Exploratory_DS_&_Basic_ML.ipynb

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        "BloomTech Data Science\n",
        "\n",
        "*Unit 2, Sprint 3, Module GIT project*\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "# Permutation & Boosting\n",
        "\n",
        "You will use your portfolio project dataset for all assignments this sprint.\n",
        "\n",
        "## Assignment\n",
        "\n",
        "Complete these tasks for your project, and document your work.\n",
        "\n",
        "- [ ] If you haven't completed assignment #1, please do so first.\n",
        "- [ ] Continue to clean and explore your data. Make exploratory visualizations.\n",
        "- [ ] Fit a model. Does it beat your baseline? \n",
        "- [ ] Try xgboost.\n",
        "- [ ] Get your model's permutation importances.\n",
        "\n",
        "You should try to complete an initial model today, because the rest of the week, we're making model interpretation visualizations.\n",
        "\n",
        "But, if you aren't ready to try xgboost and permutation importances with your dataset today, that's okay. You can practice with another dataset instead. You may choose any dataset you've worked with previously.\n",
        "\n",
        "The data subdirectory includes the Titanic dataset for classification and the NYC apartments dataset for regression. You may want to choose one of these datasets, because example solutions will be available for each.\n",
        "\n",
        "\n",
        "## Reading\n",
        "\n",
        "Top recommendations in _**bold italic:**_\n",
        "\n",
        "#### Permutation Importances\n",
        "- _**[Kaggle / Dan Becker: Machine Learning Explainability](https://www.kaggle.com/dansbecker/permutation-importance)**_\n",
        "- [Christoph Molnar: Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/feature-importance.html)\n",
        "\n",
        "#### (Default) Feature Importances\n",
        "  - [Ando Saabas: Selecting good features, Part 3, Random Forests](https://blog.datadive.net/selecting-good-features-part-iii-random-forests/)\n",
        "  - [Terence Parr, et al: Beware Default Random Forest Importances](https://explained.ai/rf-importance/index.html)\n",
        "\n",
        "#### Gradient Boosting\n",
        "  - [A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning](https://machinelearningmastery.com/gentle-introduction-gradient-boosting-algorithm-machine-learning/)\n",
        "  - [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf), Chapter 8\n",
        "  - _**[Gradient Boosting Explained](https://www.gormanalysis.com/blog/gradient-boosting-explained/)**_ — Ben Gorman\n",
        "  - [Gradient Boosting Explained](http://arogozhnikov.github.io/2016/06/24/gradient_boosting_explained.html) — Alex Rogozhnikov\n",
        "  - [How to explain gradient boosting](https://explained.ai/gradient-boosting/) — Terence Parr & Jeremy Howard"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data set: [Solar Power Generation Data](https://www.kaggle.com/anikannal/solar-power-generation-data?select=Plant_2_Weather_Sensor_Data.csv)"
      ],
      "metadata": {
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      "source": [
        "%%capture\n",
        "import sys\n",
        "!pip install category_encoders==2.*\n",
        "!pip install pdpbox\n",
        "!pip install shap\n",
        "data = 'Plant_1_Generation_Data.csv'"
      ],
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        "id": "8X53zf-Svd_G"
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error r2_score\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, roc_curve, plot_confusion_matrix, plot_roc_curve,roc_auc_score\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from category_encoders import OneHotEncoder, OrdinalEncoder\n",
        "from xgboost import XGBClassifier \n",
        "from sklearn.inspection import permutation_importance \n",
        "from xgboost import XGBRegressor\n",
        "from pdpbox.pdp import pdp_isolate, pdp_plot, pdp_interact, pdp_interact_plot\n",
        "import warnings\n",
        "warnings.filterwarnings(action='ignore', category=FutureWarning, module='xgboost')\n",
        "import shap\n",
        "import numpy as np\n",
        "from numpy import log1p"
      ]
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        ""
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      "metadata": {
        "id": "TyDJE20pvtXq"
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      "execution_count": null,
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      "cell_type": "code",
      "source": [
        "def wrangle(filepath):\n",
        "  df = pd.read_csv(filepath,\n",
        "                 parse_dates=['DATE_TIME']).set_index('DATE_TIME'\n",
        "                 )\n",
        " # Create target column...\n",
        "\n",
        "  return df\n",
        "df = wrangle(data)\n",
        "\n",
        " #Drop non-unique PLANT_ID\n",
        "df = df.drop('PLANT_ID', 1)\n",
        "\n",
        "df.rename(columns = {'DATE_TIME':'date', \n",
        "                  'SOURCE_KEY':'id',\n",
        "                  'DC_POWER':'dc',\n",
        "                  'AC_POWER':'ac',\n",
        "                  'DAILY_YIELD':'daily_yield',\n",
        "                  'TOTAL_YIELD':'total_yield'}, inplace=True)\n",
        "  #Drop HCCC\n",
        "df = df.drop('id', 1)\n",
        "  #Drop potentially leaky columns\n",
        "df = df.drop('total_yield', 1)\n"
      ],
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        "id": "yseCrLRG8UVU"
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      "execution_count": null,
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        "df.head()"
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              "      <th>daily_yield</th>\n",
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              "      <th>DATE_TIME</th>\n",
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            "text/plain": [
              "             dc   ac  daily_yield\n",
              "DATE_TIME                        \n",
              "2020-05-15  0.0  0.0          0.0\n",
              "2020-05-15  0.0  0.0          0.0\n",
              "2020-05-15  0.0  0.0          0.0\n",
              "2020-05-15  0.0  0.0          0.0\n",
              "2020-05-15  0.0  0.0          0.0"
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        "df= df[df['dc'] != 0]"
      ],
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        "df.describe()\n"
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              "      <th>dc</th>\n",
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              "      <th>daily_yield</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>36827.000000</td>\n",
              "      <td>36827.000000</td>\n",
              "      <td>36827.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5878.124201</td>\n",
              "      <td>574.851541</td>\n",
              "      <td>3603.919501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3791.741162</td>\n",
              "      <td>370.119059</td>\n",
              "      <td>2692.146275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>8.750000</td>\n",
              "      <td>0.850000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2365.857143</td>\n",
              "      <td>232.006250</td>\n",
              "      <td>904.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5969.142857</td>\n",
              "      <td>584.862500</td>\n",
              "      <td>3580.285714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8889.008928</td>\n",
              "      <td>869.226786</td>\n",
              "      <td>5909.008928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>14471.125000</td>\n",
              "      <td>1410.950000</td>\n",
              "      <td>9162.857143</td>\n",
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            "text/plain": [
              "                 dc            ac   daily_yield\n",
              "count  36827.000000  36827.000000  36827.000000\n",
              "mean    5878.124201    574.851541   3603.919501\n",
              "std     3791.741162    370.119059   2692.146275\n",
              "min        8.750000      0.850000      0.000000\n",
              "25%     2365.857143    232.006250    904.250000\n",
              "50%     5969.142857    584.862500   3580.285714\n",
              "75%     8889.008928    869.226786   5909.008928\n",
              "max    14471.125000   1410.950000   9162.857143"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "threshold = 2_000\n",
        "\n",
        "sub_optimal =  df[df['daily_yield'] < threshold]\n",
        "sub_optimal.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "32FY2Y8Ve6Xy",
        "outputId": "a7fed2ac-77ae-4fa3-eaaf-543cba83ded2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>dc</th>\n",
              "      <th>ac</th>\n",
              "      <th>daily_yield</th>\n",
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              "    <tr>\n",
              "      <th>DATE_TIME</th>\n",
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              "      <th>2020-06-17 11:00:00</th>\n",
              "      <td>6391.142857</td>\n",
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              "      <th>2020-06-17 11:00:00</th>\n",
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              "    <tr>\n",
              "      <th>2020-06-17 11:00:00</th>\n",
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              "      <th>2020-06-17 11:15:00</th>\n",
              "      <td>7522.750000</td>\n",
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              "      <td>1985.750000</td>\n",
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              "    <tr>\n",
              "      <th>2020-06-17 11:15:00</th>\n",
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            ],
            "text/plain": [
              "                              dc          ac  daily_yield\n",
              "DATE_TIME                                                \n",
              "2020-06-17 11:00:00  6391.142857  625.957143  1908.000000\n",
              "2020-06-17 11:00:00  6737.000000  659.300000  1871.571429\n",
              "2020-06-17 11:00:00  6718.000000  657.785714  1920.428571\n",
              "2020-06-17 11:15:00  7522.750000  736.525000  1985.750000\n",
              "2020-06-17 11:15:00  7388.875000  723.475000  1958.250000"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sub_optimal.hist()\n",
        "# I notice there is a skew on daily_yield, so I will np.log1p"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "J8RH-akhhxMC",
        "outputId": "f8d93724-1515-4670-a700-966b8b55ac30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8b03b4c910>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8b03b28f90>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f8b03af05d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8b03aa7bd0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_log = np.log1p(df['daily_yield'])"
      ],
      "metadata": {
        "id": "zD_jgD_TjD3U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "7GYZCPBnjLyv",
        "outputId": "826a0179-ebf7-4b40-8329-65309ffb62f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8b0397a650>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8b034d0350>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f8b03484950>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8b0343bf50>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target = 'daily_yield'\n",
        "y = df[target]\n",
        "X = df.drop(target, axis=1)\n",
        "#I set my target and do my first split"
      ],
      "metadata": {
        "id": "ylt5VYiXj--e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train1, X_test, y_train1, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train1, y_train1, test_size=0.20, random_state=42)\n",
        "#On my next split, I randomize the split between train, test and validation data\n",
        "#I could have split it by date, but chose not to."
      ],
      "metadata": {
        "id": "GE346jkGkiM6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Mean daily yield:', y_train.mean())\n",
        "\n",
        "y_pred = [y_train.mean()] * len(y_train)\n",
        "print('Baseline MAE:', mean_absolute_error(y_train, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wV3xIqIdlqkm",
        "outputId": "6ec0b02f-e9fc-480f-979e-9d2bc5cb8b1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean daily yield: 3603.351678039211\n",
            "Baseline MAE: 2401.571301712662\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_lr = LinearRegression()"
      ],
      "metadata": {
        "id": "bVryhLcPq4JA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_lr.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "patpn0igq-Ln",
        "outputId": "e0dca21b-70d4-43b8-eb86-cced3b258384"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Training MAE:', mean_absolute_error(y_train,model_lr.predict(X_train)))\n",
        "print('Test MAE:', mean_absolute_error(y_test,model_lr.predict(X_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dra-HF9FrHBG",
        "outputId": "8fc11855-c74c-4cf2-d807-8deec8538fb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training MAE: 2394.0104168522867\n",
            "Test MAE: 2362.2888772051897\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"R-Squared:\",r2_score(y_train, model_lr.predict(X_train)))\n",
        "print(\"R-Squared:\",r2_score(y_test,model_lr.predict(X_test)) )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IhRW4ThHrUWD",
        "outputId": "91f8214a-52ce-4d39-91eb-1dea3452c753"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R-Squared: 0.004991260216618332\n",
            "R-Squared: 0.004932605450963301\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"R-Squared:\", model_lr.score(X_train, y_train))\n",
        "print(\"R-Squared:\", model_lr.score(X_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZPfqFOm-rZvi",
        "outputId": "a8afec29-9fc1-4fda-c0b0-9c766de2abc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R-Squared: 0.004991260216618332\n",
            "R-Squared: 0.004932605450963301\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_xgb = make_pipeline(\n",
        "    OrdinalEncoder(),\n",
        "    SimpleImputer(strategy='mean'),\n",
        "    XGBRegressor(random_state=42,n_estimator=25, n_jobs=-1) \n",
        ")\n",
        "\n",
        "model_xgb.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "qf8k94BDxCx1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f84c104-fa2b-45d0-eb17-8a1d8d0d9d2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[00:44:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('ordinalencoder', OrdinalEncoder(cols=[], mapping=[])),\n",
              "                ('simpleimputer', SimpleImputer()),\n",
              "                ('xgbregressor',\n",
              "                 XGBRegressor(n_estimator=25, n_jobs=-1, random_state=42))])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_XGB1 = XGBRegressor(random_state=42, n_jobs=-1)\n",
        "model_XGB1.fit(X_train, y_train);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ua0VZPKl8jRK",
        "outputId": "77c6782d-0bf1-4b4a-93b7-30a90739c9f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[00:54:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_perm = make_pipeline(\n",
        "    OrdinalEncoder(),\n",
        "    SimpleImputer(strategy='mean'),\n",
        "    XGBRegressor(random_state=42,n_estimator=25, n_jobs=-1)\n",
        ")\n",
        "\n",
        "model_perm.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "xgT73OK9xDV9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b99a618-0472-4000-c92a-98111c3aa631"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[00:44:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('ordinalencoder', OrdinalEncoder(cols=[], mapping=[])),\n",
              "                ('simpleimputer', SimpleImputer()),\n",
              "                ('xgbregressor',\n",
              "                 XGBRegressor(n_estimator=25, n_jobs=-1, random_state=42))])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features = ['ac','dc']"
      ],
      "metadata": {
        "id": "ymb85p_F9IJp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "interact= pdp_interact(\n",
        "    model_xgb,\n",
        "    dataset= X_val, \n",
        "    model_features=X_val.columns,\n",
        "    features=features\n",
        ")"
      ],
      "metadata": {
        "id": "PjBORrRv9MiF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pdp_interact_plot(interact,plot_type='grid', feature_names=features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "id": "_ZIHJ9J59k3I",
        "outputId": "e6b42f37-f986-4d28-8060-0b11eb814c74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Figure size 540x684 with 3 Axes>,\n",
              " {'pdp_inter_ax': <matplotlib.axes._subplots.AxesSubplot at 0x7f8afc7a5810>,\n",
              "  'title_ax': <matplotlib.axes._subplots.AxesSubplot at 0x7f8afc7cfd90>})"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 540x684 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shap_row = X_val.tail(1)"
      ],
      "metadata": {
        "id": "co6gu_a42yqn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "explainer = shap.TreeExplainer(model_XGB1)"
      ],
      "metadata": {
        "id": "AsmTchqc291v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shap_values = explainer.shap_values(shap_row)\n",
        "\n",
        "shap.initjs()\n",
        "shap.force_plot(\n",
        "    base_value= explainer.expected_value,\n",
        "    shap_values=shap_values,\n",
        "    features=shap_row\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "ZXb9qTl3ASPr",
        "outputId": "c2c9a4d4-9cfc-43a8-d571-6c2c5ca579ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},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?v.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?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_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=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}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(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[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===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);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&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,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(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_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(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.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=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[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&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.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=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,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(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},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(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[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 U(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\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},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(96),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+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({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});return i},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:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}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=s.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=f},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(158),c=(n(1),!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){var n=a.getNodeFromInstance(t);n.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,a=o+r;return{start:o,end:a}}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(392),l=n(168),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(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),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+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},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):void 0,8===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(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},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\"):void 0,Array.isArray(c)&&(c.length<=1?void 0: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",
              "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0: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 t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "explainer = shap.TreeExplainer(model_XGB1)\n",
        "shap_values = explainer(shap_row)\n",
        "shap.plots.waterfall(shap_values[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "j3SoGYGUAdfa",
        "outputId": "a98790db-f78f-411e-dc12-651deb2363a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x180 with 3 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "sTn_TsWjA6oR"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    },
    "colab": {
      "name": "Exploratory DS & Basic ML",
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}