logistic_regression_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import findspark\n",
    "findspark.init('/home/siddharth/spark-2.4.1-bin-hadoop2.7/')\n",
    "\n",
    "from pyspark.sql import SparkSession"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "spark = SparkSession.builder.appName('log_reg_titanic').getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = spark.read.csv('./Python-and-Spark-for-Big-Data-master/Spark_for_Machine_Learning/Logistic_Regression/titanic.csv',\n",
    "                     inferSchema = True,header = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- PassengerId: integer (nullable = true)\n",
      " |-- Survived: integer (nullable = true)\n",
      " |-- Pclass: integer (nullable = true)\n",
      " |-- Name: string (nullable = true)\n",
      " |-- Sex: string (nullable = true)\n",
      " |-- Age: double (nullable = true)\n",
      " |-- SibSp: integer (nullable = true)\n",
      " |-- Parch: integer (nullable = true)\n",
      " |-- Ticket: string (nullable = true)\n",
      " |-- Fare: double (nullable = true)\n",
      " |-- Cabin: string (nullable = true)\n",
      " |-- Embarked: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PassengerId',\n",
       " 'Survived',\n",
       " 'Pclass',\n",
       " 'Name',\n",
       " 'Sex',\n",
       " 'Age',\n",
       " 'SibSp',\n",
       " 'Parch',\n",
       " 'Ticket',\n",
       " 'Fare',\n",
       " 'Cabin',\n",
       " 'Embarked']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = data.select(['Survived',\n",
    " 'Pclass',\n",
    " 'Sex',\n",
    " 'Age',\n",
    " 'SibSp',\n",
    " 'Parch',\n",
    " 'Fare',\n",
    " 'Embarked'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-------------------+------------------+------+------------------+------------------+-------------------+-----------------+--------+\n",
      "|summary|           Survived|            Pclass|   Sex|               Age|             SibSp|              Parch|             Fare|Embarked|\n",
      "+-------+-------------------+------------------+------+------------------+------------------+-------------------+-----------------+--------+\n",
      "|  count|                891|               891|   891|               714|               891|                891|              891|     889|\n",
      "|   mean| 0.3838383838383838| 2.308641975308642|  null| 29.69911764705882|0.5230078563411896|0.38159371492704824| 32.2042079685746|    null|\n",
      "| stddev|0.48659245426485753|0.8360712409770491|  null|14.526497332334035|1.1027434322934315| 0.8060572211299488|49.69342859718089|    null|\n",
      "|    min|                  0|                 1|female|              0.42|                 0|                  0|              0.0|       C|\n",
      "|    max|                  1|                 3|  male|              80.0|                 8|                  6|         512.3292|       S|\n",
      "+-------+-------------------+------------------+------+------------------+------------------+-------------------+-----------------+--------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "final_data.describe().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = final_data.na.drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import VectorAssembler,StringIndexer,OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_indexer = StringIndexer(inputCol='Sex',outputCol='sex_index')\n",
    "gender_encoder = OneHotEncoder(inputCol='sex_index',outputCol='sex_ohe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "embark_indexer = StringIndexer(inputCol='Embarked',outputCol='embarked_index')\n",
    "embark_encoder = OneHotEncoder(inputCol='embarked_index',outputCol='embarked_ohe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "assembler = VectorAssembler(inputCols=['Pclass',\n",
    " 'sex_ohe',\n",
    " 'Age',\n",
    " 'SibSp',\n",
    " 'Parch',\n",
    " 'Fare',\n",
    " 'embarked_ohe'],outputCol = 'features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.classification import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with piplines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg = LogisticRegression(labelCol='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipline = Pipeline(stages=[gender_indexer,gender_encoder,embark_indexer,embark_encoder,assembler,log_reg])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data,test_data = final_data.randomSplit([0.7,0.3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_model = pipline.fit(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = fit_model.transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.evaluation import BinaryClassificationEvaluator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_eval = BinaryClassificationEvaluator(rawPredictionCol='prediction',labelCol='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[Survived: int, Pclass: int, Sex: string, Age: double, SibSp: int, Parch: int, Fare: double, Embarked: string, sex_index: double, sex_ohe: vector, embarked_index: double, embarked_ohe: vector, features: vector, rawPrediction: vector, probability: vector, prediction: double]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "auc = my_eval.evaluate(result.select('Survived','prediction'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7957517470421366"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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