Full Code of ageron/handson-ml3 for AI

Repository: ageron/handson-ml3
Branch: main
Commit: 79d250ff988b
Files: 52
Total size: 25.3 MB

Directory structure:
gitextract_kb124ol3/

├── .github/
│   └── ISSUE_TEMPLATE/
│       ├── bug_report.yml
│       ├── clarification-request.yml
│       ├── config.yml
│       └── feature-or-improvement-request.yml
├── .gitignore
├── 01_the_machine_learning_landscape.ipynb
├── 02_end_to_end_machine_learning_project.ipynb
├── 03_classification.ipynb
├── 04_training_linear_models.ipynb
├── 05_support_vector_machines.ipynb
├── 06_decision_trees.ipynb
├── 07_ensemble_learning_and_random_forests.ipynb
├── 08_dimensionality_reduction.ipynb
├── 09_unsupervised_learning.ipynb
├── 10_neural_nets_with_keras.ipynb
├── 11_training_deep_neural_networks.ipynb
├── 12_custom_models_and_training_with_tensorflow.ipynb
├── 13_loading_and_preprocessing_data.ipynb
├── 14_deep_computer_vision_with_cnns.ipynb
├── 15_processing_sequences_using_rnns_and_cnns.ipynb
├── 16_nlp_with_rnns_and_attention.ipynb
├── 17_autoencoders_gans_and_diffusion_models.ipynb
├── 18_reinforcement_learning.ipynb
├── 19_training_and_deploying_at_scale.ipynb
├── CHANGES.md
├── INSTALL.md
├── LICENSE
├── README.md
├── apt.txt
├── docker/
│   ├── Dockerfile
│   ├── Dockerfile.gpu
│   ├── Makefile
│   ├── README.md
│   ├── bashrc.bash
│   ├── bin/
│   │   ├── nbclean_checkpoints
│   │   ├── nbdiff_checkpoint
│   │   ├── rm_empty_subdirs
│   │   └── tensorboard
│   ├── docker-compose.yml
│   └── jupyter_notebook_config.py
├── environment.yml
├── extra_ann_architectures.ipynb
├── extra_autodiff.ipynb
├── extra_gradient_descent_comparison.ipynb
├── index.ipynb
├── math_differential_calculus.ipynb
├── math_linear_algebra.ipynb
├── ml-project-checklist.md
├── requirements.txt
├── tools_matplotlib.ipynb
├── tools_numpy.ipynb
└── tools_pandas.ipynb

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

================================================
FILE: .github/ISSUE_TEMPLATE/bug_report.yml
================================================
name: Bug report
description: Create a report to help us improve
title: "[bug] "
labels: ["bug", "help wanted"]
assignees: ["ageron"]
body:
  - type: markdown
    attributes:
      value: |
        Thanks for helping us improve this project!

        Before you create this issue, please:

        * search for existing issues (both open and closed), as your question may already have been answered: https://github.com/ageron/handson-ml3/issues?q=is%3Aissue
        * make sure you are using the latest updated code and libraries: https://github.com/ageron/handson-ml3/blob/main/INSTALL.md#update-this-project-and-its-libraries
        * read the FAQ: https://github.com/ageron/handson-ml3#faq
  - type: input
    id: chapter
    attributes:
      label: Enter the chapter number
      placeholder: E.g., Chapter 2, end to end project
    validations:
      required: true
  - type: input
    id: page
    attributes:
      label: Enter the page number
      placeholder: E.g., Page 123, second code example
  - type: input
    id: cell
    attributes:
      label: What is the cell's number in the notebook
      placeholder: E.g., Cell 123, in gradient descent section
  - type: dropdown
    id: environment
    attributes:
      label: Enter the environment you are using to run the notebook
      options:
        - Colab
        - Kaggle
        - Jupyter on Windows
        - Jupyter on Linux
        - Jupyter on MacOS
        - Other (specify in description)
  - type: markdown
    attributes:
      value: |
        Please describe the problem and copy/paste the failing code using code blocks like this:

        ````
        ```python
        def inverse(x):
            return 1 / x

        result = inverse(0)
        ```
        ````
  - type: textarea
    id: description
    attributes:
      label: Describe your issue
      placeholder: I got an error when running...
    validations:
      required: true
  - type: textarea
    id: expected
    attributes:
      label: Enter what you expected to happen
      placeholder: I expected...
  - type: textarea
    id: workaround
    attributes:
      label: If you found a workaround, describe it here
      placeholder: I worked around the issue by...


================================================
FILE: .github/ISSUE_TEMPLATE/clarification-request.yml
================================================
[File too large to display: 1.9 KB]

================================================
FILE: .github/ISSUE_TEMPLATE/config.yml
================================================
[File too large to display: 27 B]

================================================
FILE: .github/ISSUE_TEMPLATE/feature-or-improvement-request.yml
================================================
name: Feature or Improvement request
description: Suggest an idea for this project
title: "[feature] "
labels: ["feature request"]
assignees: ["ageron"]
body:
  - type: markdown
    attributes:
      value: |
        Thanks for helping us improve this project!

        Before you submit this feature request, please:

        * search for existing issues (both open and closed), as your feature request may already have been submitted: https://github.com/ageron/handson-ml3/issues?q=is%3Aissue
        * make sure you are using the latest updated code and libraries: https://github.com/ageron/handson-ml3/blob/main/INSTALL.md#update-this-project-and-its-libraries
        * read the FAQ: https://github.com/ageron/handson-ml3#faq
  - type: input
    id: chapter
    attributes:
      label: Enter the chapter number
      placeholder: E.g., Chapter 2, end to end project
    validations:
      required: true
  - type: input
    id: page
    attributes:
      label: Enter the page number
      placeholder: E.g., Page 123, second code example
  - type: input
    id: cell
    attributes:
      label: What is the cell's number in the notebook
      placeholder: E.g., Cell 123, in gradient descent section
  - type: dropdown
    id: environment
    attributes:
      label: Enter the environment you are using to run the notebook
      options:
        - Colab
        - Kaggle
        - Jupyter on Windows
        - Jupyter on Linux
        - Jupyter on MacOS
        - Other (specify in description)
  - type: markdown
    attributes:
      value: |
        Please describe your idea below. If you want to include some code, please use code blocks like this:

        ````
        ```python
        def inverse(x):
            return 1 / x

        result = inverse(0)
        ```
        ````
  - type: textarea
    id: idea
    attributes:
      label: Feature or improvement request
      placeholder: It would be useful to have...
    validations:
      required: true
  - type: textarea
    id: motivation
    attributes:
      label: Why is this needed?
      placeholder: Describe the problem or limitation this feature would address

================================================
FILE: .gitignore
================================================
[File too large to display: 163 B]

================================================
FILE: 01_the_machine_learning_landscape.ipynb
================================================
[File too large to display: 235.0 KB]

================================================
FILE: 02_end_to_end_machine_learning_project.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 2 – End-to-end Machine Learning project**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*This notebook contains all the sample code and solutions to the exercises in chapter 2.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/ageron/handson-ml3/blob/main/02_end_to_end_machine_learning_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml3/blob/main/02_end_to_end_machine_learning_project.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to Machine Learning!\n"
     ]
    }
   ],
   "source": [
    "print(\"Welcome to Machine Learning!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project requires Python 3.7 or above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "assert sys.version_info >= (3, 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It also requires Scikit-Learn ≥ 1.0.1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from packaging import version\n",
    "import sklearn\n",
    "\n",
    "assert version.parse(sklearn.__version__) >= version.parse(\"1.0.1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Welcome to Machine Learning Housing Corp.! Your task is to predict median house values in Californian districts, given a number of features from these districts.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import tarfile\n",
    "import urllib.request\n",
    "\n",
    "def load_housing_data():\n",
    "    tarball_path = Path(\"datasets/housing.tgz\")\n",
    "    if not tarball_path.is_file():\n",
    "        Path(\"datasets\").mkdir(parents=True, exist_ok=True)\n",
    "        url = \"https://github.com/ageron/data/raw/main/housing.tgz\"\n",
    "        urllib.request.urlretrieve(url, tarball_path)\n",
    "    with tarfile.open(tarball_path) as housing_tarball:\n",
    "            housing_tarball.extractall(path=\"datasets\")\n",
    "    return pd.read_csv(Path(\"datasets/housing/housing.csv\"))\n",
    "\n",
    "housing = load_housing_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Take a Quick Look at the Data Structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"ocean_proximity\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell is not shown either in the book. It creates the `images/end_to_end_project` folder (if it doesn't already exist), and it defines the `save_fig()` function which is used through this notebook to save the figures in high-res for the book."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extra code – code to save the figures as high-res PNGs for the book\n",
    "\n",
    "IMAGES_PATH = Path() / \"images\" / \"end_to_end_project\"\n",
    "IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = IMAGES_PATH / f\"{fig_id}.{fig_extension}\"\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# extra code – the next 5 lines define the default font sizes\n",
    "plt.rc('font', size=14)\n",
    "plt.rc('axes', labelsize=14, titlesize=14)\n",
    "plt.rc('legend', fontsize=14)\n",
    "plt.rc('xtick', labelsize=10)\n",
    "plt.rc('ytick', labelsize=10)\n",
    "\n",
    "housing.hist(bins=50, figsize=(12, 8))\n",
    "save_fig(\"attribute_histogram_plots\")  # extra code\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def shuffle_and_split_data(data, test_ratio):\n",
    "    shuffled_indices = np.random.permutation(len(data))\n",
    "    test_set_size = int(len(data) * test_ratio)\n",
    "    test_indices = shuffled_indices[:test_set_size]\n",
    "    train_indices = shuffled_indices[test_set_size:]\n",
    "    return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16512"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set, test_set = shuffle_and_split_data(housing, 0.2)\n",
    "len(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4128"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To ensure that this notebook's outputs remain the same every time we run it, we need to set the random seed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sadly, this won't guarantee that this notebook will output exactly the same results as in the book, since there are other possible sources of variation. The most important is the fact that algorithms get tweaked over time when libraries evolve. So please tolerate some minor differences: hopefully, most of the outputs should be the same, or at least in the right ballpark."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: another source of randomness is the order of Python sets: it is based on Python's `hash()` function, which is randomly \"salted\" when Python starts up (this started in Python 3.3, to prevent some denial-of-service attacks). To remove this randomness, the solution is to set the `PYTHONHASHSEED` environment variable to `\"0\"` _before_ Python even starts up. Nothing will happen if you do it after that. Luckily, if you're running this notebook on Colab, the variable is already set for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from zlib import crc32\n",
    "\n",
    "def is_id_in_test_set(identifier, test_ratio):\n",
    "    return crc32(np.int64(identifier)) < test_ratio * 2**32\n",
    "\n",
    "def split_data_with_id_hash(data, test_ratio, id_column):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_: is_id_in_test_set(id_, test_ratio))\n",
    "    return data.loc[~in_test_set], data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id = housing.reset_index()  # adds an `index` column\n",
    "train_set, test_set = split_data_with_id_hash(housing_with_id, 0.2, \"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id[\"id\"] = housing[\"longitude\"] * 1000 + housing[\"latitude\"]\n",
    "train_set, test_set = split_data_with_id_hash(housing_with_id, 0.2, \"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set[\"total_bedrooms\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find the probability that a random sample of 1,000 people contains less than 48.5% female or more than 53.5% female when the population's female ratio is 51.1%, we use the [binomial distribution](https://en.wikipedia.org/wiki/Binomial_distribution). The `cdf()` method of the binomial distribution gives us the probability that the number of females will be equal or less than the given value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10736798530929946\n"
     ]
    }
   ],
   "source": [
    "# extra code – shows how to compute the 10.7% proba of getting a bad sample\n",
    "\n",
    "from scipy.stats import binom\n",
    "\n",
    "sample_size = 1000\n",
    "ratio_female = 0.511\n",
    "proba_too_small = binom(sample_size, ratio_female).cdf(485 - 1)\n",
    "proba_too_large = 1 - binom(sample_size, ratio_female).cdf(535)\n",
    "print(proba_too_small + proba_too_large)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you prefer simulations over maths, here's how you could get roughly the same result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1071"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – shows another way to estimate the probability of bad sample\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "samples = (np.random.rand(100_000, sample_size) < ratio_female).sum(axis=1)\n",
    "((samples < 485) | (samples > 535)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing[\"income_cat\"] = pd.cut(housing[\"median_income\"],\n",
    "                               bins=[0., 1.5, 3.0, 4.5, 6., np.inf],\n",
    "                               labels=[1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing[\"income_cat\"].value_counts().sort_index().plot.bar(rot=0, grid=True)\n",
    "plt.xlabel(\"Income category\")\n",
    "plt.ylabel(\"Number of districts\")\n",
    "save_fig(\"housing_income_cat_bar_plot\")  # extra code\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "splitter = StratifiedShuffleSplit(n_splits=10, test_size=0.2, random_state=42)\n",
    "strat_splits = []\n",
    "for train_index, test_index in splitter.split(housing, housing[\"income_cat\"]):\n",
    "    strat_train_set_n = housing.iloc[train_index]\n",
    "    strat_test_set_n = housing.iloc[test_index]\n",
    "    strat_splits.append([strat_train_set_n, strat_test_set_n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "strat_train_set, strat_test_set = strat_splits[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's much shorter to get a single stratified split:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "strat_train_set, strat_test_set = train_test_split(\n",
    "    housing, test_size=0.2, stratify=housing[\"income_cat\"], random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350533\n",
       "2    0.318798\n",
       "4    0.176357\n",
       "5    0.114341\n",
       "1    0.039971\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set[\"income_cat\"].value_counts() / len(strat_test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Overall %</th>\n",
       "      <th>Stratified %</th>\n",
       "      <th>Random %</th>\n",
       "      <th>Strat. Error %</th>\n",
       "      <th>Rand. Error %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.98</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.24</td>\n",
       "      <td>0.36</td>\n",
       "      <td>6.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.88</td>\n",
       "      <td>31.88</td>\n",
       "      <td>30.74</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-3.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.06</td>\n",
       "      <td>35.05</td>\n",
       "      <td>34.52</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.63</td>\n",
       "      <td>17.64</td>\n",
       "      <td>18.41</td>\n",
       "      <td>0.03</td>\n",
       "      <td>4.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11.44</td>\n",
       "      <td>11.43</td>\n",
       "      <td>12.09</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>5.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Overall %  Stratified %  Random %  Strat. Error %  \\\n",
       "Income Category                                                      \n",
       "1                     3.98          4.00      4.24            0.36   \n",
       "2                    31.88         31.88     30.74           -0.02   \n",
       "3                    35.06         35.05     34.52           -0.01   \n",
       "4                    17.63         17.64     18.41            0.03   \n",
       "5                    11.44         11.43     12.09           -0.08   \n",
       "\n",
       "                 Rand. Error %  \n",
       "Income Category                 \n",
       "1                         6.45  \n",
       "2                        -3.59  \n",
       "3                        -1.53  \n",
       "4                         4.42  \n",
       "5                         5.63  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extra code – computes the data for Figure 2–10\n",
    "\n",
    "def income_cat_proportions(data):\n",
    "    return data[\"income_cat\"].value_counts() / len(data)\n",
    "\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "\n",
    "compare_props = pd.DataFrame({\n",
    "    \"Overall %\": income_cat_proportions(housing),\n",
    "    \"Stratified %\": income_cat_proportions(strat_test_set),\n",
    "    \"Random %\": income_cat_proportions(test_set),\n",
    "}).sort_index()\n",
    "compare_props.index.name = \"Income Category\"\n",
    "compare_props[\"Strat. Error %\"] = (compare_props[\"Stratified %\"] /\n",
    "                                   compare_props[\"Overall %\"] - 1)\n",
    "compare_props[\"Rand. Error %\"] = (compare_props[\"Random %\"] /\n",
    "                                  compare_props[\"Overall %\"] - 1)\n",
    "(compare_props * 100).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "for set_ in (strat_train_set, strat_test_set):\n",
    "    set_.drop(\"income_cat\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discover and Visualize the Data to Gain Insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Geographical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", grid=True)\n",
    "save_fig(\"bad_visualization_plot\")  # extra code\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", grid=True, alpha=0.2)\n",
    "save_fig(\"better_visualization_plot\")  # extra code\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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