Day #3-Telecom_case_study,_identifying_propensity_drivers.ipynb

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      "name": "Day #3- Telecom case study, identifying propensity drivers",
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        "<a href=\"https://colab.research.google.com/github/skyprince999/100-Days-Of-ML/blob/master/Day%20%233-Telecom_case_study%2C_identifying_propensity_drivers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "Date: 6th January 2020\n",
        "\n",
        "The following notebook will walk you through a Dummy Telecom data. We create a sample model & then use its SHAPely values to identify the main drivers.\n",
        "\n",
        "The source file is uploaded to Kaggle Datasets: [Link](https://www.kaggle.com/skylord/telecom-case-study)"
      ]
    },
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      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)"
      ],
      "execution_count": 0,
      "outputs": []
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        "# In case you have downloaded the file to your local system you can use the files.upload() command to \n",
        "# Upload raw data file from your local file system\n",
        "\n",
        "from google.colab import files\n",
        "\n",
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              "      Upload widget is only available when the cell has been executed in the\n",
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          "text": [
            "Saving RawdatafileV0.0.csv to RawdatafileV0.0 (3).csv\n"
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        "fname = list(filename.keys())[0]\n",
        "data = pd.read_csv(fname)\n",
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              "      <td>15</td>\n",
              "      <td>Cash</td>\n",
              "      <td>Postpaid</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>300</td>\n",
              "      <td>300</td>\n",
              "      <td>600</td>\n",
              "      <td>300</td>\n",
              "      <td>10</td>\n",
              "      <td>50</td>\n",
              "      <td>1500</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>300</td>\n",
              "      <td>300</td>\n",
              "      <td>20</td>\n",
              "      <td>500</td>\n",
              "      <td>20</td>\n",
              "      <td>Poor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1002</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Central</td>\n",
              "      <td>High</td>\n",
              "      <td>Private</td>\n",
              "      <td>50</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>600</td>\n",
              "      <td>7</td>\n",
              "      <td>Card</td>\n",
              "      <td>Postpaid</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>600</td>\n",
              "      <td>300</td>\n",
              "      <td>600</td>\n",
              "      <td>300</td>\n",
              "      <td>10</td>\n",
              "      <td>50</td>\n",
              "      <td>1500</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>600</td>\n",
              "      <td>300</td>\n",
              "      <td>100</td>\n",
              "      <td>500</td>\n",
              "      <td>20</td>\n",
              "      <td>Poor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1003</td>\n",
              "      <td>Yes</td>\n",
              "      <td>South</td>\n",
              "      <td>High</td>\n",
              "      <td>Private</td>\n",
              "      <td>20</td>\n",
              "      <td>Male</td>\n",
              "      <td>3</td>\n",
              "      <td>600</td>\n",
              "      <td>7</td>\n",
              "      <td>Card</td>\n",
              "      <td>Postpaid</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>600</td>\n",
              "      <td>300</td>\n",
              "      <td>300</td>\n",
              "      <td>100</td>\n",
              "      <td>10</td>\n",
              "      <td>200</td>\n",
              "      <td>1500</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>300</td>\n",
              "      <td>600</td>\n",
              "      <td>100</td>\n",
              "      <td>500</td>\n",
              "      <td>20</td>\n",
              "      <td>Poor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1004</td>\n",
              "      <td>Yes</td>\n",
              "      <td>North</td>\n",
              "      <td>High</td>\n",
              "      <td>Private</td>\n",
              "      <td>20</td>\n",
              "      <td>Female</td>\n",
              "      <td>3</td>\n",
              "      <td>600</td>\n",
              "      <td>15</td>\n",
              "      <td>Card</td>\n",
              "      <td>Postpaid</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>600</td>\n",
              "      <td>600</td>\n",
              "      <td>300</td>\n",
              "      <td>300</td>\n",
              "      <td>10</td>\n",
              "      <td>50</td>\n",
              "      <td>500</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>600</td>\n",
              "      <td>600</td>\n",
              "      <td>100</td>\n",
              "      <td>500</td>\n",
              "      <td>20</td>\n",
              "      <td>Poor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Cust_id Plan_Chg_Flag     Var1    Var2  ... Var24  Var25 Var26  Var27\n",
              "0     1000           Yes    North  Medium  ...   100     50    20   Good\n",
              "1     1001           Yes  Central     Low  ...    20    500    20   Poor\n",
              "2     1002           Yes  Central    High  ...   100    500    20   Poor\n",
              "3     1003           Yes    South    High  ...   100    500    20   Poor\n",
              "4     1004           Yes    North    High  ...   100    500    20   Poor\n",
              "\n",
              "[5 rows x 29 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "TP4SSybdAyTv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "c3fdd083-1adf-4761-ee65-8f166252669d"
      },
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cust_id             0\n",
              "Plan_Chg_Flag       0\n",
              "Var1               25\n",
              "Var2                0\n",
              "Var3                0\n",
              "Var4                0\n",
              "Var5                0\n",
              "Var6                0\n",
              "Var7                0\n",
              "Var8                0\n",
              "Var9                0\n",
              "Var10               0\n",
              "Var11               0\n",
              "Var12               0\n",
              "Var13               0\n",
              "Var14               0\n",
              "Var15               0\n",
              "Var16               0\n",
              "Var17               0\n",
              "Var18               0\n",
              "Var19               0\n",
              "Var20               0\n",
              "Var21               0\n",
              "Var22               0\n",
              "Var23               0\n",
              "Var24               0\n",
              "Var25               0\n",
              "Var26               0\n",
              "Var27            4172\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z2CUB7PvAyTx",
        "colab_type": "text"
      },
      "source": [
        "Only Var1 & Var2 has null values. Filling with a placeholder symbol 'UNK' "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "HvJIZlDbAyTy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data.fillna('UNK', inplace = True) # Replace nulls with a placeholder"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "bR9ZQoohAyTz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f7f2b39b-298c-407a-fcc2-8ad03105f32c"
      },
      "source": [
        "## There are 90 rows which are duplicates, dropping them \n",
        "selCols = data.columns\n",
        "\n",
        "data.drop_duplicates(subset = selCols[2:], inplace= True)\n",
        "print(data.shape)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4910, 29)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Kxa5_5soAyT1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "outputId": "d9c84b06-d70b-4210-f5ff-734547d65090"
      },
      "source": [
        "data.describe().T"
      ],
      "execution_count": 8,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cust_id</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>3539.583910</td>\n",
              "      <td>1425.673126</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>2317.25</td>\n",
              "      <td>3544.5</td>\n",
              "      <td>4771.75</td>\n",
              "      <td>5999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var4</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>33.177800</td>\n",
              "      <td>12.352316</td>\n",
              "      <td>20.0</td>\n",
              "      <td>20.00</td>\n",
              "      <td>30.0</td>\n",
              "      <td>50.00</td>\n",
              "      <td>120.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var6</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>1.501222</td>\n",
              "      <td>1.500152</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var7</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>649.124236</td>\n",
              "      <td>401.583552</td>\n",
              "      <td>200.0</td>\n",
              "      <td>200.00</td>\n",
              "      <td>600.0</td>\n",
              "      <td>1200.00</td>\n",
              "      <td>1200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var8</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>11.073320</td>\n",
              "      <td>3.999735</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.00</td>\n",
              "      <td>15.0</td>\n",
              "      <td>15.00</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var11</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>1.492057</td>\n",
              "      <td>0.499988</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var12</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>1.493890</td>\n",
              "      <td>0.500014</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var13</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>460.386965</td>\n",
              "      <td>149.655178</td>\n",
              "      <td>300.0</td>\n",
              "      <td>300.00</td>\n",
              "      <td>600.0</td>\n",
              "      <td>600.00</td>\n",
              "      <td>600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var14</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>453.604888</td>\n",
              "      <td>149.971949</td>\n",
              "      <td>300.0</td>\n",
              "      <td>300.00</td>\n",
              "      <td>600.0</td>\n",
              "      <td>600.00</td>\n",
              "      <td>600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var15</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>453.360489</td>\n",
              "      <td>149.977626</td>\n",
              "      <td>300.0</td>\n",
              "      <td>300.00</td>\n",
              "      <td>600.0</td>\n",
              "      <td>600.00</td>\n",
              "      <td>600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var16</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>205.376782</td>\n",
              "      <td>99.865517</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.00</td>\n",
              "      <td>300.0</td>\n",
              "      <td>300.00</td>\n",
              "      <td>300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var17</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>165.336049</td>\n",
              "      <td>144.645869</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.00</td>\n",
              "      <td>300.0</td>\n",
              "      <td>300.00</td>\n",
              "      <td>300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var18</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>127.902240</td>\n",
              "      <td>74.951459</td>\n",
              "      <td>50.0</td>\n",
              "      <td>50.00</td>\n",
              "      <td>200.0</td>\n",
              "      <td>200.00</td>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var19</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>1013.645621</td>\n",
              "      <td>499.864668</td>\n",
              "      <td>500.0</td>\n",
              "      <td>500.00</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>1500.00</td>\n",
              "      <td>1500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var20</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>1.510591</td>\n",
              "      <td>0.499939</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var21</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>1.520367</td>\n",
              "      <td>0.499636</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var22</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>455.071283</td>\n",
              "      <td>149.929518</td>\n",
              "      <td>300.0</td>\n",
              "      <td>300.00</td>\n",
              "      <td>600.0</td>\n",
              "      <td>600.00</td>\n",
              "      <td>600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var23</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>458.676171</td>\n",
              "      <td>149.764122</td>\n",
              "      <td>300.0</td>\n",
              "      <td>300.00</td>\n",
              "      <td>600.0</td>\n",
              "      <td>600.00</td>\n",
              "      <td>600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var24</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>61.547862</td>\n",
              "      <td>39.974111</td>\n",
              "      <td>20.0</td>\n",
              "      <td>20.00</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.00</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var25</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>282.881874</td>\n",
              "      <td>224.884806</td>\n",
              "      <td>50.0</td>\n",
              "      <td>50.00</td>\n",
              "      <td>500.0</td>\n",
              "      <td>500.00</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Var26</th>\n",
              "      <td>4910.0</td>\n",
              "      <td>34.547862</td>\n",
              "      <td>14.994711</td>\n",
              "      <td>20.0</td>\n",
              "      <td>20.00</td>\n",
              "      <td>20.0</td>\n",
              "      <td>50.00</td>\n",
              "      <td>50.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          count         mean          std  ...     50%      75%     max\n",
              "Cust_id  4910.0  3539.583910  1425.673126  ...  3544.5  4771.75  5999.0\n",
              "Var4     4910.0    33.177800    12.352316  ...    30.0    50.00   120.0\n",
              "Var6     4910.0     1.501222     1.500152  ...     3.0     3.00     3.0\n",
              "Var7     4910.0   649.124236   401.583552  ...   600.0  1200.00  1200.0\n",
              "Var8     4910.0    11.073320     3.999735  ...    15.0    15.00    15.0\n",
              "Var11    4910.0     1.492057     0.499988  ...     1.0     2.00     2.0\n",
              "Var12    4910.0     1.493890     0.500014  ...     1.0     2.00     2.0\n",
              "Var13    4910.0   460.386965   149.655178  ...   600.0   600.00   600.0\n",
              "Var14    4910.0   453.604888   149.971949  ...   600.0   600.00   600.0\n",
              "Var15    4910.0   453.360489   149.977626  ...   600.0   600.00   600.0\n",
              "Var16    4910.0   205.376782    99.865517  ...   300.0   300.00   300.0\n",
              "Var17    4910.0   165.336049   144.645869  ...   300.0   300.00   300.0\n",
              "Var18    4910.0   127.902240    74.951459  ...   200.0   200.00   200.0\n",
              "Var19    4910.0  1013.645621   499.864668  ...  1500.0  1500.00  1500.0\n",
              "Var20    4910.0     1.510591     0.499939  ...     2.0     2.00     2.0\n",
              "Var21    4910.0     1.520367     0.499636  ...     2.0     2.00     2.0\n",
              "Var22    4910.0   455.071283   149.929518  ...   600.0   600.00   600.0\n",
              "Var23    4910.0   458.676171   149.764122  ...   600.0   600.00   600.0\n",
              "Var24    4910.0    61.547862    39.974111  ...   100.0   100.00   100.0\n",
              "Var25    4910.0   282.881874   224.884806  ...   500.0   500.00   500.0\n",
              "Var26    4910.0    34.547862    14.994711  ...    20.0    50.00    50.0\n",
              "\n",
              "[21 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "b8MpUbepAyT3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Copying out the index variable & the target variable\n",
        "custID = data['Cust_id']\n",
        "target = data['Plan_Chg_Flag']\n",
        "\n",
        "# Drop Var10 since it has the same value (POSTPAID)\n",
        "# Removing the target variable & index variable (cust_id)\n",
        "\n",
        "data.drop(labels=['Cust_id', 'Plan_Chg_Flag', 'Var10'], inplace = True, axis =1) # Var10 is a constant (POSTPAID)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "VOQX0fUaAyT5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "outputId": "f156ed78-9514-4c56-82fc-97cf88f67650"
      },
      "source": [
        "# Check the memory usage\n",
        "data.info(memory_usage='deep')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4910 entries, 0 to 4999\n",
            "Data columns (total 26 columns):\n",
            "Var1     4910 non-null object\n",
            "Var2     4910 non-null object\n",
            "Var3     4910 non-null object\n",
            "Var4     4910 non-null int64\n",
            "Var5     4910 non-null object\n",
            "Var6     4910 non-null int64\n",
            "Var7     4910 non-null int64\n",
            "Var8     4910 non-null int64\n",
            "Var9     4910 non-null object\n",
            "Var11    4910 non-null int64\n",
            "Var12    4910 non-null int64\n",
            "Var13    4910 non-null int64\n",
            "Var14    4910 non-null int64\n",
            "Var15    4910 non-null int64\n",
            "Var16    4910 non-null int64\n",
            "Var17    4910 non-null int64\n",
            "Var18    4910 non-null int64\n",
            "Var19    4910 non-null int64\n",
            "Var20    4910 non-null int64\n",
            "Var21    4910 non-null int64\n",
            "Var22    4910 non-null int64\n",
            "Var23    4910 non-null int64\n",
            "Var24    4910 non-null int64\n",
            "Var25    4910 non-null int64\n",
            "Var26    4910 non-null int64\n",
            "Var27    4910 non-null object\n",
            "dtypes: int64(20), object(6)\n",
            "memory usage: 2.5 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "VRpvgY97AyT7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "outputId": "eacdfd47-7106-48bf-ce0f-08fca110d504"
      },
      "source": [
        "# Copying all column names \n",
        "# Converting all columns to categories \n",
        "\n",
        "selCols = data.columns # Read the column names\n",
        "\n",
        "data = data.apply(lambda x: x.astype('category')) # Convert all columns to category \n",
        "\n",
        "# Check the memory usage \n",
        "data.info(memory_usage='deep')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4910 entries, 0 to 4999\n",
            "Data columns (total 26 columns):\n",
            "Var1     4910 non-null category\n",
            "Var2     4910 non-null category\n",
            "Var3     4910 non-null category\n",
            "Var4     4910 non-null category\n",
            "Var5     4910 non-null category\n",
            "Var6     4910 non-null category\n",
            "Var7     4910 non-null category\n",
            "Var8     4910 non-null category\n",
            "Var9     4910 non-null category\n",
            "Var11    4910 non-null category\n",
            "Var12    4910 non-null category\n",
            "Var13    4910 non-null category\n",
            "Var14    4910 non-null category\n",
            "Var15    4910 non-null category\n",
            "Var16    4910 non-null category\n",
            "Var17    4910 non-null category\n",
            "Var18    4910 non-null category\n",
            "Var19    4910 non-null category\n",
            "Var20    4910 non-null category\n",
            "Var21    4910 non-null category\n",
            "Var22    4910 non-null category\n",
            "Var23    4910 non-null category\n",
            "Var24    4910 non-null category\n",
            "Var25    4910 non-null category\n",
            "Var26    4910 non-null category\n",
            "Var27    4910 non-null category\n",
            "dtypes: category(26)\n",
            "memory usage: 166.6 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8lSnfSUTAyT8",
        "colab_type": "text"
      },
      "source": [
        "Impact of converting to categorical variables. Size of the dataframe:\n",
        "\n",
        "Before: **2.5 MB**\n",
        "\n",
        "After converting: **166.6 Kb**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "_1d4XIB8AyT9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "a5ce0a31-509c-4666-b78b-de66105eabd2"
      },
      "source": [
        "# Convert to one-hot-encoding \n",
        "df_with_dummies = pd.get_dummies( data, columns = selCols)\n",
        "df_with_dummies['Cust_id'] = custID\n",
        "\n",
        "print(df_with_dummies.shape)\n",
        "df_with_dummies.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4910, 63)\n"
          ],
          "name": "stdout"
        },
        {
          "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>Var1_Central</th>\n",
              "      <th>Var1_North</th>\n",
              "      <th>Var1_South</th>\n",
              "      <th>Var1_UNK</th>\n",
              "      <th>Var2_High</th>\n",
              "      <th>Var2_Low</th>\n",
              "      <th>Var2_Medium</th>\n",
              "      <th>Var3_Govt</th>\n",
              "      <th>Var3_Private</th>\n",
              "      <th>Var3_Unemployed</th>\n",
              "      <th>Var4_20</th>\n",
              "      <th>Var4_24</th>\n",
              "      <th>Var4_30</th>\n",
              "      <th>Var4_50</th>\n",
              "      <th>Var4_99</th>\n",
              "      <th>Var4_120</th>\n",
              "      <th>Var5_Female</th>\n",
              "      <th>Var5_Male</th>\n",
              "      <th>Var6_0</th>\n",
              "      <th>Var6_3</th>\n",
              "      <th>Var7_200</th>\n",
              "      <th>Var7_600</th>\n",
              "      <th>Var7_1200</th>\n",
              "      <th>Var8_7</th>\n",
              "      <th>Var8_15</th>\n",
              "      <th>Var9_Card</th>\n",
              "      <th>Var9_Cash</th>\n",
              "      <th>Var11_1</th>\n",
              "      <th>Var11_2</th>\n",
              "      <th>Var12_1</th>\n",
              "      <th>Var12_2</th>\n",
              "      <th>Var13_300</th>\n",
              "      <th>Var13_600</th>\n",
              "      <th>Var14_300</th>\n",
              "      <th>Var14_600</th>\n",
              "      <th>Var15_300</th>\n",
              "      <th>Var15_600</th>\n",
              "      <th>Var16_100</th>\n",
              "      <th>Var16_300</th>\n",
              "      <th>Var17_10</th>\n",
              "      <th>Var17_300</th>\n",
              "      <th>Var18_50</th>\n",
              "      <th>Var18_200</th>\n",
              "      <th>Var19_500</th>\n",
              "      <th>Var19_1500</th>\n",
              "      <th>Var20_1</th>\n",
              "      <th>Var20_2</th>\n",
              "      <th>Var21_1</th>\n",
              "      <th>Var21_2</th>\n",
              "      <th>Var22_300</th>\n",
              "      <th>Var22_600</th>\n",
              "      <th>Var23_300</th>\n",
              "      <th>Var23_600</th>\n",
              "      <th>Var24_20</th>\n",
              "      <th>Var24_100</th>\n",
              "      <th>Var25_50</th>\n",
              "      <th>Var25_500</th>\n",
              "      <th>Var26_20</th>\n",
              "      <th>Var26_50</th>\n",
              "      <th>Var27_Good</th>\n",
              "      <th>Var27_Poor</th>\n",
              "      <th>Var27_UNK</th>\n",
              "      <th>Cust_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>1000</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1004</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Var1_Central  Var1_North  Var1_South  ...  Var27_Poor  Var27_UNK  Cust_id\n",
              "0             0           1           0  ...           0          0     1000\n",
              "1             1           0           0  ...           1          0     1001\n",
              "2             1           0           0  ...           1          0     1002\n",
              "3             0           0           1  ...           1          0     1003\n",
              "4             0           1           0  ...           1          0     1004\n",
              "\n",
              "[5 rows x 63 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "EAowKnEdAyT_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "00ca4a53-e7a4-4bfe-b57c-9c5850b4bc64"
      },
      "source": [
        "# just checking the size of the DF after converting to dummy values \n",
        "df_with_dummies.info()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4910 entries, 0 to 4999\n",
            "Data columns (total 63 columns):\n",
            "Var1_Central       4910 non-null uint8\n",
            "Var1_North         4910 non-null uint8\n",
            "Var1_South         4910 non-null uint8\n",
            "Var1_UNK           4910 non-null uint8\n",
            "Var2_High          4910 non-null uint8\n",
            "Var2_Low           4910 non-null uint8\n",
            "Var2_Medium        4910 non-null uint8\n",
            "Var3_Govt          4910 non-null uint8\n",
            "Var3_Private       4910 non-null uint8\n",
            "Var3_Unemployed    4910 non-null uint8\n",
            "Var4_20            4910 non-null uint8\n",
            "Var4_24            4910 non-null uint8\n",
            "Var4_30            4910 non-null uint8\n",
            "Var4_50            4910 non-null uint8\n",
            "Var4_99            4910 non-null uint8\n",
            "Var4_120           4910 non-null uint8\n",
            "Var5_Female        4910 non-null uint8\n",
            "Var5_Male          4910 non-null uint8\n",
            "Var6_0             4910 non-null uint8\n",
            "Var6_3             4910 non-null uint8\n",
            "Var7_200           4910 non-null uint8\n",
            "Var7_600           4910 non-null uint8\n",
            "Var7_1200          4910 non-null uint8\n",
            "Var8_7             4910 non-null uint8\n",
            "Var8_15            4910 non-null uint8\n",
            "Var9_Card          4910 non-null uint8\n",
            "Var9_Cash          4910 non-null uint8\n",
            "Var11_1            4910 non-null uint8\n",
            "Var11_2            4910 non-null uint8\n",
            "Var12_1            4910 non-null uint8\n",
            "Var12_2            4910 non-null uint8\n",
            "Var13_300          4910 non-null uint8\n",
            "Var13_600          4910 non-null uint8\n",
            "Var14_300          4910 non-null uint8\n",
            "Var14_600          4910 non-null uint8\n",
            "Var15_300          4910 non-null uint8\n",
            "Var15_600          4910 non-null uint8\n",
            "Var16_100          4910 non-null uint8\n",
            "Var16_300          4910 non-null uint8\n",
            "Var17_10           4910 non-null uint8\n",
            "Var17_300          4910 non-null uint8\n",
            "Var18_50           4910 non-null uint8\n",
            "Var18_200          4910 non-null uint8\n",
            "Var19_500          4910 non-null uint8\n",
            "Var19_1500         4910 non-null uint8\n",
            "Var20_1            4910 non-null uint8\n",
            "Var20_2            4910 non-null uint8\n",
            "Var21_1            4910 non-null uint8\n",
            "Var21_2            4910 non-null uint8\n",
            "Var22_300          4910 non-null uint8\n",
            "Var22_600          4910 non-null uint8\n",
            "Var23_300          4910 non-null uint8\n",
            "Var23_600          4910 non-null uint8\n",
            "Var24_20           4910 non-null uint8\n",
            "Var24_100          4910 non-null uint8\n",
            "Var25_50           4910 non-null uint8\n",
            "Var25_500          4910 non-null uint8\n",
            "Var26_20           4910 non-null uint8\n",
            "Var26_50           4910 non-null uint8\n",
            "Var27_Good         4910 non-null uint8\n",
            "Var27_Poor         4910 non-null uint8\n",
            "Var27_UNK          4910 non-null uint8\n",
            "Cust_id            4910 non-null int64\n",
            "dtypes: int64(1), uint8(62)\n",
            "memory usage: 374.0 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JtL3knaMAyUA",
        "colab_type": "text"
      },
      "source": [
        "We will be creating a base model & then use SHAP to understand the interaction between variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "RT5c5BjFAyUB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importing the sklearn libraries\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import metrics\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import accuracy_score, roc_auc_score, confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "# Converting the target variable via LabelEncoding\n",
        "label_encoder = LabelEncoder().fit(target)\n",
        "targetLE = label_encoder.transform(target)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "qY4XcVDLAyUD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Create train and validation set\n",
        "train_x, valid_x, train_y, valid_y = train_test_split(df_with_dummies, targetLE, test_size=0.3, shuffle=True, stratify=target, random_state= 93456)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Z3G4PTG2AyUE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "a7aa7358-2445-479b-d93f-5507b9c5960a"
      },
      "source": [
        "# Removing the cust_id before creating the model\n",
        "custID_train = train_x['Cust_id']\n",
        "custID_valid = valid_x['Cust_id']\n",
        "\n",
        "train_x.drop(labels= ['Cust_id'], axis= 1, inplace= True)\n",
        "valid_x.drop(labels= ['Cust_id'], axis= 1, inplace= True)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:4117: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "fc40BNQkAyUG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#------------------------Build LightGBM Model-----------------------\n",
        "import lightgbm as lgb\n",
        "\n",
        "train_data=lgb.Dataset(train_x,label=train_y)\n",
        "valid_data=lgb.Dataset(valid_x,label=valid_y)\n",
        "\n",
        "#Select Hyper-Parameters\n",
        "params = {'metric' : 'auc',\n",
        "          'boosting_type' : 'gbdt',\n",
        "          'colsample_bytree' : 0.9234,\n",
        "          'num_leaves' : 13,\n",
        "          'max_depth' : -1,\n",
        "          'n_estimators' : 200,\n",
        "          'min_child_samples': 399, \n",
        "          'min_child_weight': 0.1,\n",
        "          'reg_alpha': 2,\n",
        "          'reg_lambda': 5,\n",
        "          'subsample': 0.855,\n",
        "          'verbose' : -1,\n",
        "          'num_threads' : 4\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "noY0h5V8AyUI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "273c54e1-4b68-4577-faa8-bcf83a58859f"
      },
      "source": [
        "#Train model on selected parameters and number of iterations\n",
        "lgbm = lgb.train(params,\n",
        "                 train_data,\n",
        "                 2500,\n",
        "                 valid_sets=valid_data,\n",
        "                 early_stopping_rounds= 30,\n",
        "                 verbose_eval= 10\n",
        "                 )"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/lightgbm/engine.py:118: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
            "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training until validation scores don't improve for 30 rounds.\n",
            "[10]\tvalid_0's auc: 0.962818\n",
            "[20]\tvalid_0's auc: 0.992743\n",
            "[30]\tvalid_0's auc: 0.995345\n",
            "[40]\tvalid_0's auc: 0.997338\n",
            "[50]\tvalid_0's auc: 0.998597\n",
            "[60]\tvalid_0's auc: 0.998699\n",
            "[70]\tvalid_0's auc: 0.998753\n",
            "[80]\tvalid_0's auc: 0.998735\n",
            "[90]\tvalid_0's auc: 0.998591\n",
            "[100]\tvalid_0's auc: 0.998248\n",
            "[110]\tvalid_0's auc: 0.998254\n",
            "Early stopping, best iteration is:\n",
            "[86]\tvalid_0's auc: 0.998771\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "zp_QP5nFAyUK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "92444cad-70fa-4322-9418-1e10f2c72c6e"
      },
      "source": [
        "lgbm.params"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'boosting_type': 'gbdt',\n",
              " 'colsample_bytree': 0.9234,\n",
              " 'max_depth': -1,\n",
              " 'metric': 'auc',\n",
              " 'min_child_samples': 399,\n",
              " 'min_child_weight': 0.1,\n",
              " 'num_leaves': 13,\n",
              " 'num_threads': 4,\n",
              " 'reg_alpha': 2,\n",
              " 'reg_lambda': 5,\n",
              " 'subsample': 0.855,\n",
              " 'verbose': -1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OIXn4Jv_AyUM",
        "colab_type": "text"
      },
      "source": [
        "Light GBM has a method which prints out the feature importance of each variable from the training dataset. We will first plot these values as a bar chart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "nOSNPnJsAyUM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 766
        },
        "outputId": "cce25027-c15f-4ace-9a9e-5527fde0de5b"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "\n",
        "# Feature importance \n",
        "feature_imp = pd.DataFrame(sorted(zip(lgbm.feature_importance(),train_x.columns), reverse=True), columns= ['Value', 'Feature'])\n",
        "\n",
        "plt.figure(figsize=(20, 10))\n",
        "sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\", ascending=False))\n",
        "plt.title('LightGBM Features (avg over folds)')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "plt.savefig('lgbm_importances-01.png')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9B2LHJjl1iuu3bi0yep87kpw8MOdFfePXJflpkkpycN+cBUm+leSqJCuT/H7f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0mSJEmSJEk7jQVmWAYcn2QuMKOqVgGnAjOBee0gv/uAPdv4h3dgrZ8B+/XfJNkf+Okg484HPkC3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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "0MuCycleAyUO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "outputId": "9f1ed6cb-8209-4135-cdac-a3d5ad7809c0"
      },
      "source": [
        "# If you are running it on your local systems then you will have to install the package using the following\n",
        "!pip install shap==0.32.1\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: shap==0.32.1 in /usr/local/lib/python3.6/dist-packages (0.32.1)\n",
            "Requirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.6/dist-packages (from shap==0.32.1) (4.28.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from shap==0.32.1) (1.17.5)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from shap==0.32.1) (0.25.3)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from shap==0.32.1) (0.22.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from shap==0.32.1) (1.4.1)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->shap==0.32.1) (2.6.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->shap==0.32.1) (2018.9)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->shap==0.32.1) (0.14.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas->shap==0.32.1) (1.12.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k79aBM63CfIP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "e10bf27a-60ef-4e8a-888a-496a4bf4710b"
      },
      "source": [
        "import shap\n",
        "\n",
        "# Explain model predictions using shap library:\n",
        "lgbm.params['objective'] = 'binary'\n",
        "explainer = shap.TreeExplainer(lgbm)\n",
        "shap_values = explainer.shap_values(valid_x)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/shap/explainers/tree.py:194: UserWarning: LightGBM binary classifier with TreeExplainer shap values output has changed to a list of ndarray\n",
            "  warnings.warn('LightGBM binary classifier with TreeExplainer shap values output has changed to a list of ndarray')\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "O_0QLfMJAyUS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "?shap.summary_plot"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "wRpHLDu-AyUV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "outputId": "fb64603a-712b-4ca2-8ba6-8591a41005ba"
      },
      "source": [
        "# Plot summary_plot\n",
        "shap.summary_plot(shap_values, valid_x)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xmflU2f9Liv1Xf06xl+pzwMzM9Bt+kiT1s65Go9Wqkga7rjmb++UPozFrcn+E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\nTp060aNHDyB26QGMGzeOSZMmscMOO9CnTx+mTZsGwLBhw+j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            "text/plain": [
              "<Figure size 576x684 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "H9kiJfFGAyUY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "d5c6bdc7-051d-465b-905b-6bad97901ebd"
      },
      "source": [
        "# Low values of Var27 has a high impact on the prediction values. When Var27_UNK =0, it is either a GOOD ore POOR value.   \n",
        "# For Plan_chg_flag = 1 customers have either a Var27 (Good/Poor) value \n",
        "\n",
        "valid_x.Var27_UNK.value_counts() "
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    1252\n",
              "0     221\n",
              "Name: Var27_UNK, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "KnAA-th7AyUZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 42
        },
        "outputId": "c3e59650-6ba7-45dc-ed8c-2a578d14f4d2"
      },
      "source": [
        "# load JS visualization code to notebook\n",
        "shap.initjs()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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RegExp(\"^(data|aria)-[\"+r.ATTRIBUTE_NAME_CHAR+\"]*$\")),Properties:{accept:0,acceptCharset:0,accessKey:0,action:0,allowFullScreen:o,allowTransparency:0,alt:0,as:0,async:o,autoComplete:0,autoPlay:o,capture:o,cellPadding:0,cellSpacing:0,charSet:0,challenge:0,checked:i|o,cite:0,classID:0,className:0,cols:u,colSpan:0,content:0,contentEditable:0,contextMenu:0,controls:o,controlsList:0,coords:0,crossOrigin:0,data:0,dateTime:0,default:o,defer:o,dir:0,disabled:o,download:c,draggable:0,encType:0,form:0,formAction:0,formEncType:0,formMethod:0,formNoValidate:o,formTarget:0,frameBorder:0,headers:0,height:0,hidden:o,high:0,href:0,hrefLang:0,htmlFor:0,httpEquiv:0,icon:0,id:0,inputMode:0,integrity:0,is:0,keyParams:0,keyType:0,kind:0,label:0,lang:0,list:0,loop:o,low:0,manifest:0,marginHeight:0,marginWidth:0,max:0,maxLength:0,media:0,mediaGroup:0,method:0,min:0,minLength:0,multiple:i|o,muted:i|o,name:0,nonce:0,noValidate:o,open:o,optimum:0,pattern:0,placeholder:0,playsInline:o,poster:0,preload:0,profile:0,radioGroup:0,readOnly:o,referrerPolicy:0,rel:0,required:o,reversed:o,role:0,rows:u,rowSpan:a,sandbox:0,scope:0,scoped:o,scrolling:0,seamless:o,selected:i|o,shape:0,size:u,sizes:0,span:u,spellCheck:0,src:0,srcDoc:0,srcLang:0,srcSet:0,start:a,step:0,style:0,summary:0,tabIndex:0,target:0,title:0,type:0,useMap:0,value:0,width:0,wmode:0,wrap:0,about:0,datatype:0,inlist:0,prefix:0,property:0,resource:0,typeof:0,vocab:0,autoCapitalize:0,autoCorrect:0,autoSave:0,color:0,itemProp:0,itemScope:o,itemType:0,itemID:0,itemRef:0,results:0,security:0,unselectable:0},DOMAttributeNames:{acceptCharset:\"accept-charset\",className:\"class\",htmlFor:\"for\",httpEquiv:\"http-equiv\"},DOMPropertyNames:{},DOMMutationMethods:{value:function(t,e){if(null==e)return t.removeAttribute(\"value\");\"number\"!==t.type||!1===t.hasAttribute(\"value\")?t.setAttribute(\"value\",\"\"+e):t.validity&&!t.validity.badInput&&t.ownerDocument.activeElement!==t&&t.setAttribute(\"value\",\"\"+e)}}};t.exports=s},function(t,e,n){\"use strict\";(function(e){function r(t,e,n,r){var i=void 0===t[n];null!=e&&i&&(t[n]=o(e,!0))}var i=n(24),o=n(174),a=(n(85),n(96)),u=n(177);n(2);void 0!==e&&e.env;var c={instantiateChildren:function(t,e,n,i){if(null==t)return null;var o={};return u(t,r,o),o},updateChildren:function(t,e,n,r,u,c,s,l,f){if(e||t){var p,h;for(p in e)if(e.hasOwnProperty(p)){h=t&&t[p];var d=h&&h._currentElement,v=e[p];if(null!=h&&a(d,v))i.receiveComponent(h,v,u,l),e[p]=h;else{h&&(r[p]=i.getHostNode(h),i.unmountComponent(h,!1));var g=o(v,!0);e[p]=g;var m=i.mountComponent(g,u,c,s,l,f);n.push(m)}}for(p in t)!t.hasOwnProperty(p)||e&&e.hasOwnProperty(p)||(h=t[p],r[p]=i.getHostNode(h),i.unmountComponent(h,!1))}},unmountChildren:function(t,e){for(var n in t)if(t.hasOwnProperty(n)){var r=t[n];i.unmountComponent(r,e)}}};t.exports=c}).call(e,n(156))},function(t,e,n){\"use strict\";var r=n(82),i=n(364),o={processChildrenUpdates:i.dangerouslyProcessChildrenUpdates,replaceNodeWithMarkup:r.dangerouslyReplaceNodeWithMarkup};t.exports=o},function(t,e,n){\"use strict\";function r(t){}function i(t){return!(!t.prototype||!t.prototype.isReactComponent)}function o(t){return!(!t.prototype||!t.prototype.isPureReactComponent)}var a=n(1),u=n(3),c=n(26),s=n(87),l=n(15),f=n(88),p=n(39),h=(n(9),n(168)),d=n(24),v=n(51),g=(n(0),n(81)),m=n(96),y=(n(2),{ImpureClass:0,PureClass:1,StatelessFunctional:2});r.prototype.render=function(){var t=p.get(this)._currentElement.type,e=t(this.props,this.context,this.updater);return e};var _=1,b={construct:function(t){this._currentElement=t,this._rootNodeID=0,this._compositeType=null,this._instance=null,this._hostParent=null,this._hostContainerInfo=null,this._updateBatchNumber=null,this._pendingElement=null,this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._renderedNodeType=null,this._renderedComponent=null,this._context=null,this._mountOrder=0,this._topLevelWrapper=null,this._pendingCallbacks=null,this._calledComponentWillUnmount=!1},mountComponent:function(t,e,n,u){this._context=u,this._mountOrder=_++,this._hostParent=e,this._hostContainerInfo=n;var s,l=this._currentElement.props,f=this._processContext(u),h=this._currentElement.type,d=t.getUpdateQueue(),g=i(h),m=this._constructComponent(g,l,f,d);g||null!=m&&null!=m.render?o(h)?this._compositeType=y.PureClass:this._compositeType=y.ImpureClass:(s=m,null===m||!1===m||c.isValidElement(m)||a(\"105\",h.displayName||h.name||\"Component\"),m=new r(h),this._compositeType=y.StatelessFunctional);m.props=l,m.context=f,m.refs=v,m.updater=d,this._instance=m,p.set(m,this);var b=m.state;void 0===b&&(m.state=b=null),(\"object\"!=typeof b||Array.isArray(b))&&a(\"106\",this.getName()||\"ReactCompositeComponent\"),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1;var x;return x=m.unstable_handleError?this.performInitialMountWithErrorHandling(s,e,n,t,u):this.performInitialMount(s,e,n,t,u),m.componentDidMount&&t.getReactMountReady().enqueue(m.componentDidMount,m),x},_constructComponent:function(t,e,n,r){return this._constructComponentWithoutOwner(t,e,n,r)},_constructComponentWithoutOwner:function(t,e,n,r){var i=this._currentElement.type;return t?new i(e,n,r):i(e,n,r)},performInitialMountWithErrorHandling:function(t,e,n,r,i){var o,a=r.checkpoint();try{o=this.performInitialMount(t,e,n,r,i)}catch(u){r.rollback(a),this._instance.unstable_handleError(u),this._pendingStateQueue&&(this._instance.state=this._processPendingState(this._instance.props,this._instance.context)),a=r.checkpoint(),this._renderedComponent.unmountComponent(!0),r.rollback(a),o=this.performInitialMount(t,e,n,r,i)}return o},performInitialMount:function(t,e,n,r,i){var o=this._instance,a=0;o.componentWillMount&&(o.componentWillMount(),this._pendingStateQueue&&(o.state=this._processPendingState(o.props,o.context))),void 0===t&&(t=this._renderValidatedComponent());var u=h.getType(t);this._renderedNodeType=u;var c=this._instantiateReactComponent(t,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,r,e,n,this._processChildContext(i),a);return s},getHostNode:function(){return d.getHostNode(this._renderedComponent)},unmountComponent:function(t){if(this._renderedComponent){var e=this._instance;if(e.componentWillUnmount&&!e._calledComponentWillUnmount)if(e._calledComponentWillUnmount=!0,t){var n=this.getName()+\".componentWillUnmount()\";f.invokeGuardedCallback(n,e.componentWillUnmount.bind(e))}else e.componentWillUnmount();this._renderedComponent&&(d.unmountComponent(this._renderedComponent,t),this._renderedNodeType=null,this._renderedComponent=null,this._instance=null),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._pendingCallbacks=null,this._pendingElement=null,this._context=null,this._rootNodeID=0,this._topLevelWrapper=null,p.remove(e)}},_maskContext:function(t){var e=this._currentElement.type,n=e.contextTypes;if(!n)return v;var r={};for(var i in n)r[i]=t[i];return r},_processContext:function(t){var e=this._maskContext(t);return e},_processChildContext:function(t){var e,n=this._currentElement.type,r=this._instance;if(r.getChildContext&&(e=r.getChildContext()),e){\"object\"!=typeof n.childContextTypes&&a(\"107\",this.getName()||\"ReactCompositeComponent\");for(var i in e)i in n.childContextTypes||a(\"108\",this.getName()||\"ReactCompositeComponent\",i);return u({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?d.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o&&a(\"136\",this.getName()||\"ReactCompositeComponent\");var u,c=!1;this._context===i?u=o.context:(u=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,u);var f=this._processPendingState(l,u),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,u):this._compositeType===y.PureClass&&(p=!g(s,l)||!g(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,u,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=u)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=u({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var c=r[a];u(o,\"function\"==typeof c?c.call(n,o,t,e):c)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(m(r,i))d.receiveComponent(n,i,t,this._processChildContext(e));else{var a=d.getHostNode(n);d.unmountComponent(n,!1);var u=h.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){s.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t=this._instance;return t.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==y.StatelessFunctional){l.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{l.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||!1===t||c.isValidElement(t)||a(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n&&a(\"110\");var r=e.getPublicInstance();(n.refs===v?n.refs={}:n.refs)[t]=r},detachRef:function(t){delete this.getPublicInstance().refs[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===y.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=b},function(t,e,n){\"use strict\";var r=n(4),i=n(372),o=n(167),a=n(24),u=n(12),c=n(385),s=n(401),l=n(171),f=n(408);n(2);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&($[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML)&&g(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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Y)Y.hasOwnProperty(n)&&t._wrapperState.listeners.push(M.trapBubbledEvent(n,Y[n],e));break;case\"source\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e),M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topReset\",\"reset\",e),M.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function p(){P.postUpdateWrapper(this)}function h(t){Z.call(Q,t)||(X.test(t)||g(\"65\",t),Q[t]=!0)}function d(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function v(t){var e=t.type;h(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var g=n(1),m=n(3),y=n(346),_=n(348),b=n(20),x=n(83),w=n(21),C=n(160),k=n(22),E=n(84),M=n(53),T=n(161),S=n(4),N=n(365),A=n(366),P=n(162),O=n(369),I=(n(9),n(378)),D=n(383),R=(n(11),n(56)),L=(n(0),n(95),n(81),n(173)),U=(n(97),n(2),T),F=k.deleteListener,j=S.getNodeFromInstance,B=M.listenTo,V=E.registrationNameModules,W={string:!0,number:!0},z=\"__html\",H={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},q=11,Y={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},K={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},G={listing:!0,pre:!0,textarea:!0},$=m({menuitem:!0},K),X=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,Q={},Z={}.hasOwnProperty,J=1;v.displayName=\"ReactDOMComponent\",v.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=J++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(f,this);break;case\"input\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this);break;case\"option\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o);break;case\"select\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(f,this);break;case\"textarea\":O.mountWrapper(this,o,e),o=O.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this)}i(this,o);var a,p;null!=e?(a=e._namespaceURI,p=e._tag):n._tag&&(a=n._namespaceURI,p=n._tag),(null==a||a===x.svg&&\"foreignobject\"===p)&&(a=x.html),a===x.html&&(\"svg\"===this._tag?a=x.svg:\"math\"===this._tag&&(a=x.mathml)),this._namespaceURI=a;var h;if(t.useCreateElement){var d,v=n._ownerDocument;if(a===x.html)if(\"script\"===this._tag){var g=v.createElement(\"div\"),m=this._currentElement.type;g.innerHTML=\"<\"+m+\"></\"+m+\">\",d=g.removeChild(g.firstChild)}else d=o.is?v.createElement(this._currentElement.type,o.is):v.createElement(this._currentElement.type);else d=v.createElementNS(a,this._currentElement.type);S.precacheNode(this,d),this._flags|=U.hasCachedChildNodes,this._hostParent||C.setAttributeForRoot(d),this._updateDOMProperties(null,o,t);var _=b(d);this._createInitialChildren(t,o,r,_),h=_}else{var w=this._createOpenTagMarkupAndPutListeners(t,o),k=this._createContentMarkup(t,o,r);h=!k&&K[this._tag]?w+\"/>\":w+\">\"+k+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"select\":case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return h},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(V.hasOwnProperty(r))i&&o(this,r,i,t);else{\"style\"===r&&(i&&(i=this._previousStyleCopy=m({},e.style)),i=_.createMarkupForStyles(i,this));var a=null;null!=this._tag&&d(this._tag,e)?H.hasOwnProperty(r)||(a=C.createMarkupForCustomAttribute(r,i)):a=C.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+C.createMarkupForRoot()),n+=\" \"+C.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=R(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return G[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&b.queueHTML(r,i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&b.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)b.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"option\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"select\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"textarea\":o=O.getHostProps(this,o),a=O.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":N.updateWrapper(this),L.updateValueIfChanged(this);break;case\"textarea\":O.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(p,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(\"style\"===r){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else V.hasOwnProperty(r)?t[r]&&F(this,r):d(this._tag,t)?H.hasOwnProperty(r)||C.deleteValueForAttribute(j(this),r):(w.properties[r]||w.isCustomAttribute(r))&&C.deleteValueForProperty(j(this),r);for(r in e){var c=e[r],s=\"style\"===r?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(\"style\"===r)if(c?c=this._previousStyleCopy=m({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(V.hasOwnProperty(r))c?o(this,r,c,n):s&&F(this,r);else if(d(this._tag,e))H.hasOwnProperty(r)||C.setValueForAttribute(j(this),r,c);else if(w.properties[r]||w.isCustomAttribute(r)){var l=j(this);null!=c?C.setValueForProperty(l,r,c):C.deleteValueForProperty(l,r)}}a&&_.setValueForStyles(j(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=W[typeof t.children]?t.children:null,o=W[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return j(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"input\":case\"textarea\":L.stopTracking(this);break;case\"html\":case\"head\":case\"body\":g(\"66\",this._tag)}this.unmountChildren(t),S.uncacheNode(this),k.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return j(this)}},m(v.prototype,v.Mixin,I.Mixin),t.exports=v},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(97),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" 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c=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<c.length;p++){var h=c[p];if(h!==o&&h.form===o.form){var d=l.getInstanceFromNode(h);d||a(\"90\"),f.asap(r,d)}}}return n}var a=n(1),u=n(3),c=n(160),s=n(86),l=n(4),f=n(12),p=(n(0),n(2),{getHostProps:function(t,e){var n=s.getValue(e),r=s.getChecked(e);return u({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:o.bind(t),controlled:i(e)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&c.setValueForProperty(l.getNodeFromInstance(t),\"checked\",n||!1);var r=l.getNodeFromInstance(t),i=s.getValue(e);if(null!=i)if(0===i&&\"\"===r.value)r.value=\"0\";else if(\"number\"===e.type){var o=parseFloat(r.value,10)||0;(i!=o||i==o&&r.value!=i)&&(r.value=\"\"+i)}else r.value!==\"\"+i&&(r.value=\"\"+i);else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=l.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=p},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(162),c=(n(2),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){a.getNodeFromInstance(t).setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length;return{start:o,end:o+r}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(405),l=n(172),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(1),i=n(3),o=n(82),a=n(20),u=n(4),c=n(56),s=(n(0),n(97),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var s=n._ownerDocument,l=s.createComment(o),f=s.createComment(\" /react-text \"),p=a(s.createDocumentFragment());return a.queueChild(p,a(l)),this._stringText&&a.queueChild(p,a(s.createTextNode(this._stringText))),a.queueChild(p,a(f)),u.precacheNode(this,l),this._closingComment=f,p}var h=c(this._stringText);return t.renderToStaticMarkup?h:\"\\x3c!--\"+o+\"--\\x3e\"+h+\"\\x3c!-- /react-text --\\x3e\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n&&r(\"67\",this._domID),8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(1),a=n(3),u=n(86),c=n(4),s=n(12),l=(n(0),n(2),{getHostProps:function(t,e){return null!=e.dangerouslySetInnerHTML&&o(\"91\"),a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a&&o(\"92\"),Array.isArray(c)&&(c.length<=1||o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t||c(\"33\"),\"_hostNode\"in e||c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in 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i=n(345),o=n(347),a=n(349),u=n(351),c=n(352),s=n(355),l=n(357),f=n(360),p=n(4),h=n(362),d=n(370),v=n(368),g=n(371),m=n(375),y=n(376),_=n(381),b=n(386),x=n(387),w=n(388),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof Symbol&&Symbol.for&&Symbol.for(\"react.element\")||60103;t.exports=r},function(t,e,n){\"use strict\";function r(t){i.enqueueEvents(t),i.processEventQueue(!1)}var i=n(22),o={handleTopLevel:function(t,e,n,o){r(i.extractEvents(t,e,n,o))}};t.exports=o},function(t,e,n){\"use strict\";function r(t){for(;t._hostParent;)t=t._hostParent;var e=f.getNodeFromInstance(t),n=e.parentNode;return f.getClosestInstanceFromNode(n)}function i(t,e){this.topLevelType=t,this.nativeEvent=e,this.ancestors=[]}function o(t){var e=h(t.nativeEvent),n=f.getClosestInstanceFromNode(e),i=n;do{t.ancestors.push(i),i=i&&r(i)}while(i);for(var o=0;o<t.ancestors.length;o++)n=t.ancestors[o],v._handleTopLevel(t.topLevelType,n,t.nativeEvent,h(t.nativeEvent))}function 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          "metadata": {
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    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "UxmMwpUnAyUb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "outputId": "d1367183-36b1-4f5b-ad53-715624d10897"
      },
      "source": [
        "# When customer is employed in a Private organization, lower values of Var26_20 have a larger impact on the prediction \n",
        "# Var3_Private = 1\n",
        "# Var26_20 = 0\n",
        "\n",
        "shap.dependence_plot(\"Var3_Private\", shap_values[0], valid_x)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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FvRwwYAVwgLvfkfE6zem5a5nZRoDWjezMsCEdt4dvUp04RESkZnTZwjazO939sPT78e5+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\nHULyLWoW/QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 540x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "XULbu7_nAyUd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "outputId": "889b6da1-8d31-4f6b-a2e3-5927e877c33d"
      },
      "source": [
        "shap.dependence_plot(\"Var3_Private\", shap_values[1], valid_x)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Jc/dxJEl9tpnNdPdXStgnIiJSzG9JKrs3ktSmHwTqSSb6KKpTI5iZWTCzT5nZ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\nKVmLiIjknJK1iIhIzv1/AwNa28oZcMMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 540x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "cMbXYqaVAyUf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "outputId": "3c038f31-311f-4008-d5ed-62e092de72e5"
      },
      "source": [
        "# Var27_UNK = 0\n",
        "\n",
        "shap.dependence_plot(\"Var27_UNK\", shap_values[0], valid_x)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/Lzv2KeCHwONZ8YXufkTDgxcREcm0S0t5CrDC3UcDhwIzzGyTMuWeBg51912A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\n",
            "text/plain": [
              "<Figure size 540x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "KhafzCN9AyUg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "outputId": "b99b35b3-771f-4089-d696-b281bf7c291d"
      },
      "source": [
        "# Have a large impact onthe prediction (Plan_Chg_Flag = 1)\n",
        "# Var27_Good = 1 \n",
        "# Var3_Private = 1\n",
        "\n",
        "shap.dependence_plot(\"Var27_Good\", shap_values[1], valid_x)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RwaBSQv4f4NRsnugjgJtLjm0JaEyxiIhIFVRKyN8F9gD+Tlqx4qclxw4C7qxR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\nKYD/B1y4N2oQie8sAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 540x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "AS3MtcraAyUi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "outputId": "3d212916-2f7b-4ed2-f700-356e9adcd4d6"
      },
      "source": [
        "# calculating the interaction values \n",
        "\n",
        "shap_interaction_values = shap.TreeExplainer(lgbm).shap_interaction_values(valid_x)\n",
        "shap.summary_plot(shap_interaction_values, valid_x)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RNMbf9TZbHj+H6LVPBR1KrzGu6wYdg/SMcBXk0MNhdc365dmHwf8d2Gq3OXPmADBr1qy+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\nO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DZI/yZ6YoVngPettXPD+/OBaqIjRi4IgsnA0dQ3IztyKdHRoMgOt6z3V+CyIAieABYB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\niIiIiIiE/h/ZLEY2r8HOVAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 828x475.2 with 7 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "tzJHmXJLAyUj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "outputId": "7ef5ae3d-805c-47d7-bd06-497331bfaf28"
      },
      "source": [
        "# Interaction between Var7_1200 and Var3_Private is not conclusive\n",
        "# When VAr7_1200 =0 , and Var3_Private = 1 , has a higher impact on the shap values & vice versa\n",
        "# When Var7_1200 =1, Var3_Private = 1 has a negative impact & vice versa\n",
        "\n",
        "shap.dependence_plot((\"Var7_1200\", \"Var3_Private\"), shap_interaction_values, valid_x)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7c2avmcCe1dzUzIpXnYK0QISIiEi/F0IYWs11eR8/P29ma2bLLj5rZluRZuYqmdTNbAXg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\nkrKIiEiTUFIWERFpEkrKIiIiTeL/Azo9AisJEFwQAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 540x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "ZjUMQdrmAyUl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "outputId": "4ba9d70b-626b-48f9-a3c3-37e5b07c8aa1"
      },
      "source": [
        "# Var26_20 =1 & Var3_Private =1 has a positive impact\n",
        "\n",
        "shap.dependence_plot((\"Var26_20\", \"Var3_Private\"), shap_interaction_values, valid_x)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ERAazDwM7xBifDSF0AcQYHwkhjMqtIHeVqHe5+y9L32tMrdlb5/ZDnSIiIs0yHHixfEcI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\n",
            "text/plain": [
              "<Figure size 540x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "f5_P5jIlAyUn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "outputId": "21e31a85-5804-4beb-cc66-58f821c9e8fe"
      },
      "source": [
        "shap.dependence_plot((\"Var4_30\", \"Var3_Private\"), shap_interaction_values, valid_x)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KYiPg7hDCraQVoS6MMb7ezTKOdeUOiWomIQN8BJhYnZCL+l4A/rc4LiIiMmjF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\n",
            "text/plain": [
              "<Figure size 540x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "txhJF1uGAyUo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "82940793-b7ca-47dd-f751-956c0df0c2ac"
      },
      "source": [
        "import numpy as np \n",
        "\n",
        "shap_sum = np.abs(shap_values[1]).mean(axis=0)\n",
        "importance_df = pd.DataFrame([valid_x.columns.tolist(), shap_sum.tolist()]).T\n",
        "importance_df.columns = ['column_name', 'shap_importance']\n",
        "importance_df = importance_df.sort_values('shap_importance', ascending=False)\n",
        "importance_df"
      ],
      "execution_count": 36,
      "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>column_name</th>\n",
              "      <th>shap_importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>Var27_UNK</td>\n",
              "      <td>0.0967409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Var3_Private</td>\n",
              "      <td>0.0394421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Var27_Good</td>\n",
              "      <td>0.0185841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Var4_30</td>\n",
              "      <td>0.0117184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Var7_1200</td>\n",
              "      <td>0.0112631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Var17_300</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Var4_120</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Var1_North</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Var5_Male</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Var1_Central</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>62 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     column_name shap_importance\n",
              "61     Var27_UNK       0.0967409\n",
              "8   Var3_Private       0.0394421\n",
              "59    Var27_Good       0.0185841\n",
              "12       Var4_30       0.0117184\n",
              "22     Var7_1200       0.0112631\n",
              "..           ...             ...\n",
              "40     Var17_300               0\n",
              "15      Var4_120               0\n",
              "1     Var1_North               0\n",
              "17     Var5_Male               0\n",
              "0   Var1_Central               0\n",
              "\n",
              "[62 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "98wbVYofAyUq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "71a136bf-fdbc-411b-f3e7-abdcd095a033"
      },
      "source": [
        "shap.__version__"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'0.32.1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M3AI87F9FYZf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}