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Repository: crawles/automl_service
Branch: master
Commit: b8fe66e6e69e
Files: 20
Total size: 8.4 MB
Directory structure:
gitextract_hy50erba/
├── .gitignore
├── LICENSE.txt
├── README.md
├── automl_service.py
├── conftest.py
├── data/
│ ├── data_test.json
│ ├── data_train.json
│ ├── label_test.json
│ └── label_train.json
├── environment.yml
├── manifest.yml
├── modelling_and_usage.ipynb
├── parameters/
│ ├── test_parameters.yml
│ ├── test_parameters_model2.yml
│ ├── train_parameters.yml
│ ├── train_parameters_model2.yml
│ └── train_parameters_model3.yml
├── resources.py
├── test_api.py
└── utilities.py
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FILE CONTENTS
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FILE: .gitignore
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# Created by .ignore support plugin (hsz.mobi)
### Python template
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# Jupyter Notebook
.ipynb_checkpoints
# PyCharm
.cache
.idea
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FILE: LICENSE.txt
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The MIT License (MIT)
Copyright (c) 2017 Chris Rawles, Pivotal
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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FILE: README.md
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# AutoML Service
Deploy automated machine learning (AutoML) as a service using `Flask`, for both pipeline training and pipeline serving.
The framework implements a fully automated time series classification pipeline, automating both feature engineering and model selection and optimization using Python libraries, `TPOT` and `tsfresh`.
Check out the [blog post](https://content.pivotal.io/blog/automated-machine-learning-deploying-automl-to-the-cloud) for more info.
<p>
<img src="https://github.com/crawles/Logos/blob/master/automl.gif?raw=true" width = 80%>
</p>
Resources:
- [TPOT](https://github.com/rhiever/tpot)– Automated feature preprocessing and model optimization tool
- [tsfresh](https://github.com/blue-yonder/tsfresh)– Automated time series feature engineering and selection
- [Flask](http://flask.pocoo.org/)– A web development microframework for Python
## Architecture
The application exposes both model training and model predictions with a RESTful API. For model training, input data and labels are sent via POST request, a pipeline is trained, and model predictions are accessible via a prediction route.
Pipelines are stored to a unique key, and thus, live predictions can be made on the same data using different feature construction and modeling pipelines.
<p>
<img src="/img/architecture.png?raw=true" width = 55%>
</p>
An automated pipeline for time-series classification.
<p>
<img src="/img/training.png?raw=true" width = 55%>
</p>
The model training logic is exposed as a REST endpoint. Raw, labeled training data is uploaded via a POST request and an optimal model is developed.
<p>
<img src="/img/serving.png?raw=true" width = 55%>
</p>
Raw training data is uploaded via a POST request and a model prediction is returned.
## Using the app
View the [Jupyter Notebook](https://github.com/crawles/automl_service/blob/master/modelling_and_usage.ipynb) for an example.
### Deploying
```bash
# deploy locally
python automl_service.py
```
```bash
# deploy on cloud foundry
cf push
```
### Usage
Train a pipeline:
```python
train_url = 'http://0.0.0.0:8080/train_pipeline'
train_files = {'raw_data': open('data/data_train.json', 'rb'),
'labels' : open('data/label_train.json', 'rb'),
'params' : open('parameters/train_parameters_model2.yml', 'rb')}
# post request to train pipeline
r_train = requests.post(train_url, files=train_files)
result_df = json.loads(r_train.json())
```
returns:
```python
{'featureEngParams': {'default_fc_parameters': "['median', 'minimum', 'standard_deviation',
'sum_values', 'variance', 'maximum',
'length', 'mean']",
'impute_function': 'impute',
...},
'mean_cv_accuracy': 0.865,
'mean_cv_roc_auc': 0.932,
'modelId': 1,
'modelType': "Pipeline(steps=[('stackingestimator', StackingEstimator(estimator=LinearSVC(...))),
('logisticregression', LogisticRegressionClassifier(solver='liblinear',...))])"
'trainShape': [1647, 8],
'trainTime': 1.953}
```
Serve pipeline predictions:
```python
serve_url = 'http://0.0.0.0:8080/serve_prediction'
test_files = {'raw_data': open('data/data_test.json', 'rb'),
'params' : open('parameters/test_parameters_model2.yml', 'rb')}
# post request to serve predictions from trained pipeline
r_test = requests.post(serve_url, files=test_files)
result = pd.read_json(r_test.json()).set_index('id')
```
| example_id | prediction |
| ------------- | ------------- |
| 1 | 0.853 |
| 2 | 0.991 |
| 3 | 0.060 |
| 4 | 0.995 |
| 5 | 0.003 |
| ... | ... |
View all trained models:
```python
r = requests.get('http://0.0.0.0:8080/models')
pipelines = json.loads(r.json())
```
```python
{'1':
{'mean_cv_accuracy': 0.873,
'modelType': "RandomForestClassifier(...),
...},
'2':
{'mean_cv_accuracy': 0.895,
'modelType': "GradientBoostingClassifier(...),
...},
'3':
{'mean_cv_accuracy': 0.859,
'modelType': "LogisticRegressionClassifier(...),
...},
...}
```
## Running the tests
Supply a user argument for the host.
```bash
# use local app
py.test --host http://0.0.0.0:8080
```
```bash
# use cloud-deployed app
py.test --host http://ROUTE-HERE
```
## Scaling the architecture
For production, I would suggest splitting training and serving into seperate applications, and incorporating a fascade API. Also it would be best to use a shared cache such as Redis or Pivotal Cloud Cache to allow other applications and multiple instances of the pipeline to access the trained model. Here is a potential architecture.
<p>
<img src="/img/cloud_architecture.png?raw=true" width = 55%>
</p>
A scalable model training and model serving architecture.
## Author
`Chris Rawles`
================================================
FILE: automl_service.py
================================================
# -*- coding: utf-8 -*-
"""
automl_service.py
~~~~~~~~~~~~~~~~~
App implements an automl pipeline.
"""
import os
import json
from flask import Flask, request, jsonify
import numpy as np
import pandas as pd
import requests
import sklearn
import resources
from flask_restful import reqparse, abort, Api, Resource
from utilities import build_features, read_file, read_params,\
train_model, ModelFactory
app = Flask(__name__)
api = Api(app)
model_factory = ModelFactory()
api.add_resource(resources.Train, '/train_pipeline',
resource_class_kwargs={'model_factory': model_factory})
api.add_resource(resources.ServePrediction, '/serve_prediction',
resource_class_kwargs={'model_factory': model_factory})
api.add_resource(resources.Models, '/models',
resource_class_kwargs={'model_factory': model_factory})
# note: not used if using gunicorn
if __name__ == "__main__":
if os.environ.get('VCAP_SERVICES') is None: # running locally
PORT = 8080
DEBUG = True
else: # running on CF
PORT = int(os.getenv("PORT"))
DEBUG = False
app.run(host='0.0.0.0', port=PORT, debug=DEBUG)
================================================
FILE: conftest.py
================================================
def pytest_addoption(parser):
parser.addoption("--host", action="store",
help="run all combinations")
def pytest_generate_tests(metafunc):
if 'host' in metafunc.fixturenames:
option_value = metafunc.config.option.host
print option_value
if option_value:
metafunc.parametrize("host", [option_value])
================================================
FILE: data/data_test.json
================================================
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gitextract_hy50erba/ ├── .gitignore ├── LICENSE.txt ├── README.md ├── automl_service.py ├── conftest.py ├── data/ │ ├── data_test.json │ ├── data_train.json │ ├── label_test.json │ └── label_train.json ├── environment.yml ├── manifest.yml ├── modelling_and_usage.ipynb ├── parameters/ │ ├── test_parameters.yml │ ├── test_parameters_model2.yml │ ├── train_parameters.yml │ ├── train_parameters_model2.yml │ └── train_parameters_model3.yml ├── resources.py ├── test_api.py └── utilities.py
SYMBOL INDEX (30 symbols across 4 files)
FILE: conftest.py
function pytest_addoption (line 1) | def pytest_addoption(parser):
function pytest_generate_tests (line 5) | def pytest_generate_tests(metafunc):
FILE: resources.py
class Model (line 15) | class Model(Resource):
method __init__ (line 17) | def __init__(self, model_factory):
method get (line 22) | def get(self):
class Models (line 31) | class Models(Resource):
method __init__ (line 33) | def __init__(self, model_factory):
method get (line 36) | def get(self):
class Train (line 45) | class Train(Resource):
method __init__ (line 47) | def __init__(self, model_factory):
method post (line 57) | def post(self):
class ServePrediction (line 109) | class ServePrediction(Resource):
method __init__ (line 111) | def __init__(self, model_factory):
method post (line 119) | def post(self):
FILE: test_api.py
function test_train_model_1 (line 10) | def test_train_model_1(host):
function test_train_model_2 (line 19) | def test_train_model_2(host):
function test_serve_model (line 29) | def test_serve_model(host):
function test_serve_model_2 (line 44) | def test_serve_model_2(host):
function test_get_models (line 59) | def test_get_models(host):
FILE: utilities.py
function read_file (line 26) | def read_file(d):
function read_params (line 33) | def read_params(d):
function build_features (line 37) | def build_features(data, params):
function train_model (line 45) | def train_model(X, y, params):
function cross_validate (line 60) | def cross_validate(cl, X_train, y_train):
class ModelFactory (line 68) | class ModelFactory(object):
method __init__ (line 70) | def __init__(self):
method __getitem__ (line 74) | def __getitem__(self, item):
method add_pipeline (line 77) | def add_pipeline(self, cl, params):
method use_pipeline (line 84) | def use_pipeline(self, df, pipeline_id):
function load_module_from_string (line 95) | def load_module_from_string(module_name):
Condensed preview — 20 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (9,836K chars).
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{
"path": ".gitignore",
"chars": 204,
"preview": "# Created by .ignore support plugin (hsz.mobi)\n### Python template\n# Byte-compiled / optimized / DLL files\n__pycache__/\n"
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"path": "LICENSE.txt",
"chars": 1087,
"preview": "The MIT License (MIT)\nCopyright (c) 2017 Chris Rawles, Pivotal\n\nPermission is hereby granted, free of charge, to any per"
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{
"path": "README.md",
"chars": 4971,
"preview": "# AutoML Service\n\nDeploy automated machine learning (AutoML) as a service using `Flask`, for both pipeline training and "
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{
"path": "automl_service.py",
"chars": 1178,
"preview": "# -*- coding: utf-8 -*-\n\"\"\"\nautoml_service.py\n~~~~~~~~~~~~~~~~~\n\nApp implements an automl pipeline.\n\n\"\"\"\n\nimport os\nimpo"
},
{
"path": "conftest.py",
"chars": 352,
"preview": "def pytest_addoption(parser):\n parser.addoption(\"--host\", action=\"store\",\n help=\"run all combinations\")\n\ndef p"
},
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"preview": "{\"example_id\":{\"0\":8,\"1\":15,\"2\":20,\"3\":23,\"4\":27,\"5\":29,\"6\":48,\"7\":54,\"8\":56,\"9\":57,\"10\":61,\"11\":67,\"12\":68,\"13\":72,\"14\""
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{
"path": "environment.yml",
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"preview": "name: automl_dependencies\ndependencies:\n - python=2.7.13\n - pandas==0.20.3\n - Flask==0.11.1\n - pytest==2.9.2\n - num"
},
{
"path": "manifest.yml",
"chars": 211,
"preview": "---\napplications:\n- name: automl\n memory: 1GB\n disk_quota: 2GB\n instances: 1\n buildpack: https://github.com/cloudfou"
},
{
"path": "modelling_and_usage.ipynb",
"chars": 306993,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# AutoML service\"\n ]\n },\n {\n "
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"preview": "pipeline_id: 1\n"
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"preview": "pipeline_id: 2\n"
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"path": "parameters/train_parameters.yml",
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"preview": "pipeline_id: 1\nextract_features:\n column_id: example_id\n impute_function: tsfresh.utilities.dataframe_functions.impute"
},
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"preview": "pipeline_id: 2\nmodel_training:\n model_args:\n generations: 20\n population_size: 10 \n verbosity: 2\n max_time_"
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"preview": "pipeline_id: 2\nmodel_training:\n model_args:\n generations: 300\n population_size: 50 \n verbosity: 2\n max_time"
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"path": "resources.py",
"chars": 4371,
"preview": "import json\nimport time\n\nimport pandas as pd\nimport sklearn\nimport tpot\nimport yaml\nimport werkzeug\n\nfrom utilities impo"
},
{
"path": "test_api.py",
"chars": 2288,
"preview": "import os\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nimport requests\n\nfrom sklearn.metrics import roc_auc_sco"
},
{
"path": "utilities.py",
"chars": 2810,
"preview": "# -*- coding: utf-8 -*-\n\"\"\"\nutilities.py\n~~~~~~~~~~~~\n\nUtility functions for automl service.\n\"\"\"\nimport copy\nimport json"
}
]
About this extraction
This page contains the full source code of the crawles/automl_service GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 20 files (8.4 MB), approximately 2.2M tokens, and a symbol index with 30 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.