7AjPyYQhcDka1vDWDiKFX9Nbmb4sb6tKcgM/8TLFc/6Ob1jSkFCkGv0F7cH2/3QgRo3+OmxrY82nWZCbZ2xLIsqw/k8vcmgxQGjo++6at+J1v79P2ExuWkC0ZALx6/d+jc+JZxNu3mXZOwaXUUM0oesusE0/f+xsEo6eRaemnfj6NrODokxWMJGDw1PeRCG9ApOdagKVbtzHbdq0legYAkAyvp36upiLGfNSl4X3ooYc0EmZ0dBSjo6OGXZAkScjlcrjyyivhcrnwxBNPaL87deoULl26hD179gAA9uzZgyNHjmB6elp7zWOPPYZQKIQtW7Zor8k/BnkNOUYT5qGcIsZvJ0WMusA9PZXApbyMmFphx4wYQgoFGrRIAszNPtAUMZTyYQi2r2yFz+VAhhcrFvaswBm1K3q9gYvcekN/te9VA51vtEAUMYIkawGSjcDMAkY50BhTRjVFjM2IGLWbMZETqGc2HSX5MCuMH1/IOMmLMvXu7lLQ1CBlQurrBZlLaBaqSHGnO+hZZMlI7C+mEzlIZYKhia1XNe/BQLtPO6ZdQOw5jS5w0J7HXxiOQJaVDmmiXqoVJL9KzAvlvkCsyYooYgJ5ihjZBF/tfJhvTWagIkb19q8328FIEOssmmOmJMl4VVXEbF8ZpnYeAqv3BCOkoaSO/UytIPZktWYP1gNiTUaj6N1ik4yYrz5xBmPRDPrDPnzwtesMP75PezYbVcQUV67WgvyGp00NWpMB5iti4llBq2X0XQaKGAC4elU73rRT79g3miikDdnhxszK1yLn76n8YoNgtrJCdLUg2rUbYOhngeoFffpkhT9xERsOfxk7nv8zU9RMokWKGHOzfZafksmuqOvbKEkSHnzwQbS2tmJoaAhDQ0MIh8P43Oc+B0mqftH88Y9/HM888wwuXLiAI0eO4OMf/zieeuop/P7v/z5aW1vx7ne/Gx/5yEfw5JNP4pVXXsE73/lO7NmzB9dddx0A4HWvex22bNmCt7/97Th8+DAeffRRfPKTn8T73/9+Tc3y3ve+F8PDw/joRz+KkydP4pvf/CZ+8IMf4IEHHqjn1ptoAOWJGHoZMcRuodqCMek4PTWZ0Kwwhjpq7xCkSS7VC9K1bIgixmOO5Y4kyVoRajNFazJAUYi0qYotkgliF8SzPCZVcqiRTc5CEEuCDF/95yiIEi7MKt8NO2fEAEDWgEIOISusJmJojCkayWQzRUzI69RCf2nnxBwfV4pxWykQMQG3E8RBJJ6xZkwhhAINRYwZdjulbMkAhVhhGECU5LIFl1qsyYiaIZLiTC/kl8LpKVURY3CBQ7fOo/NsEluyG9bWp4YBFKsjMvaSNRlpkimaEaM+k5JM39pqIchYRduaTLMSMmgukGUZ52pUj9MEaaYQDWqmKIbh2SSSOQE+lwPrTWgoIc9l8nJQxJBmGBOtyWgUvQnxb4YFcymcnIzjO8+eBwB87t6thllT5sPvqq8hayGMUMoPdvjhZBl4nCzWdzegiLHImoyoYdoDbuq5U3bCx+/crNUWtlDeqxsGWTYlBL0YCFnh4kxQVph8j4IrCJGluwYiCEWUOItEeIMpJBNvqtqHRyB+DgCQaKWviOHdrYh0XYVIz9WWfS8uN9Q1m3/iE5/Ad77zHXzxi1/EDTfcAAB47rnn8JnPfAbZbBaf//znqzrO9PQ0/uAP/gATExNobW3F9u3b8eijj+L2228HAHz5y18Gy7J485vfjFwuhzvuuAPf/OY3tb93OBz4xS9+gfe9733Ys2cPAoEA3vGOd+DBBx/UXrN69Wo8/PDDeOCBB/DVr34VK1euxL/8y7/gjjvMCXVqQkdMLWyHixExHmM3lQRpTtCksgtDfkuBBOESKxOWqc+2J+ChRy7VC/L+GmtNRre4eDGSRoYX4XGyWFWk6GI0Wv1ujMeyiFpUNC0FUiTpCXmKkpn1QiNiuOo3zKPzGXCiBK+LtaUHsicviDbHi3XnuhDoRIy11mQ0xhTNmsxmihiGYdAecGMqnsNckqMaenp0TFXEGGT5lw9WzeGIZwXEswK6LdifTsdJRgyFjmETgtHPlSFinA4WnS0ezCRymIpnSxItRBHTWYUihnTS5gQJGV6kUviqBbKsNyM0UpgqBqKIiVMqNO49NwcA2LO2s6HjDLb7MTyTwkgkjUSW14prxYgYXx4Rn8wJplqMRjRFDF1rY62D3SC14HQihxQnwsEyWiaPlci3iORUOzqjcWhEIeCv6G+lcvyFCFDI9akFZmXEAIBT7T7gzCBiaCpi1LVjhhfBixJcJjwn+ZAkGZ/48VEIkozXb+3FrZvodOyTOa5Wi+J8ZDhR3283QGy2+lz4P39wFdxOtiEiQ7e3NVfZShRafa3L35YsH72tXvzrH16NExNxXGlQjilttERPYcdzf4axtffjwpZ3m3puwaWs5WirDxx8Gtc89nuYXvlanN/6x5Ac9GMXJlbdg4nVb6R+HgDoHlVcj6Jdu005n6YaEejbd/njF8BKAnhXC7ImZMTwnjBeee13qJ+nCR117S6/973v4V/+5V/wxjfqX7Lt27ejv78f//N//s+qiZjvfKf8h+31evGNb3wD3/jGN0q+ZmhoCL/85S/LHueWW27BwYMHq7qmJuihnCJG36AYS8QQNUx7wI22Kq0i/G4nBtv9WuflirBPs0moBT7NHsOOihgDrMlMCPkFgJOqLdmGnqApm2VCFEbT5gc8lsMZtRhpdCFO95qv/jklXW+rO1sMDQw1CizLwO1gwYmSIYoYsrlcboqYeJbXvr92y4gBgI6AB1PxHGZT9DbSs8kcJuNZMAw9xV3Q60I8Sy+HoxK0jJgq1CC1Qifk6c1zlexOekIKEaPcZ3EyrRZFjN/tgNvJghMkzKd5y4mY6UQO8awAB8sYnt+hK2KMn8en41mcnU6CYYDr1rQ3dCxSPL4USWv5MB0Bt3b9+WBZBn63A2lOVHJiTHTaIgRRh2nWZMZ878h3bKDNZ3hOVj1wFzRTSKARuUPyYXaYkA8DFDacSZJs6tpJkmSMmajsJYSFYII1mZ7HYfx9kfkNUFQx1e4jjcIPXh7BKxfnEXA78Ok3bqF2HiOsyYZnk5BloM3vatia8TWbuhv6e0BXJdJWVC/EmKrQotk8ZFdct6YD19WZBWcF+i48DH9qFKH5E6af2ywbqJ6RR9ESPw9m5Nc4d8UHqJ5Lg0lZmM5cDJ0TzwIAJobeYMo5BVcQgtMH0UH/+90xtR8AkAxvNO09bcJc1FXVjEQi2LRp06Kfb9q0CZFIpOGLamJ5QiNi/Is3zj5K3sm6TLq24kW+D3uxjstqYHX3WzHoGTGNb7SDWgGO7v2RfJjNDYQ21oKwTa3JytnzNALdmqx2IsYOFial4HGpHvMGdAwTSw+rVSNGe8yTwkzY7zIkN8pokI30HMWwVRIkvrozQO09oK06qISZZPUkRK0w1ZqshKq1R1X6TMZKE3a1KGLyLSrnTS7iFANRw6zq8Buu7tAzYoyf7/YNK2qYrStCCDdYSSc5fSPzGY2IKafc0Elr875zsixrzwvtjBgf6WA3iIghTUtrGrAUMhIOlqGuqjg8GgUA7BgIUzn+QuQrc41SMlWL6UQOnCjBwTKmdOoTW1GesiKGFyXNepOGNZnLwcKrriVp73UWYi6ZwxceOQkAeOD2DVTzRowgds9q1ob2GEOsyoiZIMTgZaaIWXKQRPRdfAQAMLHqbtNPryliuDjV8/QP/wQAML763mVXzO8Z+TVYSUAivBEpE8LsASAX6MWTv/0CnnvjI9TPNde7Bxc3/D7G1txH/VyL0LQmMwV1ETE7duzA17/+9UU///rXv44dO3Y0fFFNLE+QwnY5RYzR6pFzdS4MN/bqrx8sEgZbDcjCNmWRH3QxkKKEER2+ZFNJ2zv5hFqE2tRrjqcPKRjZjYg5o2YEGE7E1BH6e25azV2yyYarGEhXb5ZvrBCQzAmYV58Fq1Ujfq3obcyYYmaHbD0gRXOa1hJHxxR7mm0r6HVFh3x0czjKQZZlutZkHjLP0ZkHsryoqVNLKmLUgseUmqFVDMRqtDNYXYE8PyfGatDKhwHyiRjjP7+9ZxUi5voGbckAYKA9TxETUeafVWWy+zQbRxOJmHhGgCApm1faRAzJdDCqoK8RMZ32aa4gqphcg3N4MeQEUWvy2bEybPjxi8HjZLW8MJrEdTGQZpK+Vq8pynKiiKFtTTYZy0KWlWeFlgqNqO7iJs/ff/PLk4hleGzuC+EPr19F9Vxkv8qJUt25PufUMcToPUq96FDtISMpDpJkXlGRpkKrCePQPrUfnuwMOHcYs703mH7+TKAP46vuwdTA7dTOEYgNIzx7CBLjwLhJVmEA4MrOYeczf4pdT72X6nn6LjwMAJhYdRfV81iFVOtanN79UUysvse0c17z69/HrT+4Gq1zh0075+WMuqqxf/d3f4e77roLjz/+OPbs2QMA2LdvH0ZGRirahDVx+aKcNZmfmiKmvoKxIYoYj7Edi0YgrRZwAwZmxCQobyhPThJFjFlEjKqIyVhfgMuHbk1Gi4ipQxFjkw1XMZAuxpzQ2PcvXzVSzAbHTBiuiCH5MDbdMJLCCs1i+HFVEbN1Bb3xJUSx2F0J8aygBZZ3h+gpYmjNAxfmUpBkhTAopeghihhiwbYQvChpz1BXFYoYQC+kz9vAopIoYvLXJUYh5KVHEj5/bhYAcP3axq1KBtqVMWo0ksbFWaWoXG5tpjX3mNgIM6daKAbcDuq5NPU0UJQDmdPtoogBFOIizYkNz+HFcGIiAV6U0RFwm9aIwDAMAh4nElnBdCJmRCVizMiHAaCRPbStySZiypjf1+qlZvUW9Dgxk8hRbzrLRyTF4UcHRwEAf3PfNurkWX4WS5oXEarjfPU2PtICmcNFSUY8yzesyqwW4+SZtOm6ugkFfReVIv7U4B2QHebv7TLBQRy77q+pnmPF8I8BALMrbgLn66J6rkIw6Bp/Rvl/kgCZNd5twMGn4U1PQQaDycHXG378yxWMxMMhcXBydC3zmlBQ18y+evVqnD59Gvfddx+i0Sii0Sjuv/9+nDp1CkNDQ0ZfYxPLBNURMQYrYip4y5dCfufpUHt9GxdNEWNDazK/ARY8NAs4BIksj5GIUiw2zZpMfT5jNlLEpDlBC1pdb3AxTrcmq74LbilYk5FCWKOKGNJJagfVSMBoRYxGxFgfzlwMHWrRfJaSNZksyzhCFDH99BQxQRPGylKYUcmJoNdJpTjcQtmaLN+SkSlhq9CjEkxT8eLKKULCOFhGU7pUQpsJJGC1OK2+BzSIGFqKmJFIGqPzGThZBlevaiwfBtAVMXMpDsdVJUNZIsYCRQx5Vtpb6Bf8jMh0yMfwrP3mdKJqzRmQ87YQej5MuOS4QgNWEIQAMBoxV/3qNsmajASjr6Bo20VTNVgKh0bmIcvAmq4Adg3SDz53O3S1Vr3Ng/Xut2nB7WS1z84se7IMJ+KYuqZcXUax2YS1YIW0FvK+XNUUjMhjxYWfA4Dp1lbEdg0AHDydUHvR5cdz9zyMF+74T+T8PVTOUQpb9n8KVz/+DgSiZ6gcnxVz2Pzig2ib2g/IdOfQhdAs84QmEWMG6qrGrl69GhMTE/j85z9f8PO5uTkMDAxAFO2jAGjCPiBETLGuFFJgTBu4ORElGcOz9Sli1nS2wMkyECS5rA95OWj3ZCdFjFqUMEQRo95flpfAi5JmRWAkSCdwX6vXtG4mooixQyc0AbEC6wi4Dbc80YiYKgtWkRSnWXWt6bTHhqsYPKqtSbbBblpCgK20AVlhdHGRqH2szr4pBS0jJmWcNZkkyTg0GsWvjk7iV0cnNdsrmooYLSMmYz4pP11DSH090AuLlImYMnN4T4hkxBRXxMyo70F7wF1113S7nyhirCXkJUnWbCmXEhGzV1XD7BwIG5K9FPK6EPa7EE3zODquFLqGyhS6iP1q0lRFDMmHofNdy4ffVbuStRSyvKjNc3ZSxGjWZBSJmO0r6RHwxUDmcLMbtDRFTJ2NZbWC7Ad4yooY0kzSF6aXx0EaKczMiDl4KQoA2DVAn4QBFLWW3+1EMifUtWdtZL9NEx0BNxJZAXNJDmtNEAQ8dmIKKU7EQLsP2/rNcXFoonZ0jz4Jp5BBumUQsY7tppxTkCSMRRNY0RrUxkdWzMHJJcB7woarRrrGn4Y7N4+ctwtzfTcaeuxKkB0uiA4PHGIOTj4JwUNpnmVYJNsWZ5bTRihyHMHYGXiyc0jB+GyarrEnsXL4v9Ex+Tyeu/uXgInRPoJbGb+bihhzUNe3Xi4R4JNMJuH1NsPJmlgMXpS0RWwxRYyPgnpkdD4NTpDgdrI1FxrdThZ/9rqNGJ5JYnOd2SR6Rox9FDEkg8eQjBivfoxUTqBClBAP700UvPFLQcuIydhHEXNmmk4+DJBncVKl1zzpeusP+wrsDOwG0v3fqL+8nRQxfoOztEaXiDXZXIOKGFGS8eL5CH51dAKPHpvCZF6WiNfF4u3XDVEles1QD5YCISG6KRExZB6gVaTKV8SUAiFiSlmTzagZQ9XakgFAGyHkLVbEjEUzSHMi3A4Wq+psCimHoIfOs7n3HMmHadyWjGCw3Y9oOqZliJZTK1upiKGVVZEPMhcY0ehzcS4NWbX/6zRBzVMtPE5j7EWL4dBoFICiiDETeoOW2Rkx5ipinGYpYqLKmE9TEUOazsycvw+pROGuwbBp5/S5HSoRU/uzSfbbnjr22zTR0eLBhbk0IgY285TDTw+OAQDu3dlvqtKuidqQbF2LsTX3IRVabVqAfTLHQZJl5AQBLocyz97yo9+CQ8zi2bsfRrZlpaHnS4XWYGTd7yDr66ViDbYQyRwHv9sFVn0/BVeLRsQYDScXh+j0QWatsQsXXEoTEI17A4AVwz8BAEysugdg6dZZEjkObodDUyALTvXeBDpKpiYKUdM38yMf+QgApXPiU5/6FPx+fRMkiiL279+PnTt3GnqBTSwPxPOK2iHv4seOdNVmOBGyLBuygNE8rzsDcNThHfy+W9Y2dH4jN8pGIa0Wy0iRohG4HCy8LhZZXkIiS4mIURUxZuXDAPa0JtPyYXooEDGuGokY4gNtE/uBUjCqiGN2AaMcjFYfjNno3oqBWJPNJWvfRHOChH3Dc/jV0Qn8+thUgTVFi8eJWzd1485tvbh5Y5chxHQ5WGFtQjAdJ0QMnSaZFoPt8hbibBW+88SabDbJFVVnzqpkVGcNZJRmTWaxMpKoQtd2t1DJCaDxbMqyrBExe9Z2GnbcgTY/Xh1V1DBBj7OsOtRvgQWUZk1mAhFTawNFOQzn5cPYqXjoUXPeOIMVMbEMj2E1P3LHyrChx66EgAVKLcA6RYwg0SVizAhGD5qUh0kgSTIOqYqYnSYShaR5MFvHmEL226vr3G/TAhmLzbAmm0vm8PTpGQDAm3b2Uz9fE/Uj2bYJx6/5jLnnzPEIeT2Q8prZFbIiCxefQPE2ovqRal2Dk1d90uCjFgcniBiLJdEXCiDkVdbZgqsFnuwcFbJizdFvo+/iL3F650cwsfqNhh9/ISbjKbT6PPC5lLlAs+/iE4afy5OaRMfkCwCA8dVvMvz4+ZBlGdOJFAJuN3pDCgGjK2KMv7cmFqOm6sPBgwcBqL7qR47A7dY3G263Gzt27MCf//mfG3uFTSwLEFuyoMdZtJjgV4kBQZLBiZLGzDYCYudklUw6kKfyMYpcahREcWRU4bHF40KWz1ErMGqKGDOJGDsqYqZUIqbbeGVQrV7zSyEfBjBSEUPICuutycg4aQQRk+VFzKoEh90VMbMprqoxNMuLeOb0DH51bBKPH59CPG9cavW5cPuWHty5rRc3rOukHqadD2JtEreCiFFVIrQUMQGKGTH5diflFDFtfjdcDga8KGMmkVtUlKtHEUMKOFYrYk5ptmR01jE0ns2z00nMJHLwOFnsHgobdtz8IvJgh7/seBBwm6+ImVaVduYoYoyzJtPm9E57zeluBx1rsiMqmTfY7jeFNMuHptQyUSkviJKmHDGr6UKzJhPoWpPpwej03DhaTG6kGJ5NIpET4HWxproBkKasepoHq1GuWgGjVNXV4OEjExAkGVf0t9rufWjCWsiyDEmW4XawECR9TOTdQXiys0veBirDC2hxuyBKhSQTYDxZwUgCei8+AnduHrwnbOixi4EXRaQ4Dk6WySNiyL0Z/7mtuPBzMJAR6boKmeCA4cfPByeK8DqdBeSg6FTvramIMQU1VWOffPJJAMA73/lOfPWrX0Uo1PS/bKI6ECImVMSWDND9rgElJ8YQImbG2s59v1qgkmVlI2lm4a8UyALbCEUMoHSKzSZzVGxpJEnWuoG39JlpTaY8o9E0B0mSq84UoImzqjXZehrWZK7auuDOzdjPB7oYjMuIUa3J2q0nKwJ5KrtGyV3ire53O7Rn3m4gGTGcoFhbkqJxPlI5AU+emsYjRyfx5MnpgiJCZ4sHd2ztweu39eK6NR1UcqyqgZYRY6E1GbWMGHUuSVJoOMi3Fy3Xyc2yDLqDXoxFM5iMZxcRMbMJpQjTGay+6NqmEvIRi4kYmvkwQL4ixrhnk6hhrl7VbshajmAw7xlYVSEIOUBZqVUMx8aVxhFan1U+yLzNiRIEUWpILUXUIXZTuZJnx2gi5rBFtmRAfnaReUTMZDwLQZLhcjDooaSMXAgXsSYzSRFDs5kkaLK16AFVDbO9P0xFBVkK/hqbsvJhdeNjKZA1pBnz+I+JLdmuphrGzlh1/DuI9FyLePtW02zJsoIIr9MBlmEg5YWvUynoyxLWH/oypgduU/JvTLjHNMejxeuCIOYTMUGIrBusaOx3r31qPzy5CDhPG+Z69xh67GLI8AJaPO5CJRNRjfAGkxWyjBXnfwoAGF9DVw0DKHWfgMeFZE6f21LBIcx3XYlMoDmOmYG62uIfeugho6+jiWUOoi4oVfBzOli4nSw4QUKaF2FEPKHVnfu+POIllRNsQcSQrmXC6jcKmt7JlyJpxRvfyVYsuhgJkmEkyYoVQbFMIzOR5UUtUHwdDWuyGjtr9e+VvTZcC2GEIiaZE7SwbjuoRoxUDhJbsv6wzxZqvWLwu53wux1IcyLmkpxWEImleTxxcgqPHJ3EM6dnCgp1K1q9uGNbL+7c1ocrh9psYZNBGhAssSYjGTEhShkxeQ0HaU40JJidoBZ70Z6QB2PRjKZKyEcjipioxRaVp1Q15EZKxX2SX5QTJOQEY5pg9p6bBQDsMTAfBgAG8sjwwQp5OTSVWsUgiBKOjitKix0D9APg8/PZ0ryIUAMF23Oq6myNzRQxtKzJSP7GjpX0P6eF0DNizCMIR/PmerMai5yaIoYeEZPmBK3Jr6+VHsEUVD+zpEnztxX5MIBOEtajsjtrceNjKbQHiG0p3YyYi3MpHLwUBcsA9+zoo3quJupHIHYW61/9GiTGiWfufcIURQWg5Ke0+jxwsmyBSpeGaqR96kWsOvVv6B/+MZ550+OQnPTJd0GS4HE6keN10uXgzd+gkk3Te+FhAMDk4OtMyYhJcwJaPC4kcvq90VL7hGcOwJ8cgeD0Y2rgNkOPXQwZnke734dEVh8fJ9a8CRMmkEBNKKCf3tREE9AzYsoVtQNuh0LEGLRxrsZbniYcLAOfy4EMLyLNiTC2JFEfaChiADrdfScnSXcpHW/8UvC6HNrnFkvzlhMx52dTkGTlu1NLIbFa1GJHkOVFjKik0NpuexVtFsKrFnHq8bsmIGRF2O8qqsYwG0YqB4kixk7BqsXQ0eJGOpLBmekk9g3P4ZGjk9h7drZA3j/U4cfrVfJlx8pW2xFLNFQH1UIjYih1QvtcDrCMQlyncoKhRMzZGvKoekLK/U3FFxdcZutQBZGmkUi6Ols8GhBEScvk2kjJoqYlL7MvkRXgaWlsbSBKMl4YjgAArjeYiClUxJQnYvx51rBm4PRUElleQovHiTWd9NecHierfe8ynKgRarVCluWCjBg7QbcmM5a0eFVVxJiZv0GgWRabqIghazaz8mEA/bPLn6eNBlFhhLxOquszszPeDlqQDwPUblNMIMuybk1mszGk0yRFzE8OjgMAblzfRW2t1UTj6FOL+LMrbjSNhAGALC9gc28HYpkcRLlQNQIYW9DvH/4RAGBy6A2mkDCiJIFhmEVqHxokjINPo2f0CQDA5NDdhh+/GDhRhM/lQyyj7y0EVxCiw/j31sknkW4ZxHz3lZCc9OdrXpTgdjoA2GvPfDmhScQ0YQpiVRAxfrcT82keKQM6xSIpTutkX2NhlkXAoxT0zSoGVALZ/BmXEUNvg3J8QlmYbO413wIx7HchExMxn+Yqdt7Sxplpkg9DJ0i3Fmuyi3NpSLKyMaVBChkJQlI0Yk2m2ZLZhKxwOlh4nCxygoQUJ2iB4vUgXxFjZ3QEPBiJZPCef3u54Ocbelrw+m19uHNbLzb1Bm1HvuQjZHIhJx9EIULLmoxhGAQ8TiSyApI5Ad0GHruW4o5OxCxWxJBu2M46FDGcIBmu9KkWF+bS4EQJPpeD2vfUwTIIuB1IcSISWaGm96gYjo/HEcvwCHqcuKLfWMXBirBPIx+GKlmTuc1VHpDi/hX9raaoDhiGgd/tRDInNHSPs0kOiawAhlEIbTvBY1DOWz4mY1lMxXNwsAy2rrBOEWPmnkDPuTNvrneq3wFOpKeIee6sory7ZnU7tXMAeRkxJpBnaU7AKbUJbdegEd4Q1UNvyqrtPudSHGIZHgxj7X67GMg8TpOIkWUZPzmk2pLtXEHtPE00CFlC78VfAgAmVplTxAd0oqIz4EOGExZYk6lEjEEZMa5cFN2jvwEAjK29z5BjVkKaF+B3O+FzOQsyYmiga+xJOMQs0i0DiHVcQfVcACDJMhgADpYtINAubH4XLmx5t+Hnm+2/GbMrfgsOIWP4sReCWK3ZwX7/ckaTiGnCFBB7j3JZBH4Dw1WJpUl/2GcY6VAPfFr3m3k2BKUgy7LhipgWmoqYCWUzsqnPfCKm1efCRCyrWepZibNqRsB6SmHNWuhvFURMvi2ZnQvfgG5r0kgRRytghO1ToAp4nMgJXMMFxqWiiFnTGdCsOrb1h3Dntj68fluv7a3x8pHvMW+muiLLi1oIezclIgZQCPlEVjB8nqslAJhYr02Wsyar4T3wuRwa6RlJcZYQMXo+TAvVzVLQ61KJmMbnO2JLdu2adsOVrC4Hi5vWd+HERBxbV5RfFxAbR7OUB4fVAPjtJtiSEfjcDiRzQl1WQgRkTl/Z5rOFfW4+SM6bkcV8Mpds6AkW2LuZhYDH/D3BiNZQYt46xuUk1mT0CnPPnpkBANy0vovaOYD8+Zv+WPLqaAySrFit9VK0WysGf402xQREtWnHMYQQMbNJekTM4dEYzs+m4HM5cMfWXmrnaaIxdEzugy89Cd4VxOyK3zLtvGmOh9/tRGfAh+lEuoCsiHbuACvlkAyvM+RcvRceBivxiLdtRqJtsyHHrIQMx6Mj4EPY58GFPLKia/QJ9A//FJHuq3Bp0x8Ycq6+i4qiaWLoLlOybzKcAJ/biaDHVagZoXluhoHooj9X53gBXpcT7X4vLs7FtJ+HIsew8+kPgPN24IU7f0j9Oi53NImYJkwBUcSEyipiVCLGgA3KuRosTWhC78q0XhHDiZJmE2AUORWiGGJ5clJVxPTRD75dCBLUHE1bG9QM6IqYdd103gdvDXYEw0skHwZYnooYQBknI6nGC4xjWpesfUimYvjYGzbh5o1d2D3YZqq1ipEg46QkAylO1JSEtDGjWnK5nSxVi0VCUiRyxs0DBXYnVczjvaoiZnqBNRknSFojSC1qD4Zh0B5wYyKWxXyas+TZO6URMXTnwJDPicm4MYXGvefmAAB71nY2fKxi+O47rwYvynA7y5M8ZmdxHFYL/DtXhk05H5DfRFH/5zY8Y8+QbQDaZ2ykIuawZktmvhoGMD+7CLBGEaNbk9FRxKQ5AS9fmAcA3LSezlhDoFsw02/MssqWDMizJqvRzvecjccQMufPpzlIkkyloeEnBxU1zOu29ljSsNFEZXSOP4sr9v4lAGBy6PWQHOY5OiQ5HgPhIHxuF1xOB/KpaUPzOGRZsyUbW2OOGgZQrEN7QwE42MI1mS81ga7xpyE6jZt3zuz4MJKt6zGx6i7DjlkOGZ5H0OtGq48+Kd4+sRfR7itNezYzggCfy4mwz1PwTEqsC55cxBSiqwnAvOCFJi5rkEJIJWsyoPZFYDHonfvWyqT9NlLE5Hc5+Q3qBGyhFGKZyPJaQL1V1mSATiBaiTM1FCPrQS3WZNqGy+b5MICeEWOIIsZGRIxRljuaIsbm1mTdQS/etLN/yZIwgPIsEqsWM3NiSD5MV4uHqgpHLy4aN8/NJHOIZwWwDLC6ihDxUtZkcynlPXCwDMI1klGEkCc2p2bjtErE0MqHIQga1FDBCRJeukAnH4aAYZiKJAygj5NmWEBleVEjzbabWECtJd+tFLR8GBNybWoFUcQYmRFDCLMdJhJm+TDbMg8ARiPmK2JoW5PtPx8BJ0roD/uqmh8aQZCiBfNCHBpRyKVdg2Hq51qIehUxds2HAfQ5XJRkxCmsvXhRws8PK/kw9+7sN/z4TTQOB5/C1hc+CaeQQqT7Kpzd/kFTz8+LIvpa1XB3lqGWxhGKHEUwdhaiw4PJoTspnaUQsqqAafN74VxAxOiB9sbYrgFAMrwBZ3Y+gExw0LBjlkNG+P/Z+/M4ye76vBd/zlL70tV7T/f07BrNjEYzowVJo10gS4BwhBF2HCBwfwZjJwIHHMc2jmNInFxuSPyzQ4Lt69iB2MYxBiw2sQnQrpEE0oxGy4w0m2bv7um1uruWs94/zvmeOl1dVV3LWWs+79dLrxfSNF3nTJ31+3ye51EwkkkjUuXuTuZPYd/jH8PuZ37Xkc9J5t/EdY//C9z2rXvBKwVHfudaFCUFQ+kkooIADpXv0o3vjagPCTGEJ7AF7VyifqcBs+wXHJgUs0p+fX4wZAtUnUwsOgXr3omK/KqbSru4lZ3MFqBGsvGOejDahQkxc8v+CjGSouHNaUP8uMIlIaaVl68ToXTEtL8QUInvCo4I4ETkjqJqVoRTkESmboXjOM8Lf4GKI8atfhhG2oUYKHYPH+9LNhV3MmxGk1ULMdOLhqtxIB1teRq2N8XuA/44I1+fYLGUbgsxxrGZ7/DYPHxuHgVJRV8qiitd3ua1SHpYiv7qhQWomo6BdAyjHsYJtVuubeek+XwRtG4HoOKIkTq4h9vRNB2HzQi5vT44DoDKO4Ebcb61kJTKvX68z7t7PYsmU1R3osmefMOIQLx9+4DrUZ9MqF4qKdZilRvoum5zxHjbDwNUhiFbjiZj7wU+J1DUIiry1v3NjXiyp45PY2ZZQn8qiltddmYR7aFGUnj55v+Mc1sfwIt3/DmUqHfDnWVFRVQQ0J80rr0RQVjZi67r4JUSRCnf8WdFy3MoJkcxuf5uz/axZMZb5RLxVVG0YV/Q13UduqajLxWHUPXuwKtlDF58En1TzzvyWaOnvgUAWOjbDU30Zr1B03X0pxMQBR48z1mdMex7E9QyONX/YeRuh4QYwhPyxbUdMQlrgtEJR0wwrNJBcsQwgSvlYC522qVJsdcuGgtQO3yIJQOAHlMwnC/6G012emYZiqYjFRWwzqUFHjZVW5TVhi+Zuq5XIv9CIMQwR0wzTp96dKsjZiJfgqrpiAo8Bjss5yaag8Vy5j102V1aNBbg3OyHAWzOSAcXF1u91gyZjph8aWVnxrTZD9NOCT2bpnWz6LceZUXFmzPGZJzbooZTHQhPHzdjybb0+14AakWTefDs9dJZc3F/fY+n3WntTrDbsRwxARRi2DBF2SEh5uT0EpbKChIRwbXBlrVIOdiF2QwXF4rQdMNd5OW9PmKe/7JLjhiv+mGAysCZoukoORiTV83FhRKmFssQeA5Xj3kfnWc57Fp8Zm4lQtQP2L3fjfv4N81Ysp/fO+rYgCPROaKUR3bmFevfZ0duwpG3/AF0wb2I3losSxLSsSh6U8bzqchzsOdADVx4Am/72o249rFf7/izpkdvx1M//zBev84Zl0YzFGQF6VgEmXjUckEyKkLMYsefk8yfwlUHfg99E892/LuaRVJVxCIiehIxd90+mop1bxpCzIUt7+789zWBqmngOA49cWPfBI6zuosUsfIsKCrhFNHCBN01CE9gC9rMaVCLlPVS2dkLSklWrXJKvx8Mg9QRwwQup/phAFt2ssNCzNGLxnTIznXex5IBQC+LJvMpkoZhxZINZ1xb4GEdMZreeMFjMl/GsqRC5Dls7A+OQ6Qe8Q4XcZbLivXiFqRCe0vc7eCawvph1uXivi+YXi744Yhh0WSsyN4t3Og9aHVxJxMTrYUkuyumE1cQK/qd86Er7OSlZaiajmxctNw+blE5Nju73z1zwphSv3mbO7FkrZCyXSfdnGIHDCcQ4L3LIhFhjuv2hJiyoloRsEEcrog57Ig5ZApmV4/1rJre9YqkCzGOjbA7C70UCdmitOyCI+biQhHHppbAc+5FINpJRQUrLt/JHrRqmBtmx0jGcrt5SbKNd/CipFrO8SBeQ4DKfXxmqbzGT7bGclnBD16dBAC8+xqKJQsKicWzeMuPPohrH/t1JPNv+roty5KMsVzaWsgXeX5FH4fjrhGO99TxU5BkDGdT4Dmu7r4J8nLHn7PuzYcxevphbHjjbzv+Xc1SkBQkmRAj8ODtYkXEGI5ywjXSP3EA8eIlSNEcLo3e0fF2N0NRNvphsvEoRN7YN+aIAS9AMXt9RImEGLchIYbwhIUmHDFJhxwxb84sQ9eBbFzEQNr7WCs7lRihADliYs494FdKLJ1dXDxiCjE7XM7GrwcTDOd97og5NmncBN2c3kzYYn8auUdY/MCG/mQoJr9iHTpi2MtlTyJila0HAScmvcPSD9NNZGKmI8bLjhizuH4o425cUtoFIYa5WpvNnec4DiM9q3tiLoXUEWPvh3E/eqdzkbAoqdZC4s1b/Y9oYQveaw0YOMFLZtzVnvXeTrEnO4wmOzNTgKYb56/brrl2cLojpiKYee82YFgxjh4NZz1zwnCpvWWTt1FXoiXEOH/uPXnMEHz3rM8hl3T/HY/jONfc/3b87IcB2os6ZO8FvcmIJXgEDUuIcfg+/sPXJlCUVWzqT2Kvx9d+oja5qRdxwyMfQDp/CqqYAK86K761gq7r0HVgMF0ZXGQL+tUxUKLUvmskO/sqtrzy5+A07wd+dcCKXau7b52KTLqOdacfBgBc3HhfZ7+rBYqyjKFMEiLPG2IFz0HTjfuZGnHONTJ66psAgIlN7/TMsVUynUzJaAQiz0Hg+YoQA9t3R44Y13FuNJ4gGtCcEONMR8yJKVYonvZ0AqwWyS53xKTNxUUnhRhN061s/F0+OWJYNJkfk9B2jk2ZHQEuCjERgUdE4CCrOoqyilydnwtTPwzQuSPm3BwruA2WWOGEI4ZFrpEQ4x2+dMQsedMRw8RBJ7vCrJ63Fq59Q5kYTk0vY3Kx8vLNHDHtCDFsAWfeB2ckuwdu96BrJWtFk7W/ny+cnoOkaljXE8emADgmk7YBg+Wy0lTPUDssFGScMntW9nhcAN/OBLsdJnZuGUz5/qxci4oQ48xi/ktn5wH41w8D2AbOPOqIefq4IVrcss1bcTQiGMeT4qIQc7uHnRzZeASLJcXV+7ef/TCALZqsDSHG7/SJRrCBTKcHKh46eAGA4YYJ4vXzciMzdwTXPfar4DUFC31X4aXb/gTlxJBv21OQFCSjIvpTlfcskech8Iazghc4KFHj+a5dsUKQC9h94HeRWjwD6BpOXv0vHdn2ZjD6b3jkEsazdb194zUZ0DWAa2+Ac92b30Fi+QIUMYlLY3c6tflromg6BkwRjcV3MbFC50UoYgKiUoQoLUGOtXfNji+dx9D5RwEA5zff78yGN0FJUbAt0wuO4ypuH5sQk+/bjaK0AJ0jmcBt6G+YcJ2SrFq5uj0NosmYe6ST7gPAtoATgAXjyqJpABwxknuOmE4jTeycnStgWTJu8JsH/MktzwUkmowdy1cMu3ssxyMCZFVpeO6FqR8GqDhiym06YoLYDwNU3AedXCdZNFmQIte6HasjxktHjMcdMU4tLi6WZKtgupUFnmGzJ2bK5oiZ7kCMYvcBPx0xXggxToiELJZs/9b+QCxK8TyHZFRAQVJRkFS4FWB0+Pw8AGBDX9LzifB4Gwundk5Om/0wPj1nrQXriHEimqysqHjNdFrv9Vgws8NEa1nVISkaoqJ77uJLi2UcNQXd/Vu8jQt0K5pM03Q8xfphtrvfD8OwetBcEmJkVcPL5w1nnV+OGCYSttI5FYb3AjeiyS4tlq3j8N37KJYsCIyd+EfwmoKZ4Rtx6Lb/Bk309/1mWZLQn0ogE6s8F1RioDQAfCW+S5PAq2VoQmvPqVe++J+RWjyDUmIYZ678gJObvyZFWUYyGkFPgvXfrNw3KdaHH//i8y3vk53eyeex66efAQCc2f4+z75TRdMgcBx6mMhkFtqzaDIAUMW0IcR04PjpmTkM6BrmBvZhqXdHx9vdLJoO9CZt3xvPQbPt20u3/Yln23K5Q0IM4TqsnJjngHQDN4YTJdRAsCb3rX3yaPqtESwejeWKO4Ebdv0jF00XyHDatxxvFknjZzSZomo4aU7aXjHk7mJcIiJgsarkuho2Pbs1gKW+tWCLVO07YpgQ4/90tx0nJmopmsx7fOmI8SiazOrjcCiCk11rBjOxhi7aaliXysTCaiGmnZhSPzti3jBjKcMixDxtRiAFIZaMkYyKKEiq49Gpdg77FEsGdB5NdtJyxPj/rFyLqIOOmCMXFyGrOvpSUV+HK1LRlU6tqOieeMfE0V3rsuhvwxHYCUyIkRx2xLx6IY+5gox0TMQ+D51Nbgyd2Tl6cRFlRUNPIoLN/f48Y1vRZHLz10srQjTAjpj+lHHsOxlN9u2XLkDTgX3jOWwKqJB9WaFrGDr3EwDA6R0f8l2EAYz4p/W5ldGyq7pG7MXo8hKkFkSLoTM/xNipb0AHh1f2/9+edsMAxnDv5v6sda23osnYgj7HdSTCpOePYe9TnwSvKZgc/zmc2O2d26coyUhEReRWiUwVsUKOpiHKixDUYtufM7nxHZBjfVjObOx4m5tFVlVEBB49ceO7EUy3j6y5G+FL1IaEGMJ12GJ2NhFpWAydcCByBwiWVdopl48TuOmIKSuaY9N9RyeMqcWdPsWSAbaOmIIETdN9KTQ/O1eEpGiIR3jXF8zZgk4zHTGtRAX5CYsma7cjJqjRZCkHrimWEBOwfetmMg7EP7XCQlG2RAjWneIW6bizEZXMCdhsPwyDOWJqRZO144jxqyOmIClWifp2l92QQKW/qN1jM1+S8bLZv+FFeXazpGICppfcjYZlcVdeLgozKtFk7d0L2D19S0CHK5zsiLFiydb3+OrYEgUeMZFHWdGwLCnoddFFxWLJbvUwwoshuhRN9oTpQti/td/TrsI0E2JcEnUPmv0we8dzvrxvAPbrSfPfWZAGH+vR70I02TcOnQcA/MI15IYJAj3TLyFWmoYcyWB26Aa/NweKqkHg+RWxZEAlvstejC5H0ojISxClRUjx5p6f4ssXseun/wEAcGrXRzA3dL2j298MiqZiMG0Tklg0me6MC3Lg/OOIyEuYG7wWr9z0nwDenXjZWhRkBUOZpDW4vOp7A3Dg7V9ve5s4VYIumNelkRs73+AWKMoKEqKIbKLy7BEVBJRaEOAJ5yAhhnAd1g+TW2Oy1QlHjKbp1pRfECb3nXL5OAGbVna2I6byu5bKCvocmO47YsZH7BhxfxK4HmwKW9ONF69WprKd4pgZTbNtKO36i9laESdLZQUXzSnzrQPBfeGyw6LJ2hdiutMRo2m6JcSszwVr37qZrMeOmKeOTUPTjeuH2x0xVgG100JMi6KvJcSsiCYzFl8GO+iImStI0HXdswXcY6YbZiAd82SSvVNHzPMnZ6HpwOaBFEYD5LKrXCvde/56yRSgvO6HAYAEixJq4x6n6/Zn5WDe09k93IlosiD0wzBSMRFlRXL1vUDXdTx9nLnUvBdHoy5Fkz1pCjFe9sMA9kEKd+7fh8x+mGt87S9qrXNK1XTLtR+Ewcd6VKLJnBFiTlxawuFzCxB4Du/as86R30l0Rr7vKhy8/b8jVpz2rPC8EcuSjHQsgr46Qow94mpq/d3gdKV594imYvezv4eIvIj5/qtxcvevObnpTWFEd/HIJSvbXN2jAgDbDv0xUotv4sTVH8NS7oqWPuPNqz4CKTGAqbG7OnLWtIOkqBhOV96PRZ4Dz/ErBwvaFGFG3nwYW179f3Hots+jkN3U4Za2TtEUmWJiZf0uKgorv7eXPo/RU9/A6R0fwukdH/J8Gy8n/Mn9IS4rWM/GWovZlZiF9h90L+ZLKMoqIgKH8T7/FxmdKNZ2CssRE3VuqkAUeKvg0ansZJZpvctHR0w8Ilj75VdPzDHWD+NyLBlQcaPVW9A5ZS7YDKRjDXueggRzxHQeTRachUWg4ohp95oyvVyGpGjgOfedEkQFVoie9yju8NHXpwAAd13pfo4+6z1w3BHTphDDOmLKimoNggy0IWgwR4ys6p72vL1u9cN4s7jFFhnzbd7DnzFjyfYHyA0DVJ513HLETCyUMJkvg+eA3WPeP690Ek02uyxhoSiD4+BbF99aRIXO7uF2DpmCWTCEGPOZ2cXIvNMzBZyfLyIicLhhc59rn1OPSkeMc46Y5bKCF04bzpHbrvCuHwaoiNVudcQcZM46n/phAHs0mQq9ian2c3MFSIqGmMgHSoCvxulosm8eNNwwd2wf9Dzyj6iNLkQxPXo7zm99j9+bAgBYKksYyaatnjMGW9C3L3q/duO/x6s3/SeUUs2Jepn515GdfRWKmMQrN30WOu/9O3lRUozorrhdiDH2zS4y9U8+j6HzjyFeuNjU7+XVMji1cp5e2PJuKDFvY1/Zd9ObrFzTOI5DVFj5vbVDduYwdj3/GaQWT2Pk9Pc6+l3tIikqhtIr10djorDCycRrZcRKM4iUZr3evMsOEmII12HRZD3Jxm6JTvOugcoCzsb+lKe29XqwBaqCixOZzcL+XpMxZ41wFct+5wuMS2UFp2eMSJYdPgoxgC2erOh9PwDQ/mJkO6wVTVaJHwjmgk0t7I6YZl4q7SyXFSvGIGjxXZ1OeZ83BabhbNzVomBiJV52xGiajsffMCaH77xyyPXPc1qIaTde1OqIyZeg67o1ARsRuLZcjYmogLh5HZnzMJ7smCXEeOMK7bT/gHVRBCmWDKgcl245YpgbZvtwxlGncbOwYZFiG5ESbJJ9tCdhOWKDBruHdyrELBRly/2z1wfnUjWV7kj33gueMmPJrt3Q68uxyaLJnBRinjs1A1nVMd6XwMZ+bwftMrHOrpGNmFuWcMo8H/f5eHyy40TXmzvn2DvKlsE0BJ/i1JqBRZPNmVHTnaDrOh4yY8nu3zfa8bYR3Yeu61B1HcPZ1e/LTizoL/btwnP3/B+8vP+zKGbGO9nUtinIMnqTcSSiledqjuMQFVfumxwxnuFFqYlCe13DVc/+Pq59/F9AlPKOb3OzlGQF8YiInsRKkTUqCCuuH2Mn/hH7Hv8Y1p36dlO/N7Y8gX1PfgKCJmFq7C5fnEy6rkMHrO4bRqRq3xTR+N4ichPfG9ERtApDuA6bSF1rIcQJ0eLEVLAWjJ3qvXGCZRccMYD9BaXzfXzddMMMZ2OWndwv2PE675sjxvi7uMIDISaxRjRZ2PphgIojRtMBpcWXLxbd1ZOIWE6GoNDplLfVDxPgCcZuxO1oEzuvXczj0mIZyaiA6zf1uv55aWvBu/N9KysqTs+0F5nEHDElWUO+pFgdOf2pWNvxjn0+9MQcuWhc+6/0KJ6TXeNY11srTC+VLRfr/i1BE2Lcff46bMWSeTuxyUh0MLx0MuD9MEClI6bTaLJXzi8AADb0JX1/rgQqgy9uOmKYOHrLNu/7YYBKNFmrz16NeOINY59uu2LQ854fyxHjwnfG3FqbB1KudgatRcImyDZzTQnLgBZztqqabq1HtMuLZ+ZwdraIVFTAPbtGnNg8okPG3/gytr30J0jm3/R7UwAAJUVFXBTQn6ydOBAVhRWuEcBwgvBquebP12K5Zyumx+7sZDM7ojq6i1EtVqgR49ogNrGgv/3gH2Hk7A+Rmz6E9MJx5za2RYqygnQsgkx85bU4WuUaSeZPYfDik0gvHFvzd/JKAfue+leIlWaw2HOF0XnDeb8EX1ZUxERhlcgUqxLQlKjx7iXIy55u3+UICTGE61SEmMZTWU6IFu1O0rpFkDpimMDltCPGSct+pR/GXzcMUHHEzBW8d8Romu6pI4ZNxNYr/Q1DIWc1bJoWaL0n5tyc4coKWiwZUDl/O3XEBM3p0+2w62TehYnaah4zY8lu2TawKhrBDdIOOg/enC5A043fyRwuzRKPCJaAPpUv4dKi8WLbSUcOWxib9eg+kC/JeP6UEQdw3Ub3RTSg4moFWp/4fvakEUu2YyQTuJgWtztiDp8zFvj9iruqdDq0vn8nAt4PA8BybJaVzr6/QwHqhwFsQ2cuCYSqpltxgX4JMcwR40S/D8Ovfhigco9zY5DiYAD6YQBA4DlL/Gzm2Dw+FY73gqjIWx19ncaTPWTGkt27e8RasyB8RNcxfuzvsfnIF5GZO+L31gAAlssSsvEYcnWEmJgoQNMr18Wrnv19vO2rN2D82N83/L3bXvoT9EwfcnJT26JWdBejWqxQIsYwkSgvNvydG47+NTa+8bcAgFdv+EPMD17r1Oa2TEGSMZxJga8S+6u/N6VZt4+uYfez/w7ZuaOQYr04dPvnLYHKa0qygmREXCUyRYSV1zJFbF5AIzqDhBjCdRbMBYxcovGkDxMtilLrUUKMoC0YWx0xLk6+NYtbjpi0g5NiRycMIWanz7FkQGWKqtMJqnY4P19ESdYQFXhs8KDrKLlGR8yJKbZoE+zJNzsxW+xWq9EmQe2HAcgRE1ayCe8cMY+9zmLJvMnRZwuLktq6o6Iau/uunalnJt5M5suWI2Yg3f6UMZug9yqa7EevTUJSNVwxlPYsmkzgOeu60urxyRZ8b97qz4JvI9zsiNF1vVIA71OcUCdxvidDMM3OROSy3Nk1pfI9+eNcqiZlCYTu3Ateu5DHfEFGOib6ts8Rhx0x5+eLOHFpGTwH7PfhWlPp0XL+fYAJhdf42A/DSLQg7jIxNyiDj41gQwIzS827DqqRFA0PHza6Ln7hmjFHtovojNTCcaQWz0DljY6YIFCUFYzlMqsW8hlRQVzhiFFF4x2/0aJ37tKL2Hzki7j+Jx9BrDjl7Aa3CIvuyiVXD96sEitMZ4XYwFmRWDyD7Yf+/wCAN/Z+EhOb3unwFreGDqA/tfr9OCasdDJV9q2xyLTx9b/B8LkfQeNFHLr1j1FK+RdpWJQV9KcTEPmVy//V/65EmYBGQozbkBBDuE6z0WRJM0ZC0XRIbeYKB23Kjy1QlRVtlRXVa6yOGIfzoq1JMQdeKlkky8513ixANcLqiPEhmozFkm0ZTEH0oOso0cARo2q6lV8dlPOqGTiuMt3XuiOGCTHe5pA3g+WIadNlR44Yf7BHm7h5L5gvSHjxjFFo7EU/DLBS3O90cdFyArZ5rWHxZBM2R8xAB04NJsjPeXQfYAs977y6ueJWp2g3Ou+AJcQEK5YM6Nw92Ig3ZwrIlxRERd6zCLlqEhFzeKnF+xsAqzNlS4Dv6ez+Xe6wZ4R1+QTNEdPuPXwtWD/MTVv6PXl+rEXE4Y6Yp0w3zL7xXFt9X53i5MCZHU3Tcci8X+8b98YB2YjkGjHFDF3XQ+OIAYD+VOcRo0+8cQlzBRmDmVggBw8uR4bP/RgAMDuy3zeXgR1V08FxwGC6/vtVTOSrXCNsQb/+ovfwme8DACY2vAPlhDfP9fVg0V3p2OoBp2i1WNHEvg2dfxQcdMwOXY/TOz7k/Aa3gKSoiAr8quguABDFatfI2vsGABc2vQtzA/vw2vV/gIXBa5zb2DZQNA0DqdXrGqLAAzbdsBlHjCjlcfOPPoA9x/4XoPk/bB5WSIghXGe+WSHGnk/bxovzQlG2Fl6CknudjNozd/29ULEFMpab7hSVBZzOFqo0Tbc6YoIQTdZjOrh8EWImvY3YizdwxJybK0BSNcREPnQuiooQ06ojJrjRZGzRW1K0thY5yBHjDxlb/JOb3QBPHpuGpgPbh9OefceiwFul9p3uW6eRjEyImcyXML1kLLp0FE3GIio9cMQsFGU8YS443rfHayGm9TLqC/NFnJo2ptRv2NLn1qa1TdrFCCjmsrhqNGtN/3tNu9FksqrhzKxxjwvKs3ItoraOmHZd8hMLJUzmyxB4DleN+v9cCdi6i1y6D1T6YfwTRy1HjOrM0METxyr9MH5QuT46+52dmllGvqQgJvLYEYABtGZ7p2aWJSwUZXBcsK8hDOZs7SSa7KFDRizZP9k7CqHNzjnCWYbO/ggAMLn+bT5viUFBkpGMRtBXw1HBiAqCfc274qyQ6jgrNBXDZw3BaWLDvU5tatsUZRnrsumajp+IKMB+xWdCjKDUd8QMnn8cADC1/m2Ax91f1RRlBYmoiFwtIabqnK84YhoLMXK8Hz976//CxS33O7ehbWBEynG1RSaeg/2Lk+J9WMxtx3J2c93fN3DhKeTmXsOmiz8GeGcHvC8n6G+OcB3LEZNsLMSIAo+oyENSNBRkFa3OBrFIk+FszBIH/CYm8hB4DqqmoyCpvm6X246YTjtizs0VsVRWEBX4QDzYVxwx3nfEHDMXI68Y8ubFzHLE1BBiTlilvum2C6/9Ih4RkC8pLWfMW66RAIoV9vO3IKnoSTS/CKjreqBj17qZmChY97d8UXZtqvdRsx/GKzcMIx0TUZKlAAgxxkvGVL6EaXPRpSNHjIcdMT96bRKyqmP7sHexZIxKh1Hz3x+LJbt6fQ7ZgDxz2bGiYV1wHlguC59iyQD7oqkCXdebjvI7M1uAoulIRgWMZGvn2AcBe7yoMQzS+hARi33aPpxx/Nm3XVIuOrVKsmp1TN3qUz8MUIk6aTfdwI6q6XjadPncvt2ffcrEjOubE12Ydlg/zNVjPb4JunbYObKWi/yEeZ9e35uwOiaDTL8ZTzqz1N59fLEk40evTQKgWLKgkFw8jczCMWiciEs+FtfbWZYkjPakkYrWfx6qdinKkcYxULnpQ4iVpiFHMpgdvtG5jW0TTQd66/TfiDy3QmQ6e8Uv4+wVvwxNqP0MzmkyeLUEALg0eofTm9oyBUnGhr7sqs4UoEZ8VxNuHwve/2ukESknoCdeS4hZuW9LvTvw7Nu/2vD3DZ5/FABwdvg2eDs21l0E46mU6GqajSYDjGlvSdFQaGMx54SH5ebNwnEckhEBi2XF954YNhWadLgjJuOQZf+I2Q+zbSgdiBcSNgk970NHDFuMvGLYm2OZHROlGgtWYeyHYcQi7TpightNFhV5RAQOsqqjICktLejni4p1no4GUGTqdrLxCKaXyq71xGiajife8LYfhpGKiZhekjq6z2majpPTTjliypZ40okjxsuOmIdf9ieWDGjP2WpN3gcwlgxwt4vD6h0Z9693hAkxmm7E3za7IMqelTcPpNrqYfKKaFXPWztCDBPM9vn4PVXjZnfRi6fnUFY0DGVivr4LRUXnosleOb+A+YKMTEz0TfhsxzHYDIfOGrFkQeiHAZp3xBy/1FmEqNf0p4xngNnl9jpivv/KBMqKhq2DqcA46y53hsxYsrmh66HE/L2+a7qOkmwM/Y32NB6iqb+gX9sRM3z2hwCAS2N3QRf8HXgpKwqigtBAiFm5b/UEGIbOR/D8PX+HWHHK98g1AJA1DUPp2u/9q78343vmtdr3BLG8gG0vfwFT69+G2eEbAuH2ScciSMVWH0Miz4PjjOO4XrdRNRe2vBuykMDpkbtIiOkAEmII11koNC/EJKMi5gpyWxOMQeuHYSRjhhDTTqGqk7Dpu5TDU4FOWfaPWv0wwXjIrUSTeeuIsWcvX+HRi3SiQS60VZ4dsPOqGeKs7LcFR0xBUqz4gqD2qCSjIhaKcssTtefmjTiavlQ0MNPBlxPZuGgKMe6Iu69eyGN6SUIqKuD6jd5GRVnOyA4Wvc/PF1GSNUQFHuNtnntDmUpHDCtWdqIjppNs+WZYKMp4ksWS+SLEtHYf13Xd1g8TzLz8pEsRULKq4dULxuDIHh8dMfY436KkNi3EnAxJ51vUNpBTljWgDfOOJZj5+D1Vk3LgWlmPp61YsgFfRTYno8nYdfHmbf513rDr47KkQtV0x6KpmCMmCP0wQGUoay2RsDKgFexrCIMNVEy3eR//hhlL9gvXjAVavL6c0MFBivVicvxuTz+XiS4lRUFJVqBoOjhwiEcEjGRTWNfTeGix3oJ+zWgyXbNiySY3/JwzO9ABBUlBKirWjLcCVu9bswRBhFE1DTyHuvtWPSC8mLsSP/rFn9UVxwYvPIHx419B7tKLePYdX3N8e1ulKCvY2NdTU2gReR4Cx0PTdPBCc9e3mXW3YGLoJswuF53e1MsKWokhXEXXdcsRk1sjmgxo/iGwFkFdMDaEj7KvjhhV063YqaTDHTHpWHslv9UcuWgsbOwMQE4yYIsm89gRM5EvYamsQOQ5bOz3xoUSbyKabGuAnGbNwvar3IIjhsWSZeOiL6WwzZCKClgoyi1fJ4McuXY54FbOPIPFkt16xcCKaXIvcCJu57htUr/dBbeRHmO1dipfwqJ5zx3MrC4VbRa2gON2V9gjZizZlcMZXOFxLBlgd8Q0d2y+OVPAxYUSogKP6zYGYxGxGjZ04vQQzBuTiygrGjJxEZs9ukfXQhR4RAUekqqh2EKc70krbjTYLleO4xATeZQVra2IK03T8fK5BQDA3vGcw1vXPm4dlwDw1HFDHL3Fx1gyoBK/40Q0md/9MACQrup4c+LZsCipOGr2YgbFEZNs0Bdpx3LEhOS9gEWTzbYRTTaxULJiOO/fR7FkQeH0zv8fTl/5QXC6d0Ou5xcWoWk64hERiYiIsZ4M+lJxZOMxZONRpGPRNR0FosBDB6w40VJyGJdG78ByZuOqn40VL0GJJMGrGcwM3+TSXjVPUZaxZSBXV3CpFiviyxew9eUvQOOjOHLDp1f8Gacp4FUJaiQYyRNFWUEyEkGugdtnhWuEF6Cj/noac2xNrX+rK9vbKrquo6/evgk8eJ6DquuWMLD/u7+AiJTHs/f+PaSEf/feboeEGMJVCpIKRTMmoppzxJhCTBuLOSxuIWhCDBM+Cms82LqJ/aHaaUdM2oom62yh6ugEE2KC4YipdMR4K8QcmzSO400DKc8WU5k7orYQE+JoMpFFkzV/7gU5loyRbHPR+/w8CTF+wha78y45Yh7zqR8GqDhiOhk46LQfBqh0xEwulqGazx6D6fZ7MNh9wO2OmIcPXwDgTywZYAjPQPPRO6yz4ZoNOSvOJmhUOmKcFT5fOmss7u9Z3+N7b1oiKkAqai0t6p807+lbAvasXIuoKcSU23h+Pjm9jMWygniE98xd3AzsncBpR8xCUcbLZhTbLdv8jQuMmFO1SodCzFJZwYunjfiu230UYuwdb04JMS+fX4Cq6RjOxrCuJxhdTYlIcyKh9b4doPOqEZVostbv499+6QJ0HXjLpl6M9wX3veCyZI3FcCdRNA3QgZu3jJk9MNG2nHGG+4CDpusQOA7LuStw6PbP1/zZcnIYz7zzm4gVp6AL7Q8UOYWi6RisE90FrBYreLWM0Te/AzmSWSXE9E79DPue+DgmN7wdr970h25v+poUJQW9yXjdfp9WXCO8UkD/xDMAgKn1b3N8W1tF0TTwPFfX7SPwHHjzmGTEStOISHlEpMWVQoyuYfNrf4WZkf2Y6bnS7U3vevwvYiC6GuYmiAq8FX/UCLYg3KpoISkaTs8asTtbh4K1YGztkwvFnM3CJuc5DohHnD3tMw7ELCyXFev72zESDEcMi6SZL0jQtM7jFZrlmMexZACQiBrHRLHq5Wt2WbJeXLYMhOOFy47liFGaXww4N2cch0Eus283Y/68JTIFd9+6mWzCPUfM3LKEg2YMj9f9MEDFEbPogBDTyeLOQDoGjoMlwkQF3vp7bwd7R4yuu3MfWCjIeMoUNu7bM+LKZ6xFq26toMeSAZVj0ulnr8PmYrefsWQMa4K9FSHGjCbbMhCsZ+VaxMTW7+EMFkt29ViPb5FWtbCOS4cFwmdPzkDTDafTuh5/7/ERc2Ja0yvX4nZ49sQMFE3Hxv4kNvT7uwjO3nWcihY9eMYQmPaN5wITd8XeBRoJMQVJsYZ6gjb4WA92H59poyPmoYNGLBm5YYJDav4YoHfutmuFoqwgERWxsTeLbDzWdjyhyHPg+ZWL3g3hOJSTw219lpMoqgaB55BL1BeN7WIFYOu/UZaBqv0dPP8YBE2CxgfDE1BSFQxn6z8T2V0jjF3P/QH2PfExRIuXVvzswMVnIKhlFFJjWMptd22bm6UoK0hERPTEa4t5ljioVc4p9t0JytKKn83OvoZtL/8PXPfoR+r24xDNE5wnU6IrYf0w2USkqQfNFHOPtLiYc2Z2GaqmIxUVMJINxmQRI+XSVGYrFGz9ME4/8DsRt/P65CJ0HRjKxNDfQZ6/k7CJN00Hljz87o5PGVEFXgox9aLJWITJWC4R2KnnRnSrI8aKgWox2sRyxJAQ4wsZK8bR+YfXJ45dgq4bQrYfi3BpB/o4jlvxou0vEEcE3pp+BYxIkk7ueUyQVzS9I5GpET98bQKyqmPHSAbbhvwZRLCiyZpwtmqajgMnWQSSv5P3jUg54NKqxaEA9Y5U+t2a28c5+3BFCFyu7B4utSPEmIJZEL4nO8yV3kmMYy2YS+1Wn2PJACBic3PLHbhiWD/MbVf4v0/sXWfJoUEKdh25ZkNwoh0td3yD6wlz1PWlopbAEXRYNNlcQW5psO71iUW8djGPiMD50t1GrCZRnsH+7/8ibvvW2yHIBc8+tygp6InHkKjjmGgWUTAWvasFak6VVohLYnkBvNq6cOgWRVlGMhKp66oAVosVbDGf0zUIiu270nUMnn8cADA9dod7G90kuq5D14HeZIN94/lVrpGBC09i8MKTiJbnVvzs4LmfAAAurX+rMQHtMyVZQTZW/9ittW+WiCatFGIGzz8GAJgZuQWaEIz1ujBDQgzhKvNF44Wvp8mJ1ES0vQXG46w4cCgdmMkiRtKayvRPiGEiUNKFxfS0Ay8nrB9mR0BiyQBDnGDuoQUP48lYNNk2D3sCKi9fK8+7EyHJkq9He46Y4LtGKi67Fh0xFE3mK252xDz+urFgdYcPbhig82gyXdcdiSYDgJGeysvBYKazF4V4RLDum3NtFv2uxcMvXwTgXywZ0Nqx+frkImaXJSSjQiBcIfWwD8E45WYqSqrlWt073uPI7+wENiDRrIv85LSx7aM9ces+EmSYENOJIyZI/TBAZeDMaYGQuer87ocBjKlvRmdCjP/9MIy0w/fvg2fmARiOmKBQEXbrX09OODAw4TVsoELVKr21zfCNQ4Yb5s4rh9AbEtGp29k4+QQ46CglhzztFykpjR0TzRLheQg8X1n01nXc9bWbcPdX34KYzVmx5bW/wB0P3YX1x77S8Wc6QUFW0JuMWfGFtaiOuNKEODTO+HlRrizopxeOI1G4AFWIYWb4Rnc3vAlKioq4KDR0+wg8t9o1El0tVnCqjMELTwAAJgMQSwYYQsxwtkGkXA23zwo3k43B848CAKbW3+XCll5+kBBDuErefODJJZt7gElZMQutPehWHgyDZ5NORthigJ/RZKYjJub8i3fagUiaoxcNF8jOdcGIJWPkEmyKyt1+AIau6/5Ek9VxxFT6YYJ3XjVDe46YEESTxdq7prBoMnLE+INbHTGapuPxN4wXuLt86IcBKveWdiMqZ5YlLBRlcFzn15vhTOVlasABhyVbxJlzQZCfL0h4ylxs9FeIYcfm2t8fKy5+y6Y+z3rM2oENwWh6ewv5tXj1gtHrMJSJBcJ93Wo02YkQ9cMAsI6vstLava6sqHjNHPAJ0kI3YHfEOCfEXFwo4uSlZfAccNMW/11q9tJmRW1PBD07W8DJ6WUIPIf9W/3fJ+ZodeL+fXGhiIl8CTxndE0FBet60uCZ+YRDAxNeEhV5qwet2XgyTdPxrUNGd9u7KZYsMGyaNJwUXnZvGIMcOnobLNQ3iygY7gNr0ZvjoPHGM6YoL5ofqGHo7I8gKsso+1iUrqgairKCfKmMoqxgJNv4nF8VccVxUCKGeGXtGyquitnhG6GJ/r+PFmUZyWgE2TrRXYDpGlklVhhrVvZ9SyyfhxLJoBzvx8LAXvc2ukV6Ghy7PMdB5PkVbsGKI8a2b0vnkFk4Do0TML3uNvc29jIi+ONQRKhhkyfNFhsm23TEWMWBAZzQcSsPuhXYC58bjhi2gCMpGsqKamV6twJzxOwcCY4jBjCKmifyJcx75Ii5tFTGQlEGzwGbPcxvt4SYakdMyAo5q4l15IgJbjRZO46YoqRixpzoX58L7r51M6yrpJnF7lZ4+fwCZpYlZGIirtvoT8xJp44Y5oZZ35uwnGztMmRbIB90QohJRXB+vuiKI+aHr01C0VgsmX/X2YojZu173TPm5P3NAVgcbYS9l3C5rHR8XAGVOKE964PR65Co42atx0lLiAnes3It2D281WiyIxcXIas6+lLRwA1VWO8EsmoU/7bZNWDn6eOGOHr1+pwjRfKdYkxGGyJou44Y5vC5ZjyHbNz/fbLc/w4IaIdMN8yVI9lAOdOaEXbDOqA1kI4hX1IwsyRhWxPzKj99cxbn54vIxES8bac/Ay7ESvjiHIbnDgEAptbf7dnnlhUVcVFsGMvVLGxBf+WidwZRacFyjWRnX0GiMAFFTGJm5OaOP7MZFktlLJZlqJoKwLgnCTyPiMAjwvNYl01hKNP43bG2WJFesW8AMHDBENMujd3p+H60iq7rWC7LGB/OQODrDxbVipRTRCYyVfatkN2Ep37+u4gVpwDO/0ElSVEREfi6/TCMqChgWao8/9dyxDA3zPzgtVBiPYDq34B5txCcuz/RlbAF7OaFmPY6YgLtiGHxGA7nQbeC5Yhx4YE/bXPZLJUUxNKtLXbouo6jE4biviNojpikcdzOt2Bl74TjZizZhr6kI4tGzZKwTcHpum4tMIUxgsAOi5Zr1hFTkBRLrAiya6QSudP8NYXFkqVjYkfl5UT7WD0cDgsxj74+BcCIpIn4VErdqSPGiiVz4B4+nK28LA9kOo8TYY6YWReEmIcPG7FkfufPNxtNpqganjs1CwC4eav/EUiNEHgOiYiAoqyiIKlwQjY6fG4BALA3IFPszHHdbDSZFTfq4aBHJ8SE9qLJrFiy9T2BEMzsMEerrhuRKE4sxFf6YYIjjkYEHmVFg9xCJ4edSj+M/7FkgLPRopV+mFzHv8tJmLDbKJrseEgHtPpSUZycXm76Ps5iyd5x9Yin72NEfeInfwheV7GYuxLFzLhnn1uUlTUdE80imK4RuTriarkScTV85hEAwKXRO6CJ3jhv54plbO3PoS8VRzwiIi6KZkS78b+jTQzZ1hQrqrpGosVp5GZeBgBcGr3dhT1pjqIkY6EkoawqSEejWNfT+HrGcxxEgYekVq7/VjSZvDK+CxyHcnLY8W1uh6KsIBERkY03FhFjAr+iI6aYXo/F3Hbr+wOAwXOPAQCmxiiWzCloNYZwlZYdMbG182mr0XXdmtAJolWaLVA1O7HoBpYjJub8w6TAc0hGBRQkFUtlBf0tTiCfmytiqawgInCBE9JYNNmCR9Fkb0wagpTXhc0Jm1OqJGtIRAWUFRVnZo2YLicWR/2AubNKcnOLOCy6KxsXAzFVWo9kG+4DFrk2lksEbmHqcqEV10ErPGb2w9y1w78FK8eEGAfu4fbIKCeiyVghsdMRlfMFyVpAfecef4WYrCUSNj42Xz6/gKWygp5EBLtGg+VgrUUqJqIoq1ZPXqdYBfABibtKtBjne9LqfQvHPT0WaS+aLGjfk51ERADHGULMUlnpWIjRdT1Q/TAMS4hpIxZQ1XQrsvG27cHYJ3aN7KQPkxHEfhhgbUeMquk4NW2+b4fkGsJg9/HpJoSYsqJaQxLvvoZiyYJC4vh3AXjfvVGUZYz29DZ0TLRCTBRQkm0L+uZid0ReBHQdw2d/BACYGvfG9aNqGgSOw46Rfox00INTS6x44a7/CU2IWcXumhDF0Wt+G6nF05A8jl0r2WLWEhERQ5kkNvZlMZJNNeyHYcREAUvlGq4RM5pMlBahinHofDDWD1RNx7IkYX0uu6aQFhWFFQLaiasfxImrH7T+nVfLSOVPAgiGk6lbICGGcJVWhZhUE9M41UwtlrFUViDwHDb0By9yJ2krjPULNx0xgDFlX5DUtibFWCzZtqGMb9Pc9WCOGDe6AWrxoyPGZLvX07ZxW85/UVaRiAo4PVOApgOZmNhx4bVfxFtcxAlDLBmwsoS6WZgjJshOn26HCTF5Bx12M0tla9Hxju3+xWekY505P9mkvhNCzLA9msyBa5dbjpgfvmrEku1cl/V9CIEdmyVZg6xqde/FrB/mpi19EByIVHKbVEzA9JIzfRzzBQmnZwxBOyi9DkyIaeaZWVE1a7giLNPsrOet1WiyiiMm5/AWdQ7HcUhFRSyVFRTKKtDh3M3xqSVcWiwjJvK4doM/0ZS1iAjG9aGdaLLD5+aRLynIxkXsGQvGuWb1YXY4SCGrGg6fnwcAXBs4Rwxz2NW+Xp6dLUBSNcREHmO5cD1LsiHB2aW17+OPHr2EfEnBSDaOmzYHx2V2WVPKI3bmSQDA1Li3Qoyi6RhIO3e8R0VhhfvA3jWSnX0VicIFKGIC0+tudewzG1GSVcQjotWD1QmrxIrYyuu3Es3i7JXv7/hzWmGxLGF2uYh4RERfMo5r+nswkkmhNxlvaTAxKghVbh/jexMU4/1682v/E2MnH8LxPR/HuW2/5OxONIGsaijKMgqSAklVwIFHMipidA23DwDERHHFMVmNJsTw5P2PIDN3BKU0idNOQUIM4SrzLQoxiTYWGFmPxYa+ZFv9JG7TjrjkNOzvM+FCRwxgLOJMLZbbEmJYLNnOgMWSAUDOXIDzoiPmwnwRT58wJgC9nsASBR5RgYekalZJJzuvtgylQ+ugaNURw1wjQcuUr4Y5YgotLHozt0/YXp67iawL0WRPHpuGrgM712Ux0uNfeXjafIHrtCPGCSFmyB5NFmBHzHdeZrFkI47+3nawR4wulhRrn6s5YAoxQY8lY1i9gw5Ew7JYsk39SevZwG+SdfrdanF2rghZ1RGP8FiX9e9a0QpRsfVosnxJtlzyQRHMqklGBSyVFUf6Rpgb5obNfYGKUGJirqy2Hk325LGKw0cMyICW5Wjt8Dt7fWIRJVlDJi5iy0CwBFEr6rDO9eSEzVHnRLeRl/Sb97SZ5fKaP/uNg0Ys2f37RkO3n11LNIXp9/wfnDn4XSxnt3r2sYrpFnGiH4YRq7OgL8pLGD5rxJJNj97uWSxZSTGcmclo50JMtVgRBOYKReweHcS2gRz6Ugnwba5pVAtox/f8Bo7t/SR0IQLoOobO/hgRKQ8p1ufUpq9JUVYwu1yEpusQeR6JiCG8DGeTyCXiyCViSDXxvUYEAXoDIQYAdF5Evv9qpzadAAkxhMuwyV/mLFiLdkSL4wHvsUhak8I+OmLKzBHjzktamln229hH5ojZORK8mJNKR4z70WQPHTwPXQdu3NyH8T7vHRmJqACpqFkRJyenWSFnMM+rZiBHTAVyxPiPG0LMY2Y/zJ1X+pujz3oP2rkHLJUVXFwoAQC2DXYuyDvviDGdkcvOCfJzy7ZYMp/7YQBDjGcRo/miXFOIKckqfvom64cJx5RwynKMdH7OMZfFngC5LJItOGJYLNnmgfAsorJhilYcMS+bgtl4X6LlqFyvSMeM4SUnBrSeDmAsGWAXYlp3xAStHwYA0g51xBw0ryP7xnOBOw8Ta0STWf0wIXwv6LOEmMbvcwsFGT85ajxXUSxZgOAFSGM34lB+CBs8HA4sygoSURG5NTo2WiEWEVYU2uf7diIiLaCYGsPU+rdBiaSw0L/Hsc9bi7KiYLw348jQZbVYMXT2EQyefxwzIzejkBlHev4YpsfugBT35hlS13Vw4DCaTWMg3dm7fbVrxC6UpReOIbl8DqoQw/S6mzv6nFaYWS5iPJfBeG8WuUQMuWQMMbH15X1RWPnd9194Clce/C9Y7L0SL+//fwBwQEiHcoMMCTGEqzAnQdMdMW28NJ+wHgyDNVnEaOVF2S3Ygm0y5s4pn7H6AVpfqKo4YgIoxJjH7YLLjhhd1/H1F84BAN573XpXP6seiYiAhaKMomS8NAf9vGqG1h0x4RArkm0I1uSI8R82UVuU1YbxT82iajoef8Psh7nSv1gyoOKoaGfggF1rBtIx9DQ5tNGIvmQUI9k4liUFoz2dH++95gLOrIOOmB++NgFV07FrXTYwfR2ZeOOI0YNn5lFWNAxmYoHs46tFpU+r8+evl8wF/iC5LFi5NnOyNqIyzR6eRdSo0Loj5lCAY8kYTg1oKaqGZ08a4ugtAXOpsWgyRWtNiFksyXjR7FC57Yrg7FPGoY6YQ+a+XROwfhhg7fdVJyNEvaY/bd7H14gm+94rFyGpGq4czgTyvZTwlqKkoD8VR8IBtwgjKogr3Adnt78PZ7e/z/r3U1d91LHPagZV09GbdMZ9Uy1WZOZex+ib34YSSaF36mdYf/LrODP/T/H6db/nyOetRVlRERMFZOKdu5ijAl/XNTJ09scAgJmR/dBE74Y5NV3H+t4MrhzuzIUjVvUf8bqC1OKbUCJpDFx8Cjt+9p9wfst7cGr3r3X0OcRKSIghXGWhRUdMO6IFiyAIaua1FY3hY0dMUXLXEZNpc1KsICl4c8b4/nYEMpqMdcS464h58cw8Tk4vIxER8A6fpqOtSTgWTXYp/EJMy46YeeaICbZYkWpjEYccMf7DJmqBxvFPzXL43DzmCjIycdH3rPkUW/CWVGia3tKk7wmHXa08z+FbH78Fiqo7EsfZZ8ZQzTnYEfMdswj4vj3+u2EYmXgEk/ly3Q6EA2Z05s1b+0MTV8m6izp1xOi6bnUxBalge61ybTsn2bPyQHiEmBi7hzchNDFesjkOgkrKofeCl84tYKmsIJeMYNdosBaNWaSYpLQWU3PgxAxUTcfmgZQv7vB6sIGzxTYGzuwcPDsHANgXsH4YYG1h13rfDuF7QX/KcDSsFU32kBlLRm4YAjBiu4Y7KLCvRbX7wE90XQc4IBNzJm61WqywCu2lRfRPPgcAmB69w5HPaoayoiIWEZF2YP+qxYrM3BFsPPrXKCWHMXDxaQDA1Hrv+os0XQfv0HdXvW/sexOUZQyeexSJwgSi5ZmOP4dYCQkxhKsstNgRk2qj+yDoC8ZW3JoDE5ntsmy+pDNRyGkqJZatvVS+PrEIXTfiY5zI8ncaqyPGwXLtWnzNdMO84+qRFVn9XpKIVIQYXdetF65tQ+FZtKmG5aWXm3TEnA9LR0yLjhhZ1TCZN6Kfgr5v3UzEFv+0WKod/9QKj77O4lv8z9G3X7eWJcWaHm4GJ/thGEMZ57K1ex3uiJldlqzS+yDEkjHYQEW+zn38GasfJhyxZEDlWrnU4fPXRL6ES4tlCDyHq0YD5IiJNC80nQz40FItYqwjpoV4KyaY7Q2yENOBg9AOiyW7eWs/hIDFXDHHZ6uOGNYPEyQ3DFC5PnbiiFkoyNZ5uG+815HtchLWESMpGlRNX3FM6bruyr3aK9jz1myDgYrz80U8d8pwmN2/b9ST7SKCiyEo6OhNONvVUr3obX4Ydv7sP2J2+AZMjd0FXfCmh66sqIgJzjhGgPoL+v2TzyNWmoYiJjE79BZHPqsZirKC0Z5UxwkEgDlcYLvNRkpzWHf6u1DEBESlCI0TcMlLkUlWEBMdEpmq9k2JGGs/ESmPwQuPAwAujd3V8ecQKwlGAx7RlWiajrw5WZltUohJtNh9YM+WD2pmbdKhicxOKJgve2yS3mnYpHer/QBHLhqxZDtGgueGASqOGDejyUqyiu8cvgAAeO+1/sSSAfZsaAVTi2UslRUIPIcNfcE8r5qBLeKUmnDEFCUV02ZkQeA7Ylq8pkwslKDpRvHxQCp4guflRLvuwVo8bvXD+BtLBhjnGlu0aTUGKuiLO32WECOvWWbZDD98tRJLtjlA7oSM1WG0+n63XFasyKebAxaB1AinOmJeOmvEkm0fzjjisnKKRCsdMdNmNFnACsIbEWVCTJPDFBMLJUzmmWAWLIeInZRDkXlPBbQfBqhEk7XaERPEfhjAmY6YQ6ZIuLE/2fEghhvYr23V18yZZQkLRRkch0Ddt5plIF0RYrQ6ZeLfPGS4YW7a0odRivG97CkrKuKiiJ6Es+9NIr9y0Xvg/OP4ua/sw/oTX8NVz/078Jp360UlRUHcIccIUGNBP2o8b8RKxnV9ZuRmz0QmAJBUFf0pZ85lgecB26WD7ZuoGIkTc0PXQ4l5N6hTUlTERQHpWOexeRGeh67DesdRIsa6XKw0g1hpBnIkjbnB6zv+HGIlJMQQrrFYUsDWLJp2xDBbtKQ2teDBykcH0lHLvRA0UrbpdScWcdrB6ohxyRHTaAGnEUcn8gCAXQHN4c0lKo4Yt767H742icWSgrFcAjdt8W/S2O6IYZ0NG/uS1kJIGGnFEXN+3nDDZOJi09crv7BiTZpcxDln64cJWjns5Qa7VuZbvFZWM71UxuHzxuLwndv9X7DiOM5yxbQqyB8PeO48E+RVTa/rFmmFh18OXiwZ0FgkfP7NWSiajvW9iUDFBa2FUx0xlssiQP0wgC2abI3oroWCbA0abA7o0FItWM9bsx0x7HvaPpxx7XnXCZwQCAuSgoNnjJiroPXDABVHjKw2/+x8ZqaAN2cKEHkON23pLPPeaSrvOe1/Z+z7CmI/DGAMVLBHxOq4QzYwMd6btJ6twwRztmp67ZQDXdfxDRZLto9iyQjDTZGMRpB1yC3CEIWVC/qaUBF6ptfdCjXi3TNWSVbRm4zXdum0gVgtVkRWDtpeGrvTkc9phWzcGSFNrHp/tu/byas+ivNbH3Dkc5qlrCjoTcYNgahDBJ6HwHFWvw9zxDBm1t0KXQj22kgYCe8KGxF45ovGS18iIlgvU2vB3COKpkNqYoqqUj4azAUcoPV9cgM2LemWI4ZlJ7dq2T9y0RBigtgPA6xcgFvsMEKiHl83Y8nec+2Yr4vkFUeMForzqhlaccScnWP9MMFfZLQvvql1JvvsWP0wNOHnO1b8U7Gz68kTb1yCrgNXjWYxlHU2NqFd0m3E7UiKhtMzhggaVCEmJgrWwmmnPTH2WLL7AhRLBgDZBguNB0IYSwY454g5HNC4q0STHTEnTDfMcDbmW/xpO7B7uNSsEGO6toImmFWTskTr9gXC50/NQlZ1jOUS2NgfvOcWtmjViiPmyePG1PS1G3pbirf0AnbvllSt6d7Bapir8JoNwYslA4yBinrRt053uXlNROCtIavZGj0xRy4u4o3JJUQF3reuTiJYFGUZg+mkIwvddiLm76u4DyrPvlPjP+foZ62FrKoYcMgxAtSPJgMAneMxPXqrY5+1FpKiIiLwjsaucdzq703neJzY/S8xueFeRz6nWWRVQ3/amXu/yHPgeZsQI658H5vyQUC7HCAhhnCNR16bBABsasHCnLRN2TTTqXJiivVYBHMBB2h9n9yALYy51hHTRjSZrus4akWTBdMRE48IVuG7G/Fkk/mSFcPwgI+xZMDKrHmrkDPE/TBAa46Yc5YQE3yxImVbSFtrEhoAzs+REBMUsm26B6t5zOyHufNK/90wDCb0tyLEnJ5ZhqrpSMdEjAREUKoFm6ad7bAn5gdmLNlVo9mWno28IGs5YlYfm8+cCG4EUiOSVil6+89emqbjsBlNtidgC/zN9oWxXoowxZIBtmiyJhe+w9APAzgjELJ+mFu3DYDjgud0Zd9dS0LMG8HshwEqTmSgPVeMruuWELMvwMdnvbhD5ogJah9rM/Sb93HmDrTzDTOW7G07hwLviie8QdF0DKSdf28SeX7ForcUqwizl0Zvd/zzGqEDjsWSAYbbp5ZYAQDz/Xshx7wTocuKgoQoOlJmD7B946BW7RunaxCUgiOf0Qo6gHTUmWuVKKx0xFS7X2bWeSegXU6EZyyKCBWyquGLT78JAPjg/o1N//9EgUdU5CEpGgqyirUu15UJneA+GNr3aVlSrAUdL7EcMW4JMbHWs5PPzxexWFYQEbhAf3+5RBQTcgnzBRnjDiclPHTwPDQduH5jr++LckyIKclqKM6rZmAiWjOOmHNzxkNUGIQYFh+h6Ub/01oTzmzfxkKwb92OEx0xqqbjiWNMiPG/H4bBBMJW3IOVxZ1UIBcTGX2pKM7NFTt2xDx8OJixZED9Y3O+IOHVC4Z7db+P8ZntwK6NhQ4cradmlrFYVhATeWwfDpZ7t9loMhbjG7bhilaiyeyC2d71OTc3q2OSbcY42nn6uOlS2xbMc7LVaDJF1fC0KfjeFoC4zWoE3ojfXCorWCopGEi3Fnfz5kwB8wUZUZHHzoDGMQP2mOKVxyYb0Ary4ONa9KWiODm9jNmq+7iq6VY/zP0US0YAUDQNAsc53g8DVBa9VU2HwAOl9Bhevun/hhTvhxrx7h4tq846RgDTWcHxUHUdIsehkNmAx+//EXhNgaAsO/Y5zcBi1+IRZ9a+RBbfpekAD2hCZXgsWppBMcTfHRMH7SkbT77ru9jwxpchyktQosF69u0WSIghXOG7L1/E+fkiBtJR/MI1rT3UpKKCIcQ08YISFqu0tU8dTGV2guWIcSuarI3FxSOmG2brYDrQPSS5ZAQT+RLmOpyErkbXdXzNjCV773X+umEAW8SJrSMm7EKMtYjTkiMmeBEf1XAch1RMxGJJaWrSm6LJgoMTHTGHzs5jviAjGxcDlTXfTjSZJcQEfHGnN1kp+m2XmaWy5SwJWiwZYOtAKK88Np89OQNdNxbgghKD1yzsmaeTBW8WS7Z7rMdaXA4KdidrI8LqiGklmuzktCGYxSM8tg8Hez9TlkDY3jvBzFIZr5nRvjcHsB8GaD2a7KVzC1gsKehJRHD1WLCcZwwmxLQzSMH6YXaPZgP9zpOs44g5EZJ7dSP608Z9fKbqPv7cyRlM5svIxkXctSN4IiDhPUVZQSIqIudQv4gdkefB29wHADCx6T7HP2ctSrKKuIOOEcC2b6ZYofMRSAl/zqmyqmDQoeguoLJvzBEDjkMxuQ6JwkUI6uq4QzcpySpiouCYm0mocUyW0mN449rfduT3E7UhIYZwHF3X8T+fPAkA+OD+TS2X+iWjIuYK8poLjIqq4dS0GaEU8AVja59c6hlphK7r1gM1e8B2mkwb0WRHzZfIXQGeDAMqPTG1yh074fC5BRyfWkI8wuOdAZiOZkLMzJKECwslAMEXONciZnPE6LrecOI+TNFkgOFuWywpTV1TLCEmJPvWzWQTnTtiHnt9CoAxNSwGaGG4LSHGHKYI+pRtn+lkne8govIHr05C04HdY1ls7A/etbXeQMUzIe2HASou4E6GYF4KaCwZULlvl2QNmqbX7Zk7Oc1634J33DWilWgy1g9z9VhPoK6LtWDRZMttRpOxc3LHSAaDGecXCp0gYn53SpOOGBbTe+u2AQg+9iU2IhMXMZFfLVY3Q9D7YRi1eqcKkmI9R24L+Pt2I/pSxrkys7Ry0fShg4Yb5r49o0132hLdTVFS0J+KI+FQ9JMdUeAh8HxTHZ9uUlIU9Cacc4wAbN9sYoWPaDqQTTgbuybwvCEymfz0bV+CKC9iOXeFY5/TDGVFQTYRRcJJt0+VI4Zwn2A/qRKh5MDJGbxyPo94hMcHbmo+loyRbDI7+excEbKqIx7hAz/pzbLz1ypUdQNJ1aCYF1bXOmJixoNKK0LMkQlDiNmxLth2x5x5E19w2BHD3DD3XjVi9Ub4CZusZRE0A+kocknvY/SchL1Q6bpxHjTifIiiyYDKpPdaC4yapuPivCGsBf06eTngREcM64e5K0CxZEB7BdTMERP0xR0myHfSEfPwyxcAAPddPerINjlNxa3VPUJMssMFb6DSOxLEXgf7cE29eDJV0/HmtHF/C/rQUjUxS4hZ21Vh9cMEPJYMqFwr2x3OCkNnU6RFR8yTx4LbD8NIdxAtevDMPIBgXkfs1Io7ZI66vlTUl3htp2AdMXZna0lW8b1XJgCg5QQPonspKQqGs+4MLlgRV3rz/VluUJIVxztwBLsjxkdYtJyzsWsre1QAoJwa8VyEAYwB04GUk24fDjz4FftGuA8JMYTj/OWTpwAAv3jduDVF2grJJi37zCa9ZSBddwowKDhRGNsu9r9Htx0xiyXZKmhbi6NmNFmQs5IBmyOmg0noasqKim+9ZCzKBSGWDKgIMa9PGN/LlpAt2NSCdcQAjRdyipJqlXeGIZoMqEx6r7XAeGmpDEnVwHPASE+4YoW6kU47Yi4tlvHyeWNC/46A5ei36ojRNN1a4Al63EmfKUq32xEzvVTGAVPQCGIsGbDyPs6YypdwfGoJHAfcFLJ+GKDzCChJ0azhhD0BXOCPi2sLMefmCpBUDVGRx2jIxPiY+VzSTDQZc8TsDfhCN2C7f7d5XD513BAtbg2yENNCR8xCUbYcI7cGWIhhYvVSi/fvkqziiJkCcM2GnNOb5Shswto+5MNiwIM+MLEWtaLJfnxkCktlBWO5BK7fGGy3EuENxjqGjt6EO+9MVh9HABa9ne7ACYrIVJYVxCMisk7HrvFcIMQKXdcd/e44jkNUJCHGa0iIIRzl2OQifnJ0ChwHfPjWzW39jmSkuQlGqx8m4As4QMURs5bLxw0K5st5VORdyzdnU2Kyqjc1uViQFJyaMRbgdowEW4jpMYWYOQeFmB8fmcJCUcZINh6YfG8WR8CcI2GbnK1FVODB0shKDcqMz88b08KZuIiehP/upGawnINrLOSwyLWRbDxw/QaXI2yxu92OmMffMNwwu8eygYukSbXYx3FhoYiirCIicNjYF2wBtLfGJG0r/ODVCWi6EZu0oT+Y+1pLJDxw0hCPrhrNhtIhaTkP2nz2emNyEZKiIRsXsSmA3xvPc5Vy7TqDPpV+mFRgI5/qERWac8SUFdXqHQy64wCoXCvbOS7PzBRwdrYIkedww+Y+pzfNMURLiFn7neDAiRmomo4tg6lAD8NkYqvF6mZ45fwCFE3HYCYWeGdyrY6YSj9MuKINq2HDofZoMhZLdv++0cAPdRLeUFaM7hSnRQqGwHMQqyKuvEbVNHAc52g/DBAckamkqEhGRSQdjJZj35vqs8hkiCXOf3dRQfDdyXS5QasyhKMwN8w9u4axaaC9B7ZmY7wsISYEmddswqjd6bdOKJiLYimX3DDG765EnjWzCPfG5BJ0HRhIxwK3mFgNK2meLzoXTfZ1M5bsPdeOBWZhJFHV5RSG82otOI6rRJvI9R+czlr9MMFdAKim2QVG6ocJFpVosvYWhlk/TNBiyQB7NFlz+8ZiyTb1pwLf6cAWcObajCZ7+PBFAMB9AegDq0et2Lynzcn7oAwMtIrVxVFWmnbr2rHirsZzDTvG/KReuTaDPSuHrR8GqPS8rdURc/TiIiRVQ28yEop4Ucup1YZLnrlhrt3Qa/2eIBIVjPNFaUKIYf0wt18RLJdnNe06Wu2xZEG9jjCsaDLbs+Vx63073ANaA2njfZMNVMwuS9YzFcWSEYyirCAZjSDrYKxVNVFR8FWsKCsq4hEBaYf3MQgiE2DGrqUSjl9vgyBWWN+d00KMz8fk5Uiw33yJUDG1WLImSz56+5a2f0+iyRgvtogThgdDPx0x7O/RrX4YwLjxsliaZiz7zKK/M+D9MACQMx0SCw45YqYWS3jMnGp/ICCxZEDFEcMIg9OsGeKmwNRoIeecJcQEfwGHkbQtMDbifAhFpm4m04EQo6ganjCvHXdeGbwFq1ajyax+mBBca5gg344zcnqpjGdPBjuWDKgsMpZkzZpiZ/0w+0PYDwNUom41vbmekWpY3NWe9T1ObpajsHtcvefLk9PMERP886waNkixVjRZGAQzO62K1naeNvthbt4W7HOSietSE9FkrB8myFFrQOUe1+r3xmLXgh5LBlTeBVY6YsIRIboWFUeMIcQ8/PJFKJqOXeuyuGI4+O+jhDcUZRmD6SQE3r1l0pgo+BoDVZIVJCMRpBx0jDCCsKCv6Tp6k86/00dF3vd9K8kK4qJg9TM7RVQQfI+Uu9wgIYZwjL9+5jQkVcO1G3K4bmP7dvlUjWmcanRdxwmWLR8CISbZYR50J1iOmJh7jhig8oLSzALjUVOI2TES/AdfqyOm6IwQ882DF6BqOq7ZkAvUsVstxIQ9C5rBFnJKDRwx5+aMaLIwCTHMhbbWRC2LXQt6HMblQq0ejmY5dHYe+ZKCXDKCfePByzJvdZHKyp0PweJOb8qMqGwjmuz7rxixZHvW92A8wBFsadt0/WJJwdnZAs7NmRFIm4IbgdQIu9OznWL0w+eMPqYgF8BXJtjrOGKmQuyIEZuLJmML3UH+nuyw9xxJ0ZousweMXq1nQtAPA1Q6YtZyxJyeWcaZ2QJEnsNNARd82SBFvmVHzByAcMTmVTvsFFXDKVPMDft7Qb/N2appOr5pDo+SG4awo2i64yX21UQFAaqPzoqSoqI/lQDvwuBCTPC3a8SK7nLB0RQTg+GI6U3GHRcKY6K/x+TlCAkxhCMUJAV/8+xpAJ25YYDmiu1nliUsFGVwXDheLlPR7nbEAJWemMXy2guMLMt757pg98MAQE+is0gaO7qu42tmLNkD1wbHDQOsXLCKhbDUtx6tOGLCJFYkY605YiiaLBhYHTHF1u8Fj71uuGFuu2IwMJGGdlJd7IjpS65cwGkFK5YswG4YwJhgZ4twiyUZz5iT9/vGc4GOQGqEYOtQaTUGqiApeGPSeFYJcgH8WtFkliMmhIuoMbEiWDSCOZfCsNANrHweX6vnzc5rF/OYK8hIRYVAH5MAEDGjydYSmp4w3TDXbuxdIQYHEXb/bsURM5kv4cJCCTwH7AmBUFjdOXVurghJ1RAT+VA9I9eCdb1pOnD4/AJ+dnoOHAf8k32jPm8ZERQUTYPAca71wzD8dsSomobeZNyV3x31eUGfRXc53aECBMPtI6uGiOY0EVEAgvdq2dWQEEM4wtdeOIeFooyN/Un83K6Rjn5XpYS6/oMum/Bb35uwFlqDTLKDPOhOYeKPV46YtaLJdF3HkQnmiAm+EMMmoZ2IJnv1Qh6vTy4iKvL4+T3BevC3CzGbQ1jqW49mHDFhjO9q3hETPpGpm8maUYeSqqEkt3Y/eNTMMr9ze/BiyQB7NFlz+xWmeFFWVK/pQL4FN9OlxTKeO2XEe70z4EIMsLID4enjxnbfHPAp9bVotxj9lfN5aDownI1hOOvOgoUTMDdrscb1ZLEk49KiUUwdhqGlaqJNOGLyJdlyyAc5Qs5OVOQRNR0jrRyXTBy9cUu/5TgJKpEmo8mefIP1wwTb4QPYBs5auAewfpjtw5nAC01AJR68YF5PjluOunToy+wjAo8e8xnsfz1ldNresnUg0Nd3wluKsoJEVEQu7q4QExWFtnrrnIB9rhtCBQDERNH32DU3orsAICqIvn1vdpzuhwEAkeNIh/GYYD/FEaFA1XT85ZPGA82Hb93c8QJuMrb29GKYYskAW2GsH46YsjeOmGZLLC8slLBYUiDyXCgmoXOmI2a+KHd882VumJ/bNYyepPMPCJ2QtEWThT0H2k7XdsQ04YjRdb3i9gnRvnUz6agIlgTQSk/MVL6EVy8YAvYdAeyHAVrrPZhZKmOuYLhaw3Afj4o8Mub+zbYQT/b9V41Ysr0BjyVjWNE7RdnWDxP8BdJGpFoUCBmHWe9IwKfY2bNdrWiyk+az8mAmhmw8WM8czcAGKVRNrxtx9bIZHzfel0B/2t3FMydp1tVq5ylTHL0l4LFkQHPRZLKq4YB5nbntimDe1+xk480NnNk5eNaIJQtDPwywOuowTBGizcDiyb77suFUvZ/cMISNoqSgJx5DwoXuFDsRF/tn1qKsqIiJgivRXYC/IhMAlBQFfamEKx0/ouCvVCGrKkSBd0VEEwUeAdCYLitIiCE65oevTuDMbAG5ZATvdaB8vJlJ7zBN0gJ2l4+Pjpiou46YZi37R8zFxG1DaWvaMciwjhhV09sqVmVIioZvHjLyiJ04T5zG7iwLy3nVDGs5YkqyiuklY2J4vMscMfMF2fpzcsQEA57nkI62PlX7mDk1vGd9DwYCutiYNhcWm7lOsnv4WC6xqp8qqPRa+fLNf28PH74AALhvT/DdMEDlPn7w7Dyml8qIiXxoFhDrUenoa69gO+gRUJXotdX7d3LanGYfCJ8bBqhEkwGGi7AWYeuHYaSaiGG2U1ZUPG+664LeDwM0F0320tl5LJaN3rPdY8F3M7EJ61aGKA6ZjpjwxOYxh52xj0yI2RpCR10t+tPGfVzRdMREHm/f3VmKB9FdlBQFw1n3j3VR4H2LgSorKmIR0RVXBQDfEzUUVXMlugsARB8FNMDo9omJAtIuiGh+79vlCP2NEx2h6zr+3ydOAgD++U0bHXFdJJpwj1QeDMOxYFzpvfHPEZNwuyOmyWnoo2YsWRj6YQBDoGCL+fMdxJM9+voU5goyhjIx3BbAl2j7Ymi3vHABaztimGMkExORTQQ/NoKRbOI6yWLJBtLRUEQ4Xi5YPTEtLOY8bvbD3HnlkCvb5ARskWq5rKw5DXc8hFO2vaYoP9ekI2ZqsYTnTs0CCEcsGVBxxPzg1QkAwPWbekN/7Wi3o++w6bQI+gI/u3cXakSTnZgKbz8MgBXDOuU6wxTMuRSWhW5GqkVHzMEz8yjJGgbSMWwfDv73yRwxcoOuANYPc8u2Ad8X75qh1Y4YRdWs68g1G3pd2y4nqe7UClOXWzP0pSoLmD+3a9i65xGE8dyqozfhflSdyPOAT+6DkqIgF4+5Fm/p54K+ruvQAdfcPn6LFWVZQSoaQSLi/HqF3/t2OUJ/40RHvHB6DofOziMq8Pjg/k2O/M5mJr3DZpVONRG35hbeOWLMSJM1pryPXDTKb3eMZFzdHifpNfsBOhFiWCzZL1wzZkzCBIwV0WQhXbSpBZuoreeIOTdXAGBEd3Fc8BcCGCxup5HLzoolIzdMoGA9Mc06YhRVwxPHmBAT3PgWdp9TNL1hpwNQWSAO07WGOWJmC80JMT94ZQK6bjgqwtI/xRYa2eLhzSGPJQMqHX2tRJPNLks4M2vcG64OeO9IdZSQHeaICetwhcBzEM0F+nrXlJfOGsdqGIrQ7VQi85pb1H/6OBMt+kPxrMIiXOQG94Inj4WnHwaodMQ02xP2xuQSirKKTEzEtpDc6+xRh7quhy4KfC36UhVH8bv3jfm4JUTQKCsq4qKInoT7rnM/F70lVcVA2r33QpH3z+0jqWbsmktuH7/Xb0qKioG0O+8TosCD4xCIDpzLheCtBhKh4n8+abhh3nPtGAYzzty4Kh0xtV9OipJqTXqH5eWy3WgMJ2AT80mXSyItR8waU95HQuaIASrxZPPF5rsB7MwslfHoUaNo+4EAxpIBQFwUsK4njt5kJDQCZzPEIiyarLEjJiwLpYxWHDHUDxMsmu3TYrx4Zh6LJQW9yUigp/NTNtflWve6MDpi+kxBvllHzHcOGxn07wqJGwaodCAwbt7a79OWOEc7jhjmstgykLLKnYNKopEQ0wWLqMyRLNVY0J9YKGEiXwLPAbvHwvNMCTQ3dGbnqeMV90gYsDpi6jhiFgoyXjJj5W4NQT8MsNIR08xiFeuH2TPeE5qie8thJ6mYXpKwUDS63DaHNN6wmgEzmqw3GcHt28Nx3BHeUJQVJKMRZF1yU9jxddFbB7Jx98QmUfDR7SOriLsoxLBuH7/ECl3XkXNJKBR5DjzHQyUhxjPCk8NCBI5T08v44WuTAICP3LbZsd+bjDTuUzk1vQxdNxbH7RbjIMMmhYt1FoPdhL3kBaEjpiipeHPaWBjYsS48jhi2ENOuI+abhy5A0XTsWd+D7cPB3G+e5/Dwb9wGVdNDH0VjJy6yaLJ6jhgmxIRLrLAcMQ0Wcc6TIyaQMPdgs46Yx143RNzbtw8GOr6F5zkkowIKkoqlstKwOPtECONOWnHETOVLeP5NI5bsHVeHJ4PeHtOSjom4OgS9DWuRbLGLA6g4gvYE3A0DAEkzoqI6mkzTdJyaZtFk4V1EjYo8liW1ZrzoS6Zgtn0440g0spekWujUypdk65gMjxDTuCPmmRPT0HRjoC4szygZM35T143rSXqNATfWD3PNeDhiyQBbp6mkWukT473JrnkvYJ1nH7hpYyh6SgnvKMoyRnt6XSl5r8a+6C166HB0s+ydYRcrvHZvlhQFQ5kkoqI71yuR58FzHDRdh+Dxvmm6DoBz7bsTeB48Zzw7klXDG8L11EoEisFMDP/2nTtxcnoZ24acW1xea4HxuK0fJgz2fMDuiPFBiDE/MwiOmDcmF6HpxkTSUMb9DFansBwxTUbSVPP1F41YsvcG1A3DCIuw2QprO2KM+JnQCTFNuOzOz5uxayFZ5LhcsDpiis1N6D/6evBjyRipmGgJMfVYLiuWWysscS1A5fo4v7y2gPb9V41Ysn0hiiUDjK4sxo2b+3yPYXCCNHNZt+BIZpP6YYi7qhdNdn6+iLKiISrwoToGqzHiReWawxTsewpbPwxgd8SsfVw+d3IWqqZj80B4RAurI6aOEMP6YW4LiRsGAOIRHiLPQdF0LJbkNYWYgyE8PllHTFFSrH6YsKRPNMNdVw7hwKfeipFseN5BCffRdB2KpmMo48290lrQ93jR282yd4bI8+B5f8QKSVExkHLvOxQFHgLbN9c+pTZlRUVM5JF2K3bNPCbJEeMdJMQQbZOOifjIbVsc/72JNSJ3rEnaEC3gtPLC5TTLHnfENIrbOXLRiCXbMRKuCIlOOmJeu5DHqxfyiAgcfn7PqNObRqxB846YcC1UNdM7VYkmC9e+dTvZFhwxEwslHLmYB8cBt4dgwSodE3Fpsdxw6IDFJfWnopbLJAwwQb4ZR4wVS7YnPLFkQEUkBID9XRBLBlSGUJaaHITRdR0vme6DvSFYQI3XiV5j0+wb+5OBdtKtBRumqCnEmI6YMHxP1SQtR8zax6W9HyYssA4EWV29qKPrOp54w+yH2R4Ohw8AcByHTFzEXEE2hs4aGOYWirIlZOwzXRhhwHLEyKq1/WFyrq4Fx3FY1xMOMZPwjnypjGwsirGcN6kVbEHf60Xvsqwgm4i6UvbOEAUeAsdB1XR4PcujA65Gy1lihabDa5NgWVYQj4hIx9yJyzWOSd503hBeEP5RN6LrSFUVBVbDXi63DoVnQseeuavVyUt2C7ZQ63ZsAyuxXGwwdXp0YhEAsGMkmPFc9eixOmJaF2KYG+buncOhWnTsFuJNd8SE68WsErdTP6v8fEj3rduxHDFNdMQ8/oYRS7Znfa5h1FdQSDdRQH38knEf2BqyxZ1mO2Km8iX81IolC5sQU3nBC0sE0lq02hFzYaGE6aUyRJ7DVaPBHxqx4nyrRHkmeIY5lgwAogITYlZHrx0+awpmIXAuVWO5/5twarF+mFtDdE5GRUP8U2o4Yt6cKeD8fBERgcONm8MjLgGVd5217t+sZ2q8L4GBENy7Gex9Vdcrw3Nh7pgiiGbIl8rYPJBDKupNJ5zIG2KFptUeEnSLkqJi0KWyd4Y9vstLZFVFhOddFWIEnvNNrCgpKnqTcdei8yrHJAkxXkFCDBE42JSYoumQajzAnwhh+SibXge874lhC2L2bXADK5qsXF+sYA/1O9cFf3HDTi7RniNGVjV889B5AMAD1wY7lqxbiTVwxJRkFdNLZQBGBnaYYOezrgMlefW+FSQFc+bxOkZCTKBoxj3IeMyMJbsrBLFkQHO9B2Gdsm22I+Z7rxixZNdsyIUmRoiRNfvQ+lJRXBnQPrNWabUj5rAZJ3TlSCYUvQj1oslOTldifMNMPUfMyellLJYVxCM8tg+Hbx9TtmGKRkzmSzg+tQSOA27aEh7RgkWTSTUcMU8eM+5r123stQSpsMB6Ytbq9jkYwn4YYOXQ3qsXjHe2sN2rCaIVCpKMuChiU593axMsvstrR4yu6+iJuxvLJ/okVpQUFbGI4Gr/jZ/xXZKqoj/p3ncn8pwVKUd4AwkxROBI2l58C+XVE3AnL4Xv5TIuCmAxmWu9dDmNV46YLHPE1Flc1HW9Ek22LlwLPO12xDz++iVML0kYSEdxR0gWUruNRo4Y5obJxERkE+FaDFjrmsLcMJm4aEVhEcGg4ohpLOzKqoanzBz9O68ccn27nKApR0wI40WBSkfMWo6Yh81YsvtC5oYBgBs29eGGzX34jbduAx/iOCs7qRY7Yg6Zk+xh6IcBKhPs1UM+FUdMuM6zatgwhVQlxDDHwdVjPaHsMkpZ18rGAiGLJbt6rAe5ZHhc1ZVostWDIk+8Eb5+GIbl/l/j/n3IFHSvCVEsGWBMfLMSeyY2hel9myBaZa5YwmhPBv0p7wZnWHyXl+4DVdPBcUDGRccIYOybH2JFSVaQjkWQcNHVJApm/40PrhEOOjJx99yVAs9D5HmourcurcuZcK08EZcFosAjKvKQFA0FWYV9lmhl+Wh4Jk15nkMyImBZUg1xyUMdgsVxuO6IMV9OlkpGVBJXVdB2caGEfEmByHOhm67qbTOajMWSvXvfmDUdSHhLzHyhLNdwjZybM8vsexOrjtegs+qaUnVKnWP9MCGbyL8cYK6DtRZyXjg9h8Wygr5UFHvGGoTRB4iU5YzsQkeMuQi6UJTN7OvV14zJfAk/PW3Ekr0zhEJMTzKCf/i1/X5vhqNYC95NDsFU4q7Ccc4lbXG+drovmmzlPfwlc6E7LIJZNSwyr5FoDVRiycIWFVgvmkxWNRw4YexTGHrPqsna3nXqoes6Dp6ZAwDsC2N/UVSwhM+wdbkRRCvIqgrowNaBnKfvgTzHQRR4SKp3w7llRUFMFF11jAAVt4/XYkXZo9g1geMgexwpJ6sqRMFdtw9gOFkLEjlivIJWBolAYmV6V72gHDfdMJsHUqGbgGOFsY3Ktd2ATdul3O6IMfdP0fSaMVD2rGE24RgWeqxosuYdMXPLEn50ZBIA8MB1FEvmFyxapjpfHrD3w4QrloyRbLDAGNbum8uBzBruQQaLJbtj+2Bo3AlrCTGyquH0jCGAhk2IYc5ITQfydUT57718EboOXLshh1ESQQOBFQHVRCm6pul4+bwpxIRkATUZXd0Rs1RWMJEvAQC2DoTrPKuGRZNVO2IOnQvX91RNMwKhrut45vgMAOCWreESYiqOmJWLOgfPzGNZUtGbjISig6ka9q7T6P59ZraAuYKMqMBjVwj30Z5MQW4YopuZK5QxkE5gXY/3x3lMFDyNgSorKhIRESmXyt4ZAs9D9KEjxovYNQCICgJUH7p9YqKAtMtupqjHx+Tljq8r2Z/97Gfxlre8BZlMBkNDQ3j3u9+N119/fcXPlEolPPjgg+jv70c6ncYDDzyAycnJFT9z5swZ3HfffUgmkxgaGsK/+Tf/Boqy8gHpsccew7XXXotYLIZt27bhS1/6ktu7R3RAvUzvE+Yk7dah8E34tVoY6wSqpltxFexl3S1SUdGKSqr1gnJ0wiho3hmyWDKgsgC30IIj5tuHL0BWdVw1mg1dJ043EbOiyWo5YsItVjS6prBoMnLEBI+1YhwZj70+BQC4M0Sxhpk1oslOzxSgaDqSUQHretx/YXKSiMBbIlq9npiHXzZjyfaMerZdRGPYs08zjpiT00tYMntHrgiJUMiGDez3gVOmG6Y/FUVPMtzRlBVHTOV9oKyoOGL2V+wLqyMmtlpAq+bEpWVM5EuIijyu3xSurhHmAq+OJmP9MLdeEZ4BAztWx1sDJxPrh9k1mg3d4BlQiTsEgK0huQ4SRKtouo6SLGPbYK8vqRXGgr53i95FWUF/KgHeA+dPVPRWrFA1DRzHuR67BvgjVpRlBaloBImIu0PVMUHwpf/mcsVXIebxxx/Hgw8+iGeffRaPPPIIZFnGPffcg+XlZetnPvnJT+Lb3/42vvrVr+Lxxx/HhQsX8J73vMf6c1VVcd9990GSJDzzzDP43//7f+NLX/oS/uAP/sD6mVOnTuG+++7DXXfdhUOHDuETn/gEPvKRj+AHP/iBp/tLNE+yzgLjCfPlMowTOq0WxjqBPTPc7Y4YnueQjtbPTn7N6ocJnyhR6YiRoTd5g/raC0Ys2QPXkhvGT+JiI0eMMZkfViGGndNLNSa9z7NospDuWzfDFnIadcRcXCji6MQiOC5c8S0VR0zt+xyLJds6mA5dHCDQuCdmYqGEn75pxNG88+oRT7eLqA87Jqs7B2vxkhlLFqbekWSNjpiT0+HrUqxHjLlabcMURy8uQlI19CYjGO8L5z0uFV07xpH1w7xlU68luIWFiGBc36uFmCeOsX6YcDl8GM10xIS1H4axQogJebQhQdQjXyojG49hvNefdQljQd9bsaIv5c0AlNdiRUlREY8IHgkxvPf9N4qK/pT76R1RUfCl/+ZyxdeOmO9///sr/v1LX/oShoaG8MILL+D222/HwsIC/uqv/gp/93d/h7e+9a0AgC9+8YvYuXMnnn32Wdx000344Q9/iNdeew0/+tGPMDw8jH379uEP//AP8Tu/8zv4zGc+g2g0ij//8z/H5s2b8Ud/9EcAgJ07d+Kpp57CH//xH+Pee+/1fL+JtUnWeXE+cSm8L5fJOnFrbsI+i+MqpeVuko6LWCwrNV8sj5pCTBjdIawbQNF0LJUVayG1Hm9MLuLwuQWIPIf799FktJ+wxYuudMQ0KKE+z/pvcuGMXetmsmyitk6fFgA8bsaS7RvPhSqfPbWGI4bdw8MWS8boTUZxeqaA2RpCzPdeMdww123sxbqecF5TupFWHDEvmQXwYeodYfsnqzpkVUNE4K2hpbD3wwCVnjfJtqDPvqe9497m+jtJMwLh0yHthwEqjhjFFk02X5Bw2PzuwirENBMtGuZ+GABI2qauyRFDdCv5Uhl7x4aRcrHcvRFxUfTMEcOGSN3uGGF47awoywqSkYgn32VUED0XK3RdR28y5vrnxERyxHhJoMa9FhaMSbS+vj4AwAsvvABZlnH33XdbP7Njxw5s2LABBw4cAAAcOHAAV199NYaHh62fuffee5HP5/Hqq69aP2P/Hexn2O+oRblcRj6fX/EP4R0sn7b6xfnEVIiFmJj3jhgWeWDEhrn/ssqyk6tLLEuyilPTxsLAzpHwRZPFI4K1GDBfWDue7OumG+auHUPoT7t/4yTqw763klzfNRLajpgGLjtyxAQXtpBjj46s5lEWS7Z9yLPtcoJ0rHEBNXPEhFWIYY6YWveBhw+bsWRXr/N0m4jGpGz9fGs5Wl8ye0f2rO9xfbucwj69zq4nJ03BsxuEmKh5D7c7YpjjYG+IBLNqLIGwzrVSUTUcOBnOfhgAEE1HjF1Ae/r4DHQduGIoHVqxmg1iVb/nMEqyaiUAXLshXHFyDPs1ZVsI37cJYi0Kkoy4KGJTn3/DoRFRgFdL3pKqIiq6X/bOiEa8dVaUFO9i1+IRb90+7LPSHnx3Is8jnKMt4SQwQoymafjEJz6BW265Bbt37wYATExMIBqNIpfLrfjZ4eFhTExMWD9jF2HYn7M/a/Qz+XwexWKx5vZ89rOfRU9Pj/XP+Ph4x/tINA+b9C7aFhjnliXMmFOoYXy59KMjhglZbvfDMNgCY77qBeWNyUVoupFXPpgJpzDRbE+Momp46OB5AMB7r6NYMr+xYk2qin5LsopLi2UAXeCIqbqmSIqGKXPfqCMmeCSjAgQzG7/WVK2kaHjaLGi+a0d4YsmAyqJ3vfz84yEepgAq94HqjpiLC0X87LQxBf0OiiULFOyYVDV91X3AjqRold6REE2yRwXeup6wZ2bLETMQzvPMDhumsMeLvmQKMWH6nqpJW8NZSk2B8OXzC1gsKcjGReweC48wyGDdPoptMY71w9wWorjNajLWPa72u8CrF/KQVR0D6Whony3ZO2M8wtMzJNGVzBVLGO3JoD/l3/Et8pxni94l2bvoLgCICaKnzgpV09GX9CZ2TeS9XT4vKypiHoloosCDlBjvCIwQ8+CDD+KVV17B3//93/u9KQCAT33qU1hYWLD+OXv2rN+bdFmRqDHpzTKvR3vi1ot1mLCm15vIKXcKyxHj0d9Xmk2KVS3CHbH6YTKhjZFg8WRzdUqaGU8en8bUYhm9yQjuujJc0+zdSD1HDHOMpGMiehLhLDOud025uFCErhv7PpAOT6zV5QLHcdYiXL6GsPuz07NYKisYSEexezRcC3CNosk0TQ99NFlfsnZHzPdeNgZ/rqdYssCRsHVr1HMfAMDRiTwkVUMuGcGGvvC4JDmOs1zkBUmFpuk4Nd09jhhWdi6ZIlq+JOOk6bAOk3OpGuaS1/Ta0aksluzmrQOW0BYmWMeSqulQNR26ruNJ1g+zPXwOHwYbOKvniLHHkoX1fYc5YrYMpMGH8NgjiEbIqgrowNYBf89RLxf0S4qCbDyGiODNYK4oeCcy6boOcPBMZBJ5b8WKsqIgHhGRjrm/ViHwPDyzaRHBEGI+9rGP4Tvf+Q4effRRrF9fmSAfGRmBJEmYn59f8fOTk5MYGRmxfmZycnLVn7M/a/Qz2WwWiUTtF+ZYLIZsNrviH8I7mHukaJv0PjFlvHiFNa+24vLx0BFT9tgRY0WTrVxcPHJxEQCwYyS85xFbrF8rmuxrZizZ/fvGrEgNwj/idRwx9n6YsL4s13PZnZ+rxJKFdd+6nWyitnsQqPTD3L59MHSLIJkGQszFfAkFSYXIc9jYH56Fbjusr6e6I+bhl81Ysj0USxY0BJ6zxJhCg2jYSixZ+BZQ47Z7wcV8CSVZQ0TgMB4iQakeVjSZeQ9/5dwCdN24d4c5+jVpFwhrvBcwV+Qt2/o92yYniQiVc0hWNZycXsb5+SKiAo8bN/f5uGWdwYYo6nXEsNi8a0IaSwZU3hnD+r5NEI2YK5QxkE5gXY+/x7eXQkxZUTCU9u55wNt9UxETBE+iuwBzyMBDsaIkq+hNxg2RxGXEkL1zhh1fVwl1XcfHPvYxPPTQQ/jJT36CzZs3r/jz6667DpFIBD/+8Y+t//b666/jzJkz2L9/PwBg//79ePnllzE1NWX9zCOPPIJsNotdu3ZZP2P/Hexn2O8ggket7gM2SRvWSJNGfQ5uYe+I8QJrUqxqEe7ohOGI2bkuvEIMi6SZbxBNtlCQ8chrhuhLsWTBIB6p7Yg5Z5bZhzU6ArD1TlU5Ys6xfhiKlAgsGXOyabG0+npi9cOE0FHHHDFLNZyfLJZs00DKKnIOG6wjxu6MvDBfxAsslmw3CTFBhA3C1FrwZrxk9Y6Ez2WRjFbifFk/zIa+ZGjPMzvV0WSHzLL3vSGOJQMAnufq9sQUJdW6ptyyLZzuEfuxp2g6nnzDGDC4flOv9T4URlhHTL34zYNn5gGEOzaPvWffEGLBjCBqoek6SrKMbYO9vt8fvfr8oqwgJooYy3nX0eup20c2HCPeOWK8FSskVfU0do0MMd7h65PQgw8+iL/7u7/DN7/5TWQyGavTpaenB4lEAj09Pfjwhz+M3/zN30RfXx+y2Sw+/vGPY//+/bjpppsAAPfccw927dqFf/7P/zk+97nPYWJiAr//+7+PBx98ELGYMSn167/+6/gf/+N/4Ld/+7fxK7/yK/jJT36Cf/iHf8DDDz/s274TjWEvJwXbg24lWz6cUQtJPzpimCMm5o0jptakmK7rNkeMdw8BTpNLGDf4hQbRZN8+fAGSomHHSAZXjYZXdOomWKxJWdGg67o16VxxxIR3YngtR0yYRaZup16f1oX5It6YXALPAbdfEb4FuHQDRwy7h4e5/LcSUVkR0L73ivHs+pZNvRjp8eZliWgNY+FXahgNe5gt8IewAJ45foqyipOsHybE55kdJsSwaDKrHyaE31M1qZiIgqSuOi5/+uYsJFXDaE8cmwfC+c5jX2SUFa0SSxbifhigcu+uNUQxtVjC+fkiOC7csXn/182bcPv2QWzuD+exRxD1yJfKyMZjGO/1/x1d5HlwnCEOuVkyP18oYV1PytM+HC+FmKKsYLw349lnet0RwwHIxr1x/4oCD57jXD8mCQNfpeA/+7M/w8LCAu68806sW7fO+ucrX/mK9TN//Md/jHe961144IEHcPvtt2NkZAT/+I//aP25IAj4zne+A0EQsH//fnzgAx/ABz/4QfyH//AfrJ/ZvHkzHn74YTzyyCPYu3cv/uiP/gh/+Zd/iXvvvdfT/SWaJxlbHSNhOWJCapWuTL51ryMmzV5QbItwE/kSFooyBJ7DFcPh/O4AIJcypuDmGkSTff1FI5bsvdetD120SbfCHDHAyniyc10gVtRz2Z0nR0zgsaZqqxZzHjNjya7Z0ItcMnz9PswRU5RVKOrKOMCw98MAQK/pjLR3xDx8+AIA4L6ryQ0TVNYahFkqKzhmCoV7xsO3gFrZP9U6z7qhHwawO2KYEGNEyIXdEQPUH6Z4+oQhWtyybSC0z5ICz4ENDhdkFQdOGlFrt4VwwMAOE2JKsga56h53yHTDXDGUtu7xYYTjOGwdpH4YovvIl8rYPJBDKur/+SnyPASeh6q550FQNR2KpmHLQK+nC+uiYDgrdN1df4Wu65A11Vu3j8CDM8UKt5FVDSLPe9IPA7BjknP1mCQq+OqIaebkjMfj+MIXvoAvfOELdX9m48aN+O53v9vw99x55504ePBgy9tI+AMTDpiQUFZUnJk1ooTCOk3LFqg8dcRIHnfEmC8e9hLLIxeNWLKtgynLnRBGmCOmXkfMiUtLOHhmHgLP4f59Y15uGtEA+zFXljWrM6YbosmY+6BQ5T5g+zYW4n3rdlhHTHXOvBVLtj2cU8OpmL33QEVPoiKEWo6YEAsxLJps1nRGXpgv4sUz8+A44B0kxASWRk4tAHjlvNE7sq4njqFM+FxNTJQ3osnMPsWB8J5nduyu1omFEibyJfAcsHvM/4nmTqlEOVYJMccrQkyYEQUekqLhuZMzKEgq+lNR7ApxRDFQ+c4A412H9YYBwEHWDzMe3n4Ygggri6Uy5otlAMa1R+TZPxxEnoeiaYiLIjb1BeMaVHEfaHBrNj5fKiObiGHU4z4ckechmGKF4KIAtFSWkI5GPe37sYsVvOCuuFVWFMQiAjJe9d/w7h+TRAX6GyYCSSK6Ms/79EwBmm4UAQ9mwlnO6Ysjxvws+4uDm2Riqy37LJYszP0wQKUjZqFYO5rs6y8Ybpg7tw+G9hjtRiJCZSqTZcwD3RFNlqzTe1BxxIR337qdbA1HTFlR8Yy5AHfXjvD1wwDGoikraa5e9D5hxYuGd4GYLbotFGUoqobvvnwRAPCWjX0YzoZvAf9yoV6fFiPMsWQArAGDgq0jZutQlzhiIpVospfM72n7cCbUPSOM6qEzwHDbvXrBGGC6eVu/L9vlFFEznuwnR40Bg1uvGAi9yyIi8FYUYLWAxhwx+zbkPN4qgri8kVUVc8Uyrlo3gD1jQ9jU14O+ZBwRwXCcLEkyliUZYz0ZTyO6GmEJMS66DxbLEjb39SAR8fZ+KfIceN5918hCScJoLu2ZUAFUixXuUpQUpGMRJDxycFn7Ro4YTwj/UyzRlVS/nLAFnC1D6dDa9Cv75L0jJuGRI4ZFk9lfTpgjZsdIyIWYhHETrOWIUTUd//jieQDAA9et93S7iMZwHIeYKKAoqyjJxkNTSVZxadGYmgqzI6bWIo6q6bg4XwJAjpggY3XEFCvXyp+9OYdlScVAOhbqqeF0TMRcQV4hxMwtS5gx47zCvEDM7gO6bogxD5tCzH17yA0TZOpFQDFY3FUYY8mAyqDP7HIZFxaM6/+WLnHEsMX8sqJW+mG6IJYMqAxT2J+ZD5ycga4DVw5nQunOsiOaovzjbxiRm2Hvh2Fk4iKKsoq8bZBC1XRL0L2GhBiC8JSJ/DI29GZw7fjIin4qXdchqRokRYWkqkhExMCsI1nOCpfEipKsICoIGO/1vp9XFCqOGLdQNQ06dM/7frwQ0ADj2C3IMvb2eTeYJ/Kc698bUYEcMUQgqXTEGC8nx61J2vAu4NTqvXEbyxHjlRATWx23c3SCOWK8fxBwkpxV0rzaEfPMiWlM5EvoSUTwtp3hnGTvZlhPDHPEMMdIOiaiJ+F/TnC7VFx2lfNtarEERdMh8ByGyZkVWGoV/j5mxpLdsX0w1FPDteJ2jptT+mO5RKgn2UWBt64Zr17I4yCLJds94vOWEY2o16fFYE6LsBbAs3sBc1L0JiMrIpPCjN0Rc/hc9/TDALbIYtu18inTFRl2NwwAa0GUvROEvR+GYfVh2t51jk0tYllSkYoKuGIo3O87BBEm5oslJKIi9q0fXiHCAJVhvEw8iv5UAskAdMMw7PFdbjBXLGE4k8RA2vt0BJHnwbvcNZIvSeiJx7Au6+3aoNsCGqMgKUhGI57GyomC+b2REOMJJMQQgSTJYhZMIaEbSn5rTa+7TUE2Psurha9M1ctJSa7EZHRPNNlqR8zXzFiyf7J3NNQ9ON0Ki21hjphKLFkiMJNR7VDpnapcU86b+zaSjUMU6BYfVCrRZJWFnMdeN6aG79oR7qnhdI0YKGuYIsT3cEaveS/48nOnAQBv2dSHIYolCzSsu6i6TwsAZpbK1j1h9/pwOmKY6/mVC4ZQsSXE8X/VsGeqoqxagtmekH5P1aSsGObKtZL1w9wa8n4YAIjYBgquHM50TXxjrT7Mg2Ys2Z71OQghHqQgiDAhqxrypTKuGhnAoA+CQyeIPAee410RKzRdh6Jq2DKQA+/De24lvsu9Bf2lsoSNvVnERK9j19x3+wCGwDjak0Zv0rv7psD2jaLJPIFWaYhAUr3AeIKVj4b45TIZrd3n4CZs0cFeoOwmmaposmOTS9B0Y+FqKOTT+UyImS/I0G0333xJxg9enQAAvJdiyQJJTFzpiGFl9mGOJQNqO2KsfpiQ71u3k6kSYs7NFXBsagk8B9y2LdxCTMURUxGtmRCzLcT3cAZzGjzy2iQA4F0USxZ42DFZyxHDXBZbBlOWQBo22L3g7Kxx/d8yEF73eDVR8/795nQBiyUF8QiP7cPd4TiwjkvzHn52toDTMwUIPIcbt3SBI0asLDN0ixsGsPVh2u5xrB+GYskIwjsmF5ewPpfFjuHwXS85jkNU5F1Z0M8Xy8jEoxjz0E1hRxQqhfZuICkqBJ7HWM6H2DUXBTSGqmnQdB2b+rwdOuE5DqLAkyPGI0iIIQJJwiZa6LpuOWJCLcRYEQTeOWKY6OOVIyYdM6fEysb3dmTCiMnYuS4baucBAOQSxuKboukrIne+e/giSrKGK4bSXTOl2W3Uc8SM5cItVtgFayYOWm6fkO9bt2N1xJjRZMwNc93GXvQkw7kYzKgIMasdMWF2tTL6zJhKTQc4Dng7xZIFnlQN0ZoR9lgyYPUzXnc5YsxoMtW4f+8e7VkVPxNWmFOeHZfPnDDcMPvGc5azMMzYv6fbtod7wMCONXRmd8ScnQPQPf1FBBF08qUyYqKIfWNDiIY0jSIqCq64DxbLEjb35Twrea/GcI24IzIBwEKpjN5kDEMZ711QbgpojHxJQi4Rw7oe74dqYqJAHTEe0R1PskTXwV5OipKKiwslFCQVIs9hY3+4bKd22EKAURqnefKZzFHktSNG1XSUZA1HLhpCzI6RcMeSAYY4yBYE5guVKTgWS/bAdetDLzZ1K+x7K8nMEcOiycJ7PQEqU9CKpluLVOSICQfZxEpHDOuHufPK8HdMZaqmvIHuEmLs3Rs3bOoLfaH25UCjjhhWAB/mQQo2bMAIc59iNVFx5atqt/TDAKudWk8dnwEA3NIFsWSAMTkMGN/hDZv6fN4a52AiWd68fy+WZBwz73H7yBFDEK6jaBrmCiXsGunHsMcdIU4SEwWourNrQiVZQUTgMd7nr3M0IrgnVhRlGVv6cxB4f5ayo4I7AhpjsVzGpv6c57FrABARSIjxChJiiEDCiu0VTbcW8zf0J0M9BWefWCx61BPDFsK8csQkowKYFrFYknH04iIAYOe67oiRqO6JeXN6GT87PQeeA37hmjE/N41oQMxcpCorzBHTLdFklfOaOe3O2/pviOBid8SUFRVPmwtwd14Z/qlhJvwz52BBUiyBsBuEmD6bEEOxZOGgXkeMrutdUQDPRHlGdzliVu5bmL+naqzjUlKgaTqe6aJ+GKAiot2wqc9KOugGrI4Y83py+NwCdN1wWZMwTxDuM5lfxlgujZ0j4YsksxMVRMcjruaLJQxlUr535kRFwZX4rqIkIy6KWOdT7Bpg7ptLYkVJVhAVBIz7ELsGmOIgdcR4QnhXtYmuJmmb7mMvyWHPlo+KPCKCoVIUZG96YixHjEdCDMdxKybF7NFk3QCLJ2OOmK+/aLhhbrtisGtKSLuRakfM+S5xxAg8h3jE2De2IGA5YnLh3rdux96n9ezJWRRlFUOZGHZ1wbWyuvfgpNnx1peKrhAxwkqvGU3GccC9FEsWCiqOmJXPXufni5hZliDyXKifU+xCjMBz2NDXPdf/WJUjJswRctWwZ/OlsorXJxcxsywhGRW6Jt6KDc91Uz8MAKTN+/eiGS168IwRS0b9MAThPoslCRGBx76xYV8cA04SczjiStN1SIqKLQM58D6ndBgRV84nwCyUyhhMJ9HnYYl9NW7tGwDMF8sYyqTQn/ZnoDImiOSI8QgSYohAIgq8NUn1ynlDiNnaBZO01mKARz0xliPGo2gyoBJLc+LSEuYLMgSe64opaABWd8NcQYKm6fjHF88DAN573Xo/N4tYg7jNEVOSVUwtlgF0h2uELeSwnhgmMlE0WbBhpeC6bvRMAcAd2we7It4wHauITACsjrewD1Mw2HVj/5Z+mn4OCRXnwcpnr5fOGs+XO9ZlVsV7hYmEbds39CVXxXmFGfu+9CYjGO/rnnub3an1tOmGuWFzX9d8f++5dgx71vd0nWM8W9URc8iMN7xmQ69fm0QQlw2zhSJ2DPf76ohwiojg7HNHvlRGNh7DWAD+btxw++i6EcW9qb/H1/elqOCOa0TTdUiqgs39Pb4JaRGX+2+ICuGWkYmuJhUVICkaDjMhpgsWcVJRAQtFGQXJfUeMruueO2IA07K/UMJPT80CALYMpEK9wGGn1xRi5osynj05g/PzRWTiIn5u17DPW0Y0gh1/JVnFBdMxkooKVtRcmEnGBMwsG5Pes8sSiqbrZ10PLRAHmZjIIyrwkFQN33vFEGLu2hH+fhig4ohhQgzrh9k6FN4cbzvv2D2Cz77natzVBX0+lwsV58HKZ6/D5+YBAHtD7rKwx1RuGeiO84xhd8TsHc91hVjNSNqOy6e6LJYMAN5/40a8/8aNfm+G47Bhg8WSAl3XcfDMPAB0jZOJIIIKB2CkJ4Wr1nXHdVLkeWOnHGKxJGHP6CCSUf/fb512+wDAkiQjFY1gXdbfNcGIKMANqWKpLCEdi/oqpIk87+QhSTSgO0ZuiK6EvaBcMqfXu6F8lGUke+GIkVQNiqnWe+mIYZb9n75pCDFhjvuohkWTLRQkfM2MJfv5vaNdIzR1K1Y0maLinC2WrBsWdCxHTFm1YskGMzE6JgMOx3G2nhgFAs91TUFzdTSZJcR0wTAFYDh2/9kNGzBCYmdoYMdkoerZi02yh12IsfdvdIN73I69I2ZPyL+nathxuVCU8bw5vNQt94FuhnXELJYVnJsz4g0jAoerRrvnfYcggkhvMo59Y8OIR7pjllwUeDi1ol9WFIgCj/HeYFyHnHb7AMBCsYzRnhQycX9jjkWec0WsWCiVsaG3B6mYf/sn8iQPeEV3XMWIrqS6fLQbXi6txQAPHDH2BYekh4uybFLslQtGP8yOdf6UjbkBc1Ccny/iey9PAAAeuJZiyYIO61Epy5pNiOmOeBN2nVyWFCuvfCzXHfvW7WTiImaWJQDAdRt70ZPwf4LNCTKxlRGcTIjplohKInzYr5MMVdOt6NuwF8Dbo8m6zRHDuhUBYN94j49b4jwsmuziQgkA0J+K4srh7nlm7lYy8Yoj5kWzH2bXuiwNwBCEi/SlErhmfBhjPpWYu4HIO7ecP1coYyidxGAmGB1xTi/oq5oGXdexodf/5wA3xApZVcGDw4Zef49vUXDWpUXUhyQvIrAkYxWdcCgTszL1w0xlMcB9RwxbcIiKvHFR9Qj2gsKyM7vJEcM6Yr790kUUZRVbBlK4lso5Aw+bqDUcMQUA3SPE2MVd5oihfphwkLHd0+68ctDHLXEWezSZomp4c2YZAAkxhH9UrpNGlxYAnLy0hGVJRTIqhP7YtA8ubekS5xmD4ziM9yWQjAq4Zry7OjiqY4Nv3jYA3sGFOcId0pYQI1M/DEF4RCIiYlOf/4vwTuLUgj7rFtkykPOtW6Qap9ee8iUJ2UQUI1n/h03cEGLmi2X0pxIY9nn/BJ5zzKVFNIYcMURgsbs4uiXSpBIj5L4jpmj1w3g7ocWEGMbOke4RYnqThlWU5cw/cN36roi36nbsjphZ04GwvjcYE0OdkrTFHVpuH3LEhIJsonKtvHN79/SNsCnv5bKC07MFyKqORETAaA8dl4Q/sOukqukoKxriEcFaQN091mO8eIaYlUKM/4sUTvP1X78ZRVlFb8rfOBKnScVWPi/fuq3fpy0hWiEbrwwbUD8MQRDtEhEE8BwHVdM7eg7Jl8rIxmKBcgs56fYBjP6Uq0cHAxFL57QQY/Q6y9g3NuR7NFjE/Hxd12mNy2X8P5IJog6pmD3zujteLJO2qUy3Ya6bZNTb0zxte7HMJSMYzsY8/Xw3ydmigzgOeM+1Yz5uDdEscdMRU+5GR0yUHDFhJRMzrifD2Rh2dlGEY9rmiLH6YYZSNOlN+Ib9OaggqYhHBBw+Z8aSrQ//hO1AOoZbtw0gHRPR32ViBQAMZbuzjylV1d9481bqhwkDafPevVhS8JoZw3wNueMJgmgRkefA8xw0XYOA9gZnZVXDQrGMa8ZHkIoGJz1G5HnHjBWyqoLnucAITaLAg+MMJ5ITDqSCJCMZjWA05//gucjz4DkOmq5DICHGVUiIIQJLwvbi3D2OGOMm601HjPEZ1S96bmOP29k5ku0qNZ1FkwHArdsGsI4mvENBrGZHTHc4YlK2Po7z5r5RR0w4YJ0wd24f6qrrZKqGELOtS+7hRDgReA6JiICirGK5rKAvFcVL5+YBdEcBPM9z+NuP3Oj3ZhAtEhV4iDwHRdOxsT+J8b7ueC7pduwRzCp09KWi2EDfHUEQLSIKPARz0bsddF3HxfwSNvRmsXtdsIR8Uags6HcqViwUy+hLxjEUmP4bDgLHQ9N08ELn72/zxTI29fcgl/B/6EQUeAi8KcT4vTFdDgkxRGCxR2p1ixCT8LQjxn9HzI4umvIGgFyiMmn6wLXrfdwSohVYgepCUcbUYhlA9zhikrGKuEuOmHDx/ps2YGZZwr+8a6vfm+IoGUscVHCCCTEh7+Agwk8qZgoxkoKyouLIRWOSnSKFCL/gOA6pmIiFooxbtgVrEY2oTzIqgOcAswoT+8ZzXTVMQRCEN4g8D57nrF7dVpleLiIbj+L6DSNWH2pQEHljQV91QKxYlmRctW7A99guhvW96XrHi+mqpkHT9cD0HzFHjKrpiATrkOo6SIghAotdQOiWRRwvO2KY68ZrR0za1hGzc1339MMAhtMgHuGRjIq496oRvzeHaJKYaDy4nZw2SsNTUQG5ZHDs253ArilTi2UsFGUA5IgJC3vW5/CXH7re781wHOaI0XTglQtG/FO33MOJ8GI8U0pYLqs4enERsqqjNxnpGlGeCCdpU4i5lYSY0MBxHNIxEfmS8Z51DYm5BEG0gbXo3YYjZlmSIasqbtw0ir5U8J5jKhFXGoD2BZSirCAeETHaE5z3CLvbp1MWSmXkEjGsC8j+iTwHgecd2TeiMSTEEIGFlY8mowJGuiQfmk2ve+KIKfvjiMnYHDE7R7pLiOlJRvD1f3EzklHRcjcRwYc5Ys7Msn6YZNdML7Lr5BuThvOgJxFZEQ9IEF6TjArgOEDXgWPkiCECQtIWDXvKFOX30iQ74TO/cutmPHdyBm/dMeT3phAtkIlHLCFmH/XDEATRBlYMVIuOGEXTML1UwNWjg9jSHwwnRTWWWNGm2wcw3CJTi8vY3N8TKLFJ5M1IuQ72jbFUlnDteHAcTQJv9N+0Iw4SrUFCDBFYmGixZbB7Sn7txdpuYzliPBYM2CIwzwFXDHff4ttVo8F84CHqwxwxzPrdTRPQzH1w4pKx4E1uGMJvOI5DKipiqaxA141+jg19Kb83i7jMsfdpvXTWcGp1Qz8MEW4+fOtmfPjWzX5vBtEirCeG4wxBlyAIolWMBX0esqY1/f/RdR0XF5awPpfB3rHgdkxa0WRtLuhruo7zC0sYy6Vx46bRjntmnIRFkxlun/YpSjKigoj1PcGJ8m9XHCRaJxhBewRRA7ag2E0vymwik7lV3MRyxMS81Vs39ifBccA1G3otJwJB+Ems6jjspg4Vdk2RFONhsJv2jQgv9kjMjf1JREV63CT8hQkxBUnBS+fmAQB719NgBUEQrcOEmK2DaWTJhUwQRJtERQFqC0LMbKGEVCyC6zeMIB4J7ky95RppQ4jRdR0XFhYxmE5g/+YxpGPRtf9PHsLcPp24RhRVw9RSAZv6suhPB+fdnYmDFE3mPsE9e4nLnvuuXoeeRATXbOj1e1Mcgy0EFD2IJvPLETPel8Qjn7wdA+mYp59LEPWIV9l9u8oRUxU9SI4YIggY97oyAGDbYPc5I4nwwZ6FJvNly0HYTYM+BEF4R9p8n6N+GIIgOiEmCE0vehclGSVZwc1bxjCQTrq8ZZ0h8hx4jofSgsgEmI6f/BJ6EjHcvHkMuUTw6gl4jkNE4FFsM+FG03VcyC9ivDeL6zeuC5TbBwAiAo+y4v5a5eUOjSgSgUUUeNx55RB6Et0zaWQ5YjyIJmOf4XVHDABsG8oglwzW9AJx+RKLrLzVre8N9sNrKyRj3SsyEeHF3hVG/TBEEGDPQs+enIGuG6L1YIYGRgiCaJ1NA0bc5q1XDPi8JQRBhJmoKDTlrFA1w0GxfagP2waDP6TMcRyiYuvOiqmlAhKRCPZvDrbYFBWa+95qMZFfwkAqiZs2jSIRQFeT4dIiR4zbBO+bJ4guJml1xHjgiGHRZFQqT1zmkCOGILwlRUIMETBYXN5P35wFAOwdp1gygiDa43fevgPv2jNKjhiCIDoiJgpN9XFczC9hXU8a14wPB85BUY+oIEDTpKZ/fnqpAJ7jcNOmUazLBvvdISo272Syc2mpgHhExE2bRtGTCOYwUKzNfSNagxwxBOEhlY4Y9x0xTOzxuiOGIIJGNztiUrHu7b8hwgsJMUTQqB6EoVgygiDaJR4RcN3GXvB8OBZECYIIJjFRXNNZsVAsIxGJ4C0bRgLpoKhHs24fAJgrlKBoGm7YuA4b+rIub1nnRIXWXSMLxTJUTcNbNqzDcDbl0pZ1TlQQoOmtRcoRrUNCDEF4SKUs1n1HzLJPHTEEETTikco5kIwK6E12U9whOWKI4JG2CTFbqSOGCADVz0J71pMjhiAIgiAI/xAFDmvpuflyGZv7ezCUCe7ifS0MZ8XaC/qLpTKKsozrN6wLRewa0LprpCDJyJfLuGb9MLYM5NzbMAeIUTSZJ5AQQxAekrJ1xOguW/4sR4wPHTEEESRiYuVWt743AS4klu5msEeTxSM8+lLUzUT4D3NqjfbEV7hjCMIv7MchxwFXj5EQQxAEQRCEf4g8DzRYElI0DRw4jPaEb6ipGddISVYwXyxj79gQdgz3ebRlndNKNJmkqLi0VMCukQHsHAl+r1hEoCFuLyAhhiA8hMWE6TpQkt21/LH4s+roIoK43LA7YroplgwAErYp77Fcd4lMRHhhi95bKZaMCAj2Z6Gtg2lk4t3jjCQIgiAIInyIfOPl2MWShJ54FMMhc8MAQEQUGmlMAICFUhmjPWlcPToUqndYkefRzNaqmoaJxWVsGcjh2vXDEEIQZxmGbewGSIghCA9J2BaEC5K7PTHkiCEIg3iVI6abiIo8ooKxf2NdJjIR4WXLgPHCeP3G8Ey3Ed2N/VloL/XDEARBEAThM6LANxQrFssSNvRmERXDN1gr8tyaYkVZUTDemw3d4v9aAhoA6LqOC/kljGRTuHHjutB8h83sG9E5tEJLEB4i8BwSEQFFWUVBUtHv4mcxoYccMcTljijwEHgOqqZ3nRADAMmYAKmgUT8MERh+8bpxXDXag+3DGb83hSAArHwW2jtOsWQEQRAEQfhLxFz01nV9lSNEVjXwHId1IYwlA9Ze0C8rCqKCgMF0+N5fRWFtsWKpLCMZieCmTaNIxcITHU5CjDfQ3zJBeEzS1hPjJstlwxGTIkcMQViumG6LJgMq53g3ikxEOOF5DrvHehAV6TGTCAbkiCEIgiAIIkiIPA+e52r2jSyWysglohjKhPPdda0F/aWyjGw8hv5U+N5fRZ5bM3ZtsVzGSDYVuv2LNCEyaTrAAaGKkwsa9IZMEB6TNKcymVDiBqqmoyizaDJyxBAE62ca70Ihhp3jJMQQBEHUJm3eAyIChx3ryKlFEARBEIS/iAIPgastxCxLMjb09oS2PH2tBf2CJJuxZOFbkmYdMXqN7w3mf1c1Detz4XveFHgeHIeaxyRjWZKQjkWQjcc83LLugkblCcJj2PS6mx0xTIQBKqXJBHE587tv34GjE3nsHsv6vSmOc8u2AUzmS3jLJurjIAiCqMX24Qxu3TaAveM9iIUkp5sgCIIgiO5F5HnwnBGfbasShqyqoY4lA0yxwlzQ56ucE4pmxK4Nh9XtI1ScTEINV0hBkpGMhtPNJPIcBI6v+b0xliUZW9cNNuWeIWpDK7QE4TFWNJmLjphC2RB5eA6IUTQMQeCB69b7vQmu8Zl/chV+/76dTeXVEgRBXI5ERR5/+5Eb/d4MgiAIgiAIAOaiN89DrXIf5EsSehIxDKbDt5DPEHneWNDXdPDCygX9pbKEdDyCgRD2wwBs3wwBrdbrd74sYawnHUrHiCGgcdA0vWZ+lqpp4MBhOJvyfuO6CFq1IQiPYQ6VouyeI2ZZYrFkImU3EsRlAIkwBEEQBEEQBEEQ4cCKJtNWCjEFScam/p5QOw6Ya6RaZAKMfpjRbBoxMZy+AOZkqhXfpes6ZFXFeG84UzhEgYdQ53sDjO8uHY2EWiQMAuE9swkipHjiiDFjz6gfhiAIgiAIgiAIgiAIIjiIvGBFXDEkRYXA8xgJueOgnlih6To0XcdINsyxa7WdTIAxbJ2IhFeosL43rbYQs1iWMNqTRjwSThEtKJAQQxAek/SgI6ZgOmKoH4YgCIIgCIIgCIIgCCI4CDwHkeeh6pr13/KlMnqT4Y4lA1jXCAdN01b894IkIxWNhLI/hVHPyQQYQkV/KoFcInyxZEAldk3TtVV/puk6dF0PdXdRUCAhhiA8xgtHzHKZHDEEQRAEQRAEQRAEQRBBJCoIKxb0C7KMjb1ZiHy4l2rrRZMtliUMpZNIx6I+bVnniDy/ysnEKMkKNvRmQ1sPIPD1I+UKkoxkyEW0oBDus5sgQghzqXjiiImSI4YgCIIgCIIgCIIgCCJIRMVKxFVZURERhFDHdjEEvrZrRFJVjOUyPm2VMwg8v8rJBBixZPGIGGqhgrm0aolMi6Xwi2hBgYQYgvAYyxEjeeCIiZEjhiAIgiAIgiAIgiAIIkjERAGqKVbkS2XkEjEMpBM+b1Xn8BwHUVjZo1KUFSREEYNdsH/VTiYAWCpJ6EvG0ZuM+7RVzlBr33Rdh6ypWN8bbhEtKJAQQxAew1wqhTI5YgiCIAiCIAiCIAiCIC43YqJouQ+KsoJNfT0QQh5LxoiJwgpnxVJJQi4ZRy7kQgWw0snEKCoKNvRlwYc0loxRa99KsoK4KIa+uygodMcZThAhgrlUCm46YiTqiCEIgiAIgiAIgiAIgggiUVOsKCsKogKPkWzK701yjKhQcfsAQFGWsaE3/EIFsFpkYt/fcDr8319MXO2IWSxL6EslkEvEfNqq7oKEGILwGMsR46IQUyibjpgYOWIIgiAIgiAIgiAIgiCChMjz4ADkixJ6k3H0p7rHcRC1iRWyavTfDHWJoyJaJVYsliT0JGLoS3WD20dY1RFTUhRs6M2C6wIRLQiQEEMQHpOwOmLciyYjRwxBEARBEARBEARBEEQwEc0YsoIsY1N/DwS+exa6Y0Ildm2xLCETj6IvFf5+GACICuKK+K6CLGNjb7YrYuWigggN1W6f7hHRgkD4jxKCCBmVjhhyxBAEQRAEQRAEQRAEQVxuiAIPRdMQj4gYzoQ/1spOVOSh6xoAYFmSMZ7LICJ0xxK0KHBgmpmsqhB5AcPZtL8b5RCiwMEuB3aT2ycodMdZQBAhgnXEkCOGIAiCIAiCIAiCIAji8kPkOfAch75kHP1d4hZhGG4fDqqmgQMw3EX9NyLPg5lG8iUJ2Xi0a74/+74BQEGSMd4lbp+gQH+TBOExbnfE/OzNWTx7cgYAkIlHXPkMgiAIgiAIgiAIgiAIoj1Enkc8ImJTX09XlNjbEQUe4Aw3TDoaxUAXRVuJNlGiIMnY0JvtHrcPz1s6jKxqEHi+69xafkO5RQThMcylslx21hGj6zr+6qlT+H++dxSKpmPLYAr3XDXs6GcQBEEQBEEQBEEQBEEQnREReOQSsa5yizCYs2KxJGHrYA6JSPcsP4s8D50DFFUDx3EY6aLvzy4yLZUNt89Al7h9gkL3nAkEERJYb0tZ0aBquiOFbAtFGb/9tZfwg1cnAQA/v3cUn33P1UhTRwxBEARBEARBEARBEESg6E0mcPXoIPqS3de/wRb0NV3Dui7pT2GIAg9dBxbLEnriUQx2k9tHWCnEXDU6gKhIlQdOQqu0BOEx9t6WgqR0HB/2yvkF/Msvv4gzswVEBR7/7l078YGbNoLrMmsrQRAEQRAEQRAEQRBENxAReGzs6/F7M1xBFHhwHBCPRDCU6R6hAjBEJoHjkC+XsWd0sKuECpHnwXMcZFUDOGBdprtEtCBAQgxBeExM5MFzgKYbPTHtCjG6ruP/PH8Wn/n2q5AUDWO5BP7sA9diz/qcsxtMEARBEARBEARBEARBEE0g8hx4jkd/MoFsPOb35jiKyHPgeQ68ynWd20cw922xVEY6GsVgmmLJnIaEGILwGI7jkIqKWCwrbffEFCQFv//QK/jHg+cBAG/bMYQ/+qW9yCWjTm4qQRAEQRAEQRAEQRAEQTSNyPOIRwSM92b93hTHEQXDEZPoslgyoOL2mS+VsWukH4loZwk+xGpIiCEIH0jGBCyWFRQkteX/7/GpJfzLL7+ANyaXwHPAv7l3B37t9i3gHeiaIQiCIAiCIAiCIAiCIIh2iYgCsvFYVzoqRJ6HwPMY60kjHumuZXUjUo4DBw6jPRm/N6cr6a4jhiBCQioqAii37Ij51ksX8LtfP4yCpGIwE8N//2fX4KYt/e5sJEEQBEEQBEEQBEEQBEG0QCYWxY0b16Ev1Z1CTCYWxWiu+4QKQ2TikIlHMNRlbp+gQEIMQfhAMmaUeTXriCkrKv7jd47gb549DQDYv6Uf/+2f7cNQJu7aNhIEQRAEQRAEQRAEQRBEq3SjCAMA8YiIPWODGOmyfhigEk3Wn0ogE6fqAzcgIYYgfCAZNU69ZoSYs7MFPPh3L+LwuQUAwIN3bcUn794OUeBd3UaCIAiCIAiCIAiCIAiCICp0Y/cNAIg8h3gkgvVd6PYJCiTEEIQPJKOGI2ZZahxN9uMjk/jNf3gJC0UZuWQEf/xL+3DXjiEvNpEgCIIgCIIgCIIgCIIgiMsAjuOwd2wQAxRL5hokxBCED6SYI6ZOR4yiavivP3wDf/74CQDA3vEcvvC+a7C+ly6GBEEQBEEQBEEQBEEQBEE4S7e6fYICCTEE4QMVR8zqaLKpfAkf+z8H8fypWQDA/3XzJvzeO3ciKlIUGUEQBEEQBEEQBEEQBEEQRNggIYYgfCAVYx0xKx0xz5yYxm/8n0OYXiojFRXwn9+7B+/aM+rHJhIEQRAEQRAEQRAEQRAEQRAOQEIMQfiA5YgpG44YTdPxZ4+fwB/98HVoOrBjJIM/ff+12DKY9nMzCYIgCIIgCIIgCIIgCIIgiA4hIYYgfIA5YoqSirllCb/5D4fw6OuXAADvvW49/vD+3UiYYg1BEARBEARBEARBEARBEAQRXkiIIQgfYI6YVy4s4F3//Smcny8iJvL4w/t345feMu7z1hEEQRAEQRAEQRAEQRAEQRBOQUIMQfgAE2JevZAHAGzqT+JP338ddo1m/dwsgiAIgiAIgiAIgiAIgiAIwmFIiCEIH8jEI9b/fsfuEfzn9+5B1vbfCIIgCIIgCIIgCIIgCIIgiO6AhBiC8IFbrxjAP9k7irds7sMHbtwAjuP83iSCIAiCIAiCIAiCIAiCIAjCBUiIIQgfyMYj+Pw/u8bvzSAIgiAIgiAIgiAIgiAIgiBchvd7AwiCIAiCIAiCIAiCIAiCIAiCILoVEmIIgiAIgiAIgiAIgiAIgiAIgiBcgoQYgiAIgiAIgiAIgiAIgiAIgiAIlyAhhiAIgiAIgiAIgiAIgiAIgiAIwiVIiCEIgiAIgiAIgiAIgiAIgiAIgnAJEmIIgiAIgiAIgiAIgiAIgiAIgiBcgoQYgiAIgiAIgiAIgiAIgiAIgiAIlyAhhiAIgiAIgiAIgiAIgiAIgiAIwiVIiCEIgiAIgiAIgiAIgiAIgiAIgnAJX4WYJ554Aj//8z+P0dFRcByHb3zjGyv+XNd1/MEf/AHWrVuHRCKBu+++G8eOHVvxM7Ozs3j/+9+PbDaLXC6HD3/4w1haWlrxM4cPH8Ztt92GeDyO8fFxfO5zn3N71wiCIAiCIAiCIAiCIAiCIAiCIPwVYpaXl7F371584QtfqPnnn/vc5/D5z38ef/7nf47nnnsOqVQK9957L0qlkvUz73//+/Hqq6/ikUcewXe+8x088cQT+OhHP2r9eT6fxz333IONGzfihRdewH/5L/8Fn/nMZ/AXf/EXru8fQRAEQRAEQRAEQRAEQRAEQRCXN5yu67rfGwEAHMfhoYcewrvf/W4AhhtmdHQU//pf/2v81m/9FgBgYWEBw8PD+NKXvoRf/uVfxpEjR7Br1y789Kc/xfXXXw8A+P73v493vvOdOHfuHEZHR/Fnf/Zn+Lf/9t9iYmIC0WgUAPC7v/u7+MY3voGjR482vX35fB49PT1YWFhANpt1ducJgiAIgiAIgiAIgiAIgiAIgggVzeoGge2IOXXqFCYmJnD33Xdb/62npwc33ngjDhw4AAA4cOAAcrmcJcIAwN133w2e5/Hcc89ZP3P77bdbIgwA3HvvvXj99dcxNzdX9/PL5TLy+fyKfwiCIAiCIAiCIAiCIAiCIAiCIFohsELMxMQEAGB4eHjFfx8eHrb+bGJiAkNDQyv+XBRF9PX1rfiZWr/D/hm1+OxnP4uenh7rn/Hx8c52iCAIgiAIgiAIgiAIgiAIgiCIy47ACjF+86lPfQoLCwvWP2fPnvV7kwiCIAiCIAiCIAiCIAiCIAiCCBmBFWJGRkYAAJOTkyv+++TkpPVnIyMjmJqaWvHniqJgdnZ2xc/U+h32z6hFLBZDNptd8Q9BEARBEARBEARBEARBEARBEEQrBFaI2bx5M0ZGRvDjH//Y+m/5fB7PPfcc9u/fDwDYv38/5ufn8cILL1g/85Of/ASapuHGG2+0fuaJJ56ALMvWzzzyyCO48sor0dvb69HeEARBEARBEARBEARBEARBEARxOeKrELO0tIRDhw7h0KFDAIBTp07h0KFDOHPmDDiOwyc+8Qn8x//4H/Gtb30LL7/8Mj74wQ9idHQU7373uwEAO3fuxNvf/nb86q/+Kp5//nk8/fTT+NjHPoZf/uVfxujoKADgfe97H6LRKD784Q/j1VdfxVe+8hX8t//23/Cbv/mbPu01QRAEQRAEQRAEQRAEQRAEQRCXC5yu67pfH/7YY4/hrrvuWvXfP/ShD+FLX/oSdF3Hpz/9afzFX/wF5ufnceutt+JP//RPsX37dutnZ2dn8bGPfQzf/va3wfM8HnjgAXz+859HOp22fubw4cN48MEH8dOf/hQDAwP4+Mc/jt/5nd9paVsXFhaQy+Vw9uxZiikjCIIgCIIgCIIgCIIgCIIgiMucfD6P8fFxzM/Po6enp+7P+SrEhIlz585hfHzc780gCIIgCIIgCIIgCIIgCIIgCCJAnD17FuvXr6/75yTENImmabhw4QIymQw4jvN7cxyDKXbk9CGI8EPnM0F4C51zBNE90PlMEOGBzleC6B7ofCaI8EDna310Xcfi4iJGR0fB8/WbYEQPtynU8DzfUNEKO9lslk4igugS6HwmCG+hc44gugc6nwkiPND5ShDdA53PBBEe6HytTaNIMkZ9iYYgCIIgCIIgCIIgCIIgCIIgCILoCBJiCIIgCIIgCIIgCIIgCIIgCIIgXIKEmMucWCyGT3/604jFYn5vCkEQHULnM0F4C51zBNE90PlMEOGBzleC6B7ofCaI8EDna+dwuq7rfm8EQRAEQRAEQRAEQRAEQRAEQRBEN0KOGIIgCIIgCIIgCIIgCIIgCIIgCJcgIYYgCIIgCIIgCIIgCIIgCIIgCMIlSIghCIIgCIIgCIIgCIIgCIIgCIJwCRJiCIIgCIIgCIIgCIIgCIIgCIIgXIKEmADy2c9+Fm95y1uQyWQwNDSEd7/73Xj99ddX/EypVMKDDz6I/v5+pNNpPPDAA5icnFzxM7/xG7+B6667DrFYDPv27Vv1OZ/5zGfAcdyqf1Kp1Jrb+IUvfAGbNm1CPB7HjTfeiOeff37Fn//FX/wF7rzzTmSzWXAch/n5+Zb/HgiiWwj7OT07O4uPf/zjuPLKK5FIJLBhwwb8xm/8BhYWFtr7CyEIFwn7+QYAv/Zrv4atW7cikUhgcHAQ999/P44ePdr6XwZBhJxuOJ8Zuq7jHe94BziOwze+8Y2m/w4IIix0w/l65513rvq9v/7rv976XwZBhJxuOJ8B4MCBA3jrW9+KVCqFbDaL22+/HcVisbW/DIIIOGE/X998882av5fjOHz1q19t7y8lwJAQE0Aef/xxPPjgg3j22WfxyCOPQJZl3HPPPVheXrZ+5pOf/CS+/e1v46tf/Soef/xxXLhwAe95z3tW/a5f+ZVfwT/9p/+05uf81m/9Fi5evLjin127duEXf/EXG27fV77yFfzmb/4mPv3pT+PFF1/E3r17ce+992Jqasr6mUKhgLe//e34vd/7vTb/Fgiiewj7OX3hwgVcuHAB//W//le88sor+NKXvoTvf//7+PCHP9zB3wpBuEPYzzcAuO6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      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "itemslist = list(dfhistorical.item_name.unique())\n",
    "\n",
    "for item in itemslist:\n",
    "    datah = dfhistorical[dfhistorical.item_name==item].copy()\n",
    "    dataf = dfforecast[dfforecast.item_name==item].copy()\n",
    "    \n",
    "    plot_historical_and_forecast(input_timeseries = datah, \n",
    "                                 timestamp_col_name = \"date\", \n",
    "                                 data_col_name = \"total_amount_sold\", \n",
    "                                 forecast_output = dataf, \n",
    "                                 actual = None,\n",
    "                                 title = item,\n",
    "                                 plotstartdate = \"2017-01-01\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_UEsngQXap-N"
   },
   "source": [
    "#### Plot the forecasted predictions against the actual data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jNPEn3ROap-O"
   },
   "source": [
    "Save the actual sales data to the `dfactual` Pandas dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "t-K52as0ap-O"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd3e392844ca4b86837e120cb585b5f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Query is running:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a664543cdb341ca9d8ab209bd216f4a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%bigquery dfactual --params $ARIMA_PARAMS --project $PROJECT_ID \n",
    "\n",
    "DECLARE HORIZON STRING DEFAULT \"30\"; #number of values to forecast\n",
    "\n",
    "SELECT \n",
    "    date,\n",
    "    item_description AS item_name,\n",
    "    SUM(bottles_sold) AS total_amount_sold\n",
    "FROM\n",
    "    `bigquery-public-data.iowa_liquor_sales.sales` \n",
    "GROUP BY\n",
    "    date, item_name\n",
    "HAVING \n",
    "    date BETWEEN DATE_ADD(@TRAININGDATA_ENDDATE, \n",
    "                              INTERVAL 1 DAY) \n",
    "            AND DATE_ADD(@TRAININGDATA_ENDDATE, \n",
    "                             INTERVAL 1+CAST(HORIZON AS INT64) DAY) \n",
    "ORDER BY\n",
    "    date;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 197
    },
    "id": "8dZ8pRewap-Q",
    "outputId": "79967295-00f2-431d-e482-feaf517441c3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>item_name</th>\n",
       "      <th>total_amount_sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-06-02</td>\n",
       "      <td>CANADIAN LTD WHISKY</td>\n",
       "      <td>442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-06-02</td>\n",
       "      <td>TITOS HANDMADE VODKA</td>\n",
       "      <td>658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-06-02</td>\n",
       "      <td>SOUTHERN COMFORT</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-06-02</td>\n",
       "      <td>CROWN ROYAL</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-06-02</td>\n",
       "      <td>TOOTERS ALA BAMA SLAMA</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date               item_name  total_amount_sold\n",
       "0  2017-06-02     CANADIAN LTD WHISKY                442\n",
       "1  2017-06-02    TITOS HANDMADE VODKA                658\n",
       "2  2017-06-02        SOUTHERN COMFORT                508\n",
       "3  2017-06-02             CROWN ROYAL                346\n",
       "4  2017-06-02  TOOTERS ALA BAMA SLAMA                 16"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfactual.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W8gcqtwiap-S"
   },
   "source": [
    "Plot the forecasted predictions against the training data and actual values, using the `dfhistorical` dataframe that contains the historical data, the `dfforecast` dataframe that contains the prediction data, and the `dfactual` dataframe that contains the actual sales data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "5C9tXEvwap-S",
    "outputId": "bec48383-c501-4a12-ed34-fd9b12093bd8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "itemslist = list(dfhistorical.item_name.unique())\n",
    "\n",
    "for item in itemslist:\n",
    "    datah = dfhistorical[dfhistorical.item_name==item].sort_values('date')\n",
    "    dataf = dfforecast[dfforecast.item_name==item].sort_values(['forecast_timestamp'])\n",
    "    dataa = dfactual[dfactual.item_name==item].sort_values('date')\n",
    "    plot_historical_and_forecast(input_timeseries = datah, \n",
    "                             timestamp_col_name = \"date\", \n",
    "                             data_col_name = \"total_amount_sold\", \n",
    "                             forecast_output = dataf, \n",
    "                             actual = dataa,\n",
    "                             title = item,\n",
    "                             plotstartdate = \"2017-01-01\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9b-KFF8wap-U"
   },
   "source": [
    "## Create a dashboard with Data Studio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XuRcoFQdXbrb"
   },
   "source": [
    "Follow the steps in this section to create an interactive, shareable dashboard of the forecasted data by using Data Studio. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YuiXk7b5ap-V"
   },
   "source": [
    "### Create a view containing the data for the dashboard\n",
    "\n",
    "Create a view that concatenates the historical and forecasted data. The SQL before the `UNION ALL` clause selects the historical data. The SQL after the `UNION ALL` clause uses `ML.FORECAST` to generate the forecasted value and the prediction interval. The query uses different fields for `history_value` and `forecasted_value` so that you can plot them in different colors in a following step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "id": "2C8Wbu--kvf0",
    "outputId": "92ed97b5-b5ce-4f61-a1e6-8eed6b78cf34"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery --params $ARIMA_PARAMS  --project $PROJECT_ID \n",
    "\n",
    "CREATE OR REPLACE VIEW bqmlforecast.outputdata_datastudio AS (\n",
    "  SELECT\n",
    "    date AS timestamp,\n",
    "    item_name,\n",
    "    total_amount_sold AS history_value,\n",
    "    NULL AS forecast_value,\n",
    "    NULL AS prediction_interval_lower_bound,\n",
    "    NULL AS prediction_interval_upper_bound\n",
    "  FROM\n",
    "    bqmlforecast.training_data\n",
    "  UNION ALL\n",
    "  SELECT\n",
    "    EXTRACT(DATE\n",
    "    FROM\n",
    "      forecast_timestamp) AS timestamp,\n",
    "    item_name,\n",
    "    NULL AS history_value,\n",
    "    forecast_value,\n",
    "    prediction_interval_lower_bound,\n",
    "    prediction_interval_upper_bound\n",
    "  FROM\n",
    "    ML.FORECAST(MODEL bqmlforecast.arima_model,\n",
    "      STRUCT(30 AS horizon, 0.9 AS confidence_level)) \n",
    "  ORDER BY timestamp\n",
    "  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zygxqrCLed7a"
   },
   "source": [
    "### Export the data to DataStudio\n",
    "\n",
    "1. In the **Resources** pane of the [BigQuery console](https://console.cloud.google.com/bigquery), navigate to the `bqmlforecast.outputdata_datastudio` view and select it.\n",
    "1. Click **Export** and then select **Explore with Data Studio**. A new tab opens in the browser."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G72fTP-bgj3o"
   },
   "source": [
    "![image.png](data:image/png;base64,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)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VgTJyryTed7a"
   },
   "source": [
    "### Set the chart type\n",
    "\n",
    "In the **Chart** pane, find the **Time series chart** icon and click it, as shown in the following screenshot:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lwpDOV_Med7a"
   },
   "source": [
    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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nE3i2KUced7a"
   },
   "source": [
    "### Set the dashboard data\n",
    "\n",
    "1. On the **Data** tab, find the **Metric** section.\n",
    "1. Click **Add metric** to add the `history_value` field.\n",
    "1. Repeat step 2 to add the `forecast_value`, `prediction_interval_lower_bound` and `prediction_interval_upper_bound` fields."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TrE5-GGYed7b"
   },
   "source": [
    "![image.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7QRuCS7ged7b"
   },
   "source": [
    "### Set the dashboard style\n",
    "\n",
    "1. On the **Style** tab, scroll down to the **General** section.\n",
    "1. For the **Missing Data** option, choose **Linear Interpolation**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lYyb1p4ged7b"
   },
   "source": [
    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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vlL5SbM3ed7b"
   },
   "source": [
    "### Set the dashboard filter\n",
    "\n",
    "1. In the **Filter** pane, click **Drop metric or dimension fields here to create filters** and choose `item_name`.\n",
    "1. Click `item_name` to see a list of values to filter by.\n",
    "1. Select **Five O'clock Vodka** to inspect the time series data for just that product."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "58_-WWkIed7b"
   },
   "source": [
    "![image.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8nfGixlsed7b"
   },
   "source": [
    "### Review the dashboard\n",
    "\n",
    "After you complete these steps, the following plot appears in the left panel. The input history time series is in blue, while the forecasted series is in green. The prediction interval is the region between the lower bound series and the upper bound series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vYdbrOdZed7c"
   },
   "source": [
    "![image.png](data:image/png;base64,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)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "bqml_retail_demand_forecasting.ipynb",
   "provenance": [
    {
     "file_id": "1nqpuwpP8nDxhcty-JVYirH-zfz0iBm87",
     "timestamp": 1614717584256
    }
   ],
   "toc_visible": true
  },
  "environment": {
   "name": "common-cpu.m55",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/base-cpu:m55"
  },
  "kernelspec": {
   "display_name": "Python 3 (Local)",
   "language": "python",
   "name": "local-python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}