TPOT_regression.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YGZdnzwIGZAY"
      },
      "source": [
        "TPOT regression for the car prices (homework 1)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dqkSZZ9NGXzH",
        "outputId": "d4b65bdf-fc1f-4944-b2aa-23e7e88d1fd1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting tpot\n",
            "  Downloading TPOT-0.12.1-py3-none-any.whl (87 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m87.4/87.4 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.16.3 in /usr/local/lib/python3.10/dist-packages (from tpot) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from tpot) (1.11.3)\n",
            "Requirement already satisfied: scikit-learn>=0.22.0 in /usr/local/lib/python3.10/dist-packages (from tpot) (1.2.2)\n",
            "Collecting deap>=1.2 (from tpot)\n",
            "  Downloading deap-1.4.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (135 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m135.4/135.4 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting update-checker>=0.16 (from tpot)\n",
            "  Downloading update_checker-0.18.0-py3-none-any.whl (7.0 kB)\n",
            "Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.10/dist-packages (from tpot) (4.66.1)\n",
            "Collecting stopit>=1.1.1 (from tpot)\n",
            "  Downloading stopit-1.1.2.tar.gz (18 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: pandas>=0.24.2 in /usr/local/lib/python3.10/dist-packages (from tpot) (1.5.3)\n",
            "Requirement already satisfied: joblib>=0.13.2 in /usr/local/lib/python3.10/dist-packages (from tpot) (1.3.2)\n",
            "Requirement already satisfied: xgboost>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tpot) (2.0.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.2->tpot) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.2->tpot) (2023.3.post1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22.0->tpot) (3.2.0)\n",
            "Requirement already satisfied: requests>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from update-checker>=0.16->tpot) (2.31.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=0.24.2->tpot) (1.16.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (2023.7.22)\n",
            "Building wheels for collected packages: stopit\n",
            "  Building wheel for stopit (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for stopit: filename=stopit-1.1.2-py3-none-any.whl size=11938 sha256=1312ce75e5834956e7743b37862f18507ca5d59701e3710a47cec8b64400cece\n",
            "  Stored in directory: /root/.cache/pip/wheels/af/f9/87/bf5b3d565c2a007b4dae9d8142dccc85a9f164e517062dd519\n",
            "Successfully built stopit\n",
            "Installing collected packages: stopit, deap, update-checker, tpot\n",
            "Successfully installed deap-1.4.1 stopit-1.1.2 tpot-0.12.1 update-checker-0.18.0\n"
          ]
        }
      ],
      "source": [
        "!pip install tpot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "x6IsnEHxG1-p"
      },
      "outputs": [],
      "source": [
        "# импорт библиотек\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import KBinsDiscretizer\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "\n",
        "from tpot import TPOTRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RjiJGXNIHM3W",
        "outputId": "6c12e7a3-f8fe-43ce-e97f-066f13a09e16"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1436, 10)\n"
          ]
        }
      ],
      "source": [
        "# load data\n",
        "data = pd.read_csv('https://raw.githubusercontent.com/Alexandre-aksenov/predict-car-prices-using-H2O-tpot/main/ToyotaCorolla.csv')\n",
        "\n",
        "print(data.shape)  # (1436, 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "e2D6VA1cHgP8",
        "outputId": "ed7ba398-afaa-4c70-bff4-90b027b618a8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0fe3133a-2035-4294-9a65-12c5d95d2611\" class=\"colab-df-container\">\n",
              "    <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>Price</th>\n",
              "      <th>Age</th>\n",
              "      <th>KM</th>\n",
              "      <th>FuelType</th>\n",
              "      <th>HP</th>\n",
              "      <th>MetColor</th>\n",
              "      <th>Automatic</th>\n",
              "      <th>CC</th>\n",
              "      <th>Doors</th>\n",
              "      <th>Weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13500</td>\n",
              "      <td>23</td>\n",
              "      <td>46986</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2000</td>\n",
              "      <td>3</td>\n",
              "      <td>1165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13750</td>\n",
              "      <td>23</td>\n",
              "      <td>72937</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2000</td>\n",
              "      <td>3</td>\n",
              "      <td>1165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13950</td>\n",
              "      <td>24</td>\n",
              "      <td>41711</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2000</td>\n",
              "      <td>3</td>\n",
              "      <td>1165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14950</td>\n",
              "      <td>26</td>\n",
              "      <td>48000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2000</td>\n",
              "      <td>3</td>\n",
              "      <td>1165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13750</td>\n",
              "      <td>30</td>\n",
              "      <td>38500</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2000</td>\n",
              "      <td>3</td>\n",
              "      <td>1170</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0fe3133a-2035-4294-9a65-12c5d95d2611')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0fe3133a-2035-4294-9a65-12c5d95d2611 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0fe3133a-2035-4294-9a65-12c5d95d2611');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e310d444-7b84-4065-b228-d38f878f8996\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e310d444-7b84-4065-b228-d38f878f8996')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e310d444-7b84-4065-b228-d38f878f8996 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   Price  Age     KM FuelType  HP  MetColor  Automatic    CC  Doors  Weight\n",
              "0  13500   23  46986   Diesel  90         1          0  2000      3    1165\n",
              "1  13750   23  72937   Diesel  90         1          0  2000      3    1165\n",
              "2  13950   24  41711   Diesel  90         1          0  2000      3    1165\n",
              "3  14950   26  48000   Diesel  90         0          0  2000      3    1165\n",
              "4  13750   30  38500   Diesel  90         0          0  2000      3    1170"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4hB4Q6bIQyi9"
      },
      "source": [
        "Encode the categorical variable 'FuelType' as 0,1,2."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Vl3Ho7L7VhhW",
        "outputId": "9adf78fa-e8f1-4485-bcf4-b738932a16b4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-19e2e4a5-77ad-4947-98ff-d36e222c1440\" class=\"colab-df-container\">\n",
              "    <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>Price</th>\n",
              "      <th>Age</th>\n",
              "      <th>KM</th>\n",
              "      <th>FuelType</th>\n",
              "      <th>HP</th>\n",
              "      <th>MetColor</th>\n",
              "      <th>Automatic</th>\n",
              "      <th>CC</th>\n",
              "      <th>Doors</th>\n",
              "      <th>Weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13500</td>\n",
              "      <td>23</td>\n",
              "      <td>46986</td>\n",
              "      <td>1.0</td>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2000</td>\n",
              "      <td>3</td>\n",
              "      <td>1165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13750</td>\n",
              "      <td>23</td>\n",
              "      <td>72937</td>\n",
              "      <td>1.0</td>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2000</td>\n",
              "      <td>3</td>\n",
              "      <td>1165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13950</td>\n",
              "      <td>24</td>\n",
              "      <td>41711</td>\n",
              "      <td>1.0</td>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2000</td>\n",
              "      <td>3</td>\n",
              "      <td>1165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14950</td>\n",
              "      <td>26</td>\n",
              "      <td>48000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2000</td>\n",
              "      <td>3</td>\n",
              "      <td>1165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13750</td>\n",
              "      <td>30</td>\n",
              "      <td>38500</td>\n",
              "      <td>1.0</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2000</td>\n",
              "      <td>3</td>\n",
              "      <td>1170</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-19e2e4a5-77ad-4947-98ff-d36e222c1440')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-19e2e4a5-77ad-4947-98ff-d36e222c1440 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-19e2e4a5-77ad-4947-98ff-d36e222c1440');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7f4e54d2-ed43-4d54-b35f-e22741797d10\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7f4e54d2-ed43-4d54-b35f-e22741797d10')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7f4e54d2-ed43-4d54-b35f-e22741797d10 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   Price  Age     KM  FuelType  HP  MetColor  Automatic    CC  Doors  Weight\n",
              "0  13500   23  46986       1.0  90         1          0  2000      3    1165\n",
              "1  13750   23  72937       1.0  90         1          0  2000      3    1165\n",
              "2  13950   24  41711       1.0  90         1          0  2000      3    1165\n",
              "3  14950   26  48000       1.0  90         0          0  2000      3    1165\n",
              "4  13750   30  38500       1.0  90         0          0  2000      3    1170"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# https://stackoverflow.com/a/56502961\n",
        "enc = OrdinalEncoder()\n",
        "\n",
        "# data['FuelType']  = enc.fit_transform(data['FuelType'])\n",
        "# Error! Expected 2D array, got 1D array instead:\n",
        "# Reshape your data either using array.reshape(-1, 1) if your data has a single feature\n",
        "\n",
        "data[['FuelType']]  = enc.fit_transform(data[['FuelType']])\n",
        "\n",
        "data.head()  # OK!\n",
        "\n",
        "# 'Diesel' is replaced by 1.0, as it goes after 'CNG' in alphabet."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "V25WtVrrHhjz",
        "outputId": "62d2c4c4-05a3-417b-9a46-47c605cd60f9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'discr_price'}>]], dtype=object)"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Specify the target variable.\n",
        "\n",
        "target = [data.columns[0]]\n",
        "\n",
        "# train-test split.\n",
        "# sklearn is used for stratification, making sure that the subdivision is the same\n",
        "# as for the linear regression.\n",
        "\n",
        "# N.B. Possile improvement.\n",
        "# This stratified subdivision in case of continuous variable can be transformed into a function,\n",
        "# or it would be event better if this function can be found.\n",
        "\n",
        "# Prototype.\n",
        "# data, params of KBinsDiscretizer , params of train_test_split\n",
        "# -> data_train, data_test\n",
        "\n",
        "\n",
        "price_discretizer = KBinsDiscretizer(n_bins=10, encode='ordinal')\n",
        "#  default strategy : 'quantile'\n",
        "\n",
        "y_discretized = price_discretizer.fit_transform(data[target])\n",
        "discrete_target = ['discr_price']\n",
        "\n",
        "data[discrete_target] = y_discretized\n",
        "\n",
        "data[discrete_target].hist()\n",
        "\n",
        "# the extra column 'discr_price' should be removed after train-test split."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vf-VGt_FIFDm",
        "outputId": "bd2bf312-b4bd-4e84-8d6d-f0d40a92fe10"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1148, 10)\n",
            "(288, 10)\n",
            "(1148, 9)\n",
            "(288, 9)\n"
          ]
        }
      ],
      "source": [
        "data_train, data_test = train_test_split(data, test_size=0.2, stratify=data['discr_price'], random_state=42)\n",
        "\n",
        "# removing the extra column 'discr_price'.\n",
        "data_train.drop(columns=discrete_target, inplace=True)\n",
        "data_test.drop(columns=discrete_target, inplace=True)\n",
        "\n",
        "print(data_train.shape)  # (1148, 10)\n",
        "print(data_test.shape)   #  (288, 10)\n",
        "\n",
        "# end of the function\n",
        "\n",
        "X_train = data_train.drop(columns=target, inplace=False)\n",
        "X_test = data_test.drop(columns=target, inplace=False)\n",
        "print(X_train.shape)  # (1148, 9)\n",
        "print(X_test.shape)   # (288, 9)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eAdwNQSLL7gM"
      },
      "source": [
        "TPOT regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318,
          "referenced_widgets": [
            "6dd7d881213b43e29a742cad62be2f9b",
            "28fcac8bf3a74df39ce43bf7abcbefa5",
            "9fef6fd3959a49429426a87d66fafa94",
            "e8a1223c5b454f24a126c42cb5d3317e",
            "06a7b422c05941c085f5188cd7cf7131",
            "814a012b7f7443888711be53f252f93b",
            "0d201013e6f74bf5bdbf5ddcfaa5dd06",
            "3454a473809f43cc98c78b753a4ffee5",
            "a071698ae3d547f09dc1dfcb81d0c53e",
            "997e3b0d844548e8b6d0eed43237febf",
            "8f4c6a1e0a9a43d5a8be62277a413fe6"
          ]
        },
        "id": "-DkPcmCeLn-a",
        "outputId": "57ed9289-f7f4-4acd-bcb1-69f7947483a4"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6dd7d881213b43e29a742cad62be2f9b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Optimization Progress:   0%|          | 0/100 [00:00<?, ?pipeline/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "0.50 minutes have elapsed. TPOT will close down.\n",
            "TPOT closed during evaluation in one generation.\n",
            "WARNING: TPOT may not provide a good pipeline if TPOT is stopped/interrupted in a early generation.\n",
            "\n",
            "\n",
            "TPOT closed prematurely. Will use the current best pipeline.\n",
            "\n",
            "Best pipeline: RandomForestRegressor(input_matrix, bootstrap=True, max_features=0.55, min_samples_leaf=2, min_samples_split=17, n_estimators=100)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TPOTRegressor(generations=2, max_time_mins=0.5, random_state=42, verbosity=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TPOTRegressor</label><div class=\"sk-toggleable__content\"><pre>TPOTRegressor(generations=2, max_time_mins=0.5, random_state=42, verbosity=2)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "TPOTRegressor(generations=2, max_time_mins=0.5, random_state=42, verbosity=2)"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tpot = TPOTRegressor(generations=2, verbosity=2, max_time_mins=0.5, random_state=42)\n",
        "\n",
        "tpot.fit(X_train, data_train[target])\n",
        "\n",
        "# DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
        "# y = column_or_1d(y, warn=True)\n",
        "\n",
        "# TPOT closed prematurely. Will use the current best pipeline.\n",
        "# Best pipeline: RandomForestRegressor(input_matrix, bootstrap=True, max_features=0.55, min_samples_leaf=2, min_samples_split=17, n_estimators=100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2WUyoJE2NhYl"
      },
      "source": [
        "Exporting the results."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Eq9FKGvNgrx",
        "outputId": "fb5e76d2-5b0b-4f46-f6be-c0aef530c022"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-1428054.084254198\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ],
      "source": [
        "print(tpot.score(X_test, data_test[target]))  #  -1428054 (== -MSE )\n",
        "# see: https://epistasislab.github.io/tpot/api/#tpotregressor-score\n",
        "\n",
        "tpot.export('TPOT_regression.py')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "81bRRz-HNt0C",
        "outputId": "d973c83b-e012-4ac5-a1bf-cfe2b27bce44"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'>\n",
            "(288,)\n",
            "[10178.89251453  7484.67019075 12502.25291394 14934.04173261\n",
            " 15823.57332902  8337.54311435  8511.42410845  9816.84862244\n",
            "  8468.89281445  8308.91641191]\n"
          ]
        }
      ],
      "source": [
        "pred_price = tpot.predict(X_test)\n",
        "\n",
        "print(type(pred_price))  # 'numpy.ndarray'\n",
        "print(pred_price.shape)  # (288,)\n",
        "# pred_price.head()\n",
        "print(pred_price[0:10])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nbFSactYOwNM",
        "outputId": "618a2036-c7e7-48d1-e6f5-f1fbbb37aa1a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE of the TPOT regression: 1195.012169082055\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "# metrics of quality of the fit\n",
        "\n",
        "# df_pred_price = pred_price.as_data_frame()\n",
        "rmse_test = mean_squared_error(data_test[target], pred_price, squared= False)\n",
        "print(\"RMSE of the TPOT regression:\", rmse_test)  #  1592 $ (lin reg) -> 1195 $\n",
        "# This number equals indeed the square root of the score produced above.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eI5O3HZVevfM"
      },
      "source": [
        "Looking for the exported file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WXiZXAhZeu4T",
        "outputId": "72f50698-fe41-4204-888d-9fee1ce3ad6b"
      },
      "outputs": [],
      "source": [
        "%ls\n",
        "# sample_data/  TPOT_regression.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "vGLn7jfSftiz"
      },
      "outputs": [],
      "source": [
        "from google.colab import files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "W95N9015f5bX",
        "outputId": "320713b9-0edb-4942-c3d2-6d6aa3131124"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_6eaa771b-28f4-4dc1-ad16-b39973d6f2ee\", \"tpot_homework1_v1.py\", 957)",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "files.download('TPOT_regression.py')\n",
        "# This python module is joined to the repository."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "TPOT provides indeed an improvement over the linear regression despite it being given only 30s for computation. "
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "06a7b422c05941c085f5188cd7cf7131": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "0d201013e6f74bf5bdbf5ddcfaa5dd06": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "28fcac8bf3a74df39ce43bf7abcbefa5": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_814a012b7f7443888711be53f252f93b",
            "placeholder": "​",
            "style": "IPY_MODEL_0d201013e6f74bf5bdbf5ddcfaa5dd06",
            "value": "Optimization Progress:  32%"
          }
        },
        "3454a473809f43cc98c78b753a4ffee5": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "6dd7d881213b43e29a742cad62be2f9b": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_28fcac8bf3a74df39ce43bf7abcbefa5",
              "IPY_MODEL_9fef6fd3959a49429426a87d66fafa94",
              "IPY_MODEL_e8a1223c5b454f24a126c42cb5d3317e"
            ],
            "layout": "IPY_MODEL_06a7b422c05941c085f5188cd7cf7131"
          }
        },
        "814a012b7f7443888711be53f252f93b": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "8f4c6a1e0a9a43d5a8be62277a413fe6": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "997e3b0d844548e8b6d0eed43237febf": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9fef6fd3959a49429426a87d66fafa94": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "danger",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_3454a473809f43cc98c78b753a4ffee5",
            "max": 100,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_a071698ae3d547f09dc1dfcb81d0c53e",
            "value": 32
          }
        },
        "a071698ae3d547f09dc1dfcb81d0c53e": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "e8a1223c5b454f24a126c42cb5d3317e": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_997e3b0d844548e8b6d0eed43237febf",
            "placeholder": "​",
            "style": "IPY_MODEL_8f4c6a1e0a9a43d5a8be62277a413fe6",
            "value": " 32/100 [00:23&lt;01:00,  1.12pipeline/s]"
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}