03: TPOT_AutoML_Python.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/AutoML/blob/main/TPOT_AutoML_Python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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      "source": [
        "TPOT is an AutoML library which uses Genetic algorithm."
      ]
    },
    {
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      "metadata": {
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        "outputId": "ebab0da0-e943-4f5e-fa23-e8078a64a9f8",
        "colab": {
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      },
      "source": [
        "!pip install tpot"
      ],
      "execution_count": 1,
      "outputs": [
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          "text": [
            "Collecting tpot\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/14/5e/cb87b0257033a7a396e533a634079ee151a239d180efe2a8b1d2e3584d23/TPOT-0.11.5-py3-none-any.whl (82kB)\n",
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            "\u001b[?25hCollecting stopit>=1.1.1\n",
            "  Downloading https://files.pythonhosted.org/packages/35/58/e8bb0b0fb05baf07bbac1450c447d753da65f9701f551dca79823ce15d50/stopit-1.1.2.tar.gz\n",
            "Requirement already satisfied: numpy>=1.16.3 in /usr/local/lib/python3.6/dist-packages (from tpot) (1.18.5)\n",
            "Requirement already satisfied: scipy>=1.3.1 in /usr/local/lib/python3.6/dist-packages (from tpot) (1.4.1)\n",
            "Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.6/dist-packages (from tpot) (4.41.1)\n",
            "Requirement already satisfied: scikit-learn>=0.22.0 in /usr/local/lib/python3.6/dist-packages (from tpot) (0.22.2.post1)\n",
            "Collecting update-checker>=0.16\n",
            "  Downloading https://files.pythonhosted.org/packages/0c/ba/8dd7fa5f0b1c6a8ac62f8f57f7e794160c1f86f31c6d0fb00f582372a3e4/update_checker-0.18.0-py3-none-any.whl\n",
            "Requirement already satisfied: pandas>=0.24.2 in /usr/local/lib/python3.6/dist-packages (from tpot) (1.1.2)\n",
            "Requirement already satisfied: joblib>=0.13.2 in /usr/local/lib/python3.6/dist-packages (from tpot) (0.16.0)\n",
            "Collecting deap>=1.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0a/eb/2bd0a32e3ce757fb26264765abbaedd6d4d3640d90219a513aeabd08ee2b/deap-1.3.1-cp36-cp36m-manylinux2010_x86_64.whl (157kB)\n",
            "\u001b[K     |████████████████████████████████| 163kB 8.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from update-checker>=0.16->tpot) (2.23.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.2->tpot) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.2->tpot) (2.8.1)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (2020.6.20)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (2.10)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24.2->tpot) (1.15.0)\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-cp36-none-any.whl size=11956 sha256=408af49d28d03337af727a62b7cdc512ba2cce578ab53e8451f1e83e87c01827\n",
            "  Stored in directory: /root/.cache/pip/wheels/3c/85/2b/2580190404636bfc63e8de3dff629c03bb795021e1983a6cc7\n",
            "Successfully built stopit\n",
            "Installing collected packages: stopit, update-checker, deap, tpot\n",
            "Successfully installed deap-1.3.1 stopit-1.1.2 tpot-0.11.5 update-checker-0.18.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ape-7wyVnYqO"
      },
      "source": [
        "![alt text](https://raw.githubusercontent.com/EpistasisLab/tpot/master/images/tpot-ml-pipeline.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KWneh3zkiWnO"
      },
      "source": [
        "**Import TPOT packages and load Telecom Churn dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "reu-SCFmbVc8"
      },
      "source": [
        "from tpot import  TPOTClassifier\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rvoK1Jeox5uk"
      },
      "source": [
        "df = pd.read_csv('/content/Churn.csv')"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fF2vxW2Sx-bR",
        "outputId": "455d2b1f-f05f-4b79-fe64-dfb320c47191",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Senior_Citizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Tenure_Months</th>\n",
              "      <th>Phone_Service</th>\n",
              "      <th>Multiple_Lines</th>\n",
              "      <th>Internet_Service</th>\n",
              "      <th>Online_Security</th>\n",
              "      <th>Online_Backup</th>\n",
              "      <th>Device_Protection</th>\n",
              "      <th>Tech_Support</th>\n",
              "      <th>Streaming_TV</th>\n",
              "      <th>Streaming_Movies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>Paperless_Billing</th>\n",
              "      <th>Payment_Method</th>\n",
              "      <th>Monthly_Charges</th>\n",
              "      <th>Total_Charges</th>\n",
              "      <th>Churn_Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>8</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>99.65</td>\n",
              "      <td>820.5</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>28</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>104.80</td>\n",
              "      <td>3046.05</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>49</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>103.70</td>\n",
              "      <td>5036.3</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender Senior_Citizen Partner  ... Monthly_Charges  Total_Charges Churn_Label\n",
              "0    Male             No      No  ...           53.85         108.15         Yes\n",
              "1  Female             No      No  ...           70.70         151.65         Yes\n",
              "2  Female             No      No  ...           99.65          820.5         Yes\n",
              "3  Female             No     Yes  ...          104.80        3046.05         Yes\n",
              "4    Male             No      No  ...          103.70         5036.3         Yes\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9PHcMzEPeYEI",
        "outputId": "6b57eb60-7c71-4f62-cbf9-66b64c413acc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7043, 20)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EzMAVZiP967S",
        "outputId": "fcbc72a3-784e-4c47-f12a-f3fbe37e4443",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Gender', 'Senior_Citizen', 'Partner', 'Dependents', 'Tenure_Months',\n",
              "       'Phone_Service', 'Multiple_Lines', 'Internet_Service',\n",
              "       'Online_Security', 'Online_Backup', 'Device_Protection', 'Tech_Support',\n",
              "       'Streaming_TV', 'Streaming_Movies', 'Contract', 'Paperless_Billing',\n",
              "       'Payment_Method', 'Monthly_Charges', 'Total_Charges', 'Churn_Label'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VxUGgVtl8LNI"
      },
      "source": [
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "\n",
        "categorical_columns = ['Gender', 'Senior_Citizen', 'Partner', 'Dependents','Phone_Service','Multiple_Lines','Internet_Service',\n",
        "                       'Online_Security','Online_Backup','Device_Protection','Tech_Support','Streaming_TV',\n",
        "                       'Streaming_Movies','Contract','Paperless_Billing','Payment_Method', 'Total_Charges', 'Churn_Label']\n",
        "column_trans = make_column_transformer((OrdinalEncoder(), categorical_columns))\n",
        "\n",
        "churn_transformed=column_trans.fit_transform(churn_df)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzY3BZZtNMVw"
      },
      "source": [
        "df_trans = df.copy()\n",
        "df_trans = pd.DataFrame(churn_transformed, columns=categorical_columns)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EuYAVJtVOlOd"
      },
      "source": [
        "df.update(df_trans)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "74HLZDDGST6u",
        "outputId": "14b366d2-2bf3-4dde-c5bd-79ce058a987d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Senior_Citizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Tenure_Months</th>\n",
              "      <th>Phone_Service</th>\n",
              "      <th>Multiple_Lines</th>\n",
              "      <th>Internet_Service</th>\n",
              "      <th>Online_Security</th>\n",
              "      <th>Online_Backup</th>\n",
              "      <th>Device_Protection</th>\n",
              "      <th>Tech_Support</th>\n",
              "      <th>Streaming_TV</th>\n",
              "      <th>Streaming_Movies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>Paperless_Billing</th>\n",
              "      <th>Payment_Method</th>\n",
              "      <th>Monthly_Charges</th>\n",
              "      <th>Total_Charges</th>\n",
              "      <th>Churn_Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>53.85</td>\n",
              "      <td>157</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>70.70</td>\n",
              "      <td>925</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>99.65</td>\n",
              "      <td>6104</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>28</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>104.80</td>\n",
              "      <td>2646</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>103.70</td>\n",
              "      <td>4265</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7038</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>72</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.15</td>\n",
              "      <td>770</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7039</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>24</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>84.80</td>\n",
              "      <td>1597</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7040</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>72</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>103.20</td>\n",
              "      <td>5698</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7041</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>29.60</td>\n",
              "      <td>2994</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7042</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>105.65</td>\n",
              "      <td>5407</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7043 rows × 20 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Gender Senior_Citizen  ...  Total_Charges  Churn_Label\n",
              "0        1.0              0  ...            157          1.0\n",
              "1        0.0              0  ...            925          1.0\n",
              "2        0.0              0  ...           6104          1.0\n",
              "3        0.0              0  ...           2646          1.0\n",
              "4        1.0              0  ...           4265          1.0\n",
              "...      ...            ...  ...            ...          ...\n",
              "7038     0.0              0  ...            770          0.0\n",
              "7039     1.0              0  ...           1597          0.0\n",
              "7040     0.0              0  ...           5698          0.0\n",
              "7041     0.0              0  ...           2994          0.0\n",
              "7042     1.0              0  ...           5407          0.0\n",
              "\n",
              "[7043 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2HJ729PVUBB",
        "outputId": "7f0848b2-1d32-4297-f819-c0108565ea3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "#Check missing values\n",
        "df.isna().sum()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender               0\n",
              "Senior_Citizen       0\n",
              "Partner              0\n",
              "Dependents           0\n",
              "Tenure_Months        0\n",
              "Phone_Service        0\n",
              "Multiple_Lines       0\n",
              "Internet_Service     0\n",
              "Online_Security      0\n",
              "Online_Backup        0\n",
              "Device_Protection    0\n",
              "Tech_Support         0\n",
              "Streaming_TV         0\n",
              "Streaming_Movies     0\n",
              "Contract             0\n",
              "Paperless_Billing    0\n",
              "Payment_Method       0\n",
              "Monthly_Charges      0\n",
              "Total_Charges        0\n",
              "Churn_Label          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-vVcHTp4_xCm"
      },
      "source": [
        "There is no missing values but if there is any spaces that won't be printed or shown for that use the following code"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pkwdagcUVaUq",
        "outputId": "bd36200b-07b5-4964-91ea-2f8db76d36fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "df.replace(r'^\\s*$', np.nan, regex=True).isna().sum()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender               0\n",
              "Senior_Citizen       0\n",
              "Partner              0\n",
              "Dependents           0\n",
              "Tenure_Months        0\n",
              "Phone_Service        0\n",
              "Multiple_Lines       0\n",
              "Internet_Service     0\n",
              "Online_Security      0\n",
              "Online_Backup        0\n",
              "Device_Protection    0\n",
              "Tech_Support         0\n",
              "Streaming_TV         0\n",
              "Streaming_Movies     0\n",
              "Contract             0\n",
              "Paperless_Billing    0\n",
              "Payment_Method       0\n",
              "Monthly_Charges      0\n",
              "Total_Charges        0\n",
              "Churn_Label          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1CT4ZFXbVwd5",
        "outputId": "4d697415-cc3f-4f00-9cf7-7103ed046a11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "df_trans.dtypes"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender               float64\n",
              "Senior_Citizen       float64\n",
              "Partner              float64\n",
              "Dependents           float64\n",
              "Phone_Service        float64\n",
              "Multiple_Lines       float64\n",
              "Internet_Service     float64\n",
              "Online_Security      float64\n",
              "Online_Backup        float64\n",
              "Device_Protection    float64\n",
              "Tech_Support         float64\n",
              "Streaming_TV         float64\n",
              "Streaming_Movies     float64\n",
              "Contract             float64\n",
              "Paperless_Billing    float64\n",
              "Payment_Method       float64\n",
              "Total_Charges        float64\n",
              "Churn_Label          float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Imruw5tsWBnJ"
      },
      "source": [
        "df.iloc[:, 18] = pd.to_numeric(df.iloc[:, 18], errors='coerce') "
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lSMQQJ1yWSyq"
      },
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "imp_median = SimpleImputer(missing_values=np.nan, strategy='median')"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TmXduYUSWYOF"
      },
      "source": [
        "df.iloc[:, 19] = imp_median.fit_transform(df.iloc[:, 19].values.reshape(-1, 1) )"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqVTo7XPWd2Y",
        "outputId": "9fd18140-ead7-474c-ab29-bbfb5667f5a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender               0\n",
              "Senior_Citizen       0\n",
              "Partner              0\n",
              "Dependents           0\n",
              "Tenure_Months        0\n",
              "Phone_Service        0\n",
              "Multiple_Lines       0\n",
              "Internet_Service     0\n",
              "Online_Security      0\n",
              "Online_Backup        0\n",
              "Device_Protection    0\n",
              "Tech_Support         0\n",
              "Streaming_TV         0\n",
              "Streaming_Movies     0\n",
              "Contract             0\n",
              "Paperless_Billing    0\n",
              "Payment_Method       0\n",
              "Monthly_Charges      0\n",
              "Total_Charges        0\n",
              "Churn_Label          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LPF6nQaQSgPd"
      },
      "source": [
        "df_X = df.drop(\"Churn_Label\", axis=1)\n",
        "df_y = df['Churn_Label']"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0La1Z7o00e7M"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(df_X, df_y, train_size=0.75, test_size=0.25)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PqeJWPTNTuIO",
        "outputId": "a56b6d2a-e4c2-4c78-ffd8-9b4c882c6e66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        }
      },
      "source": [
        "X_train"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Senior_Citizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Tenure_Months</th>\n",
              "      <th>Phone_Service</th>\n",
              "      <th>Multiple_Lines</th>\n",
              "      <th>Internet_Service</th>\n",
              "      <th>Online_Security</th>\n",
              "      <th>Online_Backup</th>\n",
              "      <th>Device_Protection</th>\n",
              "      <th>Tech_Support</th>\n",
              "      <th>Streaming_TV</th>\n",
              "      <th>Streaming_Movies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>Paperless_Billing</th>\n",
              "      <th>Payment_Method</th>\n",
              "      <th>Monthly_Charges</th>\n",
              "      <th>Total_Charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2642</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>70.20</td>\n",
              "      <td>90.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6734</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20.15</td>\n",
              "      <td>2017.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6851</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>48</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>19.85</td>\n",
              "      <td>6356.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>89.75</td>\n",
              "      <td>102.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6211</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>52</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>91.60</td>\n",
              "      <td>3925.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4977</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>61</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>65.20</td>\n",
              "      <td>3389.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1987</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>14</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>80.90</td>\n",
              "      <td>290.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2715</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>56.80</td>\n",
              "      <td>147.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5305</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>42</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>19.05</td>\n",
              "      <td>5829.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>814</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>19.45</td>\n",
              "      <td>1483.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5282 rows × 19 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Gender Senior_Citizen  ...  Monthly_Charges  Total_Charges\n",
              "2642     0.0              0  ...            70.20           90.0\n",
              "6734     0.0              0  ...            20.15         2017.0\n",
              "6851     1.0              0  ...            19.85         6356.0\n",
              "302      0.0              0  ...            89.75          102.0\n",
              "6211     1.0              0  ...            91.60         3925.0\n",
              "...      ...            ...  ...              ...            ...\n",
              "4977     0.0              1  ...            65.20         3389.0\n",
              "1987     0.0              0  ...            80.90          290.0\n",
              "2715     1.0              0  ...            56.80          147.0\n",
              "5305     0.0              0  ...            19.05         5829.0\n",
              "814      0.0              1  ...            19.45         1483.0\n",
              "\n",
              "[5282 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BsnfnFnQtdWQ"
      },
      "source": [
        "**Use TPOT classifier to find the best model algorithm that fits the data. TPOT uses genetic algorithm based search internally**\n",
        "\n",
        "There are two ways to call TPOTClassifier,\n",
        "1. First one is very common way to call but computational expensive. So you need to distribute the process or need multiple core.\n",
        "\n",
        "2. Second way is to import multiprocessing and run the TPOT in multiple processor.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yVEQ5PtGhNDH",
        "outputId": "69122460-c4ab-4f41-87a2-d0a0e7d48d95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445,
          "referenced_widgets": [
            "6f6d5aed6ab94fe3907d4e19b496bd36",
            "2e3d8ce88f5342daabdd0865e56242fb",
            "0122e0a2cd724e4b854d91ccfa80acc0",
            "8457b6c0f9b74455994432a8084bed13",
            "17af2f9342bb4126b13526a3ab676ce4",
            "76e93a685d0b49e38b017a8668c3805b",
            "acd4f539ee0541d1b70bd9c72a4ffe5a",
            "3144d485d1e146bea78251434461b6ad"
          ]
        }
      },
      "source": [
        "tpot =  TPOTClassifier(generations=4, population_size=10, verbosity=3)\n",
        "tpot.fit(X_train, y_train)\n",
        "print(tpot.score(X_test, y_test))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "32 operators have been imported by TPOT.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6f6d5aed6ab94fe3907d4e19b496bd36",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Optimization Progress', max=50.0, style=ProgressStyle(des…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "_pre_test decorator: _random_mutation_operator: num_test=0 Negative values in data passed to MultinomialNB (input X).\n",
            "_pre_test decorator: _random_mutation_operator: num_test=0 Solver lbfgs supports only dual=False, got dual=True.\n",
            "\n",
            "Generation 1 - Current Pareto front scores:\n",
            "Pipeline encountered that has previously been evaluated during the optimization process. Using the score from the previous evaluation.\n",
            "\n",
            "Generation 2 - Current Pareto front scores:\n",
            "-1\t0.8080237091826497\tExtraTreesClassifier(input_matrix, ExtraTreesClassifier__bootstrap=False, ExtraTreesClassifier__criterion=gini, ExtraTreesClassifier__max_features=0.45, ExtraTreesClassifier__min_samples_leaf=8, ExtraTreesClassifier__min_samples_split=15, ExtraTreesClassifier__n_estimators=100)\n",
            "_pre_test decorator: _random_mutation_operator: num_test=0 DataFrame.dtypes for data must be int, float or bool.\n",
            "                Did not expect the data types in fields Senior_Citizen.\n",
            "_pre_test decorator: _random_mutation_operator: num_test=1 Negative values in data passed to MultinomialNB (input X).\n",
            "_pre_test decorator: _random_mutation_operator: num_test=0 DataFrame.dtypes for data must be int, float or bool.\n",
            "                Did not expect the data types in fields Senior_Citizen.\n",
            "_pre_test decorator: _random_mutation_operator: num_test=0 Expected n_neighbors <= n_samples,  but n_samples = 50, n_neighbors = 100.\n",
            "\n",
            "Generation 3 - Current Pareto front scores:\n",
            "-1\t0.8080237091826497\tExtraTreesClassifier(input_matrix, ExtraTreesClassifier__bootstrap=False, ExtraTreesClassifier__criterion=gini, ExtraTreesClassifier__max_features=0.45, ExtraTreesClassifier__min_samples_leaf=8, ExtraTreesClassifier__min_samples_split=15, ExtraTreesClassifier__n_estimators=100)\n",
            "_pre_test decorator: _random_mutation_operator: num_test=0 Negative values in data passed to MultinomialNB (input X).\n",
            "_pre_test decorator: _random_mutation_operator: num_test=0 Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty..\n",
            "\n",
            "Generation 4 - Current Pareto front scores:\n",
            "-1\t0.8080237091826497\tExtraTreesClassifier(input_matrix, ExtraTreesClassifier__bootstrap=False, ExtraTreesClassifier__criterion=gini, ExtraTreesClassifier__max_features=0.45, ExtraTreesClassifier__min_samples_leaf=8, ExtraTreesClassifier__min_samples_split=15, ExtraTreesClassifier__n_estimators=100)\n",
            "0.8091993185689949\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ypOB9pPbZVYz",
        "outputId": "cd8efbab-ff88-4795-9c92-d0a285c2165c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "81ae9588a36d404fa4dc66f0cea5d301",
            "eb31102f21e84dc2b1f807d3821358da",
            "341c74f389ce4dabb8fe0fccdb3d73d3",
            "ac01dfe5ec8e4214874186b8a556cd58",
            "8938472d89744609b34bb06226d531fe",
            "3ec6aff933204dd4b0bd453506d34600",
            "9218c51ea1004922b6930aa373f9da37",
            "45978cd13c0d47d1b4dfff96e325b8cd"
          ]
        }
      },
      "source": [
        "#2.\n",
        "import multiprocessing\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    multiprocessing.set_start_method('forkserver', force=True)\n",
        "    tpot =  TPOTClassifier(generations=2, population_size=20, verbosity=2,n_jobs = 20, random_state=50)\n",
        "    tpot.fit(X_train, y_train)\n"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "81ae9588a36d404fa4dc66f0cea5d301",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Optimization Progress', max=60.0, style=ProgressStyle(des…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Generation 1 - Current best internal CV score: 0.8104827843238439\n",
            "Generation 2 - Current best internal CV score: 0.8104827843238439\n",
            "Best pipeline: LinearSVC(input_matrix, C=1.0, dual=False, loss=squared_hinge, penalty=l1, tol=1e-05)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8uO84IDzcsCE",
        "outputId": "543cdb5d-74d7-4ab0-af22-c7a3452ed44d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(tpot.score(X_test, y_test))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.807495741056218\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dtnA4gw3udNV"
      },
      "source": [
        "**Export the scikit learn code for best classifier and dispay the code**\n",
        "\n",
        "A good feature of tpot is that you can export the whole code and it will give you exact scikitlearn code, you can import and run the code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UhmmaQlWgwsx"
      },
      "source": [
        "tpot.export('tpot_churn_pipeline.py')"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRYLkktWhhjN",
        "outputId": "19e148f0-5cba-4aaf-96f9-4279d7a5d7ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        }
      },
      "source": [
        "!cat tpot_churn_pipeline.py"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "import numpy as np\n",
            "import pandas as pd\n",
            "from sklearn.model_selection import train_test_split\n",
            "from sklearn.svm import LinearSVC\n",
            "\n",
            "# NOTE: Make sure that the outcome column is labeled 'target' in the data file\n",
            "tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)\n",
            "features = tpot_data.drop('target', axis=1)\n",
            "training_features, testing_features, training_target, testing_target = \\\n",
            "            train_test_split(features, tpot_data['target'], random_state=50)\n",
            "\n",
            "# Average CV score on the training set was: 0.8104827843238439\n",
            "exported_pipeline = LinearSVC(C=1.0, dual=False, loss=\"squared_hinge\", penalty=\"l1\", tol=1e-05)\n",
            "# Fix random state in exported estimator\n",
            "if hasattr(exported_pipeline, 'random_state'):\n",
            "    setattr(exported_pipeline, 'random_state', 50)\n",
            "\n",
            "exported_pipeline.fit(training_features, training_target)\n",
            "results = exported_pipeline.predict(testing_features)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1KNxjXSQuC7v"
      },
      "source": [
        "**Get details on algorithm TPOT searched for and how the performed on the dataset**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "85Z-cIHcelgb",
        "outputId": "9c4d390b-9bfe-44f6-fdde-aef6f6ba481a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "tpot.evaluated_individuals_"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'BernoulliNB(GradientBoostingClassifier(input_matrix, GradientBoostingClassifier__learning_rate=1.0, GradientBoostingClassifier__max_depth=3, GradientBoostingClassifier__max_features=0.7500000000000001, GradientBoostingClassifier__min_samples_leaf=11, GradientBoostingClassifier__min_samples_split=19, GradientBoostingClassifier__n_estimators=100, GradientBoostingClassifier__subsample=0.45), BernoulliNB__alpha=1.0, BernoulliNB__fit_prior=False)': {'crossover_count': 0,\n",
              "  'generation': 1,\n",
              "  'internal_cv_score': 0.7502805968865571,\n",
              "  'mutation_count': 1,\n",
              "  'operator_count': 2,\n",
              "  'predecessor': ('BernoulliNB(GradientBoostingClassifier(input_matrix, GradientBoostingClassifier__learning_rate=1.0, GradientBoostingClassifier__max_depth=3, GradientBoostingClassifier__max_features=0.8, GradientBoostingClassifier__min_samples_leaf=11, GradientBoostingClassifier__min_samples_split=19, GradientBoostingClassifier__n_estimators=100, GradientBoostingClassifier__subsample=0.45), BernoulliNB__alpha=1.0, BernoulliNB__fit_prior=False)',)},\n",
              " 'BernoulliNB(GradientBoostingClassifier(input_matrix, GradientBoostingClassifier__learning_rate=1.0, GradientBoostingClassifier__max_depth=3, GradientBoostingClassifier__max_features=0.8, GradientBoostingClassifier__min_samples_leaf=11, GradientBoostingClassifier__min_samples_split=19, GradientBoostingClassifier__n_estimators=100, GradientBoostingClassifier__subsample=0.45), BernoulliNB__alpha=1.0, BernoulliNB__fit_prior=False)': {'crossover_count': 0,\n",
              "  'generation': 0,\n",
              "  'internal_cv_score': 0.7468725810613227,\n",
              "  'mutation_count': 0,\n",
              "  'operator_count': 2,\n",
              "  'predecessor': ('ROOT',)},\n",
              " 'BernoulliNB(MultinomialNB(input_matrix, MultinomialNB__alpha=10.0, MultinomialNB__fit_prior=True), BernoulliNB__alpha=10.0, BernoulliNB__fit_prior=True)': {'crossover_count': 0,\n",
              "  'generation': 1,\n",
              "  'internal_cv_score': 0.7631540093460624,\n",
              "  'mutation_count': 1,\n",
              "  'operator_count': 2,\n",
              "  'predecessor': ('BernoulliNB(input_matrix, BernoulliNB__alpha=10.0, BernoulliNB__fit_prior=True)',)},\n",
              " 'BernoulliNB(Normalizer(input_matrix, Normalizer__norm=l2), BernoulliNB__alpha=10.0, BernoulliNB__fit_prior=True)': {'crossover_count': 0,\n",
              "  'generation': 2,\n",
              "  'internal_cv_score': 0.7584214902095697,\n",
              "  'mutation_count': 2,\n",
              "  'operator_count': 2,\n",
              "  'predecessor': ('BernoulliNB(MultinomialNB(input_matrix, MultinomialNB__alpha=10.0, MultinomialNB__fit_prior=True), BernoulliNB__alpha=10.0, BernoulliNB__fit_prior=True)',)},\n",
              " 'BernoulliNB(Nystroem(input_matrix, Nystroem__gamma=0.6000000000000001, Nystroem__kernel=additive_chi2, Nystroem__n_components=6), BernoulliNB__alpha=10.0, BernoulliNB__fit_prior=True)': {'crossover_count': 0,\n",
              "  'generation': 2,\n",
              "  'internal_cv_score': 0.7353267179840027,\n",
              "  'mutation_count': 1,\n",
              "  'operator_count': 2,\n",
              "  'predecessor': ('BernoulliNB(input_matrix, BernoulliNB__alpha=10.0, BernoulliNB__fit_prior=True)',)},\n",
              " 'BernoulliNB(input_matrix, BernoulliNB__alpha=1.0, BernoulliNB__fit_prior=False)': {'crossover_count': 0,\n",
              "  'generation': 0,\n",
              "  'internal_cv_score': 0.7305873899830855,\n",
              "  'mutation_count': 0,\n",
              "  'operator_count': 1,\n",
              "  'predecessor': ('ROOT',)},\n",
              " 'BernoulliNB(input_matrix, BernoulliNB__alpha=10.0, BernoulliNB__fit_prior=True)': {'crossover_count': 0,\n",
              "  'generation': 0,\n",
              "  'internal_cv_score': 0.7584214902095697,\n",
              "  'mutation_count': 0,\n",
              "  'operator_count': 1,\n",
              "  'predecessor': ('ROOT',)},\n",
              " 'BernoulliNB(input_matrix, BernoulliNB__alpha=100.0, BernoulliNB__fit_prior=False)': {'crossover_count': 0,\n",
              "  'generation': 1,\n",
              "  'internal_cv_score': 0.7215000645050313,\n",
              "  'mutation_count': 1,\n",
              "  'operator_count': 1,\n",
              "  'predecessor': ('BernoulliNB(input_matrix, BernoulliNB__alpha=1.0, BernoulliNB__fit_prior=False)',)},\n",
              " 'DecisionTreeClassifier(RobustScaler(input_matrix), DecisionTreeClassifier__criterion=gini, DecisionTreeClassifier__max_depth=8, DecisionTreeClassifier__min_samples_leaf=8, DecisionTreeClassifier__min_samples_split=3)': {'crossover_count': 0,\n",
              "  'generation': 0,\n",
              "  'internal_cv_score': 0.787006357329205,\n",
              "  'mutation_count': 0,\n",
              "  'operator_count': 2,\n",
              "  'predecessor': ('ROOT',)},\n",
              " 'DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini, DecisionTreeClassifier__max_depth=10, DecisionTreeClassifier__min_samples_leaf=8, DecisionTreeClassifier__min_samples_split=3)': {'crossover_count': 0,\n",
              "  'generation': 2,\n",
              "  'internal_cv_score': 0.7762175324675324,\n",
              "  'mutation_count': 2,\n",
              "  'operator_count': 1,\n",
              "  'predecessor': ('DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini, DecisionTreeClassifier__max_depth=8, DecisionTreeClassifier__min_samples_leaf=8, DecisionTreeClassifier__min_samples_split=3)',)},\n",
              " 'DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini, DecisionTreeClassifier__max_depth=8, DecisionTreeClassifier__min_samples_leaf=8, DecisionTreeClassifier__min_samples_split=3)': {'crossover_count': 0,\n",
              "  'generation': 1,\n",
              "  'internal_cv_score': 0.787006357329205,\n",
              "  'mutation_count': 1,\n",
              "  'operator_count': 1,\n",
              "  'predecessor': ('DecisionTreeClassifier(RobustScaler(input_matrix), DecisionTreeClassifier__criterion=gini, DecisionTreeClassifier__max_depth=8, DecisionTreeClassifier__min_samples_leaf=8, DecisionTreeClassifier__min_samples_split=3)',)},\n",
              " 'ExtraTreesClassifier(KNeighborsClassifier(input_matrix, KNeighborsClassifier__n_neighbors=49, KNeighborsClassifier__p=2, KNeighborsClassifier__weights=uniform), ExtraTreesClassifier__bootstrap=False, ExtraTreesClassifier__criterion=gini, ExtraTreesClassifier__max_features=0.9000000000000001, ExtraTreesClassifier__min_samples_leaf=7, ExtraTreesClassifier__min_samples_split=11, ExtraTreesClassifier__n_estimators=100)': {'crossover_count': 0,\n",
              "  'generation': 0,\n",
              "  'internal_cv_score': 0.8030994667584072,\n",
              "  'mutation_count': 0,\n",
              "  'operator_count': 2,\n",
              "  'predecessor': ('ROOT',)},\n",
              " 'ExtraTreesClassifier(SGDClassifier(KNeighborsClassifier(input_matrix, KNeighborsClassifier__n_neighbors=49, KNeighborsClassifier__p=2, KNeighborsClassifier__weights=uniform), SGDClassifier__alpha=0.0, SGDClassifier__eta0=0.01, SGDClassifier__fit_intercept=False, SGDClassifier__l1_ratio=0.25, SGDClassifier__learning_rate=constant, SGDClassifier__loss=squared_hinge, SGDClassifier__penalty=elasticnet, SGDClassifier__power_t=0.0), ExtraTreesClassifier__bootstrap=False, ExtraTreesClassifier__criterion=gini, ExtraTreesClassifier__max_features=0.9000000000000001, ExtraTreesClassifier__min_samples_leaf=7, ExtraTreesClassifier__min_samples_split=11, ExtraTreesClassifier__n_estimators=100)': {'crossover_count': 0,\n",
              "  'generation': 2,\n",
              "  'internal_cv_score': 0.8032886815171583,\n",
              "  'mutation_count': 1,\n",
              "  'operator_count': 3,\n",
              "  'predecessor': ('ExtraTreesClassifier(KNeighborsClassifier(input_matrix, KNeighborsClassifier__n_neighbors=49, KNeighborsClassifier__p=2, KNeighborsClassifier__weights=uniform), ExtraTreesClassifier__bootstrap=False, ExtraTreesClassifier__criterion=gini, ExtraTreesClassifier__max_features=0.9000000000000001, ExtraTreesClassifier__min_samples_leaf=7, ExtraTreesClassifier__min_samples_split=11, ExtraTreesClassifier__n_estimators=100)',)},\n",
              " 'ExtraTreesClassifier(input_matrix, ExtraTreesClassifier__bootstrap=False, ExtraTreesClassifier__criterion=entropy, ExtraTreesClassifier__max_features=0.1, ExtraTreesClassifier__min_samples_leaf=5, ExtraTreesClassifier__min_samples_split=14, ExtraTreesClassifier__n_estimators=100)': {'crossover_count': 1,\n",
              "  'generation': 2,\n",
              "  'internal_cv_score': 0.793068396834953,\n",
              "  'mutation_count': 0,\n",
              "  'operator_count': 1,\n",
              "  'predecessor': ('ExtraTreesClassifier(input_matrix, ExtraTreesClassifier__bootstrap=False, ExtraTreesClassifier__criterion=entropy, ExtraTreesClassifier__max_features=0.1, ExtraTreesClassifier__min_samples_leaf=5, ExtraTreesClassifier__min_samples_split=8, ExtraTreesClassifier__n_estimators=100)',\n",
              "   'ExtraTreesClassifier(input_matrix, ExtraTreesClassifier__bootstrap=False, ExtraTreesClassifier__criterion=entropy, ExtraTreesClassifier__max_features=0.6000000000000001, ExtraTreesClassifier__min_samples_leaf=13, ExtraTreesClassifier__min_samples_split=14, ExtraTreesClassifier__n_estimators=100)')},\n",
              " 'ExtraTreesClassifier(input_matrix, ExtraTreesClassifier__bootstrap=False, ExtraTreesClassifier__criterion=entropy, ExtraTreesClassifier__max_features=0.1, ExtraTreesClassifier__min_samples_leaf=5, ExtraTreesClassifier__min_samples_split=8, ExtraTreesClassifier__n_estimators=100)': {'crossover_count': 0,\n",
              "  'generation': 0,\n",
              "  'internal_cv_score': 0.7938265101344572,\n",
              "  'mutation_count': 0,\n",
              "  'operator_count': 1,\n",
              "  'predecessor': ('ROOT',)},\n",
              " 'ExtraTreesClassifier(input_matrix, ExtraTreesClassifier__bootstrap=False, ExtraTreesClassifier__criterion=entropy, ExtraTreesClassifier__max_features=0.6000000000000001, ExtraTreesClassifier__min_samples_leaf=13, ExtraTreesClassifier__min_samples_split=14, ExtraTreesClassifier__n_estimators=100)': {'crossover_count': 0,\n",
              "  'generation': 0,\n",
              "  'internal_cv_score': 0.8066983099681775,\n",
              "  'mutation_count': 0,\n",
              "  'operator_count': 1,\n",
              "  'predecessor': ('ROOT',)},\n",
              " 'ExtraTreesClassifier(input_matrix, ExtraTreesClassifier__bootstrap=False, ExtraTreesClassifier__criterion=gini, ExtraTreesClassifier__max_features=0.9000000000000001, ExtraTreesClassifier__min_samples_leaf=17, ExtraTreesClassifier__min_samples_split=11, ExtraTreesClassifier__n_estimators=100)': {'crossover_count': 1,\n",
              "  'generation': 2,\n",
              "  'internal_cv_score': 0.8051842335368826,\n",
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              " 'RandomForestClassifier(RFE(input_matrix, RFE__ExtraTreesClassifier__criterion=entropy, RFE__ExtraTreesClassifier__max_features=0.15000000000000002, RFE__ExtraTreesClassifier__n_estimators=100, RFE__step=0.5), RandomForestClassifier__bootstrap=False, RandomForestClassifier__criterion=entropy, RandomForestClassifier__max_features=0.1, RandomForestClassifier__min_samples_leaf=9, RandomForestClassifier__min_samples_split=17, RandomForestClassifier__n_estimators=100)': {'crossover_count': 0,\n",
              "  'generation': 0,\n",
              "  'internal_cv_score': 0.8015846736045411,\n",
              "  'mutation_count': 0,\n",
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              "  'predecessor': ('ROOT',)},\n",
              " 'SGDClassifier(Binarizer(input_matrix, Binarizer__threshold=0.8500000000000001), SGDClassifier__alpha=0.01, SGDClassifier__eta0=1.0, SGDClassifier__fit_intercept=False, SGDClassifier__l1_ratio=0.25, SGDClassifier__learning_rate=constant, SGDClassifier__loss=perceptron, SGDClassifier__penalty=elasticnet, SGDClassifier__power_t=1.0)': {'crossover_count': 0,\n",
              "  'generation': 1,\n",
              "  'internal_cv_score': 0.6828658510363809,\n",
              "  'mutation_count': 1,\n",
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              "  'predecessor': ('SGDClassifier(input_matrix, SGDClassifier__alpha=0.01, SGDClassifier__eta0=1.0, SGDClassifier__fit_intercept=False, SGDClassifier__l1_ratio=0.25, SGDClassifier__learning_rate=constant, SGDClassifier__loss=perceptron, SGDClassifier__penalty=elasticnet, SGDClassifier__power_t=1.0)',)},\n",
              " 'SGDClassifier(MaxAbsScaler(input_matrix), SGDClassifier__alpha=0.01, SGDClassifier__eta0=1.0, SGDClassifier__fit_intercept=False, SGDClassifier__l1_ratio=0.25, SGDClassifier__learning_rate=constant, SGDClassifier__loss=perceptron, SGDClassifier__penalty=elasticnet, SGDClassifier__power_t=1.0)': {'crossover_count': 0,\n",
              "  'generation': 1,\n",
              "  'internal_cv_score': 0.6686480462142714,\n",
              "  'mutation_count': 1,\n",
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              "  'predecessor': ('SGDClassifier(input_matrix, SGDClassifier__alpha=0.01, SGDClassifier__eta0=1.0, SGDClassifier__fit_intercept=False, SGDClassifier__l1_ratio=0.25, SGDClassifier__learning_rate=constant, SGDClassifier__loss=perceptron, SGDClassifier__penalty=elasticnet, SGDClassifier__power_t=1.0)',)},\n",
              " 'SGDClassifier(PCA(input_matrix, PCA__iterated_power=6, PCA__svd_solver=randomized), SGDClassifier__alpha=0.001, SGDClassifier__eta0=0.01, SGDClassifier__fit_intercept=True, SGDClassifier__l1_ratio=0.25, SGDClassifier__learning_rate=constant, SGDClassifier__loss=modified_huber, SGDClassifier__penalty=elasticnet, SGDClassifier__power_t=0.0)': {'crossover_count': 0,\n",
              "  'generation': 0,\n",
              "  'internal_cv_score': 0.6006966543390384,\n",
              "  'mutation_count': 0,\n",
              "  'operator_count': 2,\n",
              "  'predecessor': ('ROOT',)},\n",
              " 'SGDClassifier(SGDClassifier(input_matrix, SGDClassifier__alpha=0.001, SGDClassifier__eta0=0.1, SGDClassifier__fit_intercept=True, SGDClassifier__l1_ratio=0.0, SGDClassifier__learning_rate=constant, SGDClassifier__loss=modified_huber, SGDClassifier__penalty=elasticnet, SGDClassifier__power_t=50.0), SGDClassifier__alpha=0.0, SGDClassifier__eta0=1.0, SGDClassifier__fit_intercept=False, SGDClassifier__l1_ratio=1.0, SGDClassifier__learning_rate=constant, SGDClassifier__loss=squared_hinge, SGDClassifier__penalty=elasticnet, SGDClassifier__power_t=10.0)': {'crossover_count': 0,\n",
              "  'generation': 0,\n",
              "  'internal_cv_score': 0.7067276955935897,\n",
              "  'mutation_count': 0,\n",
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              "  'predecessor': ('ROOT',)},\n",
              " 'SGDClassifier(input_matrix, SGDClassifier__alpha=0.001, SGDClassifier__eta0=1.0, SGDClassifier__fit_intercept=False, SGDClassifier__l1_ratio=0.25, SGDClassifier__learning_rate=constant, SGDClassifier__loss=perceptron, SGDClassifier__penalty=elasticnet, SGDClassifier__power_t=1.0)': {'crossover_count': 0,\n",
              "  'generation': 1,\n",
              "  'internal_cv_score': 0.63939268512944,\n",
              "  'mutation_count': 1,\n",
              "  'operator_count': 1,\n",
              "  'predecessor': ('SGDClassifier(input_matrix, SGDClassifier__alpha=0.01, SGDClassifier__eta0=1.0, SGDClassifier__fit_intercept=False, SGDClassifier__l1_ratio=0.25, SGDClassifier__learning_rate=constant, SGDClassifier__loss=perceptron, SGDClassifier__penalty=elasticnet, SGDClassifier__power_t=1.0)',)},\n",
              " 'SGDClassifier(input_matrix, SGDClassifier__alpha=0.01, SGDClassifier__eta0=1.0, SGDClassifier__fit_intercept=False, SGDClassifier__l1_ratio=0.25, SGDClassifier__learning_rate=constant, SGDClassifier__loss=perceptron, SGDClassifier__penalty=elasticnet, SGDClassifier__power_t=1.0)': {'crossover_count': 0,\n",
              "  'generation': 0,\n",
              "  'internal_cv_score': 0.5479159499440956,\n",
              "  'mutation_count': 0,\n",
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              "  'predecessor': ('ROOT',)},\n",
              " 'XGBClassifier(FastICA(input_matrix, FastICA__tol=0.45), XGBClassifier__learning_rate=0.5, XGBClassifier__max_depth=1, XGBClassifier__min_child_weight=14, XGBClassifier__n_estimators=100, XGBClassifier__nthread=1, XGBClassifier__subsample=0.5)': {'crossover_count': 0,\n",
              "  'generation': 1,\n",
              "  'internal_cv_score': 0.7928755984633469,\n",
              "  'mutation_count': 1,\n",
              "  'operator_count': 2,\n",
              "  'predecessor': ('XGBClassifier(KNeighborsClassifier(input_matrix, KNeighborsClassifier__n_neighbors=40, KNeighborsClassifier__p=2, KNeighborsClassifier__weights=distance), XGBClassifier__learning_rate=0.5, XGBClassifier__max_depth=1, XGBClassifier__min_child_weight=14, XGBClassifier__n_estimators=100, XGBClassifier__nthread=1, XGBClassifier__subsample=0.5)',)},\n",
              " 'XGBClassifier(KNeighborsClassifier(input_matrix, KNeighborsClassifier__n_neighbors=40, KNeighborsClassifier__p=1, KNeighborsClassifier__weights=distance), XGBClassifier__learning_rate=0.5, XGBClassifier__max_depth=1, XGBClassifier__min_child_weight=14, XGBClassifier__n_estimators=100, XGBClassifier__nthread=1, XGBClassifier__subsample=0.5)': {'crossover_count': 0,\n",
              "  'generation': 1,\n",
              "  'internal_cv_score': 0.7550091740489091,\n",
              "  'mutation_count': 1,\n",
              "  'operator_count': 2,\n",
              "  'predecessor': ('XGBClassifier(KNeighborsClassifier(input_matrix, KNeighborsClassifier__n_neighbors=40, KNeighborsClassifier__p=2, KNeighborsClassifier__weights=distance), XGBClassifier__learning_rate=0.5, XGBClassifier__max_depth=1, XGBClassifier__min_child_weight=14, XGBClassifier__n_estimators=100, XGBClassifier__nthread=1, XGBClassifier__subsample=0.5)',)},\n",
              " 'XGBClassifier(KNeighborsClassifier(input_matrix, KNeighborsClassifier__n_neighbors=40, KNeighborsClassifier__p=2, KNeighborsClassifier__weights=distance), XGBClassifier__learning_rate=0.5, XGBClassifier__max_depth=1, XGBClassifier__min_child_weight=14, XGBClassifier__n_estimators=100, XGBClassifier__nthread=1, XGBClassifier__subsample=0.5)': {'crossover_count': 0,\n",
              "  'generation': 0,\n",
              "  'internal_cv_score': 0.7542528525558326,\n",
              "  'mutation_count': 0,\n",
              "  'operator_count': 2,\n",
              "  'predecessor': ('ROOT',)},\n",
              " 'XGBClassifier(KNeighborsClassifier(input_matrix, KNeighborsClassifier__n_neighbors=40, KNeighborsClassifier__p=2, KNeighborsClassifier__weights=distance), XGBClassifier__learning_rate=0.5, XGBClassifier__max_depth=8, XGBClassifier__min_child_weight=14, XGBClassifier__n_estimators=100, XGBClassifier__nthread=1, XGBClassifier__subsample=0.5)': {'crossover_count': 0,\n",
              "  'generation': 1,\n",
              "  'internal_cv_score': 0.7542528525558326,\n",
              "  'mutation_count': 1,\n",
              "  'operator_count': 2,\n",
              "  'predecessor': ('XGBClassifier(KNeighborsClassifier(input_matrix, KNeighborsClassifier__n_neighbors=40, KNeighborsClassifier__p=2, KNeighborsClassifier__weights=distance), XGBClassifier__learning_rate=0.5, XGBClassifier__max_depth=1, XGBClassifier__min_child_weight=14, XGBClassifier__n_estimators=100, XGBClassifier__nthread=1, XGBClassifier__subsample=0.5)',)}}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PA3YBt2FdM-R",
        "outputId": "ff00d1ce-f9ed-4821-ebe1-b9779c448f09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "tpot.fitted_pipeline_"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('linearsvc',\n",
              "                 LinearSVC(C=1.0, class_weight=None, dual=False,\n",
              "                           fit_intercept=True, intercept_scaling=1,\n",
              "                           loss='squared_hinge', max_iter=1000,\n",
              "                           multi_class='ovr', penalty='l1', random_state=50,\n",
              "                           tol=1e-05, verbose=0))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jwn97VuWgUCv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cq2UA7l2Vlxc"
      },
      "source": [
        "\n",
        "Link to supporting video for below notebook walkhthrough - https://www.youtube.com/watch?v=oIFSpnFGX8s\n",
        "\n",
        "\n",
        "\n"
      ]
    }
  ]
}