Model_Validation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/malikbaqi12/Applied-data-science-using-pyspark-code-files/blob/main/Model_Validation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qh4hoToRpbeI"
      },
      "source": [
        "# Default Parameters"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyspark"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jIjCtdCkqYW1",
        "outputId": "d5b8690e-5c1d-43fb-915c-1894185856d7"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pyspark\n",
            "  Downloading pyspark-3.4.0.tar.gz (310.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m310.8/310.8 MB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: py4j==0.10.9.7 in /usr/local/lib/python3.10/dist-packages (from pyspark) (0.10.9.7)\n",
            "Building wheels for collected packages: pyspark\n",
            "  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyspark: filename=pyspark-3.4.0-py2.py3-none-any.whl size=311317130 sha256=7aefacd66bb8d37b199c67cc0b6e1aa9b4113aed1598022cb487506c085e5e06\n",
            "  Stored in directory: /root/.cache/pip/wheels/7b/1b/4b/3363a1d04368e7ff0d408e57ff57966fcdf00583774e761327\n",
            "Successfully built pyspark\n",
            "Installing collected packages: pyspark\n",
            "Successfully installed pyspark-3.4.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "xikgckYupbeq"
      },
      "outputs": [],
      "source": [
        "filename = \"bank-full.csv\"\n",
        "target_variable_name = \"y\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "62f6bGy7pbe3"
      },
      "source": [
        "# Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s6M0OROBpbe5",
        "outputId": "c1d4f494-db7d-4816-b958-a74bcb5efee2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---+------------+--------+---------+-------+-------+-------+----+-------+---+-----+--------+--------+-----+--------+--------+---+\n",
            "|age|         job| marital|education|default|balance|housing|loan|contact|day|month|duration|campaign|pdays|previous|poutcome|  y|\n",
            "+---+------------+--------+---------+-------+-------+-------+----+-------+---+-----+--------+--------+-----+--------+--------+---+\n",
            "| 58|  management| married| tertiary|     no|   2143|    yes|  no|unknown|  5|  may|     261|       1|   -1|       0| unknown| no|\n",
            "| 44|  technician|  single|secondary|     no|     29|    yes|  no|unknown|  5|  may|     151|       1|   -1|       0| unknown| no|\n",
            "| 33|entrepreneur| married|secondary|     no|      2|    yes| yes|unknown|  5|  may|      76|       1|   -1|       0| unknown| no|\n",
            "| 47| blue-collar| married|  unknown|     no|   1506|    yes|  no|unknown|  5|  may|      92|       1|   -1|       0| unknown| no|\n",
            "| 33|     unknown|  single|  unknown|     no|      1|     no|  no|unknown|  5|  may|     198|       1|   -1|       0| unknown| no|\n",
            "| 35|  management| married| tertiary|     no|    231|    yes|  no|unknown|  5|  may|     139|       1|   -1|       0| unknown| no|\n",
            "| 28|  management|  single| tertiary|     no|    447|    yes| yes|unknown|  5|  may|     217|       1|   -1|       0| unknown| no|\n",
            "| 42|entrepreneur|divorced| tertiary|    yes|      2|    yes|  no|unknown|  5|  may|     380|       1|   -1|       0| unknown| no|\n",
            "| 58|     retired| married|  primary|     no|    121|    yes|  no|unknown|  5|  may|      50|       1|   -1|       0| unknown| no|\n",
            "| 43|  technician|  single|secondary|     no|    593|    yes|  no|unknown|  5|  may|      55|       1|   -1|       0| unknown| no|\n",
            "| 41|      admin.|divorced|secondary|     no|    270|    yes|  no|unknown|  5|  may|     222|       1|   -1|       0| unknown| no|\n",
            "| 29|      admin.|  single|secondary|     no|    390|    yes|  no|unknown|  5|  may|     137|       1|   -1|       0| unknown| no|\n",
            "| 53|  technician| married|secondary|     no|      6|    yes|  no|unknown|  5|  may|     517|       1|   -1|       0| unknown| no|\n",
            "| 58|  technician| married|  unknown|     no|     71|    yes|  no|unknown|  5|  may|      71|       1|   -1|       0| unknown| no|\n",
            "| 57|    services| married|secondary|     no|    162|    yes|  no|unknown|  5|  may|     174|       1|   -1|       0| unknown| no|\n",
            "| 51|     retired| married|  primary|     no|    229|    yes|  no|unknown|  5|  may|     353|       1|   -1|       0| unknown| no|\n",
            "| 45|      admin.|  single|  unknown|     no|     13|    yes|  no|unknown|  5|  may|      98|       1|   -1|       0| unknown| no|\n",
            "| 57| blue-collar| married|  primary|     no|     52|    yes|  no|unknown|  5|  may|      38|       1|   -1|       0| unknown| no|\n",
            "| 60|     retired| married|  primary|     no|     60|    yes|  no|unknown|  5|  may|     219|       1|   -1|       0| unknown| no|\n",
            "| 33|    services| married|secondary|     no|      0|    yes|  no|unknown|  5|  may|      54|       1|   -1|       0| unknown| no|\n",
            "+---+------------+--------+---------+-------+-------+-------+----+-------+---+-----+--------+--------+-----+--------+--------+---+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from pyspark.sql import SparkSession\n",
        "spark = SparkSession.builder.getOrCreate()\n",
        "df = spark.read.csv(filename, header=True, inferSchema=True, sep=';')\n",
        "df.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "wGM4cC4Jpbe7"
      },
      "outputs": [],
      "source": [
        "from pyspark.sql import functions as F\n",
        "\n",
        "df = df.withColumn('label', F.when(F.col(\"y\") == 'yes', 1).otherwise(0))\n",
        "df = df.drop('y')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ox5YP36qpbfD"
      },
      "source": [
        "# Simple Random sampling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "moTk2_QUpbfF"
      },
      "outputs": [],
      "source": [
        "train, test = df.randomSplit([0.7, 0.3], seed=12345)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EU6gNac2pbfI",
        "outputId": "6009641b-2a21-4278-d05d-b502f80f309b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age\n",
            "balance\n",
            "day\n",
            "duration\n",
            "campaign\n",
            "pdays\n",
            "previous\n",
            "label\n"
          ]
        }
      ],
      "source": [
        "for k, v in df.dtypes:\n",
        "    if v not in ['string']:\n",
        "        print(k)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "XbgCEu6_pbfK"
      },
      "outputs": [],
      "source": [
        "df = df.select(['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous', 'label'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "VB8GgOAPpbfQ"
      },
      "outputs": [],
      "source": [
        "from pyspark.ml.feature import VectorAssembler\n",
        "from pyspark.ml import Pipeline\n",
        "\n",
        "#assemble individual columns to one column - 'features'\n",
        "def assemble_vectors(df, features_list, target_variable_name):\n",
        "    stages = []\n",
        "    #assemble vectors\n",
        "    assembler = VectorAssembler(inputCols=features_list, outputCol='features')\n",
        "    stages = [assembler]\n",
        "    #select all the columns + target + newly created 'features' column\n",
        "    selectedCols = [target_variable_name, 'features']\n",
        "    #use pipeline to process sequentially\n",
        "    pipeline = Pipeline(stages=stages)\n",
        "    #assembler model\n",
        "    assembleModel = pipeline.fit(df)\n",
        "    #apply assembler model on data\n",
        "    df = assembleModel.transform(df).select(selectedCols)\n",
        "\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "iKsZ4H46pbfT"
      },
      "outputs": [],
      "source": [
        "#exclude target variable and select all other feature vectors\n",
        "features_list = df.columns\n",
        "#features_list = char_vars #this option is used only for ChiSqselector\n",
        "features_list.remove('label')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "7kHUoGSVpbfX"
      },
      "outputs": [],
      "source": [
        "# apply the function on our dataframe\n",
        "assembled_df = assemble_vectors(train, features_list, 'label')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "j4kjxjmopbfZ"
      },
      "outputs": [],
      "source": [
        "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
        "from pyspark.ml.classification import LogisticRegression\n",
        "from pyspark.ml.tuning import ParamGridBuilder, TrainValidationSplit\n",
        "\n",
        "lr = LogisticRegression(maxIter=10, featuresCol='features', labelCol='label')\n",
        "paramGrid = ParamGridBuilder().addGrid(lr.regParam, [0.1, 0.01]).addGrid(lr.elasticNetParam, [0.0, 0.5, 1.0]).build()\n",
        "train_valid_clf = TrainValidationSplit(estimator=lr, estimatorParamMaps=paramGrid, evaluator=BinaryClassificationEvaluator(),\n",
        "                           # 70% of the data will be used for training, 30% for validation.\n",
        "                           trainRatio=0.7)\n",
        "model = train_valid_clf.fit(assembled_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c0fcHvy0pbfa"
      },
      "source": [
        "# Stratified Sampling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "E-VaRGcjpbfd"
      },
      "outputs": [],
      "source": [
        "# split data for 0s and 1s\n",
        "zero_df = df.filter(df[\"label\"]==0)\n",
        "one_df = df.filter(df[\"label\"]==1)\n",
        "\n",
        "# split data into train and test\n",
        "train_zero, test_zero = zero_df.randomSplit([0.7,0.3], seed=12345)\n",
        "train_one, test_one = one_df.randomSplit([0.7,0.3], seed=12345)\n",
        "\n",
        "# union datasets\n",
        "train = train_zero.union(train_one)\n",
        "test = test_zero.union(test_one)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eW2A_c3opbfj"
      },
      "source": [
        "# Holdout"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "XiNoIxbcpbfk"
      },
      "outputs": [],
      "source": [
        "train, test, holdout = df.randomSplit([0.7, 0.2, 0.1], seed=12345)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hvowNfMSpbfl",
        "outputId": "c4d751d8-5242-4c83-e19b-af63b319b689"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(31527, 9051, 4633)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "train.count(), test.count(), holdout.count()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4X9sg6T4pbfm"
      },
      "source": [
        "$$PSI=\\sum (Actual \\% - Expected\\%) * ln( \\frac{Actual\\%}{Expected\\%})$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LB3TxZefpbfn"
      },
      "source": [
        "# k-fold cross validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "t0ESBQnWpbfo"
      },
      "outputs": [],
      "source": [
        "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
        "from pyspark.ml.classification import LogisticRegression\n",
        "from pyspark.ml.tuning import ParamGridBuilder, CrossValidator\n",
        "\n",
        "#model initialization\n",
        "lr = LogisticRegression(maxIter=10, featuresCol='features', labelCol='label')\n",
        "\n",
        "#model parameters to try\n",
        "paramGrid = ParamGridBuilder().addGrid(lr.regParam, [0.1, 0.01]).addGrid(lr.elasticNetParam, [0.0, 0.5, 1.0]).build()\n",
        "\n",
        "# number of folds = 3\n",
        "crossval_clf = CrossValidator(estimator=lr, estimatorParamMaps=paramGrid, \n",
        "                                 evaluator=BinaryClassificationEvaluator(), numFolds=3, )\n",
        "\n",
        "# assembled_df is the output of the vector assembler\n",
        "model = crossval_clf.fit(assembled_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFK2qTbGpbfp"
      },
      "source": [
        "# Leave-one-group out cross validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "GYkSKWgxpbfq"
      },
      "outputs": [],
      "source": [
        "from pyspark.sql import SparkSession\n",
        "from pyspark.ml.feature import VectorAssembler\n",
        "from pyspark.ml import Pipeline\n",
        "from pyspark.sql.functions import countDistinct\n",
        "from pyspark.ml.classification import LogisticRegression\n",
        "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
        "from pyspark.sql import functions as F\n",
        "import numpy as np\n",
        "\n",
        "filename = \"bank-full.csv\"\n",
        "spark = SparkSession.builder.getOrCreate()\n",
        "df = spark.read.csv(filename, header=True, inferSchema=True, sep=';')\n",
        "df = df.withColumn('label', F.when(F.col(\"y\") == 'yes', 1).otherwise(0))\n",
        "df = df.drop('y')\n",
        "df = df.select(['education', 'age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous', 'label'])\n",
        "features_list = ['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous'] \n",
        "\n",
        "#assemble individual columns to one column - 'features'\n",
        "def assemble_vectors(df, features_list, target_variable_name, group_variable_name):\n",
        "    stages = []\n",
        "    #assemble vectors\n",
        "    assembler = VectorAssembler(inputCols=features_list, outputCol='features')\n",
        "    stages = [assembler]\n",
        "    #select all the columns + target + newly created 'features' column\n",
        "    selectedCols = [group_variable_name, target_variable_name, 'features']\n",
        "\n",
        "    #use pipeline to process sequentially\n",
        "    pipeline = Pipeline(stages=stages)\n",
        "    #assembler model\n",
        "    assembleModel = pipeline.fit(df)\n",
        "    #apply assembler model on data\n",
        "    df = assembleModel.transform(df).select(selectedCols)\n",
        "\n",
        "    return df\n",
        "\n",
        "# apply the function on our dataframe\n",
        "joined_df = assemble_vectors(df, features_list, 'label', 'education')\n",
        "groups = list(joined_df.select('education').toPandas()['education'].unique())\n",
        "\n",
        "# leave one group out validation \n",
        "def leave_one_group_out_validator(df, var_name, groups):\n",
        "    \n",
        "    train_metric_score = []\n",
        "    test_metric_score = []\n",
        "    \n",
        "    for i in groups:\n",
        "        train = df.filter(df[var_name]!=i)\n",
        "        test = df.filter(df[var_name]==i)\n",
        "    \n",
        "        #model initialization\n",
        "        lr = LogisticRegression(maxIter=10, featuresCol='features', labelCol='label')\n",
        "        evaluator = BinaryClassificationEvaluator(labelCol='label', rawPredictionCol='rawPrediction', \n",
        "                                                  metricName='areaUnderROC')\n",
        "        #fit model\n",
        "        lrModel = lr.fit(train)\n",
        "\n",
        "        #make predicitons\n",
        "        predict_train = lrModel.transform(train)\n",
        "        predict_test = lrModel.transform(test)\n",
        "        \n",
        "        train_metric_score.append(evaluator.evaluate(predict_train))\n",
        "        test_metric_score.append(evaluator.evaluate(predict_test))\n",
        "        print(str(i) + \" Group evaluation\")\n",
        "        print(\" Train AUC - \", train_metric_score[-1])\n",
        "        print(\" Test AUC - \", test_metric_score[-1])\n",
        "        \n",
        "    print('Final evaluation for model')\n",
        "    print('Train ROC', np.mean(train_metric_score))\n",
        "    print('Test ROC', np.mean(test_metric_score))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D_kGJ_A_pbfs",
        "outputId": "3d6e7e28-9334-476e-aa15-f0bc2661c49b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tertiary Group evaluation\n",
            " Train AUC -  0.8405057151274624\n",
            " Test AUC -  0.8116987180907579\n",
            "secondary Group evaluation\n",
            " Train AUC -  0.824959682035174\n",
            " Test AUC -  0.8352574426069586\n",
            "unknown Group evaluation\n",
            " Train AUC -  0.8314703855819574\n",
            " Test AUC -  0.8111358354349012\n",
            "primary Group evaluation\n",
            " Train AUC -  0.8256816145297797\n",
            " Test AUC -  0.8695816642610408\n",
            "Final evaluation for model\n",
            "Train ROC 0.8306543493185934\n",
            "Test ROC 0.8319184150984147\n"
          ]
        }
      ],
      "source": [
        "leave_one_group_out_validator(joined_df, 'education', groups)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qdIKz5GMpbft"
      },
      "source": [
        "# Regression models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "5NpataDGpbft"
      },
      "outputs": [],
      "source": [
        "from pyspark.sql import SparkSession\n",
        "spark = SparkSession.builder.getOrCreate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V0hklH4apbfy",
        "outputId": "234e884f-0878-4311-dd71-1589bb1cc692"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---+---+---+\n",
            "| x1| x2|  y|\n",
            "+---+---+---+\n",
            "| 58| 50| 12|\n",
            "| 37| 95| 27|\n",
            "| 29|137| 39|\n",
            "| 19|150| 45|\n",
            "+---+---+---+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from pyspark.sql.types import IntegerType\n",
        "from pyspark.sql.types import StructField, StructType\n",
        "\n",
        "cSchema = StructType([StructField(\"x1\", IntegerType())\\\n",
        "                      ,StructField(\"x2\", IntegerType())\\\n",
        "                      ,StructField(\"y\", IntegerType())])\n",
        "\n",
        "df_list = [[58, 50, 12], [37, 95, 27], [29, 137, 39], [19, 150, 45]]\n",
        "\n",
        "\n",
        "df = spark.createDataFrame(df_list, schema=cSchema)\n",
        "df.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "Fru2V32Epbfz"
      },
      "outputs": [],
      "source": [
        "#assembled_df = assemble_vectors(df, ['x1'], 'y')\n",
        "#assembled_df = assemble_vectors(df, ['x1'], 'y', 'group_name')\n",
        "\n",
        "from pyspark.ml.feature import VectorAssembler\n",
        "from pyspark.sql.functions import col\n",
        "\n",
        "def assemble_vectors(df, input_cols, output_col, group_name):\n",
        "    assembler = VectorAssembler(inputCols=input_cols, outputCol=output_col)\n",
        "    assembled_df = assembler.transform(df)\n",
        "    group_cols = [col(group_name)]\n",
        "    group_cols += [col(c) for c in df.columns if c not in input_cols]\n",
        "    agg_expr = {output_col: 'mean'}\n",
        "    assembled_df = assembled_df.groupby(group_cols).agg(agg_expr)\n",
        "    return assembled_df\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "tfE1ErS_pbf1"
      },
      "outputs": [],
      "source": [
        "from pyspark.ml.regression import LinearRegression\n",
        "\n",
        "reg = LinearRegression(featuresCol='features', labelCol='y')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "RqPuqjVopbf2"
      },
      "outputs": [],
      "source": [
        "#reg_model = reg.fit(assembled_df)\n",
        "\n",
        "reg = LinearRegression(featuresCol='features', labelCol='label')\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg_model = reg.fit(assembled_df)\n"
      ],
      "metadata": {
        "id": "oUd7NRrjfRty"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wvhe0ZAypbf4",
        "outputId": "55262801-7419-4195-c42a-d1cad1545f0d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0005057746066273917 -0.04097484793120063\n"
          ]
        }
      ],
      "source": [
        "print(reg_model.coefficients[0], reg_model.intercept)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "mbdwjA1ppbf5"
      },
      "outputs": [],
      "source": [
        "pred_result = reg_model.transform(assembled_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M3gWuhGmpbf7",
        "outputId": "50c190e9-bfbc-4153-a24e-294f59257c13"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared error 0.083927641064811\n",
            "Root mean squared error 0.28970267700663554\n",
            "Mean Absolute error 0.17194434269736392\n",
            "Explained Variance 0.017783728864301403\n",
            "R squared 0.17484504315196858\n"
          ]
        }
      ],
      "source": [
        "reg_summary = reg_model.summary\n",
        "\n",
        "print('Mean Squared error', reg_summary.meanSquaredError)\n",
        "print('Root mean squared error', reg_summary.rootMeanSquaredError)\n",
        "print('Mean Absolute error', reg_summary.meanAbsoluteError)\n",
        "print('Explained Variance', reg_summary.explainedVariance)\n",
        "print('R squared', reg_summary.r2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "irZ71egypbf8"
      },
      "source": [
        "# Mean square error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "HcvZt9gGpbf8"
      },
      "outputs": [],
      "source": [
        "from pyspark.ml.evaluation import RegressionEvaluator\n",
        "evaluator = RegressionEvaluator(labelCol='y', predictionCol='prediction', metricName='mse')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "HmJpiQmFpbf9"
      },
      "outputs": [],
      "source": [
        "#evaluator.evaluate(pred_result)\n",
        "\n",
        "evaluator = RegressionEvaluator(labelCol='label', predictionCol='prediction', metricName='rmse')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluator.evaluate(pred_result)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "psephmjhfn0y",
        "outputId": "eb379450-9d72-4bb3-c8c6-1b620bc06c9a"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.28970267700663554"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C4f1Kahcpbf9"
      },
      "source": [
        "# Root Mean Square error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "FIl1FMKZpbf-"
      },
      "outputs": [],
      "source": [
        "#evaluator = RegressionEvaluator(labelCol='y', predictionCol='prediction', metricName='rmse')\n",
        "#evaluator.evaluate(pred_result)\n",
        "\n",
        "evaluator = RegressionEvaluator(labelCol='label', predictionCol='prediction', metricName='rmse')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluator.evaluate(pred_result)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v_ZsKrgrf6n1",
        "outputId": "96ad1ba0-49e7-4532-aa3d-ccbaba7294a6"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.28970267700663554"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ulaPHwqpbgC"
      },
      "source": [
        "# Mean absolute error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ep2k1feLpbl8",
        "outputId": "56dcc3c0-1ec9-465b-b27f-eb46d6a3da39"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 0.17194434269736392\n"
          ]
        }
      ],
      "source": [
        "#evaluator = RegressionEvaluator(labelCol='y', predictionCol='prediction', metricName='mae')\n",
        "#evaluator.evaluate(pred_result)\n",
        "\n",
        "from pyspark.ml.evaluation import RegressionEvaluator\n",
        "\n",
        "# Assuming pred_result is a DataFrame with columns ['label', 'prediction']\n",
        "evaluator = RegressionEvaluator(labelCol='label', predictionCol='prediction', metricName='mae')\n",
        "mae = evaluator.evaluate(pred_result)\n",
        "print('MAE:', mae)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zPi05NUipbl9"
      },
      "source": [
        "# Explained Variance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l4AAv93hpbmE",
        "outputId": "edcf2fdd-82c6-433c-89f2-6c213057f63c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 0.28970267700663554\n"
          ]
        }
      ],
      "source": [
        "# option available for Pyspark 3.0 and above\n",
        "#evaluator = RegressionEvaluator(labelCol='y', predictionCol='prediction', metricName='var')\n",
        "#evaluator.evaluate(pred_result)\n",
        "\n",
        "from pyspark.ml.evaluation import RegressionEvaluator\n",
        "\n",
        "# Assuming pred_result is a DataFrame with columns ['label', 'prediction']\n",
        "evaluator = RegressionEvaluator(labelCol='label', predictionCol='prediction', metricName='rmse')\n",
        "rmse = evaluator.evaluate(pred_result)\n",
        "print('RMSE:', rmse)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMZq-Vq6pbmG",
        "outputId": "af5e2157-ade0-4832-fec0-3e02296d57fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Explained Variance 0.017783728864301403\n"
          ]
        }
      ],
      "source": [
        "# PySpark < 3.0\n",
        "print('Explained Variance', reg_summary.explainedVariance)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u3qN2zA0pbmH",
        "outputId": "1d7bd43f-0ea6-4b72-ec8e-84b8cf2d4e34"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-3"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "1 - 4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mww96xJdpbmJ"
      },
      "source": [
        "# R- Squared"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0NjH2PFmpbmK",
        "outputId": "60395503-7d87-45e0-e44b-1a91970806b8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.979251209246812"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "1 - (3.303/159.19)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p2ga_ewlpbmL",
        "outputId": "12503c9e-d60d-4cbe-adaa-a050846497ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared: 0.17484504315196858\n"
          ]
        }
      ],
      "source": [
        "#evaluator = RegressionEvaluator(labelCol='y', predictionCol='prediction', metricName='r2')\n",
        "#evaluator.evaluate(pred_result)\n",
        "#print('R squared', reg_summary.r2)\n",
        "\n",
        "from pyspark.ml.evaluation import RegressionEvaluator\n",
        "\n",
        "# Assuming pred_result is a DataFrame with columns ['label', 'prediction']\n",
        "evaluator = RegressionEvaluator(labelCol='label', predictionCol='prediction', metricName='r2')\n",
        "r2 = evaluator.evaluate(pred_result)\n",
        "print('R squared:', r2)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c2xglBHFpbmN"
      },
      "source": [
        "# Adjusted R squared"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zN0Q6qIEpbmO",
        "outputId": "9592e969-21d0-4cf1-9c1c-84571419ac47"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared 0.17466178591988835\n"
          ]
        }
      ],
      "source": [
        "print('R squared', reg_summary.r2adj)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ORTQsKyBpbmU",
        "outputId": "119fc168-e7b3-4225-a75a-107f04f11415"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.96886"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ],
      "source": [
        "1 - (((1-0.97924)*3)/(4-1-1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xuebGc0lpbmV",
        "outputId": "baf8489d-df37-4155-f47f-168b2fe7aeeb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30.75"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "(12 + 27 + 39 + 45)/4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "hx8IfddspbmW"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xaAsK_2SpbmX"
      },
      "source": [
        "# Binary Classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "DJNueNoapbmY"
      },
      "outputs": [],
      "source": [
        "from pyspark.sql import SparkSession\n",
        "spark = SparkSession.builder.getOrCreate()\n",
        "\n",
        "from pyspark.ml.feature import VectorAssembler\n",
        "from pyspark.ml import Pipeline\n",
        "\n",
        "#assemble individual columns to one column - 'features'\n",
        "def assemble_vectors(df, features_list, target_variable_name):\n",
        "    stages = []\n",
        "    #assemble vectors\n",
        "    assembler = VectorAssembler(inputCols=features_list, outputCol='features')\n",
        "    stages = [assembler]\n",
        "    #select all the columns + target + newly created 'features' column\n",
        "    selectedCols = [target_variable_name, 'features']\n",
        "    #use pipeline to process sequentially\n",
        "    pipeline = Pipeline(stages=stages)\n",
        "    #assembler model\n",
        "    assembleModel = pipeline.fit(df)\n",
        "    #apply assembler model on data\n",
        "    df = assembleModel.transform(df).select(selectedCols)\n",
        "\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S3tF9GqppbmY",
        "outputId": "2bc2c432-176d-4eb1-d1f4-68af5ea4a840"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---+---+------+\n",
            "| x1| x2|target|\n",
            "+---+---+------+\n",
            "|  0|  0|     0|\n",
            "|  0|  1|     1|\n",
            "|  1|  0|     1|\n",
            "|  1|  1|     1|\n",
            "+---+---+------+\n",
            "\n",
            "14.499620915312292 -7.577807714653773e-09\n"
          ]
        }
      ],
      "source": [
        "from pyspark.sql.types import IntegerType, DoubleType\n",
        "from pyspark.sql.types import StructField, StructType\n",
        "\n",
        "cSchema = StructType([StructField(\"x1\", IntegerType())\\\n",
        "                      ,StructField(\"x2\", IntegerType())\\\n",
        "                      ,StructField(\"target\", IntegerType())])\n",
        "\n",
        "df_list = [[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]]\n",
        "\n",
        "\n",
        "df = spark.createDataFrame(df_list, schema=cSchema)\n",
        "df.show()\n",
        "\n",
        "assembled_df = assemble_vectors(df, ['x1'], 'target')\n",
        "\n",
        "from pyspark.ml.classification import LogisticRegression\n",
        "\n",
        "clf = LogisticRegression(featuresCol='features', labelCol='target')\n",
        "\n",
        "clf_model = clf.fit(assembled_df)\n",
        "\n",
        "print(clf_model.coefficients[0], clf_model.intercept)\n",
        "\n",
        "pred_result = clf_model.transform(assembled_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ilEeO5IZpbmZ",
        "outputId": "ffe73f7a-5c2b-4dce-9670-f8dfc4fa5bbb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+------+--------+--------------------+--------------------+----------+\n",
            "|target|features|       rawPrediction|         probability|prediction|\n",
            "+------+--------+--------------------+--------------------+----------+\n",
            "|     0|   [0.0]|[7.57780771465377...|[0.50000000189445...|       0.0|\n",
            "|     1|   [0.0]|[7.57780771465377...|[0.50000000189445...|       0.0|\n",
            "|     1|   [1.0]|[-14.499620907734...|[5.04538638551251...|       1.0|\n",
            "|     1|   [1.0]|[-14.499620907734...|[5.04538638551251...|       1.0|\n",
            "+------+--------+--------------------+--------------------+----------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "pred_result.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tGlwlFbxpbma",
        "outputId": "9898a724-b223-4860-bb80-0f172671e456"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pyspark/sql/context.py:157: FutureWarning: Deprecated in 3.0.0. Use SparkSession.builder.getOrCreate() instead.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from pyspark.mllib.evaluation import BinaryClassificationMetrics, MulticlassMetrics\n",
        "\n",
        "predictionAndTarget = pred_result.select(\"prediction\", \"target\")\n",
        "predictionAndTarget = predictionAndTarget.withColumn(\"target\", predictionAndTarget[\"target\"].cast(DoubleType()))\n",
        "predictionAndTarget = predictionAndTarget.withColumn(\"prediction\", predictionAndTarget[\"prediction\"].cast(DoubleType()))\n",
        "\n",
        "metrics = MulticlassMetrics(predictionAndTarget.rdd.map(tuple))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "5onWZiczpbmb"
      },
      "outputs": [],
      "source": [
        "# confusion matrix\n",
        "cm = metrics.confusionMatrix().toArray()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "ycSDkLAhpbmc"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def make_confusion_matrix_chart(cf_matrix):\n",
        "    \n",
        "    list_values = ['0', '1']\n",
        "\n",
        "    plt.subplot(111)\n",
        "    sns.heatmap(cf_matrix, annot=True, yticklabels=list_values, \n",
        "                                xticklabels=list_values, fmt='g')\n",
        "    plt.ylabel(\"Actual\")\n",
        "    plt.xlabel(\"Pred\")\n",
        "    plt.ylim([0,2])\n",
        "    plt.title('OR Logic Gate predictions')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    return None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "7aVtX5ukpbmd",
        "outputId": "f81255b6-8eb2-4b09-ae15-01e1dc000819"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "make_confusion_matrix_chart(cm)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xKX6nFRxpbme"
      },
      "source": [
        "$$Sigmoid\\ function,\\ Probability=(\\frac{1}{1 + exp(-x)})$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mTGOOzm1pbmf"
      },
      "source": [
        "$$Accuracy=(\\frac{TP + TN}{TP + TN + FP + FN})$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0zpZJte-pbmk"
      },
      "source": [
        "$$Precision=(\\frac{TP}{TP + FP})$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x82FCy52pbml"
      },
      "source": [
        "$$Recall=(\\frac{TP}{TP + FN})$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OggEPP1zpbmm"
      },
      "source": [
        "$$F1-score=2*(\\frac{Precision*Recall}{Precision + Recall})$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6fTeK9Wlpbmn"
      },
      "source": [
        "$$Specificity=(\\frac{TN}{TN + FP})=1- FPR$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sj7i0KvKpbmo"
      },
      "source": [
        "$$False\\ Positive\\ Rate=(\\frac{FP}{FP + TN})$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "_jSCp6njpbmp"
      },
      "outputs": [],
      "source": [
        "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
        "acc = metrics.accuracy\n",
        "misclassification_rate = 1 - acc\n",
        "precision = metrics.precision(1.0)\n",
        "recall = metrics.recall(1.0)\n",
        "f1 = metrics.fMeasure(1.0)\n",
        "evaluator = BinaryClassificationEvaluator(labelCol='target', rawPredictionCol='rawPrediction', metricName='areaUnderROC')\n",
        "roc = evaluator.evaluate(pred_result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fhyCo_M8pbmq",
        "outputId": "d44fa234-3a3b-4d0f-8cfe-caeceb19c29a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.75 0.25 1.0 0.6666666666666666 0.8 0.8333333333333333\n"
          ]
        }
      ],
      "source": [
        "print(acc, misclassification_rate, precision, recall, f1, roc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "id": "X8qvqv4Epbmr"
      },
      "outputs": [],
      "source": [
        "from pyspark.mllib.evaluation import BinaryClassificationMetrics\n",
        "\n",
        "class CurveMetrics(BinaryClassificationMetrics):\n",
        "    def __init__(self, *args):\n",
        "        super(CurveMetrics, self).__init__(*args)\n",
        "\n",
        "    def _to_list(self, rdd):\n",
        "        points = []\n",
        "        results_collect = rdd.collect()\n",
        "        for row in results_collect:\n",
        "            points += [(float(row._1()), float(row._2()))]\n",
        "        return points\n",
        "\n",
        "    def get_curve(self, method):\n",
        "        rdd = getattr(self._java_model, method)().toJavaRDD()\n",
        "        return self._to_list(rdd)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K2WeeBzKpbmt",
        "outputId": "3a04d49e-e0bf-4de4-db3f-a98ea5a4a389"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pyspark/sql/context.py:157: FutureWarning: Deprecated in 3.0.0. Use SparkSession.builder.getOrCreate() instead.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "preds = pred_result.select('target','probability').rdd.map(lambda row: (float(row['probability'][1]), float(row['target'])))\n",
        "\n",
        "# Returns as a list (false positive rate, true positive rate)\n",
        "points = CurveMetrics(preds).get_curve('roc')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "ceEm00ozpbmu",
        "outputId": "573a1b1c-798f-4b9a-c83e-5c9e12c3ef8c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fe44f671c60>"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "points = CurveMetrics(preds).get_curve('roc')\n",
        "\n",
        "plt.figure()\n",
        "x_val = [x[0] for x in points]\n",
        "y_val = [x[1] for x in points]\n",
        "plt.title('ROC')\n",
        "plt.xlabel('False Positive Rate (1-Specificity)')\n",
        "plt.ylabel('True Positive Rate (Sensitivity)')\n",
        "plt.plot(x_val, y_val, label = 'AUC = %0.2f' % roc)\n",
        "plt.plot([0, 1], [0, 1], color='red', linestyle='--')\n",
        "plt.legend(loc = 'lower right')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "id": "Am_A-9T_pbmv"
      },
      "outputs": [],
      "source": [
        "evaluator = BinaryClassificationEvaluator(labelCol='target', rawPredictionCol='rawPrediction', metricName='areaUnderPR')\n",
        "pr = evaluator.evaluate(pred_result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ly4iIkEpbmz",
        "outputId": "146a5e33-f849-4d2c-982b-553b64b57643"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9583333333333333"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ],
      "source": [
        "pr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "r1ezu09Bpbm0",
        "outputId": "c56a9994-2b85-4e88-8035-536c3e7f50b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fe44f6c5cc0>"
            ]
          },
          "metadata": {},
          "execution_count": 89
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "preds = pred_result.select('target','probability').rdd.map(lambda row: (float(row['probability'][1]), float(row['target'])))\n",
        "points = CurveMetrics(preds).get_curve('pr')\n",
        "\n",
        "plt.figure()\n",
        "x_val = [x[0] for x in points]\n",
        "y_val = [x[1] for x in points]\n",
        "plt.title('PR curve')\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.plot(x_val, y_val, label = 'Average Precision = %0.2f' % pr)\n",
        "plt.plot([0, 1], [0.5, 0.5], color='red', linestyle='--')\n",
        "plt.legend(loc = 'lower right')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N1jNj8wrpbm0"
      },
      "source": [
        "# Random Value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rciqBm4Bpbm1",
        "outputId": "6acb86bf-adf6-40a4-ea2f-564172ca7509"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: [0.38       0.38383838 0.3877551  0.3814433  0.38541667 0.37894737\n",
            " 0.37234043 0.37634409 0.36956522 0.37362637]\n",
            "Recall: [1.         1.         1.         0.97368421 0.97368421 0.94736842\n",
            " 0.92105263 0.92105263 0.89473684 0.89473684]\n"
          ]
        }
      ],
      "source": [
        "#from sklearn.metrics import precision_recall_curve, average_precision_score\n",
        "\n",
        "# random target and probabilities\n",
        "#rand_y = [random.choice([1, 0]) for i in range(0, 100)]\n",
        "#rand_prob = [random.uniform(0, 1) for i in range(0, 100)]\n",
        "#rand_precision, rand_recall, _ = precision_recall_curve(rand_y, rand_prob)\n",
        "#pr = average_precision_score(rand_y, rand_prob)\n",
        "#plot random predictions\n",
        "#plt.figure()\n",
        "#plt.title('PR curve')\n",
        "#plt.xlabel('Recall')\n",
        "#plt.ylabel('Precision')\n",
        "#plt.plot(rand_recall, rand_precision, label = 'Average Precision = %0.2f' % pr)\n",
        "#plt.plot([0, 1], [0.5, 0.5], color='red', linestyle='--')\n",
        "#plt.legend(loc = 'lower right')\n",
        "\n",
        "import random\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "\n",
        "# Generate random targets and probabilities\n",
        "rand_y = [random.choice([1, 0]) for i in range(0, 100)]\n",
        "rand_prob = [random.uniform(0, 1) for i in range(0, 100)]\n",
        "\n",
        "# Compute precision and recall values\n",
        "rand_precision, rand_recall, _ = precision_recall_curve(rand_y, rand_prob)\n",
        "\n",
        "# Print the first 10 precision and recall values\n",
        "print('Precision:', rand_precision[:10])\n",
        "print('Recall:', rand_recall[:10])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aKcbNe__pbm2"
      },
      "source": [
        "# KS Statistic"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "id": "ER5AKLyfpbm3"
      },
      "outputs": [],
      "source": [
        "from pyspark.sql import SparkSession\n",
        "from pyspark.ml.feature import VectorAssembler\n",
        "from pyspark.ml import Pipeline\n",
        "from pyspark.sql.functions import countDistinct\n",
        "from pyspark.ml.classification import LogisticRegression\n",
        "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
        "from pyspark.sql import functions as F\n",
        "import numpy as np\n",
        "\n",
        "filename = \"bank-full.csv\"\n",
        "spark = SparkSession.builder.getOrCreate()\n",
        "df = spark.read.csv(filename, header=True, inferSchema=True, sep=';')\n",
        "df = df.withColumn('label', F.when(F.col(\"y\") == 'yes', 1).otherwise(0))\n",
        "df = df.drop('y')\n",
        "#assemble individual columns to one column - 'features'\n",
        "def assemble_vectors(df, features_list, target_variable_name):\n",
        "    stages = []\n",
        "    #assemble vectors\n",
        "    assembler = VectorAssembler(inputCols=features_list, outputCol='features')\n",
        "    stages = [assembler]\n",
        "    #select all the columns + target + newly created 'features' column\n",
        "    selectedCols = [target_variable_name, 'features']\n",
        "    #use pipeline to process sequentially\n",
        "    pipeline = Pipeline(stages=stages)\n",
        "    #assembler model\n",
        "    assembleModel = pipeline.fit(df)\n",
        "    #apply assembler model on data\n",
        "    df = assembleModel.transform(df).select(selectedCols)\n",
        "\n",
        "    return df\n",
        "\n",
        "df = df.select(['education', 'age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous', 'label'])\n",
        "features_list = ['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous'] \n",
        "assembled_df = assemble_vectors(df, features_list, 'label')\n",
        "clf = LogisticRegression(featuresCol='features', labelCol='label')\n",
        "train, test = assembled_df.randomSplit([0.7, 0.3], seed=12345)\n",
        "clf_model = clf.fit(train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "id": "Va79GYAApbm3"
      },
      "outputs": [],
      "source": [
        "from pyspark.sql import Window\n",
        "\n",
        "def create_deciles(df, clf, score, prediction, target, buckets):\n",
        "    \n",
        "    # get predictions from model\n",
        "    pred = clf.transform(df)\n",
        "    #probability of 1's, prediction and target\n",
        "    pred = pred.select(F.col(score), F.col(prediction), F.col(target)).rdd.map(lambda row: (float(row[score][1]), float(row['prediction']), float(row[target])))\n",
        "    predDF = pred.toDF(schema=[score, prediction, target])\n",
        "    # remove ties in scores work around\n",
        "    window = Window.orderBy(F.desc(score))\n",
        "    predDF = predDF.withColumn(\"row_number\", F.row_number().over(window))\n",
        "    predDF.cache()\n",
        "    predDF = predDF.withColumn(\"row_number\", predDF['row_number'].cast(\"double\"))\n",
        "    # partition into 10 buckets\n",
        "    window2 = Window.orderBy(\"row_number\")\n",
        "    final_predDF = predDF.withColumn(\"deciles\", F.ntile(buckets).over(window2))\n",
        "    final_predDF = final_predDF.withColumn(\"deciles\", final_predDF['deciles'].cast(\"int\"))\n",
        "    # create non target column\n",
        "    final_predDF = final_predDF.withColumn(\"non_target\", 1 - final_predDF[target])\n",
        "    final_predDF.cache()    #final predicted df\n",
        "    \n",
        "    #ks calculation starts here\n",
        "    temp_deciles = final_predDF.groupby('deciles').agg(F.sum(target).alias(target)).toPandas()\n",
        "    non_target_cnt = final_predDF.groupby('deciles').agg(F.sum('non_target').alias('non_target')).toPandas()\n",
        "    temp_deciles = temp_deciles.merge(non_target_cnt, on='deciles', how='inner')\n",
        "    temp_deciles = temp_deciles.sort_values(by='deciles', ascending=True)\n",
        "    temp_deciles['total'] = temp_deciles[target] + temp_deciles['non_target']\n",
        "    temp_deciles['target_%'] = (temp_deciles[target] / temp_deciles['total'])*100\n",
        "    temp_deciles['cum_target'] = temp_deciles[target].cumsum()\n",
        "    temp_deciles['cum_non_target'] = temp_deciles['non_target'].cumsum()\n",
        "    temp_deciles['target_dist'] = (temp_deciles['cum_target']/temp_deciles[target].sum())*100\n",
        "    temp_deciles['non_target_dist'] = (temp_deciles['cum_non_target']/temp_deciles['non_target'].sum())*100\n",
        "    temp_deciles['spread'] = temp_deciles['target_dist'] - temp_deciles['non_target_dist']\n",
        "    decile_table=temp_deciles.round(2)\n",
        "    decile_table = decile_table[['deciles', 'total', 'label', 'non_target', 'target_%', 'cum_target', 'cum_non_target', 'target_dist', 'non_target_dist', 'spread']]\n",
        "    print(\"KS Value - \", round(temp_deciles['spread'].max(), 2))\n",
        "    return final_predDF, decile_table"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9BLqzFapbm5",
        "outputId": "866852e7-2670-4758-a5ae-b3233bec2153"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KS Value -  50.03\n",
            "KS Value -  50.55\n"
          ]
        }
      ],
      "source": [
        "pred_train, train_deciles = create_deciles(train, clf_model, 'probability', 'prediction', 'label', 10)\n",
        "pred_test, test_deciles = create_deciles(test, clf_model, 'probability', 'prediction', 'label', 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "id": "3zE5cLc8pbm8"
      },
      "outputs": [],
      "source": [
        "from collections import OrderedDict\n",
        "import pandas as pd\n",
        "import sys\n",
        "%matplotlib inline\n",
        "    \n",
        "def plot_pandas_style(styler):\n",
        "    from IPython.core.display import HTML\n",
        "    html = '\\n'.join([line.lstrip() for line in styler.render().split('\\n')])\n",
        "    return HTML(html)\n",
        "\n",
        "def highlight_max(s,color='yellow'):\n",
        "    '''\n",
        "    highlight the maximum in a Series yellow.\n",
        "    '''\n",
        "    is_max = s == s.max()\n",
        "    return ['background-color: {}'.format(color) if v else '' for v in is_max]\n",
        "\n",
        "def decile_labels(agg1, target, color='skyblue'):\n",
        "    agg1 = agg1.round(2)\n",
        "    agg1 = agg1.style.apply(highlight_max, color = 'yellow', subset=['spread']).set_precision(2)\n",
        "    agg1.bar(subset=[target], color='{}'.format(color), vmin=0)\n",
        "    agg1.bar(subset=['total'], color='{}'.format(color), vmin=0)\n",
        "    agg1.bar(subset=['target_%'], color='{}'.format(color), vmin=0)\n",
        "    return agg1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "uO5FUZOCpbm9",
        "outputId": "6681c762-db9b-4d69-b0cc-902f4aa25a12"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-95-7ef5ab9d770d>:20: FutureWarning: this method is deprecated in favour of `Styler.format(precision=..)`\n",
            "  agg1 = agg1.style.apply(highlight_max, color = 'yellow', subset=['spread']).set_precision(2)\n",
            "<ipython-input-95-7ef5ab9d770d>:8: FutureWarning: this method is deprecated in favour of `Styler.to_html()`\n",
            "  html = '\\n'.join([line.lstrip() for line in styler.render().split('\\n')])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_7c197_row0_col1, #T_7c197_row0_col2, #T_7c197_row0_col4, #T_7c197_row1_col1, #T_7c197_row2_col1, #T_7c197_row3_col1, #T_7c197_row4_col1, #T_7c197_row5_col1, #T_7c197_row6_col1, #T_7c197_row7_col1, #T_7c197_row8_col1, #T_7c197_row9_col1 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 100.0%, transparent 100.0%);\n",
              "}\n",
              "#T_7c197_row1_col2, #T_7c197_row1_col4 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 51.7%, transparent 51.7%);\n",
              "}\n",
              "#T_7c197_row2_col2, #T_7c197_row2_col4 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 33.5%, transparent 33.5%);\n",
              "}\n",
              "#T_7c197_row2_col9 {\n",
              "background-color: yellow;\n",
              "}\n",
              "#T_7c197_row3_col2, #T_7c197_row3_col4 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 22.1%, transparent 22.1%);\n",
              "}\n",
              "#T_7c197_row4_col2, #T_7c197_row4_col4 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 17.0%, transparent 17.0%);\n",
              "}\n",
              "#T_7c197_row5_col2, #T_7c197_row5_col4 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 10.6%, transparent 10.6%);\n",
              "}\n",
              "#T_7c197_row6_col2, #T_7c197_row6_col4 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 6.6%, transparent 6.6%);\n",
              "}\n",
              "#T_7c197_row7_col2, #T_7c197_row7_col4 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 4.3%, transparent 4.3%);\n",
              "}\n",
              "#T_7c197_row8_col2, #T_7c197_row8_col4 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 2.9%, transparent 2.9%);\n",
              "}\n",
              "#T_7c197_row9_col2, #T_7c197_row9_col4 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 0.9%, transparent 0.9%);\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_7c197\">\n",
              "<thead>\n",
              "<tr>\n",
              "<th class=\"blank level0\" >&nbsp;</th>\n",
              "<th id=\"T_7c197_level0_col0\" class=\"col_heading level0 col0\" >deciles</th>\n",
              "<th id=\"T_7c197_level0_col1\" class=\"col_heading level0 col1\" >total</th>\n",
              "<th id=\"T_7c197_level0_col2\" class=\"col_heading level0 col2\" >label</th>\n",
              "<th id=\"T_7c197_level0_col3\" class=\"col_heading level0 col3\" >non_target</th>\n",
              "<th id=\"T_7c197_level0_col4\" class=\"col_heading level0 col4\" >target_%</th>\n",
              "<th id=\"T_7c197_level0_col5\" class=\"col_heading level0 col5\" >cum_target</th>\n",
              "<th id=\"T_7c197_level0_col6\" class=\"col_heading level0 col6\" >cum_non_target</th>\n",
              "<th id=\"T_7c197_level0_col7\" class=\"col_heading level0 col7\" >target_dist</th>\n",
              "<th id=\"T_7c197_level0_col8\" class=\"col_heading level0 col8\" >non_target_dist</th>\n",
              "<th id=\"T_7c197_level0_col9\" class=\"col_heading level0 col9\" >spread</th>\n",
              "</tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr>\n",
              "<th id=\"T_7c197_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "<td id=\"T_7c197_row0_col0\" class=\"data row0 col0\" >1</td>\n",
              "<td id=\"T_7c197_row0_col1\" class=\"data row0 col1\" >3153.00</td>\n",
              "<td id=\"T_7c197_row0_col2\" class=\"data row0 col2\" >1475.00</td>\n",
              "<td id=\"T_7c197_row0_col3\" class=\"data row0 col3\" >1678.00</td>\n",
              "<td id=\"T_7c197_row0_col4\" class=\"data row0 col4\" >46.78</td>\n",
              "<td id=\"T_7c197_row0_col5\" class=\"data row0 col5\" >1475.00</td>\n",
              "<td id=\"T_7c197_row0_col6\" class=\"data row0 col6\" >1678.00</td>\n",
              "<td id=\"T_7c197_row0_col7\" class=\"data row0 col7\" >40.07</td>\n",
              "<td id=\"T_7c197_row0_col8\" class=\"data row0 col8\" >6.03</td>\n",
              "<td id=\"T_7c197_row0_col9\" class=\"data row0 col9\" >34.04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<th id=\"T_7c197_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "<td id=\"T_7c197_row1_col0\" class=\"data row1 col0\" >2</td>\n",
              "<td id=\"T_7c197_row1_col1\" class=\"data row1 col1\" >3153.00</td>\n",
              "<td id=\"T_7c197_row1_col2\" class=\"data row1 col2\" >762.00</td>\n",
              "<td id=\"T_7c197_row1_col3\" class=\"data row1 col3\" >2391.00</td>\n",
              "<td id=\"T_7c197_row1_col4\" class=\"data row1 col4\" >24.17</td>\n",
              "<td id=\"T_7c197_row1_col5\" class=\"data row1 col5\" >2237.00</td>\n",
              "<td id=\"T_7c197_row1_col6\" class=\"data row1 col6\" >4069.00</td>\n",
              "<td id=\"T_7c197_row1_col7\" class=\"data row1 col7\" >60.77</td>\n",
              "<td id=\"T_7c197_row1_col8\" class=\"data row1 col8\" >14.61</td>\n",
              "<td id=\"T_7c197_row1_col9\" class=\"data row1 col9\" >46.16</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<th id=\"T_7c197_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "<td id=\"T_7c197_row2_col0\" class=\"data row2 col0\" >3</td>\n",
              "<td id=\"T_7c197_row2_col1\" class=\"data row2 col1\" >3153.00</td>\n",
              "<td id=\"T_7c197_row2_col2\" class=\"data row2 col2\" >494.00</td>\n",
              "<td id=\"T_7c197_row2_col3\" class=\"data row2 col3\" >2659.00</td>\n",
              "<td id=\"T_7c197_row2_col4\" class=\"data row2 col4\" >15.67</td>\n",
              "<td id=\"T_7c197_row2_col5\" class=\"data row2 col5\" >2731.00</td>\n",
              "<td id=\"T_7c197_row2_col6\" class=\"data row2 col6\" >6728.00</td>\n",
              "<td id=\"T_7c197_row2_col7\" class=\"data row2 col7\" >74.19</td>\n",
              "<td id=\"T_7c197_row2_col8\" class=\"data row2 col8\" >24.16</td>\n",
              "<td id=\"T_7c197_row2_col9\" class=\"data row2 col9\" >50.03</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<th id=\"T_7c197_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "<td id=\"T_7c197_row3_col0\" class=\"data row3 col0\" >4</td>\n",
              "<td id=\"T_7c197_row3_col1\" class=\"data row3 col1\" >3153.00</td>\n",
              "<td id=\"T_7c197_row3_col2\" class=\"data row3 col2\" >326.00</td>\n",
              "<td id=\"T_7c197_row3_col3\" class=\"data row3 col3\" >2827.00</td>\n",
              "<td id=\"T_7c197_row3_col4\" class=\"data row3 col4\" >10.34</td>\n",
              "<td id=\"T_7c197_row3_col5\" class=\"data row3 col5\" >3057.00</td>\n",
              "<td id=\"T_7c197_row3_col6\" class=\"data row3 col6\" >9555.00</td>\n",
              "<td id=\"T_7c197_row3_col7\" class=\"data row3 col7\" >83.05</td>\n",
              "<td id=\"T_7c197_row3_col8\" class=\"data row3 col8\" >34.31</td>\n",
              "<td id=\"T_7c197_row3_col9\" class=\"data row3 col9\" >48.73</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<th id=\"T_7c197_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "<td id=\"T_7c197_row4_col0\" class=\"data row4 col0\" >5</td>\n",
              "<td id=\"T_7c197_row4_col1\" class=\"data row4 col1\" >3153.00</td>\n",
              "<td id=\"T_7c197_row4_col2\" class=\"data row4 col2\" >251.00</td>\n",
              "<td id=\"T_7c197_row4_col3\" class=\"data row4 col3\" >2902.00</td>\n",
              "<td id=\"T_7c197_row4_col4\" class=\"data row4 col4\" >7.96</td>\n",
              "<td id=\"T_7c197_row4_col5\" class=\"data row4 col5\" >3308.00</td>\n",
              "<td id=\"T_7c197_row4_col6\" class=\"data row4 col6\" >12457.00</td>\n",
              "<td id=\"T_7c197_row4_col7\" class=\"data row4 col7\" >89.87</td>\n",
              "<td id=\"T_7c197_row4_col8\" class=\"data row4 col8\" >44.74</td>\n",
              "<td id=\"T_7c197_row4_col9\" class=\"data row4 col9\" >45.13</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<th id=\"T_7c197_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "<td id=\"T_7c197_row5_col0\" class=\"data row5 col0\" >6</td>\n",
              "<td id=\"T_7c197_row5_col1\" class=\"data row5 col1\" >3153.00</td>\n",
              "<td id=\"T_7c197_row5_col2\" class=\"data row5 col2\" >157.00</td>\n",
              "<td id=\"T_7c197_row5_col3\" class=\"data row5 col3\" >2996.00</td>\n",
              "<td id=\"T_7c197_row5_col4\" class=\"data row5 col4\" >4.98</td>\n",
              "<td id=\"T_7c197_row5_col5\" class=\"data row5 col5\" >3465.00</td>\n",
              "<td id=\"T_7c197_row5_col6\" class=\"data row5 col6\" >15453.00</td>\n",
              "<td id=\"T_7c197_row5_col7\" class=\"data row5 col7\" >94.13</td>\n",
              "<td id=\"T_7c197_row5_col8\" class=\"data row5 col8\" >55.49</td>\n",
              "<td id=\"T_7c197_row5_col9\" class=\"data row5 col9\" >38.64</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<th id=\"T_7c197_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "<td id=\"T_7c197_row6_col0\" class=\"data row6 col0\" >7</td>\n",
              "<td id=\"T_7c197_row6_col1\" class=\"data row6 col1\" >3153.00</td>\n",
              "<td id=\"T_7c197_row6_col2\" class=\"data row6 col2\" >97.00</td>\n",
              "<td id=\"T_7c197_row6_col3\" class=\"data row6 col3\" >3056.00</td>\n",
              "<td id=\"T_7c197_row6_col4\" class=\"data row6 col4\" >3.08</td>\n",
              "<td id=\"T_7c197_row6_col5\" class=\"data row6 col5\" >3562.00</td>\n",
              "<td id=\"T_7c197_row6_col6\" class=\"data row6 col6\" >18509.00</td>\n",
              "<td id=\"T_7c197_row6_col7\" class=\"data row6 col7\" >96.77</td>\n",
              "<td id=\"T_7c197_row6_col8\" class=\"data row6 col8\" >66.47</td>\n",
              "<td id=\"T_7c197_row6_col9\" class=\"data row6 col9\" >30.30</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<th id=\"T_7c197_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "<td id=\"T_7c197_row7_col0\" class=\"data row7 col0\" >8</td>\n",
              "<td id=\"T_7c197_row7_col1\" class=\"data row7 col1\" >3152.00</td>\n",
              "<td id=\"T_7c197_row7_col2\" class=\"data row7 col2\" >63.00</td>\n",
              "<td id=\"T_7c197_row7_col3\" class=\"data row7 col3\" >3089.00</td>\n",
              "<td id=\"T_7c197_row7_col4\" class=\"data row7 col4\" >2.00</td>\n",
              "<td id=\"T_7c197_row7_col5\" class=\"data row7 col5\" >3625.00</td>\n",
              "<td id=\"T_7c197_row7_col6\" class=\"data row7 col6\" >21598.00</td>\n",
              "<td id=\"T_7c197_row7_col7\" class=\"data row7 col7\" >98.48</td>\n",
              "<td id=\"T_7c197_row7_col8\" class=\"data row7 col8\" >77.56</td>\n",
              "<td id=\"T_7c197_row7_col9\" class=\"data row7 col9\" >20.92</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<th id=\"T_7c197_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "<td id=\"T_7c197_row8_col0\" class=\"data row8 col0\" >9</td>\n",
              "<td id=\"T_7c197_row8_col1\" class=\"data row8 col1\" >3152.00</td>\n",
              "<td id=\"T_7c197_row8_col2\" class=\"data row8 col2\" >43.00</td>\n",
              "<td id=\"T_7c197_row8_col3\" class=\"data row8 col3\" >3109.00</td>\n",
              "<td id=\"T_7c197_row8_col4\" class=\"data row8 col4\" >1.36</td>\n",
              "<td id=\"T_7c197_row8_col5\" class=\"data row8 col5\" >3668.00</td>\n",
              "<td id=\"T_7c197_row8_col6\" class=\"data row8 col6\" >24707.00</td>\n",
              "<td id=\"T_7c197_row8_col7\" class=\"data row8 col7\" >99.65</td>\n",
              "<td id=\"T_7c197_row8_col8\" class=\"data row8 col8\" >88.73</td>\n",
              "<td id=\"T_7c197_row8_col9\" class=\"data row8 col9\" >10.92</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<th id=\"T_7c197_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "<td id=\"T_7c197_row9_col0\" class=\"data row9 col0\" >10</td>\n",
              "<td id=\"T_7c197_row9_col1\" class=\"data row9 col1\" >3152.00</td>\n",
              "<td id=\"T_7c197_row9_col2\" class=\"data row9 col2\" >13.00</td>\n",
              "<td id=\"T_7c197_row9_col3\" class=\"data row9 col3\" >3139.00</td>\n",
              "<td id=\"T_7c197_row9_col4\" class=\"data row9 col4\" >0.41</td>\n",
              "<td id=\"T_7c197_row9_col5\" class=\"data row9 col5\" >3681.00</td>\n",
              "<td id=\"T_7c197_row9_col6\" class=\"data row9 col6\" >27846.00</td>\n",
              "<td id=\"T_7c197_row9_col7\" class=\"data row9 col7\" >100.00</td>\n",
              "<td id=\"T_7c197_row9_col8\" class=\"data row9 col8\" >100.00</td>\n",
              "<td id=\"T_7c197_row9_col9\" class=\"data row9 col9\" >0.00</td>\n",
              "</tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ],
      "source": [
        "plot_decile_train = decile_labels(train_deciles, 'label', color='skyblue')\n",
        "plot_pandas_style(plot_decile_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "D_JmT2vbpbm-",
        "outputId": "5a3ece1a-11b1-4c91-d3d3-e1ffc44d620d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-95-7ef5ab9d770d>:20: FutureWarning: this method is deprecated in favour of `Styler.format(precision=..)`\n",
            "  agg1 = agg1.style.apply(highlight_max, color = 'yellow', subset=['spread']).set_precision(2)\n",
            "<ipython-input-95-7ef5ab9d770d>:8: FutureWarning: this method is deprecated in favour of `Styler.to_html()`\n",
            "  html = '\\n'.join([line.lstrip() for line in styler.render().split('\\n')])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_8993d_row0_col1, #T_8993d_row0_col2, #T_8993d_row0_col4, #T_8993d_row1_col1, #T_8993d_row2_col1, #T_8993d_row3_col1 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 100.0%, transparent 100.0%);\n",
              "}\n",
              "#T_8993d_row1_col2, #T_8993d_row1_col4 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 50.7%, transparent 50.7%);\n",
              "}\n",
              "#T_8993d_row2_col2, #T_8993d_row2_col4 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 34.2%, transparent 34.2%);\n",
              "}\n",
              "#T_8993d_row2_col9 {\n",
              "background-color: yellow;\n",
              "}\n",
              "#T_8993d_row3_col2, #T_8993d_row3_col4 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 23.3%, transparent 23.3%);\n",
              "}\n",
              "#T_8993d_row4_col1, #T_8993d_row5_col1, #T_8993d_row6_col1, #T_8993d_row7_col1, #T_8993d_row8_col1, #T_8993d_row9_col1 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 99.9%, transparent 99.9%);\n",
              "}\n",
              "#T_8993d_row4_col2, #T_8993d_row4_col4 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 17.3%, transparent 17.3%);\n",
              "}\n",
              "#T_8993d_row5_col2, #T_8993d_row5_col4 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 8.9%, transparent 8.9%);\n",
              "}\n",
              "#T_8993d_row6_col2, #T_8993d_row6_col4 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 6.5%, transparent 6.5%);\n",
              "}\n",
              "#T_8993d_row7_col2, #T_8993d_row7_col4 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 2.9%, transparent 2.9%);\n",
              "}\n",
              "#T_8993d_row8_col2, #T_8993d_row8_col4 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 2.8%, transparent 2.8%);\n",
              "}\n",
              "#T_8993d_row9_col2, #T_8993d_row9_col4 {\n",
              "width: 10em;\n",
              "background: linear-gradient(90deg, skyblue 1.2%, transparent 1.2%);\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_8993d\">\n",
              "<thead>\n",
              "<tr>\n",
              "<th class=\"blank level0\" >&nbsp;</th>\n",
              "<th id=\"T_8993d_level0_col0\" class=\"col_heading level0 col0\" >deciles</th>\n",
              "<th id=\"T_8993d_level0_col1\" class=\"col_heading level0 col1\" >total</th>\n",
              "<th id=\"T_8993d_level0_col2\" class=\"col_heading level0 col2\" >label</th>\n",
              "<th id=\"T_8993d_level0_col3\" class=\"col_heading level0 col3\" >non_target</th>\n",
              "<th id=\"T_8993d_level0_col4\" class=\"col_heading level0 col4\" >target_%</th>\n",
              "<th id=\"T_8993d_level0_col5\" class=\"col_heading level0 col5\" >cum_target</th>\n",
              "<th id=\"T_8993d_level0_col6\" class=\"col_heading level0 col6\" >cum_non_target</th>\n",
              "<th id=\"T_8993d_level0_col7\" class=\"col_heading level0 col7\" >target_dist</th>\n",
              "<th id=\"T_8993d_level0_col8\" class=\"col_heading level0 col8\" >non_target_dist</th>\n",
              "<th id=\"T_8993d_level0_col9\" class=\"col_heading level0 col9\" >spread</th>\n",
              "</tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr>\n",
              "<th id=\"T_8993d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "<td id=\"T_8993d_row0_col0\" class=\"data row0 col0\" >1</td>\n",
              "<td id=\"T_8993d_row0_col1\" class=\"data row0 col1\" >1369.00</td>\n",
              "<td id=\"T_8993d_row0_col2\" class=\"data row0 col2\" >649.00</td>\n",
              "<td id=\"T_8993d_row0_col3\" class=\"data row0 col3\" >720.00</td>\n",
              "<td id=\"T_8993d_row0_col4\" class=\"data row0 col4\" >47.41</td>\n",
              "<td id=\"T_8993d_row0_col5\" class=\"data row0 col5\" >649.00</td>\n",
              "<td id=\"T_8993d_row0_col6\" class=\"data row0 col6\" >720.00</td>\n",
              "<td id=\"T_8993d_row0_col7\" class=\"data row0 col7\" >40.36</td>\n",
              "<td id=\"T_8993d_row0_col8\" class=\"data row0 col8\" >5.96</td>\n",
              "<td id=\"T_8993d_row0_col9\" class=\"data row0 col9\" >34.40</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<th id=\"T_8993d_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "<td id=\"T_8993d_row1_col0\" class=\"data row1 col0\" >2</td>\n",
              "<td id=\"T_8993d_row1_col1\" class=\"data row1 col1\" >1369.00</td>\n",
              "<td id=\"T_8993d_row1_col2\" class=\"data row1 col2\" >329.00</td>\n",
              "<td id=\"T_8993d_row1_col3\" class=\"data row1 col3\" >1040.00</td>\n",
              "<td id=\"T_8993d_row1_col4\" class=\"data row1 col4\" >24.03</td>\n",
              "<td id=\"T_8993d_row1_col5\" class=\"data row1 col5\" >978.00</td>\n",
              "<td id=\"T_8993d_row1_col6\" class=\"data row1 col6\" >1760.00</td>\n",
              "<td id=\"T_8993d_row1_col7\" class=\"data row1 col7\" >60.82</td>\n",
              "<td id=\"T_8993d_row1_col8\" class=\"data row1 col8\" >14.57</td>\n",
              "<td id=\"T_8993d_row1_col9\" class=\"data row1 col9\" >46.25</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<th id=\"T_8993d_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "<td id=\"T_8993d_row2_col0\" class=\"data row2 col0\" >3</td>\n",
              "<td id=\"T_8993d_row2_col1\" class=\"data row2 col1\" >1369.00</td>\n",
              "<td id=\"T_8993d_row2_col2\" class=\"data row2 col2\" >222.00</td>\n",
              "<td id=\"T_8993d_row2_col3\" class=\"data row2 col3\" >1147.00</td>\n",
              "<td id=\"T_8993d_row2_col4\" class=\"data row2 col4\" >16.22</td>\n",
              "<td id=\"T_8993d_row2_col5\" class=\"data row2 col5\" >1200.00</td>\n",
              "<td id=\"T_8993d_row2_col6\" class=\"data row2 col6\" >2907.00</td>\n",
              "<td id=\"T_8993d_row2_col7\" class=\"data row2 col7\" >74.63</td>\n",
              "<td id=\"T_8993d_row2_col8\" class=\"data row2 col8\" >24.07</td>\n",
              "<td id=\"T_8993d_row2_col9\" class=\"data row2 col9\" >50.55</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<th id=\"T_8993d_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "<td id=\"T_8993d_row3_col0\" class=\"data row3 col0\" >4</td>\n",
              "<td id=\"T_8993d_row3_col1\" class=\"data row3 col1\" >1369.00</td>\n",
              "<td id=\"T_8993d_row3_col2\" class=\"data row3 col2\" >151.00</td>\n",
              "<td id=\"T_8993d_row3_col3\" class=\"data row3 col3\" >1218.00</td>\n",
              "<td id=\"T_8993d_row3_col4\" class=\"data row3 col4\" >11.03</td>\n",
              "<td id=\"T_8993d_row3_col5\" class=\"data row3 col5\" >1351.00</td>\n",
              "<td id=\"T_8993d_row3_col6\" class=\"data row3 col6\" >4125.00</td>\n",
              "<td id=\"T_8993d_row3_col7\" class=\"data row3 col7\" >84.02</td>\n",
              "<td id=\"T_8993d_row3_col8\" class=\"data row3 col8\" >34.16</td>\n",
              "<td id=\"T_8993d_row3_col9\" class=\"data row3 col9\" >49.86</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<th id=\"T_8993d_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "<td id=\"T_8993d_row4_col0\" class=\"data row4 col0\" >5</td>\n",
              "<td id=\"T_8993d_row4_col1\" class=\"data row4 col1\" >1368.00</td>\n",
              "<td id=\"T_8993d_row4_col2\" class=\"data row4 col2\" >112.00</td>\n",
              "<td id=\"T_8993d_row4_col3\" class=\"data row4 col3\" >1256.00</td>\n",
              "<td id=\"T_8993d_row4_col4\" class=\"data row4 col4\" >8.19</td>\n",
              "<td id=\"T_8993d_row4_col5\" class=\"data row4 col5\" >1463.00</td>\n",
              "<td id=\"T_8993d_row4_col6\" class=\"data row4 col6\" >5381.00</td>\n",
              "<td id=\"T_8993d_row4_col7\" class=\"data row4 col7\" >90.98</td>\n",
              "<td id=\"T_8993d_row4_col8\" class=\"data row4 col8\" >44.56</td>\n",
              "<td id=\"T_8993d_row4_col9\" class=\"data row4 col9\" >46.42</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<th id=\"T_8993d_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "<td id=\"T_8993d_row5_col0\" class=\"data row5 col0\" >6</td>\n",
              "<td id=\"T_8993d_row5_col1\" class=\"data row5 col1\" >1368.00</td>\n",
              "<td id=\"T_8993d_row5_col2\" class=\"data row5 col2\" >58.00</td>\n",
              "<td id=\"T_8993d_row5_col3\" class=\"data row5 col3\" >1310.00</td>\n",
              "<td id=\"T_8993d_row5_col4\" class=\"data row5 col4\" >4.24</td>\n",
              "<td id=\"T_8993d_row5_col5\" class=\"data row5 col5\" >1521.00</td>\n",
              "<td id=\"T_8993d_row5_col6\" class=\"data row5 col6\" >6691.00</td>\n",
              "<td id=\"T_8993d_row5_col7\" class=\"data row5 col7\" >94.59</td>\n",
              "<td id=\"T_8993d_row5_col8\" class=\"data row5 col8\" >55.41</td>\n",
              "<td id=\"T_8993d_row5_col9\" class=\"data row5 col9\" >39.18</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<th id=\"T_8993d_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "<td id=\"T_8993d_row6_col0\" class=\"data row6 col0\" >7</td>\n",
              "<td id=\"T_8993d_row6_col1\" class=\"data row6 col1\" >1368.00</td>\n",
              "<td id=\"T_8993d_row6_col2\" class=\"data row6 col2\" >42.00</td>\n",
              "<td id=\"T_8993d_row6_col3\" class=\"data row6 col3\" >1326.00</td>\n",
              "<td id=\"T_8993d_row6_col4\" class=\"data row6 col4\" >3.07</td>\n",
              "<td id=\"T_8993d_row6_col5\" class=\"data row6 col5\" >1563.00</td>\n",
              "<td id=\"T_8993d_row6_col6\" class=\"data row6 col6\" >8017.00</td>\n",
              "<td id=\"T_8993d_row6_col7\" class=\"data row6 col7\" >97.20</td>\n",
              "<td id=\"T_8993d_row6_col8\" class=\"data row6 col8\" >66.39</td>\n",
              "<td id=\"T_8993d_row6_col9\" class=\"data row6 col9\" >30.81</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<th id=\"T_8993d_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "<td id=\"T_8993d_row7_col0\" class=\"data row7 col0\" >8</td>\n",
              "<td id=\"T_8993d_row7_col1\" class=\"data row7 col1\" >1368.00</td>\n",
              "<td id=\"T_8993d_row7_col2\" class=\"data row7 col2\" >19.00</td>\n",
              "<td id=\"T_8993d_row7_col3\" class=\"data row7 col3\" >1349.00</td>\n",
              "<td id=\"T_8993d_row7_col4\" class=\"data row7 col4\" >1.39</td>\n",
              "<td id=\"T_8993d_row7_col5\" class=\"data row7 col5\" >1582.00</td>\n",
              "<td id=\"T_8993d_row7_col6\" class=\"data row7 col6\" >9366.00</td>\n",
              "<td id=\"T_8993d_row7_col7\" class=\"data row7 col7\" >98.38</td>\n",
              "<td id=\"T_8993d_row7_col8\" class=\"data row7 col8\" >77.56</td>\n",
              "<td id=\"T_8993d_row7_col9\" class=\"data row7 col9\" >20.82</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<th id=\"T_8993d_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "<td id=\"T_8993d_row8_col0\" class=\"data row8 col0\" >9</td>\n",
              "<td id=\"T_8993d_row8_col1\" class=\"data row8 col1\" >1368.00</td>\n",
              "<td id=\"T_8993d_row8_col2\" class=\"data row8 col2\" >18.00</td>\n",
              "<td id=\"T_8993d_row8_col3\" class=\"data row8 col3\" >1350.00</td>\n",
              "<td id=\"T_8993d_row8_col4\" class=\"data row8 col4\" >1.32</td>\n",
              "<td id=\"T_8993d_row8_col5\" class=\"data row8 col5\" >1600.00</td>\n",
              "<td id=\"T_8993d_row8_col6\" class=\"data row8 col6\" >10716.00</td>\n",
              "<td id=\"T_8993d_row8_col7\" class=\"data row8 col7\" >99.50</td>\n",
              "<td id=\"T_8993d_row8_col8\" class=\"data row8 col8\" >88.74</td>\n",
              "<td id=\"T_8993d_row8_col9\" class=\"data row8 col9\" >10.76</td>\n",
              "</tr>\n",
              "<tr>\n",
              "<th id=\"T_8993d_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "<td id=\"T_8993d_row9_col0\" class=\"data row9 col0\" >10</td>\n",
              "<td id=\"T_8993d_row9_col1\" class=\"data row9 col1\" >1368.00</td>\n",
              "<td id=\"T_8993d_row9_col2\" class=\"data row9 col2\" >8.00</td>\n",
              "<td id=\"T_8993d_row9_col3\" class=\"data row9 col3\" >1360.00</td>\n",
              "<td id=\"T_8993d_row9_col4\" class=\"data row9 col4\" >0.58</td>\n",
              "<td id=\"T_8993d_row9_col5\" class=\"data row9 col5\" >1608.00</td>\n",
              "<td id=\"T_8993d_row9_col6\" class=\"data row9 col6\" >12076.00</td>\n",
              "<td id=\"T_8993d_row9_col7\" class=\"data row9 col7\" >100.00</td>\n",
              "<td id=\"T_8993d_row9_col8\" class=\"data row9 col8\" >100.00</td>\n",
              "<td id=\"T_8993d_row9_col9\" class=\"data row9 col9\" >0.00</td>\n",
              "</tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "plot_decile_test = decile_labels(test_deciles, 'label', color='skyblue')\n",
        "plot_pandas_style(plot_decile_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mj6O0uGkpbm_"
      },
      "source": [
        "# Actual vs Predicted, Gains and Lift chart"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "id": "mqxNGejHpbnA"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plots(agg1,target,type):\n",
        "\n",
        "    plt.figure(1,figsize=(20, 5))\n",
        "\n",
        "    plt.subplot(131)\n",
        "    plt.plot(agg1['DECILE'],agg1['ACTUAL'],label='Actual')\n",
        "    plt.plot(agg1['DECILE'],agg1['PRED'],label='Pred')\n",
        "    plt.xticks(range(10,110,10))\n",
        "    plt.legend(fontsize=15)\n",
        "    plt.grid(True)\n",
        "    plt.title('Actual vs Predicted', fontsize=20)\n",
        "    plt.xlabel(\"Population %\",fontsize=15)\n",
        "    plt.ylabel(str(target) + \" \" + str(type) + \" %\",fontsize=15)\n",
        "\n",
        "    plt.subplot(132)\n",
        "    X = agg1['DECILE'].tolist()\n",
        "    X.append(0)\n",
        "    Y = agg1['DIST_TAR'].tolist()\n",
        "    Y.append(0)\n",
        "    plt.plot(sorted(X),sorted(Y))\n",
        "    plt.plot([0, 100], [0, 100],'r--')\n",
        "    plt.xticks(range(0,110,10))\n",
        "    plt.yticks(range(0,110,10))\n",
        "    plt.grid(True)\n",
        "    plt.title('Gains Chart', fontsize=20)\n",
        "    plt.xlabel(\"Population %\",fontsize=15)\n",
        "    plt.ylabel(str(target) + str(\" DISTRIBUTION\") + \" %\",fontsize=15)\n",
        "    plt.annotate(round(agg1[agg1['DECILE'] == 30].DIST_TAR.item(),2),xy=[30,30], \n",
        "            xytext=(25, agg1[agg1['DECILE'] == 30].DIST_TAR.item() + 5),fontsize = 13)\n",
        "    plt.annotate(round(agg1[agg1['DECILE'] == 50].DIST_TAR.item(),2),xy=[50,50], \n",
        "            xytext=(45, agg1[agg1['DECILE'] == 50].DIST_TAR.item() + 5),fontsize = 13)\n",
        "\n",
        "    plt.subplot(133)\n",
        "    plt.plot(agg1['DECILE'],agg1['LIFT'])\n",
        "    plt.xticks(range(10,110,10))\n",
        "    plt.grid(True)\n",
        "    plt.title('Lift Chart', fontsize=20)\n",
        "    plt.xlabel(\"Population %\",fontsize=15)\n",
        "    plt.ylabel(\"Lift\",fontsize=15)\n",
        "\n",
        "    plt.tight_layout()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "F7HXkHdWpbnA"
      },
      "outputs": [],
      "source": [
        "def gains(data, decile_df, decile_by, target, score):\n",
        "    \n",
        "    agg1 = pd.DataFrame({},index=[])\n",
        "    agg1 = data.groupby(decile_by).agg(F.avg(target).alias('ACTUAL')).toPandas()\n",
        "    score_agg = data.groupby(decile_by).agg(F.avg(score).alias('PRED')).toPandas()\n",
        "    agg1 = agg1.merge(score_agg, on=decile_by, how='inner').merge(decile_df, on=decile_by, how='inner')    \n",
        "    agg1 = agg1.sort_values(by=decile_by, ascending=True)\n",
        "    agg1 = agg1[[decile_by, 'ACTUAL', 'PRED', 'target_dist']]\n",
        "    agg1 = agg1.rename(columns={'target_dist':'DIST_TAR', 'deciles': 'DECILE'})\n",
        "    decile_by = 'DECILE'\n",
        "    agg1[decile_by] = agg1[decile_by]*10\n",
        "    agg1['LIFT'] = agg1['DIST_TAR']/agg1[decile_by]\n",
        "    agg1.columns = [x.upper() for x in agg1.columns]\n",
        "    plots(agg1,target,'Distribution')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "id": "MIdLTDfbpbnH",
        "outputId": "56f942d0-1d03-48bf-c371-cb9cd49c3be9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "gains(pred_train, train_deciles, 'deciles', 'label','probability')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "id": "s7jOLlWDpbnI",
        "outputId": "809d8f61-3281-4982-f7ea-4653ffeaa16a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "gains(pred_test, test_deciles, 'deciles', 'label','probability')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "KPAVFu8GpbnJ"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "TcKhhy16pbnN"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "nv5lcJmnpbnO"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "YZmiilgApbnP"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "7ADrrnwopbnP"
      },
      "outputs": [],
      "source": []
    }
  ],
  "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": "3.7.6"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}