Data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import findspark\n",
    "findspark.init()\n",
    "\n",
    "import pyspark\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark import SparkContext\n",
    "from pyspark.sql import SQLContext\n",
    "from pyspark.sql.session import SparkSession\n",
    "from pyspark.sql.types import *\n",
    "from pyspark.sql.functions import isnan, when, count, col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"2004.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _init_spark():\n",
    "    spark = SparkSession.builder.appName(\"Project\").getOrCreate()\n",
    "    sc = spark.sparkContext\n",
    "    return spark, sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "spark, sc = _init_spark()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[Year: int, Month: int, DayofMonth: int, DayOfWeek: int, DepTime: string, CRSDepTime: int, ArrTime: string, CRSArrTime: int, UniqueCarrier: string, FlightNum: int, TailNum: string, ActualElapsedTime: string, CRSElapsedTime: int, AirTime: string, ArrDelay: string, DepDelay: string, Origin: string, Dest: string, Distance: int, TaxiIn: int, TaxiOut: int, Cancelled: int, CancellationCode: string, Diverted: int, CarrierDelay: int, WeatherDelay: int, NASDelay: int, SecurityDelay: int, LateAircraftDelay: int]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqlContext = SQLContext(sc)\n",
    "\n",
    "df = sqlContext.read.load(filename, \n",
    "                      format='com.databricks.spark.csv', \n",
    "                      header='true',\n",
    "                      delimiter=',',\n",
    "                      inferSchema='true')\n",
    "df.cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing as is stated in the task along with the 'Year'\n",
    "col_to_drop = ['ArrTime', 'ActualElapsedTime', 'AirTime', 'TaxiIn', 'Diverted', \n",
    "               'CarrierDelay', 'WeatherDelay', 'NASDelay', 'SecurityDelay', 'LateAircraftDelay', 'Year']\n",
    "df = df.drop(*col_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+----------+---------+-------+----------+----------+-------------+---------+-------+--------------+--------+--------+------+----+--------+-------+---------+----------------+\n",
      "|Month|DayofMonth|DayOfWeek|DepTime|CRSDepTime|CRSArrTime|UniqueCarrier|FlightNum|TailNum|CRSElapsedTime|ArrDelay|DepDelay|Origin|Dest|Distance|TaxiOut|Cancelled|CancellationCode|\n",
      "+-----+----------+---------+-------+----------+----------+-------------+---------+-------+--------------+--------+--------+------+----+--------+-------+---------+----------------+\n",
      "|    0|         0|        0|      0|         0|         0|            0|        0|    127|             0|       0|       0|     0|   0|       0|      0|        0|         7001506|\n",
      "+-----+----------+---------+-------+----------+----------+-------------+---------+-------+--------------+--------+--------+------+----+--------+-------+---------+----------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# \"CancelationCode\" has too much \"null\" (98% of the data) we will remove it too. Others have no missing values except for \"TailNum\", that has only 127 values left.  \n",
    "df.select([count(when(isnan(c) | col(c).isNull(), c)).alias(c) for c in df.columns]).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deletion of the \"CancelationCode\" and droping rows that contain \"TailNum\", \"UniqueCarrier\" \n",
    "# is represented by several values so we will explore it later\n",
    "df = df.drop('CancellationCode')\n",
    "df = df.drop('TailNum')\n",
    "## df = df.filter(df.TailNum.isNotNull() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,

   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- Month: integer (nullable = true)\n",
      " |-- DayofMonth: integer (nullable = true)\n",
      " |-- DayOfWeek: integer (nullable = true)\n",
      " |-- DepTime: string (nullable = true)\n",
      " |-- CRSDepTime: integer (nullable = true)\n",
      " |-- CRSArrTime: integer (nullable = true)\n",
      " |-- UniqueCarrier: string (nullable = true)\n",
      " |-- FlightNum: integer (nullable = true)\n",
      " |-- CRSElapsedTime: integer (nullable = true)\n",
      " |-- ArrDelay: string (nullable = true)\n",
      " |-- DepDelay: string (nullable = true)\n",
      " |-- Origin: string (nullable = true)\n",
      " |-- Dest: string (nullable = true)\n",
      " |-- Distance: integer (nullable = true)\n",
      " |-- TaxiOut: integer (nullable = true)\n",
      " |-- Cancelled: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- Month: integer (nullable = true)\n",
      " |-- DayofMonth: integer (nullable = true)\n",
      " |-- DayOfWeek: integer (nullable = true)\n",
      " |-- DepTime: string (nullable = true)\n",
      " |-- CRSDepTime: integer (nullable = true)\n",
      " |-- CRSArrTime: integer (nullable = true)\n",
      " |-- UniqueCarrier: string (nullable = true)\n",
      " |-- FlightNum: integer (nullable = true)\n",
      " |-- CRSElapsedTime: integer (nullable = true)\n",
      " |-- ArrDelay: integer (nullable = true)\n",
      " |-- DepDelay: integer (nullable = true)\n",
      " |-- Origin: string (nullable = true)\n",
      " |-- Dest: string (nullable = true)\n",
      " |-- Distance: integer (nullable = true)\n",
      " |-- TaxiOut: integer (nullable = true)\n",
      " |-- Cancelled: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# \"ArrDelay\" and \"DepDelay\" have string type. We cast them to Integer\n",
    "df = df.withColumn(\"ArrDelay\", df[\"ArrDelay\"].cast(IntegerType()))\n",
    "df = df.withColumn(\"DepDelay\", df[\"DepDelay\"].cast(IntegerType()))\n",
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before: 7129270,\n",
      "After: 6987729,\n",
      "%:98.0\n"
     ]
    }
   ],
   "source": [
    "old_amount = df.count()\n",
    "df = df.na.drop(\"any\")\n",
    "new_amount = df.count()\n",
    "print( \"Before: \" +str(old_amount) + \",\\nAfter: \" + str(new_amount) + \",\\n%:\"+str(round(new_amount/old_amount, 2)*100))"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 15,

   "metadata": {},
   "outputs": [],
   "source": [
    "df.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LIN REG with only numerical values"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 33,

   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = df.select([x[0] for x in df.dtypes if 'int' in x])"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 34,

   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix.show(5)"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Month', 0.008202777323020096),\n",
       " ('DayofMonth', 0.024984890590987054),\n",
       " ('DayOfWeek', -0.017791803836727405),\n",
       " ('CRSDepTime', 0.1267987420931976),\n",
       " ('CRSArrTime', 0.12408222835628847),\n",
       " ('FlightNum', -0.001189494331136067),\n",
       " ('CRSElapsedTime', -0.013223108616871579),\n",
       " ('ArrDelay', 1.0),\n",
       " ('DepDelay', 0.907447881121435),\n",
       " ('Distance', -0.013181189620090234),\n",
       " ('TaxiOut', 0.25188155159775766),\n",
       " ('Cancelled', nan)]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I guess it is too pythonic and we nees to change it's PEARSON CORRELATION\n",
    "\n",
    "[(c[0], df.corr(\"ArrDelay\", c[0])) for c in corr_matrix.dtypes]"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = df.select(['DepDelay', 'TaxiOut'])"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 37,

   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import VectorAssembler"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "assembler = VectorAssembler(\n",
    "    inputCols=features.columns,\n",
    "    outputCol=\"features\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = df.fillna(0, subset=(['DepDelay', 'Cancelled', 'ArrDelay']))"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 39,

   "metadata": {},
   "outputs": [],
   "source": [
    "output = assembler.transform(df).select('features','ArrDelay')"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(features=DenseVector([-7.0, 11.0]), ArrDelay=-14),\n",
       " Row(features=DenseVector([-9.0, 16.0]), ArrDelay=-4),\n",
       " Row(features=DenseVector([3.0, 15.0]), ArrDelay=5),\n",
       " Row(features=DenseVector([-3.0, 10.0]), ArrDelay=-16),\n",
       " Row(features=DenseVector([5.0, 13.0]), ArrDelay=3)]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],

   "source": [
    "output.take(5)"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train,test = output.randomSplit([0.75, 0.25])"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------+--------+\n",
      "|      features|ArrDelay|\n",
      "+--------------+--------+\n",
      "|     (2,[],[])|     -34|\n",
      "|     (2,[],[])|     -25|\n",
      "|     (2,[],[])|     -23|\n",
      "|     (2,[],[])|     -23|\n",
      "|     (2,[],[])|     -19|\n",
      "|     (2,[],[])|     -18|\n",
      "|     (2,[],[])|     -15|\n",
      "|     (2,[],[])|     -13|\n",
      "|     (2,[],[])|     -12|\n",
      "|     (2,[],[])|     -12|\n",
      "|     (2,[],[])|     -10|\n",
      "|     (2,[],[])|      -8|\n",
      "|     (2,[],[])|      -2|\n",
      "|     (2,[],[])|      -2|\n",
      "|     (2,[],[])|      -2|\n",
      "|     (2,[],[])|      -2|\n",
      "|     (2,[],[])|       1|\n",
      "|     (2,[],[])|       5|\n",
      "|     (2,[],[])|       7|\n",
      "|     (2,[],[])|      12|\n",
      "|  [-230.0,5.0]|      -7|\n",
      "|[-174.0,197.0]|       5|\n",
      "|  [-50.0,57.0]|      -6|\n",
      "|  [-50.0,70.0]|       3|\n",
      "|  [-39.0,15.0]|     -44|\n",
      "+--------------+--------+\n",
      "only showing top 25 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train.show(25)"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from pyspark.ml.regression import LinearRegression\n",
    "lin_reg = LinearRegression(featuresCol = 'features', labelCol='ArrDelay')\n",
    "linear_model = lin_reg.fit(train)"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [1.0017693299003945,0.42400278832501004]\n",
      "\n",
      "Intercept: -8.143825807640756\n"
     ]
    }
   ],

   "source": [
    "print(\"Coefficients: \" + str(linear_model.coefficients))\n",
    "print(\"\\nIntercept: \" + str(linear_model.intercept))"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 12.421156\n",
      "MAE: 7.842334\n",
      "\n",
      "r2: 0.861526\n"
     ]
    }
   ],

   "source": [
    "trainSummary = linear_model.summary\n",
    "print(\"RMSE: %f\" % trainSummary.rootMeanSquaredError)\n",
    "print(\"MAE: %f\" % trainSummary.meanAbsoluteError ) \n",
    "print(\"\\nr2: %f\" % trainSummary.r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from  pyspark.sql.functions import abs\n",
    "predictions = linear_model.transform(test)\n",
    "x =((predictions['ArrDelay']-predictions['prediction'])/predictions['ArrDelay'])*100\n",
    "predictions = predictions.withColumn('Accuracy',abs(x))\n",
    "predictions.select(\"prediction\",\"ArrDelay\",\"Accuracy\",\"features\").show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.evaluation import RegressionEvaluator\n",
    "pred_evaluator = RegressionEvaluator(predictionCol=\"prediction\", \\\n",
    "                 labelCol=\"ArrDelay\",metricName=\"r2\")\n",
    "print(\"R Squared (R2) on test data = %g\" % pred_evaluator.evaluate(predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with categorical data"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 16,

   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "AnalysisException",

     "evalue": "cannot resolve '`features`' given input columns: [ArrDelay, CRSArrTime, CRSDepTime, CRSElapsedTime, Cancelled, DayOfWeek, DayofMonth, DepDelay, DepTime, Dest, Distance, FlightNum, Month, Origin, TaxiOut, UniqueCarrier];;\n'Project [ArrDelay#929, 'features, Origin#32, Dest#33, DepDelay#946, TaxiOut#36]\n+- Filter AtLeastNNulls(n, Month#17,DayofMonth#18,DayOfWeek#19,DepTime#20,CRSDepTime#21,CRSArrTime#23,UniqueCarrier#24,FlightNum#25,CRSElapsedTime#28,ArrDelay#929,DepDelay#946,Origin#32,Dest#33,Distance#34,TaxiOut#36,Cancelled#37)\n   +- Project [Month#17, DayofMonth#18, DayOfWeek#19, DepTime#20, CRSDepTime#21, CRSArrTime#23, UniqueCarrier#24, FlightNum#25, CRSElapsedTime#28, ArrDelay#929, cast(DepDelay#31 as int) AS DepDelay#946, Origin#32, Dest#33, Distance#34, TaxiOut#36, Cancelled#37]\n      +- Project [Month#17, DayofMonth#18, DayOfWeek#19, DepTime#20, CRSDepTime#21, CRSArrTime#23, UniqueCarrier#24, FlightNum#25, CRSElapsedTime#28, cast(ArrDelay#30 as int) AS ArrDelay#929, DepDelay#31, Origin#32, Dest#33, Distance#34, TaxiOut#36, Cancelled#37]\n         +- Project [Month#17, DayofMonth#18, DayOfWeek#19, DepTime#20, CRSDepTime#21, CRSArrTime#23, UniqueCarrier#24, FlightNum#25, CRSElapsedTime#28, ArrDelay#30, DepDelay#31, Origin#32, Dest#33, Distance#34, TaxiOut#36, Cancelled#37]\n            +- Project [Month#17, DayofMonth#18, DayOfWeek#19, DepTime#20, CRSDepTime#21, CRSArrTime#23, UniqueCarrier#24, FlightNum#25, TailNum#26, CRSElapsedTime#28, ArrDelay#30, DepDelay#31, Origin#32, Dest#33, Distance#34, TaxiOut#36, Cancelled#37]\n               +- Project [Month#17, DayofMonth#18, DayOfWeek#19, DepTime#20, CRSDepTime#21, CRSArrTime#23, UniqueCarrier#24, FlightNum#25, TailNum#26, CRSElapsedTime#28, ArrDelay#30, DepDelay#31, Origin#32, Dest#33, Distance#34, TaxiOut#36, Cancelled#37, CancellationCode#38]\n                  +- Relation[Year#16,Month#17,DayofMonth#18,DayOfWeek#19,DepTime#20,CRSDepTime#21,ArrTime#22,CRSArrTime#23,UniqueCarrier#24,FlightNum#25,TailNum#26,ActualElapsedTime#27,CRSElapsedTime#28,AirTime#29,ArrDelay#30,DepDelay#31,Origin#32,Dest#33,Distance#34,TaxiIn#35,TaxiOut#36,Cancelled#37,CancellationCode#38,Diverted#39,... 5 more fields] csv\n",

     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAnalysisException\u001b[0m                         Traceback (most recent call last)",

      "\u001b[0;32m<ipython-input-16-a0fb53f77523>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0mdf1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipelineModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0mselectedCols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'ArrDelay'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'features'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m \u001b[0mdf1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselectedCols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m \u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprintSchema\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",

      "\u001b[0;32m/usr/local/opt/apache-spark/libexec/python/pyspark/sql/dataframe.py\u001b[0m in \u001b[0;36mselect\u001b[0;34m(self, *cols)\u001b[0m\n\u001b[1;32m   1419\u001b[0m         \u001b[0;34m[\u001b[0m\u001b[0mRow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mu'Alice'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mu'Bob'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m         \"\"\"\n\u001b[0;32m-> 1421\u001b[0;31m         \u001b[0mjdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jcols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1422\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msql_ctx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1423\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/opt/apache-spark/libexec/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m   1303\u001b[0m         \u001b[0manswer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1304\u001b[0m         return_value = get_return_value(\n\u001b[0;32m-> 1305\u001b[0;31m             answer, self.gateway_client, self.target_id, self.name)\n\u001b[0m\u001b[1;32m   1306\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1307\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtemp_arg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/opt/apache-spark/libexec/python/pyspark/sql/utils.py\u001b[0m in \u001b[0;36mdeco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m    132\u001b[0m                 \u001b[0;31m# Hide where the exception came from that shows a non-Pythonic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m                 \u001b[0;31m# JVM exception message.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m                 \u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconverted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    135\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m                 \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/opt/apache-spark/libexec/python/pyspark/sql/utils.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(e)\u001b[0m\n",

      "\u001b[0;31mAnalysisException\u001b[0m: cannot resolve '`features`' given input columns: [ArrDelay, CRSArrTime, CRSDepTime, CRSElapsedTime, Cancelled, DayOfWeek, DayofMonth, DepDelay, DepTime, Dest, Distance, FlightNum, Month, Origin, TaxiOut, UniqueCarrier];;\n'Project [ArrDelay#929, 'features, Origin#32, Dest#33, DepDelay#946, TaxiOut#36]\n+- Filter AtLeastNNulls(n, Month#17,DayofMonth#18,DayOfWeek#19,DepTime#20,CRSDepTime#21,CRSArrTime#23,UniqueCarrier#24,FlightNum#25,CRSElapsedTime#28,ArrDelay#929,DepDelay#946,Origin#32,Dest#33,Distance#34,TaxiOut#36,Cancelled#37)\n   +- Project [Month#17, DayofMonth#18, DayOfWeek#19, DepTime#20, CRSDepTime#21, CRSArrTime#23, UniqueCarrier#24, FlightNum#25, CRSElapsedTime#28, ArrDelay#929, cast(DepDelay#31 as int) AS DepDelay#946, Origin#32, Dest#33, Distance#34, TaxiOut#36, Cancelled#37]\n      +- Project [Month#17, DayofMonth#18, DayOfWeek#19, DepTime#20, CRSDepTime#21, CRSArrTime#23, UniqueCarrier#24, FlightNum#25, CRSElapsedTime#28, cast(ArrDelay#30 as int) AS ArrDelay#929, DepDelay#31, Origin#32, Dest#33, Distance#34, TaxiOut#36, Cancelled#37]\n         +- Project [Month#17, DayofMonth#18, DayOfWeek#19, DepTime#20, CRSDepTime#21, CRSArrTime#23, UniqueCarrier#24, FlightNum#25, CRSElapsedTime#28, ArrDelay#30, DepDelay#31, Origin#32, Dest#33, Distance#34, TaxiOut#36, Cancelled#37]\n            +- Project [Month#17, DayofMonth#18, DayOfWeek#19, DepTime#20, CRSDepTime#21, CRSArrTime#23, UniqueCarrier#24, FlightNum#25, TailNum#26, CRSElapsedTime#28, ArrDelay#30, DepDelay#31, Origin#32, Dest#33, Distance#34, TaxiOut#36, Cancelled#37]\n               +- Project [Month#17, DayofMonth#18, DayOfWeek#19, DepTime#20, CRSDepTime#21, CRSArrTime#23, UniqueCarrier#24, FlightNum#25, TailNum#26, CRSElapsedTime#28, ArrDelay#30, DepDelay#31, Origin#32, Dest#33, Distance#34, TaxiOut#36, Cancelled#37, CancellationCode#38]\n                  +- Relation[Year#16,Month#17,DayofMonth#18,DayOfWeek#19,DepTime#20,CRSDepTime#21,ArrTime#22,CRSArrTime#23,UniqueCarrier#24,FlightNum#25,TailNum#26,ActualElapsedTime#27,CRSElapsedTime#28,AirTime#29,ArrDelay#30,DepDelay#31,Origin#32,Dest#33,Distance#34,TaxiIn#35,TaxiOut#36,Cancelled#37,CancellationCode#38,Diverted#39,... 5 more fields] csv\n"
     ]
    }
   ],
   "source": [
    "from pyspark.ml.feature import OneHotEncoder, StringIndexer, VectorAssembler\n",
    "\n",
    "\n",
    "\n",
    "df1 = df.select('Origin', 'Dest','DepDelay', 'TaxiOut')\n",
    "cols = df1.columns\n",
    "\n",
    "categoricalColumns = ['Origin', 'Dest']\n",
    "stages = []\n",
    "for categoricalCol in categoricalColumns:\n",
    "    stringIndexer = StringIndexer(inputCol = categoricalCol, outputCol = categoricalCol + 'Index')\n",
    "    encoder = OneHotEncoder(inputCols=[stringIndexer.getOutputCol()], outputCols=[categoricalCol + \"classVec\"])\n",
    "    stages += [stringIndexer, encoder]\n",
    "\n",
    "\n",
    "    \n",
    "     \n",
    "label_stringIdx = StringIndexer(inputCol = 'DepDelay', outputCol = 'ArrDelay')\n",
    "stages += [label_stringIdx]\n",
    "\n",
    "\n",
    "numericCols = ['DepDelay', 'TaxiOut']\n",
    "assemblerInputs = [c + \"classVec\" for c in categoricalColumns] + numericCols\n",
    "assembler = VectorAssembler(inputCols=assemblerInputs, outputCol=\"features\")\n",
    "stages += [assembler]\n",
    "\n",
    "\n",
    "from pyspark.ml import Pipeline\n",
    "\n",
    "pipeline = Pipeline(stages = stages)\n",
    "pipelineModel = pipeline.fit(df1)\n",
    "df1 = pipelineModel.transform(df1)\n",
    "selectedCols = ['ArrDelay', 'features'] + cols\n",
    "df1 = df.select(selectedCols)\n",
    "df1.printSchema()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml import Pipeline\n",
    "from pyspark.ml.feature import StringIndexer, OneHotEncoder, VectorAssembler\n",
    "\n",
    "df = spark.createDataFrame([(0, \"a\", 1), (1, \"b\", 2), (2, \"c\", 3), (3, \"a\", 4), (4, \"a\", 4), (5, \"c\", 3)], [\"id\", \"category1\", \"category2\"])\n",
    "\n",
    "indexer = StringIndexer(inputCol=\"category1\", outputCol=\"category1Index\")\n",
    "inputs = [indexer.getOutputCol(), \"category2\"]\n",
    "encoder = OneHotEncoder(inputCols=inputs, outputCols=[\"categoryVec1\", \"categoryVec2\"])\n",
    "pipeline = Pipeline(stages=[indexer, encoder])\n",
    "pipeline.fit(df).transform(df).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "NON_corr_matrix = df.select([x[0] for x in df.dtypes if x[1] !='int'])\n",
    "NON_corr_matrix.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import OneHotEncoderEstimator, StringIndexer, VectorAssembler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "origin = NON_corr_matrix.select(\"Origin\").distinct().collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "origin[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import StringIndexer\n",
    "\n",
    "df = sqlContext.createDataFrame(\n",
    "    [(0, \"a\"), (1, \"b\"), (2, \"c\"), (3, \"a\"), (4, \"a\"), (5, \"c\")],\n",
    "    [\"id\", \"category\"])\n",
    "indexer = StringIndexer(inputCol=\"category\", outputCol=\"categoryIndex\")\n",
    "indexed = indexer.fit(df).transform(df)\n",
    "indexed.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First approach\n",
    "# from pyspark.ml.linalg import Vectors\n",
    "# from pyspark.ml.stat import Correlation\n",
    "\n",
    "# data = [(Vectors.sparse(4, [(0, 1.0), (3, -2.0)]),),\n",
    "#         (Vectors.dense([4.0, 5.0, 0.0, 3.0]),),\n",
    "#         (Vectors.dense([6.0, 7.0, 0.0, 8.0]),),\n",
    "#         (Vectors.sparse(4, [(0, 9.0), (3, 1.0)]),)]\n",
    "# df = spark.createDataFrame(data, [\"features\"])\n",
    "\n",
    "# r1 = Correlation.corr(df, \"features\").head()\n",
    "# print(\"Pearson correlation matrix:\\n\" + str(r1[0]))\n",
    "\n",
    "# r2 = Correlation.corr(df, \"features\", \"spearman\").head()\n",
    "# print(\"Spearman correlation matrix:\\n\" + str(r2[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Second Approach\n",
    "# from pyspark.ml.stat import Correlation\n",
    "# from pyspark.ml.feature import VectorAssembler\n",
    "\n",
    "# # convert to vector column first\n",
    "# vector_col = \"corr_features\"\n",
    "# assembler = VectorAssembler(inputCols=df.columns, outputCol=vector_col)\n",
    "# df_vector = assembler.transform(df).select(vector_col)\n",
    "\n",
    "# # get correlation matrix\n",
    "# matrix = Correlation.corr(df_vector, vector_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# matrix.collect()[0][\"pearson({})\".format(vector_col)].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization experiments"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in order to visualize data, it has to be transformed in Pandas\n",
    "#unfortunately, our dataset is too large, therefore we only have to get a sample\n",
    "# in this case we only get 25% of our data, with no replacement\n",
    "\n",
    "df_Pandas_25 = df.sample(False, 0.25, 42).toPandas()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#I will be using Altair for visualization, which accepts only 5000 max observations\n",
    "#from here we can tell what airports have the longest trips\n",
    "\n",
    "import altair as alt\n",
    "\n",
    "alt.Chart(df_Pandas_25.sample(n=5000, random_state=1)).mark_point().encode(\n",
    "    x='Origin',\n",
    "    y='Distance',\n",
    "    color='DayOfWeek',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x=df_Pandas_25.Origin,\n",
    "    y=df_Pandas_25.Distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing all the libraries needed\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as pyplot\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11,9))         # Sample figsize in inches\n",
    "\n",
    "df_pandas = df_Pandas_25.drop(df_Pandas_25.Cancelled)\n",
    "sns.heatmap(df_pandas.corr(), annot = True, ax=ax,cbar_kws={\"shrink\": .5},  vmax = 1, vmin = -1, center = 0, cmap='coolwarm', linewidth =.5, linecolor ='black', square = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Pandas_25s = df.sample(False, 0.001, 42).toPandas()\n",
    "\n",
    "\n",
    "\n",
    "features = ['DepDelay', 'TaxiOut']\n",
    "for feature in features:\n",
    "        sns.regplot(x=df_Pandas_25s[\"ArrDelay\"], y=df_Pandas_25s[feature])\n",
    "        plt.show(block=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}