Feature_Relevance_Extraction_through_Explainability.ipynb

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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/damzC/explainable-AI/blob/main/Feature_Relevance_Extraction_through_Explainability.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "This implementation takes a publicly available financial (bank) dataset from the UC Irvine Machine Learning Repository and builds a machine learning classification model on this data. Using *explainable AI* (*Shapley Additive Values* method), the *relevance* of each of the features in the feature set are determined to understand, which features are important for a particular outcome. The details of the implementation are as follows:\n",
        "\n",
        "**Dataset**\n",
        "1.  *y-value*: If the client has subscribed for a term deposit\n",
        "2.   *x-value*: Set of *20* features like age, marital status, education *etc.*\n",
        "\n",
        "**Classification steps**\n",
        "1.   The data is plotted and understood first to detect imbalance, missing values *etc.*\n",
        "2.   The data is then Pre-processed/Cleaned\n",
        "3.   Missing values are added to the data using *SMOTE* method\n",
        "4.   Using logistic regression a classification model is built on this dataset\n",
        "5.   A grid search strategy is used to find the optimal hyper-parameters for the logistic regression model\n",
        "\n",
        "**Explainability and feature relevance**\n",
        "1.   SHAP (*Shapley Additive Values*) algorithm is used for explainability\n",
        "2.   Each prediction made by the logistic regression model is explained in terms of the relevance/ importance of each feature in the dataset\n",
        "\n",
        "\n",
        "\n"
      ],
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      "cell_type": "markdown",
      "source": [
        "## Importing Libraries"
      ]
    },
    {
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      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from pathlib import Path\n",
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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      "cell_type": "markdown",
      "source": [
        "## Downloading data"
      ]
    },
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      "cell_type": "code",
      "source": [
        "!wget https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank-additional.zip\n",
        "!unzip  bank-additional.zip\n",
        "!mv bank-additional data\n",
        "!rm -rf __MACOSX\n",
        "data_path = Path(\"data\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-12-20 07:58:59--  https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank-additional.zip\n",
            "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
            "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 444572 (434K) [application/x-httpd-php]\n",
            "Saving to: ‘bank-additional.zip’\n",
            "\n",
            "bank-additional.zip 100%[===================>] 434.15K  1.74MB/s    in 0.2s    \n",
            "\n",
            "2022-12-20 07:59:00 (1.74 MB/s) - ‘bank-additional.zip’ saved [444572/444572]\n",
            "\n",
            "Archive:  bank-additional.zip\n",
            "   creating: bank-additional/\n",
            "  inflating: bank-additional/.DS_Store  \n",
            "   creating: __MACOSX/\n",
            "   creating: __MACOSX/bank-additional/\n",
            "  inflating: __MACOSX/bank-additional/._.DS_Store  \n",
            "  inflating: bank-additional/.Rhistory  \n",
            "  inflating: bank-additional/bank-additional-full.csv  \n",
            "  inflating: bank-additional/bank-additional-names.txt  \n",
            "  inflating: bank-additional/bank-additional.csv  \n",
            "  inflating: __MACOSX/._bank-additional  \n"
          ]
        }
      ]
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      "cell_type": "markdown",
      "source": [
        "## Loading Data and Inspection"
      ]
    },
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      "source": [
        "df= pd.read_csv(data_path/\"bank-additional-full.csv\",sep=\";\")\n",
        "\n",
        "print(df.shape) # prints data shape\n",
        "print(df.columns) # print columns\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "(41188, 21)\n",
            "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
            "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
            "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
            "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'],\n",
            "      dtype='object')\n"
          ]
        },
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              "   age        job  marital    education  default housing loan    contact  \\\n",
              "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
              "1   57   services  married  high.school  unknown      no   no  telephone   \n",
              "2   37   services  married  high.school       no     yes   no  telephone   \n",
              "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
              "4   56   services  married  high.school       no      no  yes  telephone   \n",
              "\n",
              "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
              "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "1   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
              "\n",
              "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
              "0          93.994          -36.4      4.857       5191.0  no  \n",
              "1          93.994          -36.4      4.857       5191.0  no  \n",
              "2          93.994          -36.4      4.857       5191.0  no  \n",
              "3          93.994          -36.4      4.857       5191.0  no  \n",
              "4          93.994          -36.4      4.857       5191.0  no  \n",
              "\n",
              "[5 rows x 21 columns]"
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        "id": "FRh31UFrYLwi"
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      "cell_type": "markdown",
      "source": [
        "##  Data inputs\n",
        "1.age (numeric)\n",
        "\n",
        "2.job : type of job (categorical: “admin”, “blue-collar”, “entrepreneur”, “housemaid”, “management”, “retired”, “self-employed”, “services”, “student”, “technician”, “unemployed”, “unknown”)\n",
        "\n",
        "3.marital : marital status (categorical: “divorced”, “married”, “single”, “unknown”)\n",
        "\n",
        "4.education (categorical: “basic.4y”, “basic.6y”, “basic.9y”, “high.school”, “illiterate”, “professional.course”, “university.degree”, “unknown”)\n",
        "\n",
        "5.default: has credit in default? (categorical: “no”, “yes”, “unknown”)\n",
        "\n",
        "6.housing: has housing loan? (categorical: “no”, “yes”, “unknown”)\n",
        "\n",
        "7.loan: has personal loan? (categorical: “no”, “yes”, “unknown”)\n",
        "\n",
        "8.contact: contact communication type (categorical: “cellular”, “telephone”)\n",
        "\n",
        "9.month: last contact month of year (categorical: “jan”, “feb”, “mar”, …, “nov”, “dec”)\n",
        "\n",
        "10.day_of_week: last contact day of the week (categorical: “mon”, “tue”, “wed”, “thu”, “fri”)\n",
        "\n",
        "11.duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects \n",
        "the output target (e.g., if duration=0 then y=’no’). The duration is not known before a call is performed, also, after the end of the call, y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model\n",
        "\n",
        "12.campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
        "\n",
        "13.pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)\n",
        "\n",
        "14.previous: number of contacts performed before this campaign and for this client (numeric)\n",
        "\n",
        "15.poutcome: outcome of the previous marketing campaign (categorical: “failure”, “nonexistent”, “success”)\n",
        "\n",
        "16.emp.var.rate: employment variation rate — (numeric)\n",
        "\n",
        "17.cons.price.idx: consumer price index — (numeric)\n",
        "\n",
        "18.cons.conf.idx: consumer confidence index — (numeric)\n",
        "\n",
        "19.euribor3m: euribor 3 month rate — (numeric)\n",
        "\n",
        "20.nr.employed: number of employees — (numeric)\n",
        "\n",
        "## Target Variable\n",
        "\n",
        "y — has the client subscribed a term deposit? (binary: “1”, means “Yes”, “0” means “No”)"
      ]
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      "source": [
        "class UnderstandingData:\n",
        "    \n",
        "    def __init__(self, raw_df):\n",
        "        self.raw_df = raw_df\n",
        "        self.raw_df_grouped = raw_df.groupby(\"y\")\n",
        "        self.class_name_no = \"no\"\n",
        "        self.class_name_yes = \"yes\"\n",
        "        self.raw_df_grouped_no = self.raw_df_grouped.get_group(self.class_name_no)\n",
        "        self.raw_df_grouped_yes = self.raw_df_grouped.get_group(self.class_name_yes)\n",
        "\n",
        "    def plot_histogram_continuous(self, feature_name, bin_size):\n",
        "        plt.figure()\n",
        "        plt.hist(self.raw_df_grouped_no[feature_name], bins=bin_size, label=self.class_name_no)\n",
        "        plt.hist(self.raw_df_grouped_yes[feature_name], bins=bin_size, label=self.class_name_yes)\n",
        "        plt.legend()\n",
        "        plt.title(\"Feature Histogram - \"+feature_name)\n",
        "        plt.xlabel(\"Feature values\")\n",
        "        plt.ylabel(\"Count\")\n",
        "\n",
        "    def plot_histogram_categorical(self, feature_name):\n",
        "        feature_df = pd.DataFrame()\n",
        "        feature_df[\"no\"] = self.raw_df_grouped_no[feature_name].value_counts()\n",
        "        feature_df[\"yes\"] = self.raw_df_grouped_yes[feature_name].value_counts()\n",
        "        \n",
        "        feature_df.plot(kind='bar')\n",
        "        plt.title(\"Feature Histogram - \"+feature_name)\n",
        "        plt.ylabel(\"Count\")\n",
        "        plt.xlabel(\"Feature unique values\")\n",
        "        plt.tight_layout()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mGLhgLETqW5N"
      },
      "cell_type": "code",
      "source": [
        "understanding_data = UnderstandingData(df)\n",
        "\n",
        "### Feature 1 - AGE\n",
        "understanding_data.plot_histogram_continuous(\"age\", 50)\n",
        "\n",
        "### Feature 2 - JOB\n",
        "understanding_data.plot_histogram_categorical(\"job\")\n",
        "\n",
        "### Feature 3 - MARITAL\n",
        "understanding_data.plot_histogram_categorical(\"marital\")\n",
        "\n",
        "### Feature 4 - EDUCATION\n",
        "understanding_data.plot_histogram_categorical(\"education\")\n",
        "\n",
        "### Feature 5 - DEFAULT\n",
        "understanding_data.plot_histogram_categorical(\"default\")\n",
        "\n",
        "### Feature 6 - HOUSING\n",
        "understanding_data.plot_histogram_categorical(\"housing\")\n",
        "\n",
        "### Feature 7 - LOAN\n",
        "understanding_data.plot_histogram_categorical(\"loan\")\n",
        "\n",
        "### Feature 8 - CONTACT\n",
        "understanding_data.plot_histogram_categorical(\"contact\")\n",
        "\n",
        "### Feature 9 - MONTH\n",
        "understanding_data.plot_histogram_categorical(\"month\")\n",
        "\n",
        "### Feature 10 - DAY OF WEEK\n",
        "understanding_data.plot_histogram_categorical(\"day_of_week\")\n",
        "\n",
        "### Feature 11 - CAMPAIGN\n",
        "understanding_data.plot_histogram_continuous(\"campaign\", 30)\n",
        "\n",
        "### Feature 12 - PDAYS\n",
        "understanding_data.plot_histogram_continuous(\"pdays\", 30)\n",
        "\n",
        "### Feature 13 - PREVIOUS\n",
        "understanding_data.plot_histogram_categorical(\"previous\")\n",
        "\n",
        "### Feature 14 - POUTCOME\n",
        "understanding_data.plot_histogram_categorical(\"poutcome\")\n",
        "\n",
        "### Feature 15 - emp.var.rate\n",
        "understanding_data.plot_histogram_continuous(\"emp.var.rate\", 50)\n",
        "\n",
        "### Feature 16 - cons.price.idx\n",
        "understanding_data.plot_histogram_continuous(\"cons.price.idx\", 50)\n",
        "\n",
        "### Feature 17 - cons.conf.idx\n",
        "understanding_data.plot_histogram_continuous(\"cons.conf.idx\", 50)\n",
        "\n",
        "### Feature 18 - euribor3m\n",
        "understanding_data.plot_histogram_continuous(\"euribor3m\", 50)\n",
        "\n",
        "### Feature 19 - nr.employed\n",
        "understanding_data.plot_histogram_continuous(\"nr.employed\", 50)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MB1iv3yZffPS",
        "outputId": "4acf4645-3e4b-4552-ebe4-c15442e0ea33",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "df[\"y\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no     36548\n",
              "yes     4640\n",
              "Name: y, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "GQ5gsbYeldU-",
        "outputId": "8604d367-c334-470f-f1eb-5296629b0609",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "df[\"y\"].value_counts()/len(df.y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no     0.887346\n",
              "yes    0.112654\n",
              "Name: y, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "KIjUgsELe_pg"
      },
      "cell_type": "markdown",
      "source": [
        "### Understanding graphs\n",
        "\n",
        "1. 88% of the observations are No as compared to just 11 % data as Yes.Clearly this dataset is an imbalanced dataset.\n",
        "2. There are missing values so we need to impute it.\n",
        "3. Features need to be preprocessed.\n",
        "4. Many features are useless which need to be deleted\n",
        "        1. duration\n",
        "        \n",
        "5. Some Important features are\n",
        "  1. Jobs\n",
        "  2. Education (illterate people seems to get convinced by campaign)\n",
        "  3. Months (Seasonal effect can been seen in the data. In may the sales increases)\n",
        "  4. Previous outcome seems important because new customers seems to convert more\n",
        "  5. Average age of people who converted as higher than those who didn't\n",
        "  6. People who converted were exposed to fewer campaigns than those who didn't\n"
      ]
    },
    {
      "metadata": {
        "id": "jzQm24rflyA1",
        "outputId": "d4ba8a93-3d14-4193-88d3-228c83237683",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "df.drop([\"duration\"] , axis=1,inplace=True)\n",
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age               0\n",
              "job               0\n",
              "marital           0\n",
              "education         0\n",
              "default           0\n",
              "housing           0\n",
              "loan              0\n",
              "contact           0\n",
              "month             0\n",
              "day_of_week       0\n",
              "campaign          0\n",
              "pdays             0\n",
              "previous          0\n",
              "poutcome          0\n",
              "emp.var.rate      0\n",
              "cons.price.idx    0\n",
              "cons.conf.idx     0\n",
              "euribor3m         0\n",
              "nr.employed       0\n",
              "y                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "tkV96wsLlyLY"
      },
      "cell_type": "code",
      "source": [
        "df.replace('unknown',np.NaN,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oXvdWg5HlyQt",
        "outputId": "8418734c-22f8-4ed3-9306-89a19bfcab47",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                  0\n",
              "job                330\n",
              "marital             80\n",
              "education         1731\n",
              "default           8597\n",
              "housing            990\n",
              "loan               990\n",
              "contact              0\n",
              "month                0\n",
              "day_of_week          0\n",
              "campaign             0\n",
              "pdays                0\n",
              "previous             0\n",
              "poutcome             0\n",
              "emp.var.rate         0\n",
              "cons.price.idx       0\n",
              "cons.conf.idx        0\n",
              "euribor3m            0\n",
              "nr.employed          0\n",
              "y                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "UwwxZIZFmz_z"
      },
      "cell_type": "markdown",
      "source": [
        "####  Six Columns have unknow value\n",
        "\n",
        "### We will use Decision Tree to fill these values \n",
        "### After that use logistic regression for model training"
      ]
    },
    {
      "metadata": {
        "id": "OV20lMp4ngSk"
      },
      "cell_type": "markdown",
      "source": [
        "## Fill values using Decision Tree"
      ]
    },
    {
      "metadata": {
        "id": "kLPuP4IrmzNP"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "    \n",
        "def convert_cats(df,cols):\n",
        "  for i in cols:\n",
        "    df[i] = df[i].astype(\"category\").cat.codes\n",
        "  return df\n",
        "\n",
        "def fillMissing(missing_cols , df):\n",
        "\n",
        "  df1=df.copy()\n",
        "  for col in missing_cols:\n",
        "    df1=convert_cats(df1,['job','marital','education','loan','contact','month','poutcome','y',\"default\" ,\"housing\",\"day_of_week\"])\n",
        "    test=df1[df1[col]==-1]\n",
        "    train=df1[df1[col]!=-1]\n",
        "    tree=DecisionTreeClassifier()\n",
        "    tree.fit(train.drop(col,axis=1),train[col])\n",
        "    test.loc[:,col]=tree.predict(test.drop(col,axis=1))\n",
        "    df1 = train.append(test)\n",
        "    df1.replace(-1,np.NaN,inplace=True)\n",
        "  \n",
        "  return df1  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5b265Tv2lx7_"
      },
      "cell_type": "code",
      "source": [
        "df1 = fillMissing([\"job\" , \"marital\",\"education\",\"loan\",\"default\" ,\"housing\"],df.copy())\n",
        "df1.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mlKOcv8-xCaC",
        "outputId": "6f9fb0da-a03b-4979-df81-9ab949588564",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "df1.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
              "       'contact', 'month', 'day_of_week', 'campaign', 'pdays', 'previous',\n",
              "       'poutcome', 'emp.var.rate', 'cons.price.idx', 'cons.conf.idx',\n",
              "       'euribor3m', 'nr.employed', 'y'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "ccGqQiIcxVLj"
      },
      "cell_type": "markdown",
      "source": [
        "## Creating Dummies for categorical\n"
      ]
    },
    {
      "metadata": {
        "id": "W0wfs0ffxYOC"
      },
      "cell_type": "code",
      "source": [
        "cat_cols = ['job','marital','education','loan','contact','month','poutcome',\"day_of_week\",\"default\" ,\"housing\" ]\n",
        "\n",
        "for col in cat_cols:\n",
        "    df1 = df1.join(pd.get_dummies(df1[col],prefix = col))\n",
        "\n",
        "keep = [col for col in df1.columns if col not in cat_cols]\n",
        "df1 = df1 [keep]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ntccyae6EViJ"
      },
      "cell_type": "code",
      "source": [
        "df1[\"y\"]=df1.y.astype('category')\n",
        "df1[\"y\"]=df1[\"y\"].cat.codes"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "x6-N105QxtkB",
        "outputId": "21eb9e89-8ed1-49bd-a396-f3fb41c212c7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "df1.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'campaign', 'pdays', 'previous', 'emp.var.rate',\n",
              "       'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed', 'y',\n",
              "       'job_0', 'job_1', 'job_2', 'job_3', 'job_4', 'job_5', 'job_6', 'job_7',\n",
              "       'job_8', 'job_9', 'job_10', 'marital_0', 'marital_1', 'marital_2',\n",
              "       'education_0', 'education_1', 'education_2', 'education_3',\n",
              "       'education_4', 'education_5', 'education_6', 'loan_0', 'loan_1',\n",
              "       'contact_0', 'contact_1', 'month_0', 'month_1', 'month_2', 'month_3',\n",
              "       'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9',\n",
              "       'poutcome_0', 'poutcome_1', 'poutcome_2', 'day_of_week_0',\n",
              "       'day_of_week_1', 'day_of_week_2', 'day_of_week_3', 'day_of_week_4',\n",
              "       'default_0', 'default_1', 'housing_0', 'housing_1'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "pvk0ug5DEjkw",
        "outputId": "f7cf538c-99d4-448e-cd37-9e96e334f759",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "df1.y.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "6    0\n",
              "Name: y, dtype: int8"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "WKSV2a6OzRpZ",
        "outputId": "1434dd8d-770f-4830-a719-fec4e5ba6e8d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "cols = df1.columns.values.tolist()\n",
        "y=df1['y']\n",
        "X = df1[[col for col in cols if col !=\"y\"]]\n",
        "X.shape ,y.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((41188, 56), (41188,))"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "914r9wrqxuwd",
        "outputId": "f580150e-c17f-4a13-c879-0b449f0d77cd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
        "\n",
        "print(\"Number transactions X_train dataset: \", X_train.shape)\n",
        "print(\"Number transactions y_train dataset: \", y_train.shape)\n",
        "print(\"Number transactions X_test dataset: \", X_test.shape)\n",
        "print(\"Number transactions y_test dataset: \", y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number transactions X_train dataset:  (28831, 56)\n",
            "Number transactions y_train dataset:  (28831,)\n",
            "Number transactions X_test dataset:  (12357, 56)\n",
            "Number transactions y_test dataset:  (12357,)\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "H2AFmcoT17Cn",
        "outputId": "420edf36-0213-4720-859c-60e82292365b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"Before OverSampling, counts of label '1': {}\".format(sum(y_train==1)))\n",
        "print(\"Before OverSampling, counts of label '0': {} \\n\".format(sum(y_train==0)))\n",
        "\n",
        "sm = SMOTE(random_state=2)\n",
        "X_train_res, y_train_res = sm.fit_resample(X_train, y_train.ravel())\n",
        "\n",
        "print('After OverSampling, the shape of train_X: {}'.format(X_train_res.shape))\n",
        "print('After OverSampling, the shape of train_y: {} \\n'.format(y_train_res.shape))\n",
        "\n",
        "print(\"After OverSampling, counts of label '1': {}\".format(sum(y_train_res==1)))\n",
        "print(\"After OverSampling, counts of label '0': {}\".format(sum(y_train_res==0)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before OverSampling, counts of label '1': 3221\n",
            "Before OverSampling, counts of label '0': 25610 \n",
            "\n",
            "After OverSampling, the shape of train_X: (51220, 56)\n",
            "After OverSampling, the shape of train_y: (51220,) \n",
            "\n",
            "After OverSampling, counts of label '1': 25610\n",
            "After OverSampling, counts of label '0': 25610\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "ZiODrATQ4HXq"
      },
      "cell_type": "markdown",
      "source": [
        "## Hyperparameter Search"
      ]
    },
    {
      "metadata": {
        "id": "9xHX-hGB2tAr",
        "outputId": "95fd6e81-3c20-4da3-d93f-af8c7c9ae671",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix, precision_recall_curve, auc, roc_auc_score, roc_curve, recall_score, classification_report\n",
        "\n",
        "parameters = {\n",
        "    'C': np.linspace(1, 10, 10)\n",
        "             }\n",
        "lr = LogisticRegression()\n",
        "clf = GridSearchCV(lr, parameters, cv=5, verbose=5, n_jobs=3)\n",
        "clf.fit(X_train_res, y_train_res.ravel())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=LogisticRegression(), n_jobs=3,\n",
              "             param_grid={'C': array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])},\n",
              "             verbose=5)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "dCU4-fJk3OjD",
        "outputId": "53bd4206-748e-4b06-a81d-c32803f06929",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "clf.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 4.0}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "id": "wsbO1Ql53Qg0",
        "outputId": "a137f3f0-de31-4f0b-822c-1ab2dbb2aad3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "lr1 = LogisticRegression(C=2,penalty='l2', verbose=5,solver=\"lbfgs\", max_iter=150)\n",
        "lr1.fit(X_train_res, y_train_res)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    2.4s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    2.4s finished\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=2, max_iter=150, verbose=5)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "metadata": {
        "id": "8xr7D3Tm3dg_",
        "outputId": "396b6e09-4089-419a-cea6-48722795c726",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "y_pre = lr1.predict(X_test)\n",
        "\n",
        "cnf_matrix = confusion_matrix(y_test, y_pre)\n",
        "lr1.score(X_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8551428340212025"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "8JFQVQYR3lDk"
      },
      "cell_type": "code",
      "source": [
        "y_pred_sample_score = lr1.decision_function(X_test)\n",
        "\n",
        "\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_pred_sample_score)\n",
        "\n",
        "roc_auc = auc(fpr,tpr)\n",
        "\n",
        "# Plot ROC\n",
        "plt.title('Receiver Operating Characteristic')\n",
        "plt.plot(fpr, tpr, 'b',label='AUC = %0.3f'% roc_auc)\n",
        "plt.legend(loc='lower right')\n",
        "plt.plot([0,1],[0,1],'r--')\n",
        "plt.xlim([-0.1,1.0])\n",
        "plt.ylim([-0.1,1.01])\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tPzcgiff69qy",
        "outputId": "76439f4e-d2d3-4328-8fd8-acc9cacbedf9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "roc_auc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7468517214910204"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "metadata": {
        "id": "EOSMzAjw3mxU",
        "outputId": "95f7f2ba-cb2c-4416-9d3b-273b26dc5db8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,y_pre))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.90      0.92     10938\n",
            "           1       0.40      0.50      0.44      1419\n",
            "\n",
            "    accuracy                           0.86     12357\n",
            "   macro avg       0.66      0.70      0.68     12357\n",
            "weighted avg       0.87      0.86      0.86     12357\n",
            "\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "2JdTYl793-Cw",
        "outputId": "d1cb80fd-45d6-44bc-acff-c4c03de83639",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "cnf_matrix"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9860, 1078],\n",
              "       [ 712,  707]])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Calculate Feature Relevance using ***SHAP*** algorithm"
      ],
      "metadata": {
        "id": "yT1zFRw9lpsP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install and import shap"
      ],
      "metadata": {
        "id": "LO8UVgzI1rXo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install shap"
      ],
      "metadata": {
        "id": "mE2Db1FvmvjL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Z_UBHMXCFAtq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 44
        },
        "outputId": "289e7e0d-c3e0-41b7-a42d-25479cb2e209"
      },
      "cell_type": "code",
      "source": [
        "import shap\n",
        "shap.initjs()"
      ],
      "execution_count": null,
      "outputs": [
        {
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p=this.xlabel.selectAll(\"option\").data(f);p.enter().append(\"option\").merge(p).attr(\"value\",function(t){return t}).text(function(t){return t}),p.exit().remove();var h=this.props.outNames[0]?this.props.outNames[0]:\"model output value\";f=(0,y.map)(this.usedFeatures,function(e){return[t.props.featureNames[e],t.props.featureNames[e]+\" effects\"]}),f.unshift([\"model output value\",h]);var d=this.ylabel.selectAll(\"option\").data(f);d.enter().append(\"option\").merge(d).attr(\"value\",function(t){return t[0]}).text(function(t){return t[1]}),d.exit().remove(),this.ylabel.style(\"top\",(this.height-10-this.topOffset)/2+this.topOffset+\"px\").style(\"left\",10-this.ylabel.node().offsetWidth/2+\"px\"),this.internalDraw()}}},{key:\"internalDraw\",value:function(){var t=this,e=!0,n=!1,r=void 0;try{for(var i,o=this.props.explanations[Symbol.iterator]();!(e=(i=o.next()).done);e=!0){var a=i.value,c=!0,s=!1,l=void 0;try{for(var 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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 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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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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Initialize *explainer* and extract *feature relevance* through **shap**"
      ],
      "metadata": {
        "id": "KGBa8Xwv1wVw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# explainer = shap.LinearExplainer(lr1, X_test)\n",
        "explainer = shap.Explainer(lr1, X_test)\n",
        "shap_values = explainer(X_test)"
      ],
      "metadata": {
        "id": "ZnAvHwuhm2H0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Waterfall plot visualization for first observation\n",
        "shap.plots.waterfall(shap_values[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "t9Q5TYF2tE75",
        "outputId": "51d7c544-bb4c-4254-f451-b93629880639"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x468 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shap_values.shape, X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "osf-NVNftelf",
        "outputId": "e191e599-097d-4e48-cd87-a799971f57b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((12357, 56), (12357, 56))"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Feature Values"
      ],
      "metadata": {
        "id": "CxtdLRCn2Jyy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_test[0:1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "itAIbKHJtreC",
        "outputId": "db4b2a2d-6768-4156-8cb9-dbcddeeb2e80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age  campaign  pdays  previous  emp.var.rate  cons.price.idx  \\\n",
              "23151   55         2    999         0           1.4          93.444   \n",
              "\n",
              "       cons.conf.idx  euribor3m  nr.employed  job_0  ...  poutcome_2  \\\n",
              "23151          -36.1      4.965       5228.1      0  ...           0   \n",
              "\n",
              "       day_of_week_0  day_of_week_1  day_of_week_2  day_of_week_3  \\\n",
              "23151              0              0              0              1   \n",
              "\n",
              "       day_of_week_4  default_0  default_1  housing_0  housing_1  \n",
              "23151              0          1          0          0          1  \n",
              "\n",
              "[1 rows x 56 columns]"
            ],
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              "\n",
              "  <div id=\"df-6ef1479a-e9c4-4cac-bab8-179aaf4c729c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>emp.var.rate</th>\n",
              "      <th>cons.price.idx</th>\n",
              "      <th>cons.conf.idx</th>\n",
              "      <th>euribor3m</th>\n",
              "      <th>nr.employed</th>\n",
              "      <th>job_0</th>\n",
              "      <th>...</th>\n",
              "      <th>poutcome_2</th>\n",
              "      <th>day_of_week_0</th>\n",
              "      <th>day_of_week_1</th>\n",
              "      <th>day_of_week_2</th>\n",
              "      <th>day_of_week_3</th>\n",
              "      <th>day_of_week_4</th>\n",
              "      <th>default_0</th>\n",
              "      <th>default_1</th>\n",
              "      <th>housing_0</th>\n",
              "      <th>housing_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>23151</th>\n",
              "      <td>55</td>\n",
              "      <td>2</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>93.444</td>\n",
              "      <td>-36.1</td>\n",
              "      <td>4.965</td>\n",
              "      <td>5228.1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 56 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6ef1479a-e9c4-4cac-bab8-179aaf4c729c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6ef1479a-e9c4-4cac-bab8-179aaf4c729c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6ef1479a-e9c4-4cac-bab8-179aaf4c729c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Ground-truth label: \" + str(list(y_test[0:1].values)[0]))\n",
        "print(\"Predicted value: \" + str(y_pre[0:1][0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6Tx6p0tuD66",
        "outputId": "10224d87-1e58-4d8b-c996-7c8b824cfe07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ground-truth label: 0\n",
            "Predicted value: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shap_values[0].values.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GQnnxE2gtRwj",
        "outputId": "382ff87c-0b7f-492e-dde7-eaad9ef085d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(56,)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Extract Feature Value and Relevance using ***shap***"
      ],
      "metadata": {
        "id": "3km4oeNx2vVh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "feat_value = list(shap_values[0].data)\n",
        "feat_relevance = list(shap_values[0].values)"
      ],
      "metadata": {
        "id": "LQxikq_U1A_s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feat_value[0], feat_relevance[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vJccpd44vJtG",
        "outputId": "601e8e81-e7a6-484e-fae9-49c626a3ddfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(55.0, -0.08895589826722214)"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    }
  ]
}