{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Scaling US Census Income Category Prediction with Apache Spark (PySpark)\n",
"\n",
"Have you ever wondered how Machine Learning Pipelines are built at scale? How can we deploy our ML models over a distributed cluster of machines so that our ML application scale horizontally? Do you want to convert your scikit-learn and pandas based ML workflow and take it to scale using Spark? Would it have been better if you had an working end-to-end application to refer? Then Read On... \n",
"\n",
"`\n",
"@author: Anindya Saha\n",
"@email: mail.anindya@gmail.com\n",
"`\n",
"\n",
"I hope you enjoy reviewing it as much as I had writing it.\n",
"\n",
"## Why we use Apache Spark for ML?\n",
"\n",
"Almost all of us who starts with Machine Learning in Python starts developing and learning using scikit-learn, pandas in Python. The datasets that we use fit in our laptops and can run with 16GB of memory. However, in reality when we go to the industry the datasets are not tiny and we need to scale with GBs of data over several machines. In order to scale our algorithm on large datasets scikit-learn, pandas are not enough. Spark is inherently designed for distributed computation and they have a [MLlib](https://spark.apache.org/docs/latest/ml-guide.html) package which has scalable implementation of most common algorithms. \n",
"\n",
"Data Scientists usually work on a sample of the data to devise and tune their algorithms and when they are satisfied with the model's performance they then need to deploy that at scale in production. Sometimes they themselves do it or if you are working as an Applied ML Engineer or ML Software Engineer then the Data Scientist might seek your help in transforming his pandas, scikit-learn based codes into a more scalable and distributable deployment working over large datasets. While doing that soon you will realize not exactly everything of scikit-learn, pandas is implemented in Spark. Spark community is adding more ML implementations. But there are some constraints imposed by the distributed nature of the large data sets across machines that all features and functions that the Data Scientist have leveraged from Pandas is not available. Also, sometimes some functionalities such as StratifiedSampling will not be directly implemented in Spark. You have to use the existing Spark APIs to realize the same.\n",
"\n",
"This post was inspired when a Graduate Data Science engineer sought my help to convert his own scikit-learn and pandas code to PySpark. While doing that I added more functionalities and implementation that you would find useful and handy and will serve as a ready reference implementation. \n",
"\n",
"Although Spark is written in Python there is no inherent visualization capabilities. Here we will heavily leverage the `toPandas()` method to convert the aggregated DataFrames or results into Pandas DataFrame for visualization. This work covers - EDA, custom udf, cleaning, basic missing values treatment, data variance per feature, stratified sampling (custom implementation), class weights for imbalanced class distribution (custom code), onehotencoding, standard scaling, vector assembling, label encoding, grid search with cross validation, pipeline, partial pipelines (custom code), binary and multi class evaluators and new metrics introduced in Spark 2.3.0, auc_roc, roc curves, model serialization and deserialization."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What is Apache Spark?\n",
"\n",
"![](assets/spark.jpeg)\n",
"\n",
"Apache Spark is a fast cluster computing framework which is used for processing, querying and analyzing Big data. It is based on In-memory computation, which is a big advantage of Apache Spark over several other big data Frameworks. Apache Spark is open source and one of the most famous Big data framework. It can run tasks up to 100 times faster, when it utilizes the in-memory computations and 10 times faster when it uses disk than traditional map-reduce tasks. Read this article to learn more about Spark\n",
"https://www.analyticsvidhya.com/blog/2016/09/comprehensive-introduction-to-apache-spark-rdds-dataframes-using-pyspark/\n",
"\n",
"It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.\n",
"\n",
"Even more: https://spark.apache.org/docs/latest/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem Statement:\n",
"\n",
"In this notebook, we try to predict a person's income is above or below 50K$/yr based on features such as workclass, no of years of education, occupation, relationship, marital status, hours worked per week, race, sex etc. But the catch is here we will do entirely in PySpark. We will use the most basic model of Logistic Regression here. The goal of the notebook is not to get too fancy with the choice of the Algorithms but its more on how can you achieve or at least try to achieve what you could do using scikit-learn and pandas.\n",
"\n",
"## BINARY CLASSIFICATION : LOGISTIC REGRESSION\n",
"\n",
"US Adult Census data relating income to social factors such as Age, Education, race etc.\n",
"\n",
"The US Adult income dataset was extracted by Barry Becker from the 1994 US Census Database. The data set consists of anonymous information such as occupation, age, native country, race, capital gain, capital loss, education, work class and more. Each row is labelled as either having a salary greater than \">50K\" or \"<=50K\".\n",
"\n",
"This Data set is split into two CSV files, named `adult-training.txt` and `adult-test.txt`.\n",
"\n",
"The goal here is to train a binary classifier on the training dataset to predict the column `income_bracket` which has two possible values \">50K\" and \"<=50K\" and evaluate the accuracy of the classifier with the test dataset.\n",
"\n",
"Note that the dataset is made up of categorical and continuous features. It also contains missing values. The categorical columns are: workclass, education, marital_status, occupation, relationship, race, gender, native_country\n",
"\n",
"The continuous columns are: age, education_num, capital_gain, capital_loss, hours_per_week.\n",
"\n",
"This Dataset was obtained from the UCI repository, it can be found at:\n",
"\n",
"https://archive.ics.uci.edu/ml/datasets/census+income \n",
"http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from pyspark import SparkConf, SparkContext\n",
"from pyspark.sql import SparkSession, SQLContext\n",
"\n",
"from pyspark.sql.types import *\n",
"import pyspark.sql.functions as F\n",
"from pyspark.sql.functions import col, udf\n",
"\n",
"from pyspark.ml.stat import Correlation\n",
"\n",
"from pyspark.ml.classification import LogisticRegression\n",
"\n",
"from pyspark.ml.tuning import ParamGridBuilder, CrossValidator, CrossValidatorModel\n",
"\n",
"from pyspark.ml.feature import Bucketizer, StringIndexer, OneHotEncoder, StandardScaler, VectorAssembler\n",
"\n",
"from pyspark.ml import Pipeline, PipelineModel\n",
"from pyspark.ml.tuning import ParamGridBuilder, CrossValidator, CrossValidatorModel\n",
"\n",
"from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
"from pyspark.mllib.evaluation import MulticlassMetrics"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Visualization\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Visualization\n",
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\"\n",
"\n",
"pd.set_option('display.max_columns', 200)\n",
"pd.set_option('display.max_colwidth', 400)\n",
"\n",
"sns.set(context='notebook', style='whitegrid', rc={\"figure.figsize\": (18,4)})"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from matplotlib import rcParams\n",
"rcParams['figure.figsize'] = 18,4"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# setting random seed for notebook reproducability\n",
"rnd_seed=42\n",
"np.random.seed=rnd_seed\n",
"np.random.set_state=rnd_seed"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Creating the Spark Session"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# The following must be set in your .bashrc file\n",
"#SPARK_HOME=\"/home/ubuntu/spark-2.3.0-bin-hadoop2.7\"\n",
"#ANACONDA_HOME=\"/home/ubuntu/anaconda3/envs/pyspark\"\n",
"#PYSPARK_PYTHON=\"$ANACONDA_HOME/bin/python\"\n",
"#PYSPARK_DRIVER_PYTHON=\"$ANACONDA_HOME/bin/python\"\n",
"#PYTHONPATH=\"$ANACONDA_HOME/bin/python\"\n",
"#export PATH=\"$ANACONDA_HOME/bin:$SPARK_HOME/bin:$PATH\""
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
"
SparkSession - in-memory
\n",
" \n",
"
\n",
"
SparkContext
\n",
"\n",
"
Spark UI
\n",
"\n",
"
\n",
" - Version
\n",
" v2.3.0
\n",
" - Master
\n",
" local[*]
\n",
" - AppName
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" predict-us-census-income
\n",
"
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"
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" \n",
"
\n",
" "
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"spark = (SparkSession\n",
" .builder\n",
" .master(\"local[*]\")\n",
" .appName(\"predict-us-census-income-python\")\n",
" .getOrCreate())\n",
"spark"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"
SparkContext
\n",
"\n",
"
Spark UI
\n",
"\n",
"
\n",
" - Version
\n",
" v2.3.0
\n",
" - Master
\n",
" local[*]
\n",
" - AppName
\n",
" predict-us-census-income
\n",
"
\n",
"
\n",
" "
],
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sc = spark.sparkContext\n",
"sc"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sqlContext = SQLContext(spark.sparkContext)\n",
"sqlContext"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Load The Data From a File Into a Dataframe"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ADULT_TRAIN_DATA = 'data/adult-training.csv'\n",
"ADULT_TEST_DATA = 'data/adult-test.csv'"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# define the schema, corresponding to a line in the csv data file.\n",
"schema = StructType([\n",
" StructField(\"age\", IntegerType(), nullable=True),\n",
" StructField(\"workclass\", StringType(), nullable=True),\n",
" StructField(\"fnlgwt\", DoubleType(), nullable=True),\n",
" StructField(\"education\", StringType(), nullable=True),\n",
" StructField(\"education_num\", DoubleType(), nullable=True),\n",
" StructField(\"marital_status\", StringType(), nullable=True),\n",
" StructField(\"occupation\", StringType(), nullable=True),\n",
" StructField(\"relationship\", StringType(), nullable=True),\n",
" StructField(\"race\", StringType(), nullable=True),\n",
" StructField(\"sex\", StringType(), nullable=True),\n",
" StructField(\"capital_gain\", DoubleType(), nullable=True),\n",
" StructField(\"capital_loss\", DoubleType(), nullable=True),\n",
" StructField(\"hours_per_week\", DoubleType(), nullable=True),\n",
" StructField(\"native_country\", StringType(), nullable=True),\n",
" StructField(\"income\", StringType(), nullable=True)]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Load training data and cache it. We will be using this data set over and over again.\n",
"train_df = (spark.read\n",
" .csv(path=ADULT_TRAIN_DATA, schema=schema, ignoreLeadingWhiteSpace=True, ignoreTrailingWhiteSpace=True)\n",
" .cache())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"root\n",
" |-- age: integer (nullable = true)\n",
" |-- workclass: string (nullable = true)\n",
" |-- fnlgwt: double (nullable = true)\n",
" |-- education: string (nullable = true)\n",
" |-- education_num: double (nullable = true)\n",
" |-- marital_status: string (nullable = true)\n",
" |-- occupation: string (nullable = true)\n",
" |-- relationship: string (nullable = true)\n",
" |-- race: string (nullable = true)\n",
" |-- sex: string (nullable = true)\n",
" |-- capital_gain: double (nullable = true)\n",
" |-- capital_loss: double (nullable = true)\n",
" |-- hours_per_week: double (nullable = true)\n",
" |-- native_country: string (nullable = true)\n",
" |-- income: string (nullable = true)\n",
"\n"
]
}
],
"source": [
"train_df.printSchema()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" age | \n",
" workclass | \n",
" fnlgwt | \n",
" education | \n",
" education_num | \n",
" marital_status | \n",
" occupation | \n",
" relationship | \n",
" race | \n",
" sex | \n",
" capital_gain | \n",
" capital_loss | \n",
" hours_per_week | \n",
" native_country | \n",
" income | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 39 | \n",
" State-gov | \n",
" 77516.0 | \n",
" Bachelors | \n",
" 13.0 | \n",
" Never-married | \n",
" Adm-clerical | \n",
" Not-in-family | \n",
" White | \n",
" Male | \n",
" 2174.0 | \n",
" 0.0 | \n",
" 40.0 | \n",
" United-States | \n",
" <=50K | \n",
"
\n",
" \n",
" 1 | \n",
" 50 | \n",
" Self-emp-not-inc | \n",
" 83311.0 | \n",
" Bachelors | \n",
" 13.0 | \n",
" Married-civ-spouse | \n",
" Exec-managerial | \n",
" Husband | \n",
" White | \n",
" Male | \n",
" 0.0 | \n",
" 0.0 | \n",
" 13.0 | \n",
" United-States | \n",
" <=50K | \n",
"
\n",
" \n",
" 2 | \n",
" 38 | \n",
" Private | \n",
" 215646.0 | \n",
" HS-grad | \n",
" 9.0 | \n",
" Divorced | \n",
" Handlers-cleaners | \n",
" Not-in-family | \n",
" White | \n",
" Male | \n",
" 0.0 | \n",
" 0.0 | \n",
" 40.0 | \n",
" United-States | \n",
" <=50K | \n",
"
\n",
" \n",
" 3 | \n",
" 53 | \n",
" Private | \n",
" 234721.0 | \n",
" 11th | \n",
" 7.0 | \n",
" Married-civ-spouse | \n",
" Handlers-cleaners | \n",
" Husband | \n",
" Black | \n",
" Male | \n",
" 0.0 | \n",
" 0.0 | \n",
" 40.0 | \n",
" United-States | \n",
" <=50K | \n",
"
\n",
" \n",
" 4 | \n",
" 28 | \n",
" Private | \n",
" 338409.0 | \n",
" Bachelors | \n",
" 13.0 | \n",
" Married-civ-spouse | \n",
" Prof-specialty | \n",
" Wife | \n",
" Black | \n",
" Female | \n",
" 0.0 | \n",
" 0.0 | \n",
" 40.0 | \n",
" Cuba | \n",
" <=50K | \n",
"
\n",
" \n",
" 5 | \n",
" 37 | \n",
" Private | \n",
" 284582.0 | \n",
" Masters | \n",
" 14.0 | \n",
" Married-civ-spouse | \n",
" Exec-managerial | \n",
" Wife | \n",
" White | \n",
" Female | \n",
" 0.0 | \n",
" 0.0 | \n",
" 40.0 | \n",
" United-States | \n",
" <=50K | \n",
"
\n",
" \n",
" 6 | \n",
" 49 | \n",
" Private | \n",
" 160187.0 | \n",
" 9th | \n",
" 5.0 | \n",
" Married-spouse-absent | \n",
" Other-service | \n",
" Not-in-family | \n",
" Black | \n",
" Female | \n",
" 0.0 | \n",
" 0.0 | \n",
" 16.0 | \n",
" Jamaica | \n",
" <=50K | \n",
"
\n",
" \n",
" 7 | \n",
" 52 | \n",
" Self-emp-not-inc | \n",
" 209642.0 | \n",
" HS-grad | \n",
" 9.0 | \n",
" Married-civ-spouse | \n",
" Exec-managerial | \n",
" Husband | \n",
" White | \n",
" Male | \n",
" 0.0 | \n",
" 0.0 | \n",
" 45.0 | \n",
" United-States | \n",
" >50K | \n",
"
\n",
" \n",
" 8 | \n",
" 31 | \n",
" Private | \n",
" 45781.0 | \n",
" Masters | \n",
" 14.0 | \n",
" Never-married | \n",
" Prof-specialty | \n",
" Not-in-family | \n",
" White | \n",
" Female | \n",
" 14084.0 | \n",
" 0.0 | \n",
" 50.0 | \n",
" United-States | \n",
" >50K | \n",
"
\n",
" \n",
" 9 | \n",
" 42 | \n",
" Private | \n",
" 159449.0 | \n",
" Bachelors | \n",
" 13.0 | \n",
" Married-civ-spouse | \n",
" Exec-managerial | \n",
" Husband | \n",
" White | \n",
" Male | \n",
" 5178.0 | \n",
" 0.0 | \n",
" 40.0 | \n",
" United-States | \n",
" >50K | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" age workclass fnlgwt education education_num \\\n",
"0 39 State-gov 77516.0 Bachelors 13.0 \n",
"1 50 Self-emp-not-inc 83311.0 Bachelors 13.0 \n",
"2 38 Private 215646.0 HS-grad 9.0 \n",
"3 53 Private 234721.0 11th 7.0 \n",
"4 28 Private 338409.0 Bachelors 13.0 \n",
"5 37 Private 284582.0 Masters 14.0 \n",
"6 49 Private 160187.0 9th 5.0 \n",
"7 52 Self-emp-not-inc 209642.0 HS-grad 9.0 \n",
"8 31 Private 45781.0 Masters 14.0 \n",
"9 42 Private 159449.0 Bachelors 13.0 \n",
"\n",
" marital_status occupation relationship race sex \\\n",
"0 Never-married Adm-clerical Not-in-family White Male \n",
"1 Married-civ-spouse Exec-managerial Husband White Male \n",
"2 Divorced Handlers-cleaners Not-in-family White Male \n",
"3 Married-civ-spouse Handlers-cleaners Husband Black Male \n",
"4 Married-civ-spouse Prof-specialty Wife Black Female \n",
"5 Married-civ-spouse Exec-managerial Wife White Female \n",
"6 Married-spouse-absent Other-service Not-in-family Black Female \n",
"7 Married-civ-spouse Exec-managerial Husband White Male \n",
"8 Never-married Prof-specialty Not-in-family White Female \n",
"9 Married-civ-spouse Exec-managerial Husband White Male \n",
"\n",
" capital_gain capital_loss hours_per_week native_country income \n",
"0 2174.0 0.0 40.0 United-States <=50K \n",
"1 0.0 0.0 13.0 United-States <=50K \n",
"2 0.0 0.0 40.0 United-States <=50K \n",
"3 0.0 0.0 40.0 United-States <=50K \n",
"4 0.0 0.0 40.0 Cuba <=50K \n",
"5 0.0 0.0 40.0 United-States <=50K \n",
"6 0.0 0.0 16.0 Jamaica <=50K \n",
"7 0.0 0.0 45.0 United-States >50K \n",
"8 14084.0 0.0 50.0 United-States >50K \n",
"9 5178.0 0.0 40.0 United-States >50K "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.limit(10).toPandas()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Load testing data. We will be using this data set over and over again.\n",
"test_df = (spark.read\n",
" .csv(path=ADULT_TEST_DATA, schema=schema, ignoreLeadingWhiteSpace=True, ignoreTrailingWhiteSpace=True)\n",
" .cache())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Understanding the Distribution of various Features\n",
"\n",
"Here we will do an EDA on the training set only. We will not be doing any EDA on the test data. It is supposed to be unknown data altogether. EDA should be limited to find out what type of cleaning is needed on test data. If it throws surprises in the end while testing, then we can go and do a thorough EDA to see if it was too off from training data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3.1 How many records in each data set:**"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training Samples: 32561, Test Samples: 16281.\n"
]
}
],
"source": [
"print('Training Samples: {0}, Test Samples: {1}.'.format(train_df.count(), test_df.count()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3.2 What is the distribution of class labels in each data set:**"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
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" \n",
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" | \n",
" income | \n",
" count | \n",
" %age | \n",
"
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" \n",
" \n",
" \n",
" 0 | \n",
" <=50K | \n",
" 24720 | \n",
" 0.76 | \n",
"
\n",
" \n",
" 1 | \n",
" >50K | \n",
" 7841 | \n",
" 0.24 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" income count %age\n",
"0 <=50K 24720 0.76\n",
"1 >50K 7841 0.24"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.groupBy('income').count().withColumn('%age', F.round(col('count') / train_df.count(), 2)).toPandas()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" income | \n",
" count | \n",
" %age | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" <=50K. | \n",
" 12435 | \n",
" 0.76 | \n",
"
\n",
" \n",
" 1 | \n",
" >50K. | \n",
" 3846 | \n",
" 0.24 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" income count %age\n",
"0 <=50K. 12435 0.76\n",
"1 >50K. 3846 0.24"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_df.groupBy('income').count().withColumn('%age', F.round(col('count') / test_df.count(), 2)).toPandas()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can clearly see a type in the income label for test set. The labels are `<=50K.` and `>50K.` instead of `<=50K` and `>50K` ."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"**3.3 Fix Typos in Test Set:**"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# There is a typo in the test set the values are '<=50K.' & '>50K.' instead of '<=50K' & '>50K'\n",
"test_df = test_df.replace(to_replace='<=50K.', value='<=50K', subset=['income'])\n",
"test_df = test_df.replace(to_replace='>50K.', value='>50K', subset=['income'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3.4 How are the income labels distributed between the training set and the test set:**"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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QKvH2JWAxYJ96fUQs1ChzSLNumXkWcAmle+vmjTJrANsA9T6bjgQeBPaIiCXH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\nuZdJF8Cc5AAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 277,
"width": 625
}
},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10,4), sharex=False, sharey=False)\n",
"\n",
"g = sns.countplot('income', data=train_df.select('income').toPandas(), ax=axes[0])\n",
"axes[0].set_title('Distribution of Income Groups in Test Set')\n",
"\n",
"g = sns.countplot('income', data=test_df.select('income').toPandas(), ax=axes[1])\n",
"axes[1].set_title('Distribution of Income Groups in Test Set');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see almost the same distribution of income labels in the different data sets."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3.5 How is the Age distributed in the training set:**"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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569ap5IcuXcJrniRJkiRJkqREmPAgSaq4bdvCJ14nTw5t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\nosLnmSSpw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"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 263,
"width": 1054
}
},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(train_df.select('age').toPandas(), bins=100, color='red')\n",
"plt.title('Distribution of Age in Traning Set');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clearly, the `age` feature in right skewed and we can see some people working beyond the age of 80. Taking a logarithm of the `age` feature may be beneficial in turning this skewed distribution into a normal distribution."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Skewness of the Age Feature:**\n",
"\n",
"Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The skewness value can be positive or negative, or undefined. For a unimodal distribution, negative skew indicates that the tail on the left side of the probability density function is longer or fatter than the right side - it does not distinguish these two kinds of shape. Conversely, positive skew indicates that the tail on the right side is longer or fatter than the left side. https://en.wikipedia.org/wiki/Skewness\n",
"\n",
"We can measure the skewness of a feature using Spark's `skewness` method. A variable is known to be moderately skewed if its absolute value is greater than 0.5 and highly skewed if its absolute value is greater than 1."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Row(age_skew=0.5587176292398546)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# How skewed is the 'age' feature\n",
"train_df.select(F.skewness('age').alias('age_skew')).first()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Taking Logarithm Age Feature:**"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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hxXBh94NevSAlpeLrlSRJqm7q1IGrrw6dEubO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bKuVn9ZlnVKkpSR9bErhNCd+N1qITA4SZJPq/WNBJ4n3nV6XxoFS5IKWprXqO4EugGjkyS5vdq+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\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 263,
"width": 1041
}
},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(train_df.select(F.log10('age').alias('age')).toPandas(), bins=100, color='red')\n",
"plt.title('Distribution of Age in Traning Set');"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Row(log_age_skew=-0.13172385088975794)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# How skewed is the 'log(age)' feature\n",
"train_df.select(F.skewness(F.log10('age')).alias('log_age_skew')).first()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Taking the logarithm of the `age` feature reduces the skewness from moderate 0.55 to very low -0.13."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**What about Age outliers?**\n",
"\n",
"Some people who are working are beyond the age of 80. Are they self-employed or private? Are they earning <=50K?"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" age | \n",
" workclass | \n",
" education | \n",
" occupation | \n",
" sex | \n",
" income | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 90 | \n",
" Private | \n",
" HS-grad | \n",
" Other-service | \n",
" Male | \n",
" <=50K | \n",
"
\n",
" \n",
" 1 | \n",
" 81 | \n",
" Self-emp-not-inc | \n",
" HS-grad | \n",
" Exec-managerial | \n",
" Male | \n",
" <=50K | \n",
"
\n",
" \n",
" 2 | \n",
" 90 | \n",
" Private | \n",
" HS-grad | \n",
" Other-service | \n",
" Female | \n",
" <=50K | \n",
"
\n",
" \n",
" 3 | \n",
" 88 | \n",
" Self-emp-not-inc | \n",
" Prof-school | \n",
" Prof-specialty | \n",
" Male | \n",
" <=50K | \n",
"
\n",
" \n",
" 4 | \n",
" 90 | \n",
" Private | \n",
" Bachelors | \n",
" Exec-managerial | \n",
" Male | \n",
" <=50K | \n",
"
\n",
" \n",
" 5 | \n",
" 90 | \n",
" Private | \n",
" Some-college | \n",
" Other-service | \n",
" Male | \n",
" <=50K | \n",
"
\n",
" \n",
" 6 | \n",
" 90 | \n",
" Private | \n",
" Some-college | \n",
" Adm-clerical | \n",
" Female | \n",
" <=50K | \n",
"
\n",
" \n",
" 7 | \n",
" 81 | \n",
" Private | \n",
" 9th | \n",
" Priv-house-serv | \n",
" Female | \n",
" <=50K | \n",
"
\n",
" \n",
" 8 | \n",
" 82 | \n",
" ? | \n",
" 7th-8th | \n",
" ? | \n",
" Male | \n",
" <=50K | \n",
"
\n",
" \n",
" 9 | \n",
" 81 | \n",
" Self-emp-not-inc | \n",
" HS-grad | \n",
" Adm-clerical | \n",
" Female | \n",
" <=50K | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" age workclass education occupation sex income\n",
"0 90 Private HS-grad Other-service Male <=50K\n",
"1 81 Self-emp-not-inc HS-grad Exec-managerial Male <=50K\n",
"2 90 Private HS-grad Other-service Female <=50K\n",
"3 88 Self-emp-not-inc Prof-school Prof-specialty Male <=50K\n",
"4 90 Private Bachelors Exec-managerial Male <=50K\n",
"5 90 Private Some-college Other-service Male <=50K\n",
"6 90 Private Some-college Adm-clerical Female <=50K\n",
"7 81 Private 9th Priv-house-serv Female <=50K\n",
"8 82 ? 7th-8th ? Male <=50K\n",
"9 81 Self-emp-not-inc HS-grad Adm-clerical Female <=50K"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.filter(col('age') > 80).select(['age', 'workclass', 'education', 'occupation', 'sex', 'income']).toPandas().head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Well, indeed the old people certainly earn <=50K but we cannot for sure say anything about the workclass or occupation."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" workclass | \n",
" occupation | \n",
" count | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" ? | \n",
" ? | \n",
" 22 | \n",
"
\n",
" \n",
" 1 | \n",
" Federal-gov | \n",
" Craft-repair | \n",
" 1 | \n",
"
\n",
" \n",
" 2 | \n",
" Local-gov | \n",
" Adm-clerical | \n",
" 2 | \n",
"
\n",
" \n",
" 3 | \n",
" Local-gov | \n",
" Exec-managerial | \n",
" 2 | \n",
"
\n",
" \n",
" 4 | \n",
" Local-gov | \n",
" Other-service | \n",
" 1 | \n",
"
\n",
" \n",
" 5 | \n",
" Local-gov | \n",
" Protective-serv | \n",
" 1 | \n",
"
\n",
" \n",
" 6 | \n",
" Private | \n",
" Adm-clerical | \n",
" 6 | \n",
"
\n",
" \n",
" 7 | \n",
" Private | \n",
" Craft-repair | \n",
" 2 | \n",
"
\n",
" \n",
" 8 | \n",
" Private | \n",
" Exec-managerial | \n",
" 9 | \n",
"
\n",
" \n",
" 9 | \n",
" Private | \n",
" Handlers-cleaners | \n",
" 2 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" workclass occupation count\n",
"0 ? ? 22\n",
"1 Federal-gov Craft-repair 1\n",
"2 Local-gov Adm-clerical 2\n",
"3 Local-gov Exec-managerial 2\n",
"4 Local-gov Other-service 1\n",
"5 Local-gov Protective-serv 1\n",
"6 Private Adm-clerical 6\n",
"7 Private Craft-repair 2\n",
"8 Private Exec-managerial 9\n",
"9 Private Handlers-cleaners 2"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(train_df\n",
" .filter(col('age') > 80)\n",
" .groupby(['workclass', 'occupation'])\n",
" .count()\n",
" .orderBy(['workclass', 'occupation'])\n",
" .limit(10)\n",
" .toPandas())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is one person working beyond the age of 80 in Federal Gov jobs."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3.6 How is the Hours Per Week distributed in the training set:**\n",
"\n",
"**Hypothesis:** We expect it to be more centered around 40 hours per week with occassional overtimes."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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KCP39yfR1jJALIwAAAADQSa2qjPDpJJsnObZarX6idmOl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\ns2xXrEjOPrsMCh51VOd+t0NDyde/nuy22+pBhM03L6/DG94wsUGEmle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"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 263,
"width": 1041
}
},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(train_df.select('hours_per_week').toPandas(), bins=100, color='blue')\n",
"plt.title('Distribution of Hours Per Week in Traning Set');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is indeed concentrated on 40 hours per week.\n",
"\n",
"**3.7 How is the Education_Num distributed in the training set:**"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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2qs8nfXTfvPt0NPOoSR0CAFA1EUYA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\ntvnIZq07uE7rEoqW84W8zr60w9n8rH66IuKKonDCqYBCm1pt5IhwKMg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"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 263,
"width": 1041
}
},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(train_df.select('education_num').toPandas(), bins=100, color='green')\n",
"plt.title('Distribution of Education Num in Traning Set');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3.8 How are the Capital Gain and Capital Loss attributes distributed in the training set:**"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 263,
"width": 1060
}
},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(train_df.select('capital_gain').toPandas(), bins=100, color='red')\n",
"plt.title('Distribution of Capital Gain in Traning Set');"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 263,
"width": 1054
}
},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(train_df.select('capital_loss').toPandas(), bins=100, color='red')\n",
"plt.title('Distribution of Capital Loss in Traning Set');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `capital_gain` and `capital_loss` are highly skewed. Let's check their skewness."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Row(capital_gain_skew=11.953296998194228, capital_loss_skew=4.594417456439665)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# How skewed are the 'capital_gain' and 'capital_loss' feature\n",
"(train_df\n",
" .select(F.skewness('capital_gain').alias('capital_gain_skew'), \n",
" F.skewness('capital_loss').alias('capital_loss_skew'))\n",
" .first())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Following the graphs above the skewness value is very high for both of them are highly skewed. We will check with the correlation if they should be kept or dropped."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3.9 Correlation between the Numerical Attributes:**\n",
"\n",
"Let's investigate the correlation between the numerical features. Correlation measures whether there is any linear relationship between two features. Correlation measures whether increasing/decreasing a variable will also cause increase/decrease in the other variable. If two variables are highly correlated we can almost predict the one of the variable by looking at the values of the other variable. We calculate the `Pearson correlation coefficient`, which is sensitive only to a linear relationship between two variables. The Pearson correlation is +1 in the case of a perfect direct (increasing) linear relationship (correlation), −1 in the case of a perfect decreasing (inverse) linear relationship (anticorrelation), and some value in the open interval (−1, 1) in all other cases, indicating the degree of linear dependence between the variables. As it approaches zero there is less of a relationship (closer to uncorrelated). The closer the coefficient is to either −1 or 1, the stronger the correlation between the variables.\n",
"\n",
"https://en.wikipedia.org/wiki/Correlation_and_dependence\n",
"\n",
"**Note:** We cannot use DataFrame's `corr` method here because that only calculates the correlation of two columns of a DataFrame.\n",
"\n",
"**Note:** We cannot calculate correlation for categorical features."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" age | \n",
" fnlgwt | \n",
" education_num | \n",
" capital_gain | \n",
" capital_loss | \n",
" hours_per_week | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 39 | \n",
" 77516.0 | \n",
" 13.0 | \n",
" 2174.0 | \n",
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"
\n",
" \n",
" 1 | \n",
" 50 | \n",
" 83311.0 | \n",
" 13.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 13.0 | \n",
"
\n",
" \n",
" 2 | \n",
" 38 | \n",
" 215646.0 | \n",
" 9.0 | \n",
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" 0.0 | \n",
" 40.0 | \n",
"
\n",
" \n",
" 3 | \n",
" 53 | \n",
" 234721.0 | \n",
" 7.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 40.0 | \n",
"
\n",
" \n",
" 4 | \n",
" 28 | \n",
" 338409.0 | \n",
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" 0.0 | \n",
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\n",
" \n",
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" 37 | \n",
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\n",
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" 49 | \n",
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" 0.0 | \n",
" 0.0 | \n",
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\n",
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" 52 | \n",
" 209642.0 | \n",
" 9.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 45.0 | \n",
"
\n",
" \n",
" 8 | \n",
" 31 | \n",
" 45781.0 | \n",
" 14.0 | \n",
" 14084.0 | \n",
" 0.0 | \n",
" 50.0 | \n",
"
\n",
" \n",
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" 42 | \n",
" 159449.0 | \n",
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" 5178.0 | \n",
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\n",
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" 37 | \n",
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\n",
" \n",
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" 30 | \n",
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" 0.0 | \n",
" 0.0 | \n",
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],
"text/plain": [
" age fnlgwt education_num capital_gain capital_loss hours_per_week\n",
"0 39 77516.0 13.0 2174.0 0.0 40.0\n",
"1 50 83311.0 13.0 0.0 0.0 13.0\n",
"2 38 215646.0 9.0 0.0 0.0 40.0\n",
"3 53 234721.0 7.0 0.0 0.0 40.0\n",
"4 28 338409.0 13.0 0.0 0.0 40.0\n",
"5 37 284582.0 14.0 0.0 0.0 40.0\n",
"6 49 160187.0 5.0 0.0 0.0 16.0\n",
"7 52 209642.0 9.0 0.0 0.0 45.0\n",
"8 31 45781.0 14.0 14084.0 0.0 50.0\n",
"9 42 159449.0 13.0 5178.0 0.0 40.0\n",
"10 37 280464.0 10.0 0.0 0.0 80.0\n",
"11 30 141297.0 13.0 0.0 0.0 40.0\n",
"12 23 122272.0 13.0 0.0 0.0 30.0\n",
"13 32 205019.0 12.0 0.0 0.0 50.0\n",
"14 40 121772.0 11.0 0.0 0.0 40.0"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# seggregate the numerical features\n",
"corr_columns = ['age', 'fnlgwt', 'education_num', 'capital_gain', 'capital_loss', 'hours_per_week']\n",
"train_df.select(corr_columns).limit(15).toPandas()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Vectorize the numerical features first\n",
"corr_assembler = VectorAssembler(inputCols=corr_columns, outputCol=\"numerical_features\")\n",
"\n",
"# then apply the correlation package from stat module\n",
"pearsonCorr = (Correlation\n",
" .corr(corr_assembler.transform(train_df), column='numerical_features', method='pearson')\n",
" .collect()[0][0])"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1.00000000e+00, -7.66458679e-02, 3.65271895e-02,\n",
" 7.76744982e-02, 5.77745395e-02, 6.87557075e-02],\n",
" [ -7.66458679e-02, 1.00000000e+00, -4.31946327e-02,\n",
" 4.31885792e-04, -1.02517117e-02, -1.87684906e-02],\n",
" [ 3.65271895e-02, -4.31946327e-02, 1.00000000e+00,\n",
" 1.22630115e-01, 7.99229567e-02, 1.48122733e-01],\n",
" [ 7.76744982e-02, 4.31885792e-04, 1.22630115e-01,\n",
" 1.00000000e+00, -3.16150630e-02, 7.84086154e-02],\n",
" [ 5.77745395e-02, -1.02517117e-02, 7.99229567e-02,\n",
" -3.16150630e-02, 1.00000000e+00, 5.42563623e-02],\n",
" [ 6.87557075e-02, -1.87684906e-02, 1.48122733e-01,\n",
" 7.84086154e-02, 5.42563623e-02, 1.00000000e+00]])"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# convert to numpy array\n",
"pearsonCorr.toArray()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
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iAAAM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\nxoSlJEmSJEmSJEmSpJT5fxxPqLNmG8XWAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 263,
"width": 918
}
},
"output_type": "display_data"
}
],
"source": [
"# Compute the correlation matrix\n",
"corrMatt = pearsonCorr.toArray()\n",
"\n",
"# Generate a mask for the upper triangle\n",
"mask = np.zeros_like(corrMatt)\n",
"mask[np.triu_indices_from(mask)] = True\n",
"\n",
"# Set up the matplotlib figure\n",
"plt.title('Numerical Features Correlation')\n",
"sns.heatmap(corrMatt, square=False, mask=mask, annot=True, fmt='.2g', linewidths=1);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is clear from the heat map that none of the numerical features are correlated with each other."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3.10 How is the Income distributed across Workclass in the training set:**"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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wXZ26xEC5CX0I5TvwR0ogZhHgD8BJwAaZ2SlUNi9XdHjc7rUrO60kM/8ObEQJ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\nSn/Is6ZqbPvC8+HKHSmlzyPiBWB3oDXwErBTofufKaX5S1o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"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 277,
"width": 1122
}
},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(y='workclass', hue='income', data=train_df.select('workclass', 'income').toPandas(), palette=\"viridis\")\n",
"plt.title('Distribution of income vs workclass in training set');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most people are employed in the Private Sector. Since, there are so many people working in private sector we can create two separate models, one for the private sector and one for the rest of the sectors.\n",
"\n",
"Observing the unique values for the `workclass` columns we can see there are is **a special character '?'**. It most likely signifies NaN or unknown values and we have to handle it later."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3.11 How is the Income distributed across Occupation in the training set:**\n",
"\n",
"Does Intellectual Skill Sets / Higher Skill Sets / Specialized skill sets ensure Higher Income?"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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QNDW5vvw7GQNv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\nK+m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"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 340,
"width": 1065
}
},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x='occupation', hue='income', data=train_df.select('occupation', 'income').toPandas(), palette=\"viridis\")\n",
"plt.title('Distribution of income vs occupation in training set')\n",
"plt.xticks(rotation=45, ha='right');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Although, it is evident that the proportion of people earning >50K is higher in the specialized skillsets as compared to low skilled labor, but even within the high skilled specification it is not necessary that one will always have higher income as can be seen in the case of 'Exec-managerial' and 'Prof-speciality'."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3.12 How is the Income distributed across age and hours_per_week in the training set:**\n",
"\n",
"**Hypothesis 1:** For most people the prime time of their career is between 35-45 and that is where they are likely earn higher income.\n",
"\n",
"**Hypothesis 2:** If we work hard and put in more hours per week to work we should earn more."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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ARsr75vFj7PeCavnjpmDXPEpg5iPAIygXO++onsuDKIGlE8bzHDoREU+k9O+eDywA+imB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\nvf70atdbqmUjg2s59Rrr1+5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8N6Vv+ZDG6mp5SOv+ETEPuCewnZIdJnVkzpzRX1/M9JB6\nV7sLil5oVCcMeEkdag5i9c2dS19Lh2t+U8BrzZo17NixA0kz3x133DHi9uLF81m8eGDMfaQe1Xjj\n79dme2N9Y2rTs6vlk2v2PQpYBJyfmc78oI7VBbcMeEm9q119Puv2qRMGvKQONdfwmr908ajtzeuG\nhoacqVHqEc3BrP7+ucyfP48lS0YGvJontJB62C+q5asj4m7NGyLiKcAjgc1AY7zZ6cAq4LiIOKJp\n3wXAe6ubn5rUFqvnzJ8/v6N1knrD0qVLx7VeamYeoNSh5gyv+UuXsGPryCmw5y9ZPGr//fZrdxFc\n0kxx++237/y9MZSxNeBlhpdmidOBnwBPAC6NiG8BNwH3pQx37APelpm3AWTm7RHxquq4n0fEacBq\n4BlAVOu/NuXPQjPawMBAR+sk9QYDXtodZnhJHRgcHByZ4bWkLsNryYjb1vGSekNzwGvZsgUALF06\n8svVunXrkHpdZg4CTwXeCPweeBbwJuDhwA+AJ2XmR1uOOQN4DHAu8BzgDcA24ETguMwcmrInoJ5g\nwEuaXZYtW1a73oCXOmGGl9SB22+/nW3bhjO6+pcuYdPqkZNK9S9eBH19MFT67s0BMkkzV3MwqxHw\nWrRoPnPnzmHHjsFR+0i9LDO3AR+pfjo95peUQJm02xzSKM0uS5YsGdd6qZkZXlIHWrO1WrO5APrm\nzClBrzbHSJqZmjO8li4tAa++vr4RWV7N+0iSJk/dzGzO1ib1rsWLR4+sAQNe6owBL6kDrdladUXr\ny/rhf7xmeEkz35YtW9i0adPO240ML4Dly4dnBzLDS5KmhrM0SrPLwMBA7We8XSBMambAS+rAqAyv\nmhpeMDIQduutt05qmyRNvrVrRw5dbg5yNQe/WveTJE2OOXNGf32pWyepN/T19dUGtwx4qROeHaQO\nNAe85i1cwJw2qfPzm1JrN23axMaNGye9bZImT2vm1ooVC2t/X7t2LUND1t6WpMlWl+lhwEvqbdbu\nU7c8O0gdaA541dXv2rlt2chtZnlJM9uaNWtG3F62bGHt71u3bh0x9FGSNDnqLi54wUHqbQa61S3f\nJVIHmgNXYwa8Wmp7WbhemtlaA17tMrwAVq9ePSVtkqTZbMeOHaPWDQ4OTkNLJE0VhzKrW75LpF3Y\nvHkz69ev33l7zIDXEjO8pF7SHMRasGAe3//+b/nGNy5hw4Yt7LXXorb7SpImR11wqy4IJql3tAa3\n+vr6DHipI87hK+1Ca9Cqddhis7kD85k7MJ8dW7bWHitpZmkOYs2ZM4cLLrhq5+0nPOHQtvtKkiaH\nGV7S7NP6GR8aGmJwcNCgl3bJd4i0C+MJeJXtS3f+fsstt0xKmyRNjXZBrJtvXs/SpQuYO7dvl/tK\nkiZOXcDLDC+pt23btm3Uuu3bt09DSzTTGPCSdqE1aDWwdGmbPavtTQExM7ykmWtwcJDbbrtt5+3+\n/pGnzDlz+thrr+G6fX7eJWnyGfCSZp+64FZdEExqZcBL2oXmL7FzBwaYOzD2FLjNGV7r16935jZp\nhlqzZs2IDlZ//+gZgvbddzjgZUanJE0+A17S7FMX3DLgpU4Y8JJ24aabbtr5+8DysYczAgwsG5kB\ndvPNN094myRNvtYAVn3Aa/h/wqpVq6wjI0mTrC645dAmqXcNDQ3VJhCYVKBOGPCSxjA0NNQS8Fq2\ny2MGVozcp/l4STNHa7B6VwGv7du3W8dLkiZZXXDLDC+pd23ZsqX2guLGjRunoTWaaQx4SWNYt24d\nmzdv3nm7k4DX/KVLoW+4kLUBL2lmuuGGG3b+PjAwj3nzRge87nKXkf8Tmo+RJE28uoCXGV5S72oX\n2DLgpU4Y8JLG0Bqsas3eqjNn3lzmLxmu62PAS5qZmoNX++9f/9m/612Xj7h9/fXXT2qbJGm2s5aP\nNLts2LBhXOulZga8pDH86U9/GnF7YMXyNnuO1LyfGR/SzDM4ODji87///vWf/UWL5rNixcKdt/28\nS9LkMuAlzS7tykVYRkKdMOAljaE5W2PO/H76Fy/q6LgFe6/Y+fvq1atNuZVmmBtvvHHEF6jWTK5m\nzcGwa6+9dlLbJUmzXV2fyn6W1LtWrVo1rvVSs3nT3YDxioiXAZ/fxW6DmTmi2EpEHAmcBDwcWAhc\nDnwO+HhmWulStZoDXgv2WkFfU22usTQHvKBkfRx88MET2jZJk+fKK68ccfuAA/bmkkvqhyseeODe\nXHppGbq8Zs0a1qxZw1577TXpbZSk2ahuGNMdd9wxDS2RNBVuvfXWca2Xms24gBfwa+BdbbY9Gngc\n8MPmlRFxDPANYDPwNWA18HTgX4BHAs+drMZq5tq2bduI+lsLW4JYY2nd9/rrrzfgJc0gV1xxxc7f\nBwbmtR3SCHDQQfuMOvaII46YtLZJDRExJzNHT10l9bC6gJcZXlLvahfYMsNLnZhxAa/M/DUl6DVK\nRGEjkOgAACAASURBVFxQ/XpK07plwGeAHcDRmXlRtf7vgbOBYyPiuMw8bVIbrhnnhhtuGDHN9YJ9\nOs/Y6F+ymDnz+xncWoZEXX311RPdPEmTqDnD68AD92bOnPbZnfe4x17MmdPH4ODQzmMNeKlbEfHi\nzPyPDvbrA74IvGjyWyXtOdavX9/ROkm9od2EQH/605/YsWMHc+eOnkVbauiZGl4R8UDKcMUbgO83\nbToW2A84rRHsAsjMzZQhjgCvnap2auZoDVIt2nef+h1r9PX1sXDfvdvel6Q912233cZtt9228/aB\nB+49xt4lA+xudxvO6rzssssmrW2aFT4TEY/rZD/gBZPdGGlPU1eouvl/tqTesX379hGTCA3MHb4A\n2ToaR6rTMwEv4NXV8rMtNbkancYza445F9gIHBkRA5PZOM08zUGqOf39zF++dFzHNwfIVq1aZX0J\naYb4wx/+MOL2IYfcaZfHHHzwfjt/v/HGG1m7du2Et0uzxkbgGxHxgHY7RMTHgP8D/O+UtUraQ9QF\nt5ytTepNjSyuhpX7jZxAzMmCtCszbkhjnYhYSEnp3wH8e+vmajnqkntmbo+Iq4D7A/cCLt2ddlx8\n8cW7c7j2MM1fehfut3fHBeuHjxmZEXbWWWdxwAEHTEjbJE2e888/f+fvAwPzOOCAsTO8AA455M6c\nffbwaebMM8+0bp+69SzKRbofRMTDM/NPzRsj4oPA8cAfgD+fhvZJ02bjxo219bqs5SP1puuuu27E\n7cP3W8T5N20Ysf0Rj3jEVDdLM0ivZHg9D1gBnJmZ17Vsa1QaXtfm2Mb6ziuSq+dt2LBhRD2IRfvt\nO+77WNQS8Lr55pt3u12SJtfg4OCI1Pn73Gc/5s7d9anyoIP2pr9/uIZEu3oT0q5k5jnAy4G7AT+M\niJ3pxRHxDuAtwJXA4zPTKao0q7TL5Nq0aRObNm2a4tZImmzNfTKAeyztZ6+BuW23S616IsOL4eGM\n/zadjVi5cuV0Prwm0IUXXjji9uK77Ndmz/bmLRhgYMVytqwtMdXbb7/d94i0h7v88svZunXrztud\nDGcEmDdvLve+97784Q8lsH3TTTdx2GGHWUi1h0xlFndmnhYRBwAfAL4ZEU8B3gS8A7iOEuy6ccoa\nJO0h1q1rd/0a1q5dy8KFC6ewNZIm2403Dp/qVgzMZWDuHO60cB5rtpRhjtbw0q78f/buPDzK8mr8\n+HeWbJN9IyGsUeAGAQUFVFCkKrSi1bautLVqtbZ2efW19e3VX3etVbS0ir5vW7e6oEIr7oqAKyCb\n7BDgJsgSCJCQkJ1sk5nfH5M8M09IQiCzz/lcF9fM/cyznNpkMnOe+z4n4md4KaVGA5OBg8D7XezS\n8Zexu57yHdul4Iow7N6923husVpPmK3VW76JsgMHDtDU1NTn2IQQgbN582bTePTo/r0+1nffxsZG\niouL/RaXiD1a60eAvwOXAWuAPwNlwHSt9f5QxiZEqFRVVXX7mtROFCL6+Ca0+iXZTY8dr7tcrqDH\nJSJHxCe86L5YfQfd/jii8wtKKTtQCDjxLA8QAsD0RTUpJwur/fQmQybneRNeLpeLL7/8ss+xCSEC\nw+12s2XLFmM8cGAGGRmOHo4wGz26P76l/jonz4Q4DT8D3gbGA5XA5VpraQMqYlZPM7x6ek0IEZl8\nS8LkdiS8HHHGtpaWlh4T4UJE9JJGpVQicDOeYvXPdrPbx8B3gK8Br3Z6bSrgAJZprZsDFaeILFVV\nVaY31+T83i1p6krnY7XWjB49+rTPJ4QInIMHD5q6f40ZU3BKx6elJTF4cBb793tqzGzZsoXrr78e\nqzUa7i2JQFFK/e4ku2g8N+ZWANcqpa71ec2ttX4gYMEJEWZ66ngt3bCFiD6+q2OS7Z7PUw67+XOV\nbykKITqL6IQXcD2QCbzbRbH6Dq8Bs4GblFJPaK3XgZEs+1P7Pn8PeKQiYuzYYW7WmVKQd9rnsicl\nkpiZQVOVZ5q9b+dHIUR46Vyj6VQTXh3HdCS8qqur2bNnD8OGDfNLfCJq/QFwA921Au547Rvt/3y3\nuQFJeImY0dMNBLm5IER0cblcuN1uY2xtn0Zv6/TXsq2tq0VeQnhEesKrYznjU93toLWuVUr9AE/i\n61Ol1HzgGHA1oNq3Lwh0oCJy+CalrHY7SadZv6tDSkGekfAqLS2ltraWtLS0Pp1TCOFfbrfblPDK\ny0sjP//Uf0/POWcA7723zRivX79eEl7iZP4Y6gCEiBQ9NQKRhJcQ0aVzba6ORJfVYulxPyF8RWzC\nSyk1CriI7ovVG7TWbyqlLgF+DVwLJAK7gXuBuVprd0/Hi9jhcrnQWhtjR34u1j52WUsuyKeiyHvO\nnTt3MmnSpD6dUwjhX3v37jW1ux8/fuBpnScrK5khQ7zLGjds2MB1110n3RpFt7TWkvASopd6SmrJ\n+6wQ0aVzIqsjz9Up34XT6QxSRCISRWzCS2u9g+6n/3e1/+fAzMBFJKJBSUmJqQZESkF+n8+ZnJeD\nxWbD3T7dtqioSBJeQoSZdevWmcbjxp1ewqvj2I6EV319PTt37pTafUII4QdxcXGn9ZoQIvLExcVh\ns9mMJYvHnZ4E2PFWcyIsOTk56LGJyCFzf4XwUVRUZBqnDujf53Na7XZT8fodO3bI1FshwojT6TQl\nvAYNyiQnJ+W0z3fOOQNMdx/XrFnTl/BEDFNKpSulLldKzVJKTQ51PEKEWkZGxmm9JoSIPBaLhczM\nTGNc0+xJfNW0mGt2ye++6IkkvITw4Zvwik9NIT7t9L/0+kod6J0p1tDQwP79+/1yXiFE323bto2G\nhgZjfN55g/t0vrS0JEaM8Da72LJlC42NjX06p4gt7Ymu54ByYDEwD7jD5/U7lFKHlFIXhCpGIUIh\nKyur29d8vxgLIaKDKeHV4mp/9Ca8HA4HCQkJQY9LRA5JeAnRrq6ujpKSEmOcMiAfS+dF4qcppdNM\nsc4zyYQQoeM7A8tms5x2/S5fEyZ4k2atra1s2LChz+cUsUEplQx8CtwKVAGLOLGEw7tAHt6ujULE\nhJ6SWpLwEiL6mBNeJ87wkt97cTKS8BKi3bZt20ytb1MHFvjt3AlpqabZYlu3bvXbuYUQp6+uro5t\n27xdFUeN6k9yct/vFI4ZU0BioreezKpVq/p8ThEzfgGcg2dW1xla66s676C1PgJsBy4NcmxChFR3\nX26Tk5OJj48PcjRCiEDz7Wzf0F67q8Gnhld6enrQYxKRRRJeQrTz/dLbue6WP6QO8ibQDh48SFVV\nlV/PL4Q4datXrzbV1Js4sW/LGTvExdkYN26AMd67dy+HDh3yy7lF1LseOAT8QGt9vIf9dgEDenhd\niKgTHx+P3X5iz62UFP+UoBBChBff3+1GpwuX221KeMnvvjgZSXgJgWfJ0Y4dO4xxckEeVrt/21un\nDjR/L/FNsAkhgs/tdrNy5UpjnJaWyMiRfe/M2mHSpKGmse+1hOjBGcAXWuvmk+zXBGQHIR4hwobF\nYumyI5t0aRMiOvkmtNx4kl4d3RpBfvfFyUnCSwhg165dNDd7v1ukDfLfcsYOyXk5WH1aZm/ZssXv\n1xBC9F5xcTHl5eXGeOLEIdhs/vuzOGhQJgUF3qn2a9eupbW11W/nF1GrFUjsxX6DgPoAxyJE2JGE\nlxCxo/MMruOdEl4yw0ucjCS8hAA2b95sGvuzflcHi9VK6iBv8XqttXRuEyKEVqxYYRp3npHVVxaL\nhfPP956zoaFBiteL3tDAeKVUt8XklFKZeOp8SUFIEXO6qtUl9buEiE5tbW2msc1iwebTVMy3LIUQ\nXZGEl4h5LpfLlPBy5OViT+rNzfVTlzbY2/2tra1NljUKESLV1dVs3LjRGI8Y0Y/sbP/PEDj33EHE\nxXmXR3/66aem5hhCdOE1oB8wu4d9/gykAP8OSkRChJGuZsq2tLSEIBIhRKAdP24uZZlot5Jks3T7\nuhCdScJLxLw9e/ZQX+9dFZI2ZGAPe/dN6oD+WGzeL7+bNm0K2LWEEN1bvny56a7gRRedGZDrJCXF\nM2GCtxB+SUkJ+/btC8i1RNR4EtgB/EwptUIpdW/79qFKqbuUUh8Dd+KZ3fVsqIIUIlR8S1D0tE0I\nEfk6r4ZJtFlItFu7fV2IziThJWKe7ywPgLTBgWt6ZY2zkzLAWxS7qKiIpqamgF1PCHGi1tZWPv/8\nc2OcnZ3s12L1nU2ZYk6mffrppwG7loh87Z0ZZwBrgMnAo+0vXYInGTYN2ABcqbWWaS0i5nSV3JIZ\nXkJEp4aGBuN5gs2C1WIh0WeGl+/rQnRFEl4iprlcLlNNnaScLOJTAlv4NN1nBllra6ssaxQiyNas\nWUNdXZ0xnjz5DKxWSw9H9E1+fhrDhuUa4w0bNlBZWRmw64nIp7Uu1VpPBmYC/wu8DyzBM6PrWmCS\n1ro0hCEKETJd3SiUWR5CRKdjx44Zz9PibabHzq8L0RV7qAMQIpR2795NbW2tMU4vHNzD3v6ROmgA\nFpsNd3sRxvXr1zNhwoSAX1cI4amdt2TJEmOckGBn0qQhAb/u1KnD2L37KOBJtC9ZsoRZs2YF/Loi\nsmmtPwA+CHUcQoSLlpaWLmt4ySwPIaLT0aNHjeeZCZ5EV2aiN4VRUVGB2+3GYgncjUsR2WSGl4hp\n69atM43Thw4K+DVt8XGkDvR2a9y+fbsUXBQiSNatW2eaXTVlyhkkJQW+u9eoUfkUFKQb49WrV1Nd\nXR3w6wohRDTxrbnqq6GhQbq1CRGFKioqjOdZ7YmurATvDK/m5mbTrH0hOpOEl4hZra2tpqLxjrxc\n4pIdQbm270wyp9MpxeuFCAKXy8XixYuNcVycjalThwfl2haLhUsvVcbY6XTy4YcfBuXaIjIppYYp\npR5tL1yvlVKP+Lx2vlLqTqVURihjFCLYupvJ5Xa7pSaqEFGmqanJlOQ+VN/Cv4urcNjNs7nKy8uD\nHZqIIJLwEjGrqKjI9MEpvTDws7s6pA7sjzXOOx137dq1Qbu2ELFq5cqVlJWVGeMLLigkJSUhaNc/\n++wB5OamGOPly5eb7lwK0UEpdTuwDfg5nsL1w4Acn10cwN+BbwY/OiFCp6daXVLHS4jo4vuZDaCk\nvpWVRxrYXmVuXCEJL9ETSXiJmOWbZLJYraQPDXz9rg5Wu500n+WTxcXFUsRaiABqbGzk3XffNcZx\ncTamTQvO7K4OVquF6dNHGWOn08mbb74Z1BhE+FNKTQH+CTQB9wHnA52Lk3wG1ABXBzc6IUKrrb3+\naVecTmcQIxFCBFrnhFeH2hbz+4AkvERPJOElYlJ9fb2pO2LqwP7YE4M30wMg88yhprHM8hIicJYs\nWWKq8fCVr4wgPT0p6HGMGzeQQYMyjfHGjRvZvXt30OMQYe1/ADdwhdZ6jtb6i847aK1dwEZgVOfX\nhIhmPSW1ekqGCSEiT3cJL6sFku3Wk+4nBEjCS8SodevWmT4YZXRKPgVD55pha9aswe12Bz0OIaJd\neXk5H3/8sTFOT0/kkkuCO7urg9Vq4eqrzzZte+211+SLmvB1IbBWa73qJPsdAfqfZB8hokpPCS+Z\n4SVEdDlw4IDxvPM05+wkb+H6kpKSIEUkIpEkvERMWr16tfHclhBPysDgf2ewWCymRNvRo0f58ssv\ngx6HENHM5XIxb9480xehK64YTUKCvYejAquwMJuzzx5gjA8cOCAF7IWvdOBgL/ZLAUL3gyxECEgN\nLyFiQ0tLC+vXrzfGiTZzymtIirfD9t69e2VZo+iWJLxEzCktLTXdMcg4YwhWm62HIwInY1ihaeyb\niBNC9N2nn35qSiQPHZrFuecGr15fd666aqwp6fbee+9RWloawohEGCkHCk+6FyhAfmhETKmpqTmt\n14QQkWXz5s2mJHZKvPm72vhch2ks36FEdyThJWLOqlXmVSKdk07BlJCWgiMv1xhv2LCB5ubmHo4Q\nQvRWWVkZb7/9tjGOi7Nx443nYbV2nhgffFlZDq66aowxbmtr46WXXpKljQLgc+BcpdSE7nZQSk0H\nRgCfBisoIcKBJLyEiA2+39esFkixm9MWQ9PiyUjwJsE+//xzKQ0juiQJLxFTnE4nX3zhrf+bmJVB\nUnZmD0cEXqZPwq25uZlNmzaFMBohokNLSwvPP/88ra2txraZM0eTm5sawqjMLrigkOHD+xnjAwcO\n8M4774QwIhEm/oanXMnrSqkZSinTZzWl1FTgOcAJPBGC+IQImerqauO5JSHO9FpVVVWwwxFCBMDa\ntWtNM7ZGZiZi7ZS1sFosjM/xNh86cOCA6SanEB0k4SViSlFREfX19cY4lLO7OqQNHYjV7l3aJFNy\nhegbt9vN/PnzTUVMCwuzmTLlzBBGdSKLxcINN5xrWtq4dOlSU80KEXu01mvwdGocCCwCKvF0bfyG\nUqoM+AQYAPyP1npryAIVIsja2trYvHmzMbanJWN1JBpjuWEoROTbs2cP//d//2fadmF+cpf7np+f\nbEpmLFiwQLreixNIwkvElDVr1hjPLVYrGWcMCWE0Hra4ONKGDjTGu3btorKyMoQRCRHZPvvsM9Pv\nelJSHDfdNCEsljJ2lpnp4Nprx5u2zZs3T+p5xTit9RzgSmAdniL2FiADyAW2Ad/QWj8WugiFCL4t\nW7aYli0mDs4jaXCeMd63b590axMiglVVVTFnzhxaWlqMbRf1T2ZsdlKX++c74vjmmRmmbX//+9/Z\nu3dvQOMUkUUSXiJm1NXVsXWr92Z46sD+2BMTQhiRV2anmWa+X9aFEL1XXFzMwoULjbHFAjffPIns\n7K7vDoaDc88dxNSpw4xxS0sLTz31FA0NDSGMSoSa1nqR1vp8PEmuScCFwECt9Tlaa1m3IWLOsmXL\nvAOLhaTCApLOHND9PkKIiFFXV8ejjz5qWpqsMhJOSGh1dnFBChf1937Ga25u5i9/+QtHjhwJWKwi\nskjCS8SMdevW4XK5jHE4LGfs4MjLJS7F+2a9Zs0aKbwoxCk6dOgQTz31lOn3fObMMYwYkdfDUeHh\nyivHMGyYt4FFRUUF//jHP0x3OUVs0lpXaq3Xaa3XaK0PhToeIUKhoqLCtNw7oSAHW1ICcVlp2DO9\ntRmXLVvG8ePHQxGiEOI0lZWV8fvf/559+/YZ2/ol2bllVDY2y8ln53/zzAxUhncSQ1VVFb/73e/Y\ntWtXIMIVEUYSXiJm+BartyUkkDqwfwijMbNYLGScOdQYV1RUmN70hRA9q6io4MknnzR90Rk3biDT\npg0PYVS9Z7NZufnmSWRmetts79mzh6effhqn0xnCyESwKaXuUUqNOfmeQsSOefPmmd4LfWd2OXye\n19fXm2b5CiHC265du/jd735nmpHlsFv5wegcHPbepSpsFgu3jMqmX5K3Jmp9fT0PPvig1EYWkvAS\nsaG8vJz9+/cb4/TCQVg6t/sIsYwzzfXEfBN0Qoju1dTU8MQTT5hquwwZksX115+LpRd3BsNFcnIC\nt912IYmJ3s5j27dv58UXXzTNWhNR76/AZqXUEaXUK0qp7yulQl9wshOl1GVKqTfa42xWSh1SSi1W\nSs3sYt/JSqn3lVLHlFKNSqkt7Yk9W1fnFsLX1q1bTYWo7VlpJA7yztxNGjbQVLx+8eLFUstLiAiw\ndu1aHnzwQerq6oxtGfE2fnp2Lrk+yavecNit/OTsXAameD9Dtba2MnfuXN555x1ZORPDwusbvxAB\n0jl5FA7F6jtLSEslKSfLGK9fv562trYQRiRE+GtoaODJJ5+koqLC2Jafn8btt082dT+MFAUF6dx+\n+2Ti4rx5gPXr17NgwQL5sBY7/gJsBHKAm4CngT1Kqd1KqX8opa5XSmWHMkCl1CPAh8AE4G1gDvAe\nnnpj0zrtew2wDJgKvAE8CcQDfwPmBy1oEZGcTifPP/+8aVv6pFFYfJqQWOPspE0YaYxdLhfPP/+8\nvGcKEcY+/vhjHn/8cVpbW41tA5Pj+O9x/ShIjuvhyO6lx9v42dm5nJWVaNr+6quv8sorr/QpXhG5\nJOElop7b7TbVfYhPTSEpN6TfFbrlm4irr69Hax3CaIQIb/X19cydO5dDh7xljbKyHNx55xQcjvgQ\nRtY3hYXZ3HLL+dhs3i90K1asYMGCBTLTKwZorf9Haz0BT/LoOuAfQDFwBnAnniRRmVJqQ3viKaiU\nUj8A7gNeAM7UWt+ptf5/WusfaK3PBX7ts28anoRdGzBNa3271vo+YBywCrhOKXVTsP83iMixbNky\nDh8+bIyTzhxAfG7mCfslDs4jPt/72W7nzp1s3rw5KDEKIU5NSUnJCUnpszIT+dk5uaQn9G3ib4LN\nyu1nZZsK2QO89957pu+DInZIwktEvcOHD1NWVmaM0wsHh+0yp/TCwabxpk2bQhSJEOGtrq6Oxx9/\nnIMHDxrbUlMTuPPOi0hL67p9dSQZOTKfWbMm4vtWtXz5cubPny9Jrxihta7SWr+utf6J1nokMAi4\nFXgZaMGTNPp5MGNSSiUADwIlwJ1a6xO6KmitW32G1+FJ3M3XWq/z2acJ+E378K7ARSwi3cqVK43n\nljgbqeNHdLmfxWIhbdIofN80fY8VQoQHp9PJ3//+d1NNvsn5ydw+OpsEm39SEzaLhWvPzOCawnTT\n9qeffpra2lq/XENEDkl4iai3ceNG0zhtyMAQRXJy9qREHHneTm2bN2+WZY1CdFJbW8tjjz1mmtmV\nkuJJduXkpIQwMv8aN25gex0y77bPP/+cl19+WZJeMUYpNQC4HJje/tixXiPYHQ2m40lgvQ64lFJX\nKqV+qZS6Wyl1YRf7X9r++EEXry0DjgOT2xNpQphUV1ezY8cOY5w4pD+2pO5/VOLSU4jPN5eGkE63\nQoSX119/3VRXWWUkcP2wjF51YzwVFouFrwxMZWqB93NhbW0tzz77rCx3jjGS8BJRz3eWVHxqColZ\nGSGM5uTSh3oTcvX19ezevTuE0QgRXmpqanjsscdM3XxSUxO4666L6d8/vYcjI9OkSUO58cbzTEmv\n1atXM2/ePEl6RTGlVJpS6hql1BNKqR14ZlT9C/gOcAyYC1yDp85XME1sf2zCU2fsXeBh4DFgpVLq\nM6VUrs/+qv3xhN7wWmsnsBew41muKYTJ2rVrTV9Mk4bkn/SYpCHeDtyNjY1s3bo1ILEJIU7dl19+\nyVtvvWWME20WZo3IDOjKm6uGppkK4H/xxRd8/vnnAbueCD+RV9HXh1LqMuCnwIVAJlAJbAUe11q/\n32nfyXimz18AJOGph/Ec8ITWWqbQRKny8nLTLJC0IQPDdjljh7TBAzm8xjsrbfPmzSilejhCiNhQ\nVVXF3LlzKS8vN7alpSXyox9dTL9+qSGMLLAmTBiC1Wrl1Ve/oOO735o1a2hra+N73/seNps0uosm\nSqlVwHmADbAAB/DUy/oI+EhrfaSHwwOtX/vjfcB24GJgE1CIp9j+DOA/eAvXd2ShvS1UzTq29/lO\nlNRmiT6fffaZ8dySEGeavdWdxMH9qFljoePNcsmSJQGLTwhxapYuXWpKYl83LIOMADcYirdZ+c6I\nTB7ffJSOK7/11lskJUV++QvROxGb8Gov1HofcBBPh6AKPNPsz8PzQet9n32vARbiuSO5AM/d0a/j\n6RA0Bbg+iKGLICoqKjKN0wYPCFEkvReX7CApJ4vGimMAbNu2jeuvvz7sE3VCBFJlZSVz5841dWNM\nT0/irrsujqpljN0599xBWK0WXnnlC1wuz0e2devW0dbWxm233SZJr+hyPuDGcwPv11rr90Icj6+O\nlQFO4Gqt9b728Val1DcBDVyilLpQa70qFAGK6OFb0sGaEIfFevKFKZY4Oxa7FXer51iZCStE+HA4\nHKbx4JTgNBgakBJPnNVCS/vnp+Tk5JMcIaJJRCa8OnUIOqFoqlIqzud55w5B69q3/xb4mPYOQVpr\naY0dhbZt22Y8tyUmhG13xs5SBxUYCa/KykqOHDlC//79T3KUENGpoqKCxx9/nGPHjhnbMjKSuOuu\nqWRnx86HlnHjBmK1Wpg3b62R9Nq4cSNtbW18//vfJy7u9Np4i7CzCTgHOBt4WylVAnyIZ4bXx1rr\n8p4ODrDq9seNPskuALTWx5VSi4HbgUl4ujB2zODqbr1xx/bqbl7vtfPOO6+vpxBh5ssvv6SkpASA\nttrjtB1vwuZI7PGY1mN1RrIL4IILLpCfDSHCRHp6OqtXrzbGRcea6OcI/GeX4uomI9kFcOmll8r7\nQoTpyyzuiKvhJR2CRG81NTVRXFxsjFMH9I+YWVKpA83JLd/EnRCxpKKigscee8yU7MrKcvDjH18S\nU8muDmefPYBbbjkfm837XrZlyxaeeeYZU8cjEbm01ufi+dxyPfAUno6MtwOvAIeVUluUUn9tLxgf\n7F8C3f7YXYKqqv2xY61Ix/4ntNZTStnxLIV0Anv8FaCIHqNGjTKNW8qqutnTd59jpnHncwghQueM\nM84gI8O7gn1bZWNQrrutssl4brFYGDduXFCuK8JDxCW8kA5Bope01qbp8J2TSOEsMSsTe5L3Lub2\n7dtDGI0QoVFTU8MTTzxBVZX3S05OTgo//vFUsrIcPRwZ3UaPLuDWWy/Ebvf+Cd+2bRsvvfSSLN+J\nElrrY1rrhVrru7TWChgMfB94GcgC7sZTzqEyyKF9hGe55VlKqa4+Q45pf9zb/vhx++PXuth3KuAA\nVmqtm/0apYgKw4cPNy3Xbio9+eTGZp99MjIyZHa8EGHEarUyfvx4Y/xlbQtryxoCes09Nc2s8bnG\n8OHDSUtLC+g1RXiJxCWNnTsEjfF9USm1DLhOa320Y1P7Y5cdgpRSe4HReDoE7ei8z6mQgqnhxXfK\nLBYLKQUn7+4TLiwWCykD+lO92/OdYffu3axZswa7PRJ/ZYU4dc3NzSxatMg0s6tfv1R++MOLSE+X\nQqOjRuXz/e9fyHPPrcLp9CS51q1bR319PRdeeGHEzGYVvaO1PqiU+rB9aAW+BSQCQV3HqrXeWc1z\npAAAIABJREFUr5R6B7gaT9Ltbx2vKaVmAF/FM/ur4ybja8Bs4Cal1BM+ZSUSgT+17/P3IIUvIkxi\nYiJKKeOmX9O+IzjHDcee0vUNj5ajVaZZYGPGjJH3QiHCzMUXX8wnn3xijP9dXEW+I47Bqf6v51Xd\n7ORfOypp865m5JJLLvH7dUR4i8QZXr4dgtx4OgSl4ql1sQTPHcP/+OwftA5BIrz4dmdMys7ElhCc\nwoj+klKQZzx3uVyUlZWFMBohgsfpdLJ06VJTsisz0yHJrk5GjMjjllsuwGr1fqHbuXMnGzdu7OEo\nESmUUhlKqW8ppf5XKaWB/cCzwLeBBGAL8FgIQvsJns6Rf1VKfaiUelQp9RqeZkFtwB1a6xoArXUt\n8AM8HSc/VUo90950aBOeDtuv4WkmJESXrrjiCu/A7aahaG+3+9ZvNa+MnTlzZqDCEkKcppEjR3L1\n1VcbY6cbnt1eSW1LWw9HnbpWl5vntldS1+qd+T5x4kRJeMWgSJwuErYdgqT4Xfiorq6mutpbYiS5\nf14Pe4en5Px+prHL5ZKfMRH1XC4XTz31FOXl3mUpqakJkuzqxqhR+cyaNYFXXvmCjk7fmzZtQinF\n1KlTQxtclAnWLG6l1EPA5cA4PJ95OjKae/EsKfwI+EhrXdH1GQKrfbbZecDv8Mz0mgrUAu8AD2mt\n13ba/02l1CXAr4Fr8cxM2w3cC8zVWrsRohvnnnsuQ4YMYf/+/QAc332QlLFnnlC8vrWyhubSo8Z4\n/PjxDB06NJihCiF66YYbbqCkpIRNmzYBUNPSxvM7Kvnp2blY/TQrc+HuKkrqvWW9Bw4cyI9+9COs\nvej2KqJLJCa8wrZDkAgfu3aZV7D6zpaKFHGOJBIy0mmu9vwId/7fJEQ0Wrp0KVu3bjXGiYlx/OAH\nU8jJSQlhVOFt/PhBHD/ewhtvbDa2vfbaawwZMoQhQ4aEMDJxmn7Z/ngUTw2sjgRX91Nbgqy9bMTP\n2v/1Zv/PAZluI06ZxWLhmmuuYe7cuZ4NLjfHiw+Qes5w034NusQ0vuaaa4IVohDiFFmtVn7605/y\n29/+lsOHDwOwp7aFzRWNjM/te43WQw0trC47boyTk5P5+c9/TlKS3DiNRZGY4pQOQeKkvvzyS+O5\nxWrFkZsdwmhOX3J+rvH8wIEDNDdLXV8RvYqLi3nnnXeMsd1u5fbbL6SgQFacn8yUKWcyY4a3G1lb\nWxvPPPMMx48f7+EoEaZ+DozTWudprWdprZ8Jp2SXEME2adIkcnJyjHHL0RO/ArRWeLeNGDGCESNO\n+NgvhAgjDoeDe++9l7g4bynKTw7W4Xb3fdLvJwfrTeOf/OQn5OVF3uQH4R+RmPCSDkHipHwTXkk5\nmVgjtNh7cp434eVyudi7V77ziOhUW1vLc889Z/qgc801Z1NYmNPDUcLX9OkjOessb3OOY8eO8eKL\nL/rlw6MIHq3137TWW07n2Pau1R+ffE8hIofVajUlsJzHak3va65WJ84abxc2pRRCiPA3YMAALr74\nYmNcUt/KlzUtfTpndbOT9Ue9N/tGjhzJuHHj+nROEdkiLuGltd6Pp07EYDwdggw9dAiqwNMhaILP\nvtIhKEo1NDQY02MBHP1ye9g7vDnyzLH7JvKEiBZut5sXX3yR2tpaY9v48YO44ILCEEYVeSwWCzfd\nNIHMTO9ygK1bt/Lpp5+GLigRbCMBqcgroo5vPS5Xcyuu403G2FlV1+2+Qojw1rm5xMeldd3s2TvL\nDtXj8rnPd+WVV/bpfCLyRVzCq510CBLd6jwLqnPSKJLEOZKIS002xnv2yMpbEX02btzIjh07jHG/\nfqlcd914aSd/GhyOeG6+eRI2m/e/3bvvvktNTXeNioUQIvwVFppvgLT6JLlaJeElRMQqKCgwNeXa\neawJp+v0Z6ZvrfQmw/v378/48eP7FJ+IfBGZ8NJaHwTOA54EhuOZ6TUNz8yvKVrrhZ32fxPPHc9l\neDoE/QxoxdMh6CbpEBRdTkh4RWj9rg6OXO+Srn379uFyuXrYW4jI0tLSwuuvv26MrVYL3/3uJBIS\nInMZcjgYPDiLK64YbYybmpp4++23QxiREEL0TXx8vHlDD5/cExISAhuMEMKvfBPaLrytiU+Hy2e5\nc2FhoXRlFBHZpRGQDkGie/v27TOex6emYE+M7A8+jtxsavZ42nE3NTVRXl5Ofn7+SY4SIjIsWbKE\nqqoqY3zRRWdSUNBdU13RWxdfPIwvvthPWZln5sPq1au56KKLTpglIYQQkcD37wSAzZHQ5XPw1C/M\nysoKSlxCiL5raPDW4Iu3WrBZTz/l5bBbqaTthPOK2CUpTxFVXC4X+/fvN8ZJuZH/gScpx/y/QQrX\ni2hx7Ngxli5daoxTUhKYPn1UD0eI3rLZrFxzzTmmbf/5z3+kgL0QIiJVVlaaxlZHovHc5vMcPH9b\nhBCRwzcx5bD3LT2R5HO8JLwESMJLRJmjR4/S2NhojB05kb2cESAxKwOLz3Rc34SeEJFs0aJFOJ1O\nYzxz5miSkuJ6OEKcihEj+jF2bIEx3r9/P1u2nFbzPyGECCnTDC+rBWuid4mjVRJeQkS0ujpvHb6k\nPia8fBNmvs2QROyShJeIKiUlJaZx59lRkchqs5GYlWGMO/9vFCISlZeXs3r1amPcv386EyYMCWFE\n0emqq8ZitZoL2EsdQCFEpPFtvGFNTDA1NbEmxpuK/kiTDiEiS2lpqfE8O8nWp3NlJ3orNh09epSW\nlpY+nU9EPkl4iahiSgZZLKZEUSRLys40npeWltLW1hbCaITou0WLFpkSL1/72ihTYkb4R3Z2MhMn\nehOJhw4dYuPGjSGMSAghTp3vTA1bormAvcViwZoQ3+W+Qojw1lGfuEN/R99m+vdP9h7vdrtNyTQR\nmyThJaLKgQMHjOcJGWlY7RHbl8EkMds7U83pdHL48OEQRiNE3xw6dIgvvvjCGA8alMlZZ/UPYUTR\n7fLLR2Kzef/cv/vuu6alpEIIEe7MM7ziT3jdd5skvISIHAcPHjSN+5zwcpi/+/l+NxSxSRJeImq4\nXC7Tm5rvrKhI1/l/iyxrFJHK7XafUDz9a187y7Q8RfhXZqaDCy4YaozLy8v57LPPQheQEEKcIt8k\nliS8hIge+/btM419Z2idjjxHnCnBIc2+hCS8RNSoqKigqanJGEdTwisxM91UuF7uVohItWnTJnbt\n2mWMhw/vx4gR/UIYUWy4/PKRJPrUtXj//felzk2YUkr9VSn1u9M8fAXwoj/jESIc+NbhscSdOHvf\n4jOjX2r2CBE5tm3bZjxPsFno5+jb6hy71cKAFG/SrKioqE/nE5EvIAkvpdQwpdSjSqkVSimtlHrE\n57XzlVJ3KqWio7iSCBudk0CJWdGT8LJYrSRkphvjztN/hYgELS0tvP7668bYarVwzTVny+yuIEhN\nTWTGjFHGuKmpibfffjuEEYke/Aw453QO1Fo/q7W+zc/xCBFyvsuwLV3Ue/TdJku2hYgMLpeL7du3\nG+Ph6QnY/PCZcESGt3PrwYMHzV1eRczxe8JLKXU7sA34OTAZGAbk+OziAP4OfNPf1xax7cSEV3Tl\nVH1nrB08eFA6rYmI88Ybb5jaxU+Zcib5+WkhjCi2TJlyJv36pRrj1atXs3Xr1hBGJLpxBJBv7EL4\nMDXrsXbx9cVnmyS8hIgMu3fvpr6+3hirzMQe9u49lZFgGm/evNkv5xWRya8JL6XUFOCfQBNwH3A+\npkbBAHwG1ABX+/PaQvgmvOLTUrHF9W0NeLjxnbHW0tJi6mgiRLhbvXo1y5YtM8bJyQnMmDEyhBHF\nHpvNyjXXnG3a9sILL8h7Sfj5EJiilIqOritC9FFjYyOtra3G2NJFwsvi05ijoaFBbgoKEebKysp4\n4oknTNtGdEpUna7C9ATifN4mXnzxRXbu3OmXc4vI4+8ZXv8DuIErtNZztNZfdN5Ba+0CNgKjOr8m\nxOlyu92mQu7RVL+rgxSuF5GqpKSE+fPnm7bdcMN4kpJOLDwsAkupPC68sNAYNzY28vTTT9Pc3BzC\nqEQnvweSgH8opZJDHYwQobZ27VrT2J5+4q+FPT3FeF5fX29aJiWECC+HDx/mgQceoLKy0tg2NDWe\nfkn+uc8TZ7VwXq7DGDc1NTF79mx27Njhl/OLyOLvu4cXAmu11qtOst8RYIKfry1iWGVlJcePHzfG\nSTlZIYwmMBIzM8BigfbudiUlJUyaNCnEUQnRs7q6Op5++mnT3fnLL1eMHl0Qwqhi2zXXnE1paTUl\nJZ6aFocOHeLll1/m1ltvxdrVUiERbLcCi4DbgKuVUh8C+4HGLvZ1a60fCGJsQgTd8uXLjecWu42E\ngSc2Okkamk/dBm06ZsyYMUGJTwjRe6WlpTz44INUV1cb2/ol2bltVLZfa7p+88wMKpvaKK7x3NBr\nbm5m9uzZ3HfffYwePdpv1xHhz9+fbNOB3lTTTsH/yTYRwzrPdorGGV5Wu43EDG/hepnhJcJdbW0t\nc+fONdXtUqofM2acFcKohN1u43vfO5/kZO/SgfXr1/Pqq6/KMqDw8AdgFp6SEDnATcAv27d3/Pu9\nz3MholZZWZlptlbikHysXXRptCUnEZ+fbYzXrl1ruhEqhAitlpYWFi9ezP33329KduUl2fnp2bmk\nJ9j8er0Em5UfjM42LZNsaWnhkUce4bXXXjPVDhPRzd9Jp3Kg8KR7gQJK/XxtEcM6J3+iqUOjr8Sc\nTJqqPH8kDhw4QFtbGzabf/9ACOEP1dXVzJ07l7KyMmNbZqaD73xnEtYuOmyJ4MrIcHDzzZP45z+X\nd0waZeXKlTidTr773e/K+0po3Y+nPIQQMa2mpoa//OUvpm1JZ3Q/OzjpjAJajniWSDU3N/Poo4/y\ny1/+ksRE/xTCFkKcuubmZj766CPeffddU6ILoL/Dzo/H5pIaH5jPHPE2K3eclcNzOyrYWeWZ6dXa\n2srrr7/OokWLmDFjBldccQVpadJAKZr5O+H1OXCdUmqC1npdVzsopaYDI4Bn/HxtEcP27NljPE9I\nT8MWH10F6zs4crKpLt4LeO5SHDp0iEGDBoU4KiHMjh07xuOPP05FRYWxLS0tkTvumIzDIXW7wsWw\nYbnceON5LFiw3kh6rV27FqfTya233ipJrxDRWv8h1DEIEWq1tbX8+c9/prTUe3/cnp5CfF73JSsS\nB+dRt3EXrkbPF1utNY8++ij33XefJL2ECLKmpiaWLl3Ke++9R21t7QmvFyTH8eOxOaTEBfazRrzN\nwu1n5fD8jkqKjjUZ2xsbG3nrrbf44IMPmDFjBjNnziQ9Pb2HM4lI5e8ljX/DMwX/daXUDKWU6fxK\nqanAc3jabT/RxfFCnDKn02ma4eXolxPCaALL0S/bNPZN9AkRDo4cOcLf/vY3U7IrPT2Ju+6aSl6e\n3EELNxMmDOHb355omnW3YcMGKWQvhAiZuro6HnroIVP3basjgcxp43us8WONs5M57VwsPksed+zY\nwZw5c+T9TIggOX78OG+99Rb/9V//xauvvnpCsivJbuFrg9O4+5zcgCe7OsRZLXz/rGyuOzODjE5L\nJ5ubm3nnnXe4++67eemll6iqqgpKTCJ4/Jrw0lqvwdOpcSCegquVeKblf0MpVQZ8AgwA/kdrvdWf\n1xaxq7S01FQQu3NSKJokZKRjjfPOXtu7d28IoxHCbOvWrTz66KOmml2ZmQ5+/OOp5Oam9HCkCKXx\n4wdx882TsNm8XyS3bt3KnDlzTB2URPAppdKVUpcrpWYppSaHOh4hAq22tpaHHnqI/fv3G9usSQlk\nT5+EPe3kTUvjc9LJumwCFp8v0kVFRcyZM4empqYejhRC9EVTUxNvvfUWd999NwsWLDihRpbDbuXK\nIWn8fmJ/vjYkjQRbcJvk2CwWLipI4TcT8rlxWAZZnRJfLS0tLFq0iHvuuYeXXnqJmpqaoMYnAsfv\nP2la6znAlcA6PEXsLUAGkAtsA76htX7M39cVsau4uNg0duRG7wwvi8WCI9eb0Nu9ezdut5R6EaHl\ndrtZvHgx//znP01fKHJykvnJT6aSnX3yLykitMaOHcAtt1yA3e79WFBaWsrs2bPZtWtXCCOLTe2J\nrufw1EZdDMwD7vB5/Q6l1CGl1AWhilEIf6usrOT+++9n3759xjZrYjzZ0yf2KtnVIT43g6xLJ2Cx\ne7/Qbtu2jYceekgKVQvhZy0tLbz//vvcc889LFiwgIaGBtPrKXFWvj40nd9NzGf64DQS7aHtBm23\nWriwfwq/npDPrOGZ5CSaE1+tra1G4mv+/PnynhEFAvITp7VepLU+H0+SaxJwITBQa32O1vrtQFxT\nxK4dO3YYz+2JicSnp4YwmsBz5Ocaz6uqqkxFwYUItpaWFv71r3/x9ttvm5KvQ4dm8eMfX0JGhiOE\n0YlTcdZZ/bnzzotM3RsbGhp44okn+OyzzyS5HiRKqWTgU+BWoArPjPnO67jeBfKAbwQzNiEC5dCh\nQ/zhD3/g0KFDxjZrQjxZ0ydhTz/1GcLx/TI9M718kl7FxcU88MADsmRJCD9obW1l6dKl/Pd//zfz\n5s07YeliWpyVawrT+e3EfC4blNqnRJfb7aah1dtFuqLRSVFlY58+l9isFs7PT+ZXE/L59ohMcpPM\npc2bm5t5++23ufvuu1m4cKF0fY1g/i5ab6K1rsSzrFGIgGhpaWH37t3GOGVAXo/1HaJBakE+5Ru8\nK4K3b99Ofn5+CCMSsaq8vJxnn32WgwcPmrZPmjSEb31rHHa7FD2PNGeckcPdd3+F559fxaFDnun8\nLpeLf//735SUlHDjjTcSHy+NBwLsF8A5eGZ1/UhrfVwp5fLdQWt9RCm1Hbg0FAEK4U979+5l9uzZ\npi/MVkci2ZdPOK1kV4f4fplkXT6BYx+vx93iBDwdrv/4xz/yq1/9iry8vD7HLkSsaWtrY/ny5bz+\n+uumeq0dUuKsXD4olcn5KcTb+v6drL61jfm7qjhy3Glsq2lx8fT2SsZkJXLTiMw+1QKzWSxMyktm\nQj8HG44e54P9tVQ0tRmvNzY2snDhQhYvXsxVV13FjBkzpAlGhAntnEIh+qi4uBin0/sGmDKgfwij\nCY7E7Exsid4ZGL4z3IQIlnXr1jF79mxTsstqtfDNb57D9defK8muCJaV5eCnP72EceMGmravXr2a\n2bNnc/jw4RBFFjOuBw4BP9Ba93RLeReeuqhCRKzS0lL+9Kc/mZJdtjQHOV87v0/Jrg7xuZlkzzgf\na5L3c1N5eTl//OMfpUaPEKfo+PHj/OY3v+Gpp546IdnlsFu4amgav52Yz7QBqX5JdrndbubvqmLb\nsa7r72071sT84iq/zEC3WixM6JfMr87L56bhmWR2qvFVX1/P/Pnz+cUvfiGrayKMX2d4KaV+18td\nW4AKYL3WeqM/YxCxZcuWLaZxSkH0362zWCykFORTs8dT0HXXrl00NjaSlJQU4shELGhpaWHhwoWs\nWLHCtN3hiOd73zufYcNyuzlSRJL4eDvf+c5ECgrSWbSoiI7PkkeOHGH27NnceOONXHjhhaENMnqd\nASzWWp+srVwTEL1dWkRMeOutt2hsbDTG9qw0si49D5tPgqqv4jJTyf7q+Rz78Ava6j3Xqq6uZtGi\nRdx0001+u44Q0W7ZsmWmhhIAiTYL0wakcsmAFJL8XJ9r+7GmbpNdHbZVNrH9WBOjs/3zPchmtXBB\nvmfG1+ojDSw5UEtti3eS9bFjx3j33Xe5/fbb/XI9EXj+XtL4BzxdGU/G0rGfUmoLcJvWepOfYxFR\nzul0snGjN1+alJuNPUammKYNKjASXk6nk02bNsmXTxFwZWVlPPvss5SWlpq2DxmSxXe+M4msLKnX\nFU0sFguXXqoYMCCDV15ZR0ODJ//S2trKvHnzKC4u5oYbbpCp/f7XCvTmP+ogQKrpiojldrvZtm2b\nMbZnppI9fRLWeP9XXLGnOsj+6vlULFqN67jnC7TvtYUQJ/fFF18Yz+0WuGRAKpcOTCG5D0sKe7Kr\n5mT3fTyKa5r9lvDqYLd6ujpOykvm88P1LD1Qx3GnJ/G1bt06brvtNqxWWSwXCfz9/9L9wAt4ElrH\ngbeAucBjwJtAR9uGF4Dn8EzHPwf4UCk12M+xiCi3fft2UyeQjDOHhDCa4EodVIDV7v1A6PsHSIhA\nWLNmDQ8//PAJya5p00bw4x9PlWRXFFMqj3vvvZQzzzR3wF2zZg2PPPLICT8Tos80MF4p1e0UF6VU\nJp7PT1u720eIcFdaWkp1dbUxTiosCEiyq4PNkUjiYO9KgL1790oHNiF6qba2lp07dxrjCf0cfL0w\nPWDJLoAjDa292u9wL/c7HfE2C18ZmMr0Qd6maDU1NRQXFwfsmsK//J3weha4CngVGKq1/pbW+r+1\n1j/XWl8LDGl/7UrgAWAM8A8gC0+RViF6zZTksVhIHxo7OVOr3U7aEG99nV27dpk+NArhL01NTbz4\n4ou8+OKLtLS0GNsdjnhuv30yV101BptN7nBFu/T0JH74w4uZPn0kvn1BysrKeOSRR1i+fLl0cfSf\n14B+wOwe9vkzkAL8OygRCREARUVFpnFC/8Cv0E3I917D7XZLHVQhemn9+vWmv/Nn5wS+lIqzl58r\nertfX4ztNINs3bp1Ab+m8A9/f0v5E56aEre2d2g00VpXAbe17/MnrXUbnkRXBTDDz7GIKFZXV2eq\n35U6IB97ov/qPUSC9DO8M9rcbjerVq0KYTQiGpWWlvLII4+wZs0a0/bCwmzuvfdSRo2S7qCxxGq1\n8NWvnsWdd15Eaqr3/dbpdDJ//nyeffZZadvtH08CO4CfKaVWKKXubd8+VCl1l1LqY+BOPLO7ng1V\nkEL01b59+0xja0LgO8BaE83X6ByDEKJrnX9XVh5uMJb4RTuny81nh8yzQffu3RuiaMSp8nfCawbw\nuda623mF7a+tBKa3j48Dm/HUohCiV1asWGHqzpgxrDCE0YRGSv9+2B3euw3Lly83/TcR4nS53W6W\nL1/OI488YupEY7HA5ZcrfvSji8nIkCWMsWr48H7ce+9ljBjRz7R948aNPPzww/IFso/aPxfNANYA\nk4FH21+6BE8ybBqwAbhSa93S1TmEiATDhg0zjWs36IBez+12U9fpGp1jEEJ0bcSIEabxtmNNzNlY\nxsH66P4zVNXs5IktR1neKeHV+b+HCF/+Tnhl4JlifzLJ7ft2OOrnOEQUczqdLF++3BjHJTtIGxx7\nndktVivZI4cb45qaGjZtkt4Pom9aWlp44YUXmD9/vimBmpqawJ13XsTXvjZaljAKUlMTueOOKcyc\nORqr1bvGsbKykr/+9a8sW7ZMljj2gda6VGs9GZgJ/C/wPrAEz4yua4FJWmspniYi2rRp0xg0yHu/\nu2nfYZrLjgXsek17D9NSXmWMx44dy7hx4wJ2PSGiyeTJk7ntttuw2bw1uyqb2nhsUzmrjzT0cGTk\n2lnVxF82lLO/zpzUu+qqq7j22mtDFJU4Vf6uDLkX+IpSarDWuqSrHdqL01/avm+H/sAJSyCF6Mqm\nTZuoqakxxlkjh2GJ0S4ZmSPOoHxzEe62NgA++eQTJkyYEOKoRKSqrKzkqaee4uDBg6btI0b0Y9as\nCaSmSjc+4WW1ero4Fhbm8PLLa6mubgSgra2NBQsWcODAAW644Qbi4uJCHGnk0lp/AHwQ6jiECASb\nzcatt97KAw88YGyrXbudnCsn+/1znavFaZpBZrPZuOWWW7D4FiUUQnTLYrEwffp0CgsLmTt3LhUV\nFQA43TC/uIova5q5blgGCVFwU7TN7WZJSS1LSurwvXXncDj44Q9/yMSJE0MWmzh1/v6JfAFwAJ8o\npWYppYwUsFLKppS6CfgET7vtF9q32/F0GpLewOKk3G43H374oTG22GxkDj8jhBGFlj0xwdSdct++\nfezevTuEEYlItXPnTmbPnm1KdlkscMUVo7njjimS7BLd8tR0u4zRo/ubtq9cuZLHHntMGmoIIbo1\natQopkyZYoyd1fU0fun/yYsN2/fiamw2xldeeSUFBQV+v44Q0W7YsGE8+OCDnHPOOabtX5QfZ87G\ncg41RPYSx+rmNv5vy1EWd0p2DRkyhAcffFCSXRHI3zO85uCpLfFVYB7wglLqMOAGCgAbYAEWt+8L\nMBooAl7xcywiCu3cuZMDBw4Y44wzh8RcsfrOskeNoGrXHmO8ZMkSqUkhes3tdvPRRx/x5ptvmpag\nORzx3HzzJIYP79fD0bHD7XbT4PMhrrKynu3bDzNqVL7MEMDz83LLLRfwySeaDz7YTseP0r59+3j4\n4Ye544475H3pFCml4vEsX5wGdLTlLQU+BRZqrZu7PlKIyPLtb3+bdevW0dzs+ZGu27ybpMICLHbb\nSY7snbbGZhp27DPGmZmZfOMb3/DLuYWIRampqdx33328+eabLFy40Pj8WN7o5K8by/nmmRlMzk+O\nuM9HRccaeUVX0dCpGP+0adO49dZbiY8PfGMN4X9+neGltXYCVwL3AvvxJNQGAYPbn5fg6cp4Vfu+\naK03a60v1lrP82csIjotXrzYO7BYyBkzMnTBhInEzHRSB3nvUhYVFVFaKqVdxMm53W7+85//8MYb\nb5iSXQMGpHPPPZdKsqtdQ0Mzzz+/mrKyWmNbTU0Tzz23iuefX01Dg+QdwLPE8bLLRvL9708mMdG7\njLGuro65c+eyefPmEEYXWZRSk4FdeG4e/gC4ov3fHcBLwC6l1EWhi1AI/8nMzOTKK680xq7GZhp2\n7vfb+eu3fInb2WaMr7vuOhITZdayEH1htVr51re+xa9+9SvS09ON7U43/Gd3NS/sPEZjhHRxdLrc\nvLWnmqeLKk3JroSEBH70ox9x5513SrIrgvl9ka3W2qW1fkxrfQaeRNeF7f+GaK0LtdZ/1Vq39XwW\nIU60d+9eiouLjXH6kIEkpKWGMKLwkTt2lGm8ZMmSEEUiIoXL5eLVV1/ls88+M20/99yCxfh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7xgmMp4yy0rKCiY+QbAQsykG24opbLyAnV1oZWdqqur+d3vfsd9990nCXoh4tDQ0BCNjY0cPHiQ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\n13+lqKiIu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Djd7eV0d/hn2GU1UeSxU+SxU5xsp9iTeE3yJ9LjDQwntkIJrvoeL92TaCAf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\nAABWhBcAAAAAK8ILAAAAgJUANEtakeSoedQAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 291,
"width": 606
}
},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(10,4), sharex=False, sharey=False)\n",
"\n",
"sns.violinplot(x='income', y='age', data=train_df.select('age', 'income').toPandas(), ax=axes[0], palette=\"Set3\")\n",
"axes[0].set_title('age vs income')\n",
"\n",
"sns.violinplot(x='income', y='hours_per_week', data=train_df.select('hours_per_week', 'income').toPandas(), ax=axes[1], palette=\"Set2\")\n",
"axes[1].set_title('hours_per_week vs income')\n",
"\n",
"fig.suptitle('Training Set');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Hypothesis 1:** This hypothesis holds good from the first violin plot as we can see that the bulk of people who is earning >50K are in the age range 35-55. Our income is less during our older years or very younger years."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Hypothesis 2:** This hypothesis is rejected because greater number of hours does not relate to higher income. There is same distribution of number of people across the hours in both earning categories of <50K or >=50K. Perhaps, higher income is determined by other factors such as education, experience etc."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3.13 How is the Income distributed across marital_status in the training set:**"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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QJEmSJEmSJEmS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"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 277,
"width": 1155
}
},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(y='marital_status', hue='income', data=train_df.select('marital_status', 'income').toPandas())\n",
"plt.title('marital_status vs income in training set');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Quite interestingly significant proportion of Married Couples have income >50K. Does it signify joint income?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3.14 How is the Income distributed across native_country and education_num in the training set:**\n",
"\n",
"How important is higher education for higher income?"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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pYGBGBeOrSX3O6cuBOaRz57kQwgG55LTs3DwshFA9Ga22riPVxy2AMSGEkK23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\nt5IJMggQVrakganeuguZxFB/9QwSWD355JNEii0o2OVCnXjo3nDDDUENx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KGHsHz5cjQ0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rwuRPhbrNAHGEW2C+Qt5QFrW3l9Gw4R6OnpIdtaNvxiibBOxO2STmEWS4h87V5vhEEscfHi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\nwcjISPFjlwIItt6UtZJMphGXCiTJZDK69957q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9jMhiIf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\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IIIe4+0MtLe/kINiRYxBh7zxEJotbZy+GgpFLoD13GIo1jEB26ZJFPQPNISRAPnORzLZF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\nhveQGtiHSHGxQZT4nRa+2srx6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9/7+SjwvAM5st0RUeKAYIQ1rGETZTnCRE93v6fkIU0W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\nkiRJkiR1FMMSkiRJkiRJkiRJkiSpo/x/0BQmqve31DAAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 377,
"width": 1061
}
},
"output_type": "display_data"
}
],
"source": [
"sns.violinplot(x=\"native_country\", y=\"education_num\", hue=\"income\", data=train_df.select('native_country', 'education_num', 'income').toPandas(), inner=\"quartile\", split=True)\n",
"plt.title('native_country and education_num across income level in training set')\n",
"plt.xticks(rotation=45, ha='right');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Higher Education in general seems to be a factor for higher income, but for some countries such as India, Columbia, Taiwan and Hong Kong highly educated people earn the most money.\n",
"\n",
"Looking at the unique values for the native_country columns we can see there are is **a special character '?'**. It most likely signifies NaN values and we have to handle it later."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3.15 How is the Income distributed across workclass and education_num in the training set:**"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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vf9PARGPMfsaYK4wxXwA3NqBtvoc7JtoC//DGfRSmu6N4bnNfEbhOXGSMidZN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8eBFVVVW+G8E9gOIiwWhiQWtiwWhiwUQMJa4S/QhFUYiamYiWnBqPGKmhoYEIJ/oYWZavOkM2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\nq2nNUE+95gz1UETmJdhONfk4wAD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s3bsXNTXeDFpZdMJZWwS+6SzUQyeDiUjy29hTtPEw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\nZE0cKrlPsH3OtOdxrMS051GwvLF9K3pYTnJims+LT3seOzk+bZ8CQQKBkLi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sA4HC338E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845tZ4CdfTII49o+/bt6XZvby//DgGfDQwMpEtw5dq8ebNrbTMA3kkmk/rbv/3bvO3z58/n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\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"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 333,
"width": 1062
}
},
"output_type": "display_data"
}
],
"source": [
"sns.violinplot(x=\"workclass\", y=\"education_num\", hue=\"income\", data=train_df.select('workclass', 'education_num', 'income').toPandas(), inner=\"quartile\", split=True)\n",
"plt.title('workclass and education_num across income level in training set')\n",
"plt.xticks(rotation=45, ha='right');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In some sectors we observe some people are having low income despite of higher education. Let's dig in a little more. We divide the dataset into 8 distinct buckets based on the number of years spent on education."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"edu_bucketizer = Bucketizer(splits=[0, 3, 5, 9, 13, 16, float('Inf') ], inputCol=\"education_num\", outputCol=\"education_level\")\n",
"train_df = edu_bucketizer.setHandleInvalid(\"skip\").transform(train_df)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bucket_to_level = {0.0:\"elementary-school\", 1.0: \"middle-school\", 2.0:\"high-school\", 3.0: \"Graduate\", 4.0: \"Masters\", 5.0: \"PhD\"}\n",
"udf_bucket_to_level = udf(lambda bucket: bucket_to_level[bucket], StringType())\n",
"train_df = train_df.withColumn(\"education_level\", udf_bucket_to_level(\"education_level\"))"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" education_num | \n",
" education_level | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 13.0 | \n",
" Masters | \n",
"
\n",
" \n",
" 1 | \n",
" 13.0 | \n",
" Masters | \n",
"
\n",
" \n",
" 2 | \n",
" 9.0 | \n",
" Graduate | \n",
"
\n",
" \n",
" 3 | \n",
" 7.0 | \n",
" high-school | \n",
"
\n",
" \n",
" 4 | \n",
" 13.0 | \n",
" Masters | \n",
"
\n",
" \n",
" 5 | \n",
" 14.0 | \n",
" Masters | \n",
"
\n",
" \n",
" 6 | \n",
" 5.0 | \n",
" high-school | \n",
"
\n",
" \n",
" 7 | \n",
" 9.0 | \n",
" Graduate | \n",
"
\n",
" \n",
" 8 | \n",
" 14.0 | \n",
" Masters | \n",
"
\n",
" \n",
" 9 | \n",
" 13.0 | \n",
" Masters | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" education_num education_level\n",
"0 13.0 Masters\n",
"1 13.0 Masters\n",
"2 9.0 Graduate\n",
"3 7.0 high-school\n",
"4 13.0 Masters\n",
"5 14.0 Masters\n",
"6 5.0 high-school\n",
"7 9.0 Graduate\n",
"8 14.0 Masters\n",
"9 13.0 Masters"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.select('education_num', 'education_level').limit(10).toPandas()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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uAjYADgbOBc4u3tNdizrPBI5LKT3UhLLmh1vncfxBYHuAlNIVRTDGOeRZar4b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\nvXv3OV43lrbc3nvvzd577z3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YmVP62zNJGh46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Qa0OvBRYK3MXB74MbAKsFK/+itJczqDF9L/juELac73NmA6ZVU2tZzzjM5GTfK7BjBWBbZuVtVI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\nRUQMA8jMS4F9gX8Bl0bEVnX9x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EmSJEmSJEmSpGnw/wG75h9TKd1HXQAAAABJRU5ErkJg\ngg==\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 341,
"width": 1071
}
},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x='education_level', hue='income', data=train_df.select('education_level', 'income').toPandas(), order=['elementary-school', 'middle-school', 'high-school', 'Graduate', 'Masters', 'PhD'])\n",
"plt.title('Distribution of Education Level across income in training set')\n",
"plt.xticks(rotation=45, ha='right');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we filter out people who are highly educated but earning <=50K."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" native_country | \n",
" race | \n",
" relationship | \n",
" count | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" United-States | \n",
" White | \n",
" Not-in-family | \n",
" 1521 | \n",
"
\n",
" \n",
" 1 | \n",
" United-States | \n",
" White | \n",
" Husband | \n",
" 888 | \n",
"
\n",
" \n",
" 2 | \n",
" United-States | \n",
" White | \n",
" Own-child | \n",
" 453 | \n",
"
\n",
" \n",
" 3 | \n",
" United-States | \n",
" White | \n",
" Unmarried | \n",
" 275 | \n",
"
\n",
" \n",
" 4 | \n",
" United-States | \n",
" Black | \n",
" Not-in-family | \n",
" 98 | \n",
"
\n",
" \n",
" 5 | \n",
" United-States | \n",
" White | \n",
" Wife | \n",
" 92 | \n",
"
\n",
" \n",
" 6 | \n",
" United-States | \n",
" White | \n",
" Other-relative | \n",
" 61 | \n",
"
\n",
" \n",
" 7 | \n",
" United-States | \n",
" Black | \n",
" Unmarried | \n",
" 56 | \n",
"
\n",
" \n",
" 8 | \n",
" United-States | \n",
" Black | \n",
" Own-child | \n",
" 44 | \n",
"
\n",
" \n",
" 9 | \n",
" ? | \n",
" White | \n",
" Not-in-family | \n",
" 38 | \n",
"
\n",
" \n",
" 10 | \n",
" United-States | \n",
" Black | \n",
" Husband | \n",
" 28 | \n",
"
\n",
" \n",
" 11 | \n",
" ? | \n",
" White | \n",
" Husband | \n",
" 26 | \n",
"
\n",
" \n",
" 12 | \n",
" United-States | \n",
" Asian-Pac-Islander | \n",
" Not-in-family | \n",
" 24 | \n",
"
\n",
" \n",
" 13 | \n",
" United-States | \n",
" Asian-Pac-Islander | \n",
" Own-child | \n",
" 20 | \n",
"
\n",
" \n",
" 14 | \n",
" India | \n",
" Asian-Pac-Islander | \n",
" Husband | \n",
" 15 | \n",
"
\n",
" \n",
" 15 | \n",
" Philippines | \n",
" Asian-Pac-Islander | \n",
" Not-in-family | \n",
" 13 | \n",
"
\n",
" \n",
" 16 | \n",
" ? | \n",
" White | \n",
" Unmarried | \n",
" 12 | \n",
"
\n",
" \n",
" 17 | \n",
" China | \n",
" Asian-Pac-Islander | \n",
" Not-in-family | \n",
" 12 | \n",
"
\n",
" \n",
" 18 | \n",
" Mexico | \n",
" White | \n",
" Not-in-family | \n",
" 12 | \n",
"
\n",
" \n",
" 19 | \n",
" Germany | \n",
" White | \n",
" Not-in-family | \n",
" 11 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" native_country race relationship count\n",
"0 United-States White Not-in-family 1521\n",
"1 United-States White Husband 888\n",
"2 United-States White Own-child 453\n",
"3 United-States White Unmarried 275\n",
"4 United-States Black Not-in-family 98\n",
"5 United-States White Wife 92\n",
"6 United-States White Other-relative 61\n",
"7 United-States Black Unmarried 56\n",
"8 United-States Black Own-child 44\n",
"9 ? White Not-in-family 38\n",
"10 United-States Black Husband 28\n",
"11 ? White Husband 26\n",
"12 United-States Asian-Pac-Islander Not-in-family 24\n",
"13 United-States Asian-Pac-Islander Own-child 20\n",
"14 India Asian-Pac-Islander Husband 15\n",
"15 Philippines Asian-Pac-Islander Not-in-family 13\n",
"16 ? White Unmarried 12\n",
"17 China Asian-Pac-Islander Not-in-family 12\n",
"18 Mexico White Not-in-family 12\n",
"19 Germany White Not-in-family 11"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(train_df\n",
" .filter((col('education_num') >= 13.0) & (col('income') == '<=50K'))\n",
" .groupby(['native_country', 'race', 'relationship'])\n",
" .count()\n",
" .orderBy('count', ascending=0)\n",
" .limit(20)\n",
" .toPandas())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Among the highly educated but earning low income are the White Males from United-States followed by Black Males in United States and Indian & Chinese Males."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# we are done with the analysis so drop the added column\n",
"train_df = train_df.drop(col('education_level'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3.16 Are educated females earning more than educated males across different workclass in the training set:**"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
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vCbt4JzbLomxnMTg46EZZyCBeQ2+7cOGCbRy+hUm02+ENN01kRyyU\n7Ovrc68YuK55bahbWHd3d8InNpAdiZxA2n7PWtv7+MUXX8xgRQAAt8VbXJUMn89nmxMZHh4mIJ5F\nK4bCWqsV3tCEUJi3xAqFjY6OulwJnJRoV+3wLSSXlpZ4/+a4aPPV1fWhLkMsnPMuto/MP6mEwiRx\nHhAAgDg4QoKr3Gihfsv9622Tai+88ALdwlzi9/tX7Hax5S77dha7du3KdFnIsKGhoWyXgDTMzc3Z\nxqUVxaqoDnUXIBTmTZEhoJqGClunRkJh+S18C8n5+Xk6/uW4REJhVbVlWr+jOTg+f/68zp8/n8my\nAAAucqpTmCRtuLXFNn7jjTcce24kZ6VQWFFJoWqbQ52a+d3uLXQKy0+JLqhpWmvv9Mf7N7dFC1+H\nbz137do1zh94FJ3C8k+i4ermdbUqKikMjk+cOJGpkgAA8DyOkOAqN1bLVdaUacMtoUnQnp4eTn67\nZH5+fsWtKZrX1aqmsSI43rdvn2OropEdIyMjcbeqQ26bnJy86bqGtlC3sJ6eHrac8aCbO4X5bKtg\n+b3oTYm+F5vCOoVJbCGZ6xLdasa8bZ1t/Nxzz2WiHABAFqy0uCoZlTVltrDC8ePHE+58A2dFC4VF\nhg6awraQ7Ovrc/T/AjIrVoCEUJi3RZsjiaasokRVYZ+xCYXltpt+tvple/1mZ2c1MTHhclVwQuQ8\nic/ni3FPeMH8/LzGx8cTum9BYYHaNoV2pbEsi/NMAADEQCgMrnKrhfr2e9baxkePHnXl+652iUxe\n+nw+bX9re3A8Ozur/fv3Z7IsZNjS0lLCk2bIPadOnbrpusa20ImJ6elp2uh7UOSJv+tTs6prrgyO\n+/r6WAXrQYmuWq+sKVV5VUlwTCgstyUaCqttqlT71sbguLOzU2fPns1UWQAAFzl9Amu9CS2Um5mZ\n0enTpx19fiQmWge4+Vn7gqrGsFCY3+9Xd3d3xuuCM2J9nkp02yvkpo6OjoTvGx7q7OnpIYCbw6L9\nPK4O6xQmsYWkV0WGwgoLC2PcE16Q7E5D7VtDobDFxcWkfoYDALCaEAqDq9wKhdU2Vaq6IdSN6siR\nI5z8dkGiK1rXmxaVlBcFx6+++iqdpjyOVTjeFW31VWNYpzBJ6urqcqscOCRyMnpxYUm1YaGwqakp\nVrB7UOR2r7H4fD5bt7Du7m46/uWwRF9XSbrt/g228bPPPssxLgDkAac/T63d1mjbOvzYsWOOPj8S\nE7VT2FLsTmESn728JF4ojGNv70qmW1R4V8aFhQVdunQpEyUhTYuLi1EX4oR3CpMIhXkVncLyy9DQ\nUFL3b93UoILC0GlujnkBAIiOUBhcFR4KK8zw/75125uCl4eGhpI+oETyEu10UVhUoK13tQXHIyMj\ndHPzOEJh3hVtIru2qVKFRaEf0qxW955oK5TrW6ps497eXrfKgUOSWXkefoJxdnZWly9fzkRJcECi\nx0+SVNtcqbXbQt3Czp8/L8uyMlEWAMBFTm8ZWFperOZ1oYB4R0dHwh1H4YzFxcWonWkilVWWqKqu\nLDimw6t3xAqF0U3d25IJ9DW02kOdzJ3kplg/iytrSm0B6v7+frdKgoMi37MFBZzy9LJk34dFJYVq\n3VQfHJ86dYqtuAEAiIIjJLjKFgrL8KKN5vW1tjGrtTIvmQPurXe120InL730Ep0uPIwPW94VbcKz\noLBADa2hbmHnz593syQ4IFp4qK7ZHgrr6elxqxw4JJmOUo10nfCMZF5XSbrtAXu3sGeeeYZjKADw\nsMXFxaR/FyQiPEQ8MzPDlsMuSyQQdkNTe2j+ii3o8gNbSHpXMsfVVXVltp0QLly4kIGKkK5YP48L\nCgtsodyrV6+6VRIcxPaR+SWVcGZ4c4iFhQW9+eabTpYEAEBeIBQG11y/ft3WCaGwILOpsLqmStuY\nUFjmJdPporSiWBtvWxMc9/X16cyZM5koCy5g+0/virUKNrzL0ODgIJPaHhMtqFlcWqSqutD2CExY\ne08yv2drGitUXBqaDCXcmbuSDQLUNFZqwy3NwfHFixeZ9AQAD8vUApv2LY22cUdHR0a+D6KLtnVk\nLJFb0F28eDETJcFF4+Pj2S4BKUomFObz+dSwJrSgjvduborXua+moSJ4mVCYN0XOSdMpzNtSeR+2\nbWmwNR944403nCwJAIC8wBESXBMZKMj08XlxaZEqakqD44GBgcx+QyR1slqStt+zVgrLBr700ksO\nVwS3JNNeH7kl1mvXuNbebZFtTLwl1s/jxrbQhHVPTw/bCHlMMuEhn8+nxrbQCUa2MsldqXSHufWB\njbatTp555hl+FwOAR2UqFFZWWaL6NaFOsR0dHXSWdNHExETC921aZ//sRZjfG+K9n1hU5V3J/pys\nDwuFDQ8PJ/XehzvivSY1jaFQ2MjICFu/ehDbR+aPpaUl9fX1Jf24ouJCtW8NLYawLItwNgAAEThC\ngmsiJ0Qy3SlMksoqSoKXk2ndj9QkGwqrqi2ztfft7OxkVZ1HcXLBu2J1eWtsq7YFDthuxltinVwM\n70IwPz+v3t5et0qCA5IND4WHwkZHRzUyMuJ0SXBAKltEVdWWafMdrcFxf3+/Dh065GRZAACXZHKu\nonVTQ/Dy0NCQrl27lrHvBbtkggUV1aUqrwotamRBjvcRCvOu5ENhVbYxn7FzT7xQWH1LtW3M6+c9\nkfOao6OjWaoE6bp27VrS55duWG9C3dT9fj/dwgAAiEAoDK6JPBFZmPlMmIpLi4KXk2ndj9SkctC+\n4551tvHLL7/sVDlwEd1JvCvWa1dUXKiG1tDkGKEwb4kVCmtst3ch4HX1lmR/zza22ye46RaWm1Kd\n9Lzlx9bbtkh47rnnUgqYAQCyK7OhsHrb2LKsjH0v2CUTCvP5fGpeFwrzd3V1xVy8g9xBp7D8lOz8\nVmQo7NKlS06WAwfE6xgU+fpduHAhw9XAaZEd8F988UUWnXtUOq/bmo31KqsoDo7379/PInYAAMIQ\nCoNrhoeHbeNCF/73FRWHvgknyTIvlW0v6tdUqXl9KKRw9OjRm/6vIPfxIcu74p1saA7bxmRgYID3\npofEOglVVVdm60Jw8uRJt0qCA5L9PVu/plq+sBA+obDclMr2kZJUXlmirW9pD46Hh4e1b98+p8oC\nALgkk6Gw+pYqlZSFFsudOXMmY98LdsluIde0NvTZa25uTj09PU6XBIcRCstPyc5vlVWU2IIIqWx9\nhswaGhqKeVtZZYkqqkNzJJ2dnW6UBAfNzMzcdN2RI0eyUAnS1dXVlfJjCwp82nBrS3B89epVwoEA\nAIQhFAbXhHcKqyjxyefLfKuwpaXQB/mioqI494QTUj2puePeULcwv9+v3bt3O1USXEIozLvivXYt\nG+psYzoLeEesUJjP51PbltA2Qj09PUmfsEL2JPt7tqi4ULXNlcExq55zU6qdwiTJvG2tikoKg+MX\nXniBhRAA4DGZDIX5Cny2hR5nz56lA5VLkg0Fhb9OEh19vchXEJrnJBTmXal0wq9tDnWbIhSWe+KF\nwiT73Fd3d3fUkBFyV7TjmoGBgSxUgnSFzzuXpnAqb+Nta2zjvXv3plsSAAB5g1AYXBP+Aay2ojDO\nPZ2ztEgozE2pdAqTpDUb6lTdUB4c7927N60TpHAfJxa8aaWASWNrtYqKQz+vT58+nemS4JB429W0\nbQ6Fwvx+v06cOOFGSXBAKr9nG1pDWxFdunSJwFCOWVpaSus1KSkr1o571gbH4+PjTHwCgMfE29bK\nCS3rQye7Z2dn2drMJcmGgipry2zdagiF5b7IBVblVSXBy+ELY+EtKYXCmiqClwcGBvjMlWNWCoWt\n2RjaanlpaYmumh4TbU766tWrWagE6RgaGrK9V8uKkz91XdNQoca20BzY4cOHCXkCALCMUBhc09/f\nH7zcWOnOf72FudCHguLi4jj3hBNSDXL5fD5tC9v+aGZmRgcPHnSqLLgg1UAgsitecEiSCgoLbCvW\nLcsiAOgR8U4uNq+rtXUWYrs570jlZ21jW3Xw8uLiIieCc4wTIfhtd7eruDT0nn7xxRc5EQUAHpLp\nUFjzensHqnPnzmX0+yEg2VCYz2fv6tbV1ZVyN3a4IzI8FB7qm5qa4vXzqFQ64dc0hkJhfr/fNgeO\n7Jqenl7x92zL+jpbp78DBw5kuiw4KFqQc3BwkJ/BHnPq1CnbuLwktV2GttzVGrw8NzenQ4cOpVUX\nAAD5glAYXDEzM2P7ANZU7U7XrrnroRNilZWVce4JJ6QTDNpwa4vthObrr7/OloQeQmc3b0pk28DW\nTaEVkzMzM+ru7s5kSXDA0tKShoeHY95eWFSg9aY5OO7p6VFvb68bpSFNqXUKq7aNeQ/nFidWrRaX\nFmn7W+3dwvbv35/28wIA3JHpbeaq6spVVhFaJEcozB2pdIoK38JsYWFB58+fd7IkOCxywVR4KEyi\nW5hXpdIprKbBPudMl6LckchcR0lZkdq3NgbHp0+f5v3rIdEWr/r9fhbEecyxY8eCl8uKfSopSi0U\ntnZbk0rKQuced+/ezTkmAABEKAwuifww3FTtzvaRc9cXgpcJhWVeOqGwouJC277vly9fJqTgIXQK\n86ZETkCFh8Ik6eTJk5kqBw4ZHR1dcSJ7y52ttvGePXsyWRIckkqAqLK2zDYhduHCBQcrQrqc+v0Z\n2S3s1VdfTemEFgDAffHC/E7w+XxqiuhAxe+IzJqZmdH09HTSjwvf6lMKdGpG7rqpU1hNmW280pZ1\nyD1LS0sp/Xysbii3ja9du+ZUSUhTonPLm28PzUn7/X7t2rUrUyXBYbF2NOjp6XG5EqRqYmLCtmjh\nlrYSpRYJCyyEDT/H1N/fz/EUAAAiFAaXXL582TZudiEU5vf7bZ3CKioq4twbTki3W1T4B3BJdLnI\ncQWFoY9niXScQu5JZKuaipoy1TaFfn6++eabrLDKcYmcfKhrrrJ1kDp48KAGBgYyWRYckEqAyOfz\n2V5rJkZzixOdwqRAt7Atd7YFxwMDA+ro6HDkuQEAmbO0tOTKMVhTe03w8szMDF1sMizVMFBZZYnt\ns9eZM2ecKgkZEBlEqKq1h8L4fOU9qc5rFhUXqryqJDgmFJY7Ll68mND9WjbUqaIm1O1v165ddAvz\niIWFhajX0yXdO44ePWqba75tbWmce69s61vaFJ4qe+2119J6PgAA8gGhMLgi/CC8uFBqqsp8KGx+\nblHhuYWqqqqMf8/VLt1uFzWNlbYT14cPH9b8/HycRyCbyipDE16Z3vIEmZFIKEySrY3+4OCg+vr6\nMlUSHJDoSb7t94S2m1tcXNTTTz+dqZLgkKmpqZQe19AW+t06MjKi0dFRp0pCmiYnJx17rq1vaZOv\nIDTz+corrzj23ACAzBgbG3PlM29jWChMEtsSZlhkKMyXRLuLNRtDnZovX75MKCGHRYbCyqpKVVAY\nmmonFOY9qXT4u6GqLtQtjFBYblhcXFRnZ2dC9/X5fLrtgQ3B8fz8vJ599tlMlQYHxeoUdvbsWTqj\neoDf79frr78eHJcX+7SluTjOI1ZWWVOm9i2hueyOjg719/en9ZwAAHgdoTC4InzCcV1DsQoKUm0A\nm7jwrSMlOoW5wYluF+HtfWdmZhL+8A73lRMK87xEgyFrtzXZxseOHctEOXDIpUuXErrf2m2NtiDu\nsWPH1NXVlamy4IBUT1I0tNpPBLOFZO5wMhRWXlWq9TtCP6/PnTvHCSkAyHFunaCqbaxUUUlocR7H\nfJkVuUgjmSmw1k31tvGpU6ecKAkZENmdxueTqupC3cI4DvOeVBfhSPZQ2NDQEB3Wc8CFCxeS+gy9\nwbSotqkyOD548CDbznlArHD91NQUi1o9oKurS1euXAmO795YpkIHzh1uu7vdNmbRHABgtfNsKMwY\n81FjzP9njNltjBk3xviNMY/EuO+m5dtj/Xk0he//DmPMs8aYYWPMjDHmTWPMbxljMt8Cy2PGx8dt\nqyQ3NBS58n0X5+2rREpL02s7i/j8fr8jobC12xpt7X2PHz+e9nMifdEms8qqQu8pVi97U6KhsJrG\nClXVhyY4Dx8+zARnDuvt7U3ofj6fT3f++Gbbdd/73vfS3goYmZNyKGyNvVsqobDc4WQoTJK23NVm\nGx88eNDR5wcAOMutbZ19BfbtpNlSKbPCQ2FVpT4lc2qzsa1GxWEBvpMnTzpYGZwUrTtNTUNoQerl\ny5fdLAcOSOfYvDJs+9CZmZm0uo7BGR0dHbZx8QpnbXwFPt35zk3Bsd/v1z/8wz8k3GUf2RGrU5hE\nsNoLIrd2vHdTWYx7JqdpbY3qW0JzYQcPHuS9DABY1TwbCpP0+5L+s6S7JSUa+T8u6f+J8ueHyXxj\nY8zPSNol6V2SnpD0N5JKJP2lpKQDZvku8uB7fWN67V8TtbRobw9cVOROGG21mp+fj/shLFGl5cVq\nXlsbHJ88eZJWzzkg2mtbWRMKhTHh5U2Jdnjz+XzaYJqD46GhIboL5Kj5+XnbCruVNLXXBMK4y/r7\n+/X4449nojSkaWlpKeWfs8WlRappDJ2g4kRw7nC602ZDa7WtS8GhQ4c4jgKAHBYe1C7M8AxdU9gW\nksPDwyzsyaDwUFhzdXJzUQWFBVoT1i3MsiwWbXhIeJeh8fFxTUxMZLEaJCud16uq1h5kGBwcTLcc\npGFpacm20LilpjChro1rNtZr460twfHExIQeeeQRPlPlsHjbcLPYPLdduXLF9hptaS5WY5UzPTd8\nPp923Ls2OF5YWNCuXbsceW4AALzIy6GwL0jaIalG0q8n+JhjlmV9NcqfhENhxpgaSd+StCjpIcuy\nftWyrN9RIJy2T9JHjTEfT+6vkt/CtxkrKfJpo2uhMHsXm8JCmrhlkpOBoPatoYDC5OQkHU1yQOS2\nCJJUUWOf8ArvCIjc5/f7NTw8nPD914eFwqRA0AC5p7u7O+mA7t0PbVVpeeh38/79+3X48GGnS0Oa\npqam0urQ19gWOhHc29sbd+IU7knm53AifD6f7STGyMgIIUAAyFF+v9/2Wbe0KP2tcuIJPxaQ6Bya\nKfPz87ZQWFN18nNR7VtCcyILCws6ffq0I7Uh88JDYRLdwrwmnQUbkXNkTh/nIzmdnZ0aGBgIjk1r\nScKPvfs9W23d8s+cOaPHHnuMjvk5KnLOurQiNL916dIltvLNYc8//7ztffXOHeVx7p289m1Nqghb\n1L57925HdroBAMCLPBsKsyzrFcuyzlqW5fbR+EclNUt61LKsN8Lqua5A9zIp8ZBa3puenpZlWcHx\njtYSFRVmdqLzhsJi+39vTn5m1tTUlGPP1ba5wTY+d+6cY8+N1EQLhVXWEgrzsunp6aRWnFfVlaux\nLeJCG68AACAASURBVLTlzOHDh3X9+vVMlIY0pHLCqKyyRPd9YIftukcffVQXL150qiw4IN1tBhvC\n3r8LCwu6dOlSuiXBAZno0rL+FnuIN3LbFABAbujt7bV9js50KKyhtVrh+xgSGs6My5cv2xZptNcn\n37W+dVO9fGEtbeh04h11LfZQGOFLbxkdHU35seHBAykzx/lI3O7du23jezYmviVdUXGhHvjQLSoI\nO4exd+9ePfnkkwTDclDkOZ9125tsYxa15qa+vj5bM4kNjUXa1ORsM4mCAp923LsuOJ6ZmdGePXsc\n/R4AAHiFZ0NhKWo3xvyaMeb3lr/elcJz/MTy1+ej3LZL0rSkdxhjSqPcvuqcOHHCNhl2W3viq3LS\nVVRin3gjvJBZTnYKq6gpVXlV6P8K29RlX7RQWGRr/PAVeMh9qaxa3XR7a/Dy7Ows3aRy0JkzZ1J6\n3JqN9baJktnZWX3jG9+wdTlAdqUbCgvfMkrid2suSLZjY6Iqa8ps24XSXQQActPJkydt4/KSzIbC\nikoKVRfWxYhjgczo6emxjdemEAorLi1Sy/q64PjEiROam5tLuzZkXnlVqSqqQ1PCvM+8JZ0gV0lZ\nkQqLQqdaCIVlz/DwsO13rGktUV1lcl0ba5srdd/7d9jC1C+//LKee+45gmE5rqG12raQ+cCBA2z/\nmWP8fr9++MMf2t5LD91SIZ/P+WPhTbetUVlY97hXXnmFbbkBAKtS8jMT3va+5T9BxphXJX3SsqxE\n22GY5a+dkTdYlrVgjOmWdLukLZLSOgPj9ZPtfr9fzz8fys4VF0rbWtwLhZWU2j/snT59WkVFq+2/\nvHsiJz7T4fP51Nheo0udg5Kks2fP6tChQyooWG051twRLYxQUlak4tJCzc8Ggp9nzpxRQ0PDTfdD\nbkrlPbtuR5Pe3NWl+bnAa/7CCy+otLQ0Ix/akbypqSn19fWl/Pjb375BI/0TGrg0Fny+r3/96/rQ\nhz6kqqoqp8pEitI9oVRZW6bS8mLNzgRW0R45ckR1dXUrPAqZNDk5mbHJyDUb6zU+FAjs9/X1affu\n3aqoqFjhUQAQ27333pvtEjw/RxLpwIEDwcsNlQUqdqGremN7jUYHAt3Jent7deDAAeZJHHbkyJHg\n5dIin5qqkt8+UpLWmyb19wRCJXNzc3r66ae1adMmJ0pEhjW21Wh6IrBo7ty5c8xneUg62336fD6V\nV5VqcjSwNVl3d3fe/d7yiv3799vCJvdtSbxLWLh1O5q1ML+owz8K7WDx/PPPq6urS/fffz/v6xy2\n6fY16tgbmPccHR3Vk08+qQ0bNmS5KtzQ1dWl8+fPB8dbmosd7xJ2Q2FRgbbfs1Yn9lyQFJiH+f73\nv6/bb789I98PwOqRC3MkQDJWy5HrtKQ/knSvpPrlP++W9IqkhyS9ZIypjPlou9rlr2Mxbr9x/ao/\ny3bt2jXbdnJ3ri9TcYa3QwhXXFqkkrLQ5ObYWKyXDE5wej/2pvba4OX5+XlevyyLvqLKp6q68uBo\nfHzcvYKQtomJiaQfU1RcqA23tQTHIyMjdJLKIemGhgoKC/T2D9+q+pZQAGx6elovvPBC2l2qkL50\nO3LeCFzfcO3aNVY4Z1k629OsZM3Getv42rVrGfteAIDkjY+P27rImDZ3ms03toWOBfx+vwYHB135\nvquF3+9Xf39/cLy2vijlBTTtWxptW5fRcco7GttD27bPz8/b5kaRu/x+f9rzWuG7Hji5owISNzY2\nps7O0Fr+pupCbWlOPWyy6fZW3fWuzbbrOjs79fLLL9+0bSHcF2tOY+Nta2zbMJ86dcqtkrCCubk5\nvfHGG8FxgU/60F2VGV1wvOXONtt5wo6Ojqi7ogAAkM9WxXJAy7KuSfpKxNW7jDHvl7RH0v2SPi3p\nr9yuLR6vp0y/853v2MYPbE1tVU6qfD6fahoqNHg58IF+fn7e8/+muSx84tMJ9WvsXWkaGxv11re+\n1dHvgcRduXIl6vXV9eUa6Q+ERSYnJ3XPPffQNcojzp49m9Ljtt7VrvPHQv8fLl26pA9/+MNOlYUU\nRXbnrC4r0NTskpaSzPwUlxbpwY/crtd++KYmhgNh34mJCb344ov63Oc+p3Xr1q3wDMgUJzpyNrXX\n6PL5wEmp2dlZrVu3Tq2trSs8CpmSyS1lGlrtx1FFRUUcBwPwvHz6OfbUU0/Zxre2lejySOZPLje2\nVdvG/H5w1rVr1zQ1NRUcb0ojiFBcWqTWTQ3BY7e+vj7t2LFD1dXVKzwSbnn44YejXt+ywR7OX1pa\n4n3mASMjI1pcXEzrOcrDtg5lHjo7vvnNb9qCQu+9Lf0t6ba/da0k6c1d3cHr+vr69Nprr+kzn/kM\nuyZkUazO2+WVJVq/o0kXzwS6Nvb396uhoUGbN2+Oen+455FHHrE1F3hgW7maqjN7mrqopFDb71kb\n7B43MzOjmZkZPfTQQxn9vgAA5JLV0iksKsuyFiR9e3n4rgQfdqNdUW2M229cn7ml/x7Q39+vN998\nMzje1lKs5gwf3EVT0xRqAHfp0iXHu1khJJWuQ/FU15fbxnQjyq5Yq2dqw95jU1NTGe16AmelGuSs\nri9X+9bG/5+99w5u48zSfp9GBkiAOecgQhIlSqKyJVvOacYzjmPP7nhnd2d2Z+/W3Vu1s7V17927\nW7Vb96tvdsI3d+yZ8YyjLGdZ0VaOVqZyoigRJEWJWRSTCGaAQN8/aHX3C0YQDXQDOL8qlXFaIPqV\nwe5+3/M+5zlCfP369YBaFhLy0NjYyHyni3Jn7zZhNOvx4AsLYLGJn+F0OvHGG2/A4XAENE5i9shx\nf03OYqevUrt+IvQ0Nord6zUy66n1Bh2siWK7SOm5CIIgCGUZHR1lWkcmW7XITgxNvsRiMzFONrdu\n3Zri3YS/+M6VA3GnAYA8iUuz1+tlnDUIZZnYTX0Ma4IZsfFiYSw51IQHchS7mmPE+2tfX9+UvyeE\n/NTW1uLatWtCnJukgz3dMMVPzJw5S7Kw6jtzodGK22ktLS341a9+xZyTCC3Dw8OT/t19Md999u3b\nF+zhENNw6dIlnD17VohtZg3W2S1T/IR8FJVlQG8U59sHDx6Ey+UKybkJgiAIQg1EtSjsWzq+/e9M\n20fez/CU+P6F3W7XASgAMAogqn3dd+zYwVTlrCo2T/Hu4JGaI25+er1eVFdXKzKOaEDu9o56ow4m\nSTJFbicywj8mq5aMS2GdSFpbW0MxHCJAeJ4PSGhZUs4mVqQOVYQynDhxgokX5QTWgsgca8S6l8tg\nSxKTMyMjI/jzn/+Mc+fOBfTZxOzo7u4O+DPiUmKgM2iFuK6uLuDPJGYHz/NMGyhjEFqsS11Xm5qa\nqF0oQRCESqisrGSKqpbmmULqtixtJ33r1i0SLciIVBRm1HHIiA9M7JeelwCjWRSWnT59mp7nKmG6\ntnHp+aJzUGNjoyxzeSK4yFHsJs1jer1e9Pf3B/yZxMxwuVz44osvmGNPLpC3JV1WcTLWvbyQuS8P\nDg7inXfewfbt2wN2miP8Z6o2rfGpsUgvEJ0br1+/Tq2YFeTevXvYuHEjc+z58lgYgpALmQi9UYc5\n5ZlC7HQ6UVFREZJzEwRBEIQaIFEYsOrb/850Rnj42/8+PcHfPQTAAuCUw+GY2Ls2CqitrWVcwnKT\ndAFXR86W1Jx4pn88Ve4Ej2A4RFkTRTFhR0fHFO8kgs1kiY34ZFZPS4vr8KCvr49pa+IvSZk2pGSL\notsrV66gublZjqERs6Cnp4dxDchJ1MlivW6xjgnDkrPEjUOPx4OPP/6YEp4K0NnZGfBnaDQckjLE\n77O2tpY2FhWis7MTTqdTiI16+ROh0mf08PAwcz6CIAhCGXiex6FDh4RYqwHKAnB4nQ1SUdjQ0BC5\ncsuEy+XCjRs3hLgwRQ9NgGIEjVaD3HmiW1hbWxutuVXCZC3L7pNZxLaTI5c39SNHTkMqCgPkL6Al\nJmfnzp1M7nhhthFZCfLvRySmW/Hwq4sQn8LmQw8fPow33ngDXV1dsp+TmJzpusLMW5HLxNu3b6cc\niAKMjo5i/fr1jIhvdbEZBSnyOPnNlOJFmYxb2IEDB8gtjCAIgogaokIUZrfby+12+7h/q91ufwzA\nP38bfuLzd3F2u32u3W7P8PmxzQA6Abxmt9uXSd5vAvA/vg3/JNvgwwyv14uvvvqKOfZEqbxVOf6g\nN+qQImmVdPny5YCEEMTk9PT0yP6ZFquYHKdEirJMtkAyWvSMeK+qqipUQyICQI4K2Hmr2MTKrl27\nAv5MYnYcPXqUEWitltGd02DSYe3zC5A1J5k5fvjwYfzxj3+UvXUwMTGDg4NTVsD6g1TQ6XQ6cffu\nXVk+l/AP39adpiCIwqTPZ4BcVwmCINRAdXU109J3YbYRFkNoU3PJEoE4QIU9clFdXc24R83NlGej\ns2BBOhP7OgQTyjCdKCw5Kw7mWDGnde7cORIiqJympqaAP8NkYUVItF4ODTdv3sTRo0eFOMbI4emF\nM20K4z+xcSY8/INFKCxjt41u376N//7v/0ZFRQVd7yFiOlFYYroVWcVJQnz79m1cuHAh2MMifNi6\ndSvTsjzVpsWj80LTNlIKuYURBEEQ0UzYisLsdvvzdrv9Q7vd/iGA/+vbw6vvH7Pb7b+RvP23AJrs\ndvsmu93+/3375xCAgwCMAP7D4XCc8jnFCwBuAPiF9KDD4XAC+DsAWgBH7Hb7e3a7/VcALgNYjTHR\n2EZEKadPn2YSnAuyDMhOVMYl7D75pWnCa7fbjTNnzig4msjE5XIFRWwnTaD19fWRK42CDA8PT/p3\nGQViBWxrayu1RQgDbt++HfBnpGTFMeKSqqoq1NTUBPy5hH8MDg7i5MmTQpwUq8XcDHkr7bQ6DVY+\nY0fJUrZtaF1dHX7961+joaFB1vMR45HDJew+0usWGHMLI0KP1L1Wr0VQWiZYE9gkKznBEARBKAvP\n89i3b58QcwDWloR+Q8yWHAOdXmwnTaIweZA65ms4YE6aPHNya4IZKTlssSO5fyrPdGIfjuOQOzdF\niNvb22ndpGLkKpbxdQojUVjwGRoawqeffsqIsL6zKBYWY3C3vbQ6DZY8UoSVz9ihM4jP1JGREXz+\n+ed455136F4dAmayH1H6QD7TSWb79u3TiskI+aioqGAE7UYdh1eWW6HTKmMiMZFb2HQtoQmCIAgi\nEghbURiAxQB+/O2fp749Vig59rLkvR8DuARgOcYEXf8IYA6ALwE85HA4/gf8wOFwbAewDsAxAC8B\n+CcAbgA/B/Caw+GIylKQnp4ebN++XYi1GuDR+cGrypkpWcVJMEoqtY4dO0YTPZkJVmtHsySZwvM8\nLaYVZKaiMGAsSU2oG2kyOpAl+II1+Uy8fft2eL3eAD6R8JcDBw4wVepr5piD4s7JcRwWri3Aymfs\n0OrF6eO9e/fwu9/9DseOHaNK2CAip5gnPjWWSVqTmDP0+LaXKk4zBHQvngyL1QiNJNFKrnAEQRDK\nUlNTwwiwFmQbkRSrneIngoNGwyEx3SrEUucGYna43W5UVlYKcX6yHmYZHeCKJG40Ho+H3MJUwEzy\nU9LWnwAYJyNCXcgljjVZWFEY5TGDC8/z+Pzzz5kiqoXZRszLDF1b5uySFDz2w8XMcxUYK5z8xS9+\ngUuXLoVsLNHITISX1gQz5iwRixydTid27twZzGER31JTU4Mvv/ySOfbC0lgkW3WT/ETw0Rt1mLOE\ndQs7dcrXL4QgCIIgIg/lnr4B4nA4/hPAf87wve8DeN/Pz/8QwIdT/P1JAM/685mRDM/z+PLLLxnh\nyEN2CxJiQp/g9EWj1aBgQTqqz47ZgHd3d6OiogIPPfSQwiOLHIIlCvOtsHM6nUhISAjKuYipmaqC\nKjHDBqNZj5GhMbHl0aNHsW7dOmi1yl//xHi8Xi/jFGbQASOjs/usxHQrcuwpaHKM3QOam5tx5swZ\nrF69WoaREtPR1dXFbCzEmTUoywlu8jO7JAXWRAtO77qB/ntjz3yPx4PNmzejrq4OP/zhD2E2y9e+\nkhijra1Nts/SaDgkZ9lw59ZY2+fa2lp4vV5oNOFcKxJejGsvlWHAxduTi69nC6fhEBtvhrNrrPUo\nicIIgiCUw+v1MkV0ALC2RLk5U1KmFXeb7gEYm1M6nU7YbLZpfoqYjGvXrjFr5vlZ8s7JMwuTYLEZ\nMegcKwY5ceIEnnjiCej1yjrzRzMzEfvYEi1IyY5DR3MvAODSpUt4/vnnERcXN81PEqGmrq5Ols/R\nGbTQ6jTwjI4Vy5FTWHA5fvw4U5hqNWnwdFnoC9Rj481Y90oZas434/qZRvDesYK5gYEBrF+/Hpcu\nXcIrr7wCq9U6zScR/tLf3z+j981bkYMmx10M9bsAjD1Hly5disLCwmAOL6ppaWnBe++9x3R+WTfX\nAntG6ESbk1G8OBO1l1rgHhkb28GDB/HAAw/QvIogCIKIaGj3h5CF8+fPo6qqSojT47RYM0c9m8LF\nizMZV4x9+/YxzipEYARLFGY0sxPxYLSoJGbGVIksjYZDYVm6EPf09JBbmIppaGjA4OCgEBv1gU0F\nFjyQB41W/Iyvv/6artUQsXPnToyOioq+R+dboNUE3349LjkGj7y2eEKXwF//+tdoamoK+hiiDbnb\n/qXmxAuvBwYG0NraKuvnE1Nz4cIF4TUnY3upibAmiPNxEoURBEEox/nz59HS0iLEZTlGpNqUq9NM\nymAFYOQWFhhnz54VXms1QGmmvM92TsOhaJHoatHf38+ckwg9M23vXixxI/F6vTh27FiwhkTMEp7n\nmZy2IcD6Rmm3ChKFBY+GhgZs27ZNiDkOeHm5FRYZXRr9QaPhMHdFDh55dRGsiWxr6MuXL+N//s//\nSa5hQWCm15jOoMXiR4qEmOd5fPrpp7Q/FCS6u7vx5z//mTGQmJ9pwDq7OvYL9UYdiheL7nG9vb00\nryIIgiAiHhKFEQHT3d2NzZs3CzHHAd9bYg3JxvRMMZr1KFkqTvT6+vpw4MABBUcUWUgdTOT82g0+\norCZVv8Q8nPv3r0p/76oLJMRBh08eJDaCKoUacsyADDrA7toLTYT7MvE++vAwAC++uqrgD6TmJ6G\nhgZGWJIRr8PC7NBV2xmMOqx+bh4WrM2HtFtlZ2cnfvvb3+LEiRPUTlJG5BZtpeXGM7HD4ZD184nJ\n6evrw9WrV4W4KEXe9lK+WBPEDYmenh5KehMEQSiAy+Vi2hRpNcCj8yxT/ETw8W1zJXUSJvzD6XQy\na6y5GQaYgvBsLyhNY4odDx8+TGtuBWlvb5/R+zIKEhETZxLiY8eOURGVyrhz5w66urqEONC5ubSF\nJInCgkN/fz/Wr1/POBA9Nt+C3CTlXX4SUmPx2A8Xj+1DSHIl913DPvjgA8pvy0hvb++M35tZmITs\nkmQh7ujoGOfiSgROX18f/vSnPzHfTW6SDi8stYLj1LNf6GsiceDAAeaeQhAEQRCRBonCiIDweDzY\nsGEDY5O/Zo4ZGfHq60w6Z3EWU6116NAh2Z03opXm5mbhtUEn3+Te1ymMFs3KMd0i22jRI29+qhC3\ntLTg/PnzwR4WMQsqKyuF14kxGuhk6PJpX5bDJLpPnz6N2trawD+YmBCPx4MvvviCOfZkqSXkyRWO\n42Bfmo2HXi6DOVZMfHs8Hnz55Zf45JNP4HK5QjqmSMTpdKKnp0eIbebAv2drogUmyZyIRGGh48yZ\nM0yisTzfNMW7AycuWRQd8DzPzNkIgiCI0HDw4EGmyGZVkRlxFhkm4QGgN+oYJ5OGhgYFRxPenDlz\nhhFnleUE59muN+pQuFB06O7o6MCVK1eCci5iemYqCuM4DiVLs4V4ZGQEhw4dCtawiFkgzZEAgMUQ\n2HpLmnumPKb8eDwefPjhh+ju7haOlaQb8ECxOhyIAECr02Dh2gI8/EoZYuPZcV2+fBm/+MUvxv3e\nEbNjuiJmXxY/XMTkQk6ePMkUbRGBMTAwgLfeeot5RiZbtXhtpQ06rXoEYQBgMOlQVJYhxN3d3bh4\n8aKCIyIIgiCI4EKiMCIg9u7dy7QZyIjX4eG5yla8TobOoEXZgwVCfH/TmpxMAsPlcjETfYOME3y9\nUcs40FAyRTmkgoTJsC/Nhkby/X/99deMTTShPK2trUzbmpJ0edqaaHUaLJHYsAPA559/To40QeLI\nkSPM9zg3w4D8lOC1n5uO5EwbHvuLJUjLS2COnzt3Dr/97W+pZV2A+G7SJsYEvonMcRxSJC0kb968\nCbfbHfDnElPj9Xpx6tQpIY41aWS7D09GfBrrBNPY2BjU8xEEQRAsnZ2dOHjwoBBbDBzWlqhj4zox\nLVZ43djYSK5Ts8D32W41aVCcGjynmuIlWcyae//+/ZTTUoDh4WFGkDId+fNTYbGJrs7Hjh2D0+kM\nxtAIP+F5HufOnRNim1kTcLGrtMCVnMLkZ8eOHaipqRHieIsGz5fHqsqB6D5JGTY8/peLMac8izne\n19eHd999F5999hlTaE/4B8/zM8pXSzGa9Vj6xBzm2GeffebXPZ2YmKGhIfzpT39i8pVWkwY/Wm0L\nqjt6IMxZkgWtThzboUOHaF5FEARBRCzqfBoTYUFtbS32798vxAYdh5eWqattpC859hQkZ9mEuK6u\nDhUVFQqOKPxpaWlhJssGGU3iOI5jWkiSKEwZRkdHZ7Q4jokzYc4SMdHhdDqpTavKOHv2LBMvkrGK\nPS0vATn2FCHu7Oxk2uQQ8tDV1YU9e/YIsUHH4ZmyGAVHNIbRrMea789H6eo8pkVCa2srfvOb35CL\nQQD4isISZBCFAWwLSbfbzYj8ieBw+fJldHZ2CvGSXGPQ580xNiMMJnFyRk4wBEEQoWXbtm0YHR0V\n4sdLY2DSqyMVJ20h6VvsRcwMh8PBtJ0rzzNCE8RnuznGgLx5aULc0tKCa9euBe18xMQ0NTX59X6N\nVoN5K3KF2OVyYffu3XIPi5gFjY2NzL1vYbZxinfPDKkobGBggAS3MnL+/HkcPnxYiPVa4LWV6hWc\nAIBWN1ak/vAPyhAbz+bgTp8+jV/+8peor69XaHThzdDQ0Kzc6dPzE1G8OFOIBwcHsX79ema+RvjH\n8PAw3n77baYILcbI4cdr4xR3x52Ksc4n4ryqtbUV1dXVCo6IIAiCIIKHemfMhKrp7e3Fhx9+yIiB\nni2LQVKseid5wJjIaMkjxeAkSbpt27ZRNUgA1NXVMbFRJ+9txUiiMMXp6uqacZWMfXk2TDGi68nh\nw4fR0dERrKERfuByuRhRWJpNi3SZW/0uWlfItEo4duwYtZGUEZ7nsXHjRibp9eg8C2xmdTx7OY7D\n3BU5ePD5Bcy9e3h4GO+//z45GcwSaYI4MSbwyvX7pEpEYQAo8RVkvF4v9u3bJ8RaDbCsILitI4Gx\n6zJB4hbmcDiY9pUEQRBE8Lh+/TrTHiorQYfFuYELDuQiPjWWiaXODsTMOH78uPCaQ/DbQgOAfVk2\n46i+b98+mmOHGH9FYQCQOy8VsQmiS2BFRQU5uKqA06dPM/EiGe7R0rWw1+slJyiZaGlpweeff84c\n+365FWlx8ua1gkVSxpjDeqGkXR0w1rLuzTffxMGDB0lA6CfSgit/WbAmH/EpYoFlQ0MDtm/fLsew\noo6hoSG89dZbTO7KbODwV2viVL9XCGDMyU8yr5IKTwmCIAgikiBRGOE3Ho8HH374IWOBXZZtxKLc\n4Ce/5MCWZMG8lTlCPDIygi+++IKSaLNEKviwmTXQyTzXJ9t15fFH1KU36LBgTZ4QezwebN26NRjD\nIvzk1KlTjLBycRDu2UazHkseLRZinufxySefYHBwUPZzRSOnTp1ihDuZ8TosL1Tfszc1Nx6P/nAx\nEjPYtnU7d+7EF198QYIUP3C5XIyDV26SfO2IzLFGWBPFjSlpCw5CfiorK9HW1ibES/JMIRN0ZhSI\nrV0HBgZw+/btkJyXIAgimnG73di8eTNz7NmyGFW1t4pLtjCbYM3NzcoNJgzp7OxEVVWVEJekG0Ly\nbI+JMyF3bqoQNzY24saNG0E/LyEyGzGXRsNh0UMFQszzPLZs2UK5SAUZGBhgWkdmxuuQYg1cYCQt\nlAMolykHAwMDeO+99+B2u4VjD8wxozRLPULrmaDTa7HkkSKsfb4U5lixoNbr9eLrr7/Gu+++i4GB\nAQVHGF4EUoSs1Wmw8tm50BnE5/axY8fGdVggpua+IEyaYzDqOLz+QBxSbeEh2IyNMyGrOFmIHQ4H\nuecSBEEQEQmJwgi/2bVrF27evCnEyVYtvrM4doqfGA/P8xgZZRMfPe39IUuG2JdmM9Ug1dXVOHXq\nVEjOHUl4PB6mCiRfxs3q+0gXyffu3ZP984np8XeRnTs3lWlFUlVVxSTLidDjdrtx6NAhITbrOSzJ\nC07yLKsoCblzxTaSPT09+PLLLynZHSCdnZ3Ytm2bEGs44LklsdCoaGNRisVqxLqXFqJoEVsFW1FR\ngbfffpuqpWfIrVu3GBFdQYq8z9nUHNEtrKmpiQScQcLr9TJtXzUcsHaOeYqfkJeMgkQmlrrWEARB\nEMHh4MGDjINFeb4JmQnyr5cDQavTwipxLrpz546Cowk/jh07xqxxVhSFrljDvjyHEfTt2bOH1lsh\nZLYCyvT8RKRLxPq3bt0a51RFhI6TJ08yLtwrZCq4kha3AtT1IFC8Xi8++ugjplVvYYoej823KDiq\nwEjLS8DjPypH1pxk5nhVVRV+9atfkYvgDAnEKQwAYuPNWPZkCXNs48aNs3KDjEYGBwfxxz/+EQ0N\nDcIxk57DX62xIUPmzhTBRtpOFABOnDih0EgIgiAIIniQKIzwi8rKShw8eFCI9VrgByusfrUyGhzx\nYuOZPjiHWEvkmgvNqNh5AyND7kl+Uj40Wg2WPVHCtJHcvn07s8Akpqeuro5JoOQlB0EUZhWFK06n\nkxxmFMDfRTbHcVj8cBFzbOvWrfTdKcjx48fR29srxKuKzTDqgzcFWPxwESySa/fixYtUbRcAZTe2\nWQAAIABJREFUXq8Xn3zyCXO/fXiuBekqb5Og0Wqw+OEiLFpXyByvrq7GG2+8QRWwM8C3/WqBzM9Z\nqSiM5/lxLaEJeaioqEBra6sQL841Is4SujYKFpsJccliMcS5c+eYKnuCIAhCXjo6OnDgwAEhNhs4\n1W5eWxPEcZEobOYMDw8zYp4Uq1b2edpUWBPMjFtYQ0MDuYWFiJGRkYDcaRY9VAiNVsxFbtu2jQog\nFcDlcuHYsWNCbDVpsCBbnsI5EoXJy549e5j7W5xZg5eWWVVbIDdTDEYdVj5jx+KH2XtCT08P3nzz\nTVy/fl3B0YUHcrg5ZRUlwb4sW4jdbjfee+89cvibhv7+fvzhD39gBIxjgrA41RVBzISkDCuTMzlz\n5gxGRkYUHBFBEARByA+JwogZ09nZiU8++YQ59tziWL+stXmex9eX+uG445rw79vqu3HhYG1IKhzj\nUmIwf2WuEI+MjOCzzz6j6ko/uHLlChPPSTdM8s7ZY4kVkzI8zzPCFiI0zCbhmZAWi4IF6cxnUAWs\nMnR3d2P37t1CbNRxWFEQ3Cp2vVGH5U+VMNXrmzZtIvvtWXL48GHGlTE7QYc1IXQZCpTixZlY/dw8\naHXitLO1tRV/+tOfMDw8rODI1I80EZwcq4VV5pZEydlxzHVKLSTlZ2hoCDt37hRivRZYNzf0woAc\niYNjX18fLl26FPIxEARBRAtbt27F6OioED8+PwYWgzrTb7ZE8ZnU09PDFCEQk3P27FlmHruyyBzy\n1qBzfdzCdu/eTfmsENDa2hrQ/+fYePPYd/ctw8PD2LhxI313IebYsWNwOp1CvLLIBK1GnmvYQKIw\n2bhx4wb27dsnxDoN8OpKGyxGdT5T/YXjOBQtysTDryyCxSbmv10uF9555x2cOXNGwdGpH2nhVSCU\nrs5DWp5YMNfT04MPPviAmcsRIk6nE7///e8Z10yzgcOP18SFnUPYfcauRbHTwfDwMDmsEwRBEBFH\nZMygiaDjdrvxwQcfMO2elhWYsDDHP2FBbbt7UkHYfdrqu3HnVs+sxukvJcuykZAmtr6sra1FRUVF\nSM4d7ni9Xly9elWIcxJ1sJrkv6VIncIAkJubAsy2CrZ0dR50elHAsHfvXnImCTE8z2Pz5s3M5s4j\n8ywwhWBTKjkrjkl2u1wurF+/nn4H/KSxsZERlOi0wPNLrdDIlLAOFZmFSVj38kIYLWKCvLGxEe+8\n8w5tPk5CT08Pk2QrTpO/2tJg1CEhlZ0HEfKyb98+xhVvzRwLbDKL+2ZCQWk6I8w8cuQIbT4SBEEE\ngaqqKlRVVQlxdoIuaG3b5SA2Xszp8DyPnp7Q5GLCGa/Xi6NHjwqxWc+hTCaHIX/wdQtrbGwkt7AQ\n0NLSEvBn2JdlM44kVVVV5KwdQgYGBsa5OS7Nl69wztcpjByyZ4fT6RxXnP6dxbFhKzqZioS0WDz2\nF0sYJ2+v14tPP/2U+V0lREZHR2UrPOU0HFY8PRcxceJ94ObNm9iyZYssnx9J9Pb24s0330RbW5tw\nzPKtICw9zK/N7JJkJmdCz2WCIAgi0iBRGDEjtmzZwmxMZsbr8NSCmCl+YmJudcxs47ejOTTW6RoN\nh2VPljA2zdu3byfr9hlw8+ZNpqpubob8LmHAWBWllLt37wblPMTEeDyeWW8MGC16FC/JFOLe3l6c\nOHFCrqERM+DcuXO4du2aEGfE67C8MLguYVLmrcxFcqZNiFtbW7Ft27aQnT/cGRkZwYYNG+D1iu2W\nnyyNQVJs6AUlcpCQZsVDLy6EwSQmiurq6vDhhx9Se9kJkF67AGDPCM5mY0p2nPC6ra2NNi1kpKWl\nBUeOHBHiOLMGDyjk8mcw6ZA3P02Im5ubqSUJQRCEzLjdbmzdupU59uyi2JA7SPmD1JkEGHMZJqbm\n+vXrTOHU0gIT9DplvmNft7A9e/aQ6DvIyNFmVaPVYOkTc8CRs7Yi7N+/nyl6fqjEApNevi0SrU7D\nFEiSU5j/eL1efPzxx0wLvyV5RizODV0+K9QYjDqs+f585NhTmOM7duygAvYJuHv3rqx5JINJhwee\nm89cuydPnsTx48dlO0e4c7+1qXRvJtakwV8/GIe0uPAWhAGA3qBDZlGSEDscDupYQxAEQUQUJAoj\npuXcuXM4deqUEJv1HF5ZYYVO63/S665zZpP13q5Bvz97ttgSLZi3QmwjOTw8jM2bN4fs/OGK74J0\nXmZwNqtj4kzgJI44lCQLLd3d3YwgxV/mlGdBbxQXhgcOHMDIyIgcQyOmobW1FV9++aUQcwC+uzgW\nmhBuSmk0HJY/bWdEQCdOnMDFixdDNoZwZsuWLcyGU0m6AcuC3Poz2NiSLFj7fCl0BjHRdu3aNaYl\nBDGG1GXEpOeQmxicJFtyZhwT37x5MyjniTa8Xi+++OIL5hn6eGkM9LOYP8tF8eJMJt6+fTsJMgmC\nIGTkyJEjzNytPN+kekcTcyy7jiensOmRCr41HLBcwfm5NcGMXImAoKGhAQ6HQ7HxRANyiMIAICE1\ndpyz9ocffkjO2kGmpaWFcfqLt2iCssY2mMV7P4nC/Ofw4cPMvSzZqsUzC2On+InIQKPVYPlTJShZ\nmsUc37hxI6qrqxUalTppbGyU/TNtSRYsf7qEObZlyxbU1dXJfq5wo6enB7///e+Zea7NrMFfr41D\nilXdc11/yJ0nOrDyPM90ySEIgiCIcIdEYcSUdHR0MKICAHhhmRXxltm5lHi8M6tY9HpmL0KZDSVL\nsxCXbBHiq1evUiJtCgYGBnD58mUhLkzRIyEmOM41Gg2HWIl9MzmFhZbOzs6Aft5g1MEuSWb09/eT\nW1gIGB4exgcffMC05VtTYkamAptSFqsRy55kkyqff/45XcvTcPHiRZw+fVqIY00afH+Jup0mZkpC\nmhUPPDePcencv38/mpqaFByVuhgcHGTmIXPSDEFrGZokcfMDSBQmFydOnEBDQ4MQF6XqUZoVHFfV\nmWJNMCN/gegW1t7eTs9kgpghbrcbZ86cwVdffcX82bVrF+OoTUQvfX192L9/vxCb9Bwem2eZ4ifU\ngSmGfTaRY+jUtLW1oaamRojnZxkVaQstZe6KXMYtTPp7SMiPVBQW6PR8ro+zdktLCzlrB5GJijYe\nmx8zq6Ln6TCYxBaSdF/1j7a2NuzatUuItRrg5WVWxRwZQw3HcVi4toARjXq9XnzwwQdobW1VcGTq\n4vbt20H53MzCJJSuzhNir9eL9evXR7Vovru7G2+++SazRxD3rSAsXDsZTEZqdhxT3O7r4E8QBEEQ\n4QyJwohJGR0dxYYNGxhXn7UlZsxJU3ZDKxhotBqUPzaHObZlyxZyT5iEs2fPYnR0VIjL84NbGWtN\nFJPp0p71RPCRowq2aFEmUyV58OBBcgsLIl6vF5988gkjuspP1uORucptSmUUJDKVjiMjI1i/fj1V\nQU9Cd3c3Nm7cyBx7vjwWFmPkTNtSsuOx5JFiIb7/e0u/E2NUVlYyc5B5mcGbexlMOkYYLxUyEbOj\np6cHO3bsEGKdFviOStqHla7KY5z69uzZw7RlIQhiYvbt24dPP/0Uhw4dYv7s27cPv/vd78gFhMCe\nPXuYNc4j8yxhMXfT6jRMCztpSzViPL5tpFYWKu/ia00wI3tOshDX1dWhvr5ewRFFLr29vXA6nUKs\nC/ASn8xZW1ocRMjH8ePHQ1a0If1OBwdD1w0j3PF6vdi4cSOzFn5qQUxEtKbzl/mrc5FTIjpBDg8P\n49NPPw2om0MkIRWFmfWTv2822JdnI0vyXO3r68P7778flfmq+w5hXV1dwrF4y1jLyGAZBCiJRqtB\nen6CENfU1NAeBkEQBBExqD9DRSjGrl27GCve7ESdoqKCYJOYbkWexCL2zp0741okEoDH42Gs1mOM\nHOZmBFcoKN2s7unpoSq7ECJH5ZXOoIV9abYQDwwMUKu4IMHzPDZv3szYW8eaNHhpmTVoLkMzpXR1\nHhIzrELc0tKCr7/+WsERqROv14uPPvqI2ZBbXWxGUWrkCbLz5qcyyZa2tjYcOHBAwRGph0uXLgmv\nDToOxUEW5CekiddmU1MTieIDgOd5bNy4kUkcPjzXopqEqSnGwFSdDw4OYtOmTQqOiCDCg3Pnzk36\ndy6Xi5wbopz29nacOnVKiBNjNFga5MIpueA4jnFEGB4eVnA06mZoaAhnz54V4sx4HbITZd6JniVz\nl+Uw8cGDBxUaSWTj20JMDucii9WIpU+wRaobN24MmgtOtNLe3s7kH4JdtGEkp7BZUVFRwYhaC5L1\nQWnvGQ5wHIelT8xhnL2bmpqmnJNGC0NDQ0zReHqcvM9ijuOw7PE5sCWJ+xGNjY3YunWrrOdRO06n\nE3/84x8ZQVhCzJhD2Gy7CIUDGYWJwmuPx0PtQwmCIIiIgURhxITU1NTg0KFDQmzUcXhpqfKigmCz\nYE0+455w8OBB2hj14dKlS+ju7hbi8jwTtEH+vYhLiWVi2nQJHXI5xhSWZcBoERfphw4dwq1bt2T5\nbELkwIEDTCswrQZ4ZbkVsSblH/carQYrn7Yzm05Hjx5FZWWlgqNSH/v372eSoBlxWjw2PzIF2RzH\nofyxYuiN4nP3+PHjUVl9KWVgYADV1dVCbE83QB+EliZSpKIwt9sti0tktHL+/Hlcv35diNPjtFhV\nZFZwROMpXpyJ2ARxTJcvX2aEiARBsPT09DAtYzTc2BxLCs/zIR4VoSZ27drFOHc8XhoT9DWynEjz\nPORAMjlnz56Fy+US4uUqcAm7T1xKDFNsUVVVhY6ODgVHFJnU1tYKrzUcZJujZxYmYe4KUdjn8Xjw\n/vvvo7e3V5bPj3Y8Hg8+/vhjZp0Z7KINqVs+OYXNjN7eXnz11VdCrNMA312sDrdlpdDqNFj+VAk0\nknvN119/HfWunvX19czcOz1O/mtZZ9Bi9XPzmXzVyZMno0aUNzAwgLfeeovpQnFfEBYXwYIwAEjN\niWdi2r8gCIIgIgXld4kJ1eFyuca1rXpuSSziVeJwEExMMQYUL8oU4u7ubly4cEHBEakLnueZilOd\nBlgZgo3O+OQYJm5paQn6OQmgq6uLEQAGkobR6bUoe7BAiHmexyeffEIWzDJSUVGBnTt3MsdeWGpF\nbpI6qtcBwGIzjauC/uyzz5gWHNFMY2Mj9u7dK8R6LfDiMmtYbSr6iznWiHkrc4V4YGCAcbqLRq5c\nucJsyM4PYuvI+ySmseJrqVMsMXP6+vqY6mGOA763RH3XsFanwTKfe/GmTZuojSRBTILvRsCPHrDh\n9QfiFBoNoTba2tpw+fJlIc5J1AXdSZtQBmlLP7OBw4Iso4KjGc+c8izhNc/zjMM7ETgej4cp3MhO\n1EFOrcr8VblILxCFfb29vXjvvfcYISIxO/bu3cusb3KTdFhdHNxcprQYbmhoiAS3M2Dv3r2MW+VD\ncy1IjI38vYjpiLGZUCLpvtDX14czZ84oOCLlkQp0ASAzMTjtRWPjTFj+lJ059uWXXzJCqUjE5XLh\nz3/+M1OUbzNr8Fdr4mAzR/41aTTrERsvPiOoJTdBEAQRKZAojBjH/v37mYrCsmwjSlWW7AomxUsy\nodWJl8Y333yj4GjURVVVFbMgWJJnQowx+LcRi83IOLiRKCw0HD9+nIl1Aa77cuwpyCpOEuKOjg5s\n3LiRkmMycO7cOXzxxRfMsacWxqjy3p1VlISiRRlCPDAwgC+++CLqHTbcbjc+/fRT5np4emEskq3B\nSW6pibz5adBILFek7ZeikYsXLwqvjSFoHQkAtiQLOIlwqb29PejnjES2bt3KtIdZM8eMjHh1XsNJ\nGTZm87i/vx+bNm2K+nsxQUyEtGUIxwFZCeoR3BPK49v6+pF5lrBzNZHe+sNt7KGiubmZyUMsyjFC\nF2QnV39JyY5DnKSg7vTp09QOVEbOnj3LFM0VpMg7R+c4DiuesjNurg0NDePWiIR/1NbWYv/+/UJs\n0HF4odwKTZDvdQYTuwaIdmen6eju7maEtylWLR4IsnAvnLAvy2bc56qqqhQcjfJIRWFpNi3M+uDt\nTWQUJMK+TBTljYyMYMOGDRHbWcbr9eLjjz9mOofEGjn81ZrIbhnpS1Km6Kbf2NhIz2EZ8Hg86Orq\nYv709/crPSyCIIiogkRhBENrayvjBGU2cHhqYcwUPxF5GM165JemCXFLSwu1UcJYpanUwYbjEPTK\nOvFcHJPcJFFY8BkZGUFFRYUQJ8dqoQvQ6YTjOCx5tJhpI3n+/Hl89NFHEbuYDgWXLl3CJ598wmzk\nry42q65dmZSFawtgTRRbIl67dg1nz55VcETKs2/fPrS1tQlxSboBS/LUJ+oLBgajDjklyUJcW1sb\nte5xTqeTSXDOzTCEZMNRo9UgNk5sgUSiMP9xOByMu2xyrBbr7Opu/Vq6OpepgL18+TI55BLEBEjv\ny1nxOhh06hKCEMrR2dnJiLlzEnXITw4v0SDP83C7RoXYaIyO+ae/+LqyLM5VT+vI+3Ach+IlovO9\ny+Wi9tAy4Xa7sWfPHiHWa4Fl+fL/DuiNOjzw3HzGZerSpUvMuYmZMzAwgI8++ojJlTxbFhOSThjS\n7xAgUdh0HDhwgMkLPjzXojq3ZSXR6bVIz08U4rq6uqjtvDA4OIjm5mYhDsW8a/7qPCRliCKhpqam\ncUUBkcKOHTtw5coVITbrOby+Jg5JUebaF58iuum7XC7cu3dPwdGEP01NTfj3f/93/Nd//Rfz59/+\n7d+wefNmpYdHEAQRNZAojGDYvHkzo3x/akEMLCFwglIbefNSmVia6I1Wrl+/ztitl2UbkRDClqJS\nUdidO3dIRBRkKioqmKTVyiJTYP0jv8Vo1o+1rJJ81sWLF/H+++/D7XYHfoIoo7KyEhs2bGCSnMsK\nTHiiVN1CBK1Og+VPljDtNrZu3Rq1rcsaGhqYhJJJz+G7i2OjyqkhU+IiCCDi7fgn48qVK8z1XJod\nuo1Za6IoDiJRmH+43W5s2rSJOfbdxbGqcxDxRavTYtmT7DN506ZNlPAkCAlOp5O5J+anhJfghwgu\nR48eZfInD5aEn0uYZ9QLr0ece8TERFdR4EzweDw4f/68EGfEaZEWp04n0Ow5yYzLurTQi5g9x44d\nY+ZHK4vMiDUFJ1dqTTBj1XfmMi6++/btI+G+n/A8j88++wy9vb3CsYXZRpTlhGZ9pTew+VIShU1O\nT08P4xKWatNiXia1YfYlPU9sL+vxeMa1N48WamtrmZxJQQjm5hoNh+VP25nn6969e5luKpFARUUF\nDh06JMQaDnh1pQ2pNnXOeYKJ1LUTANNVifCfPXv2MK76Uo4fPx61IleCIIhQE31qH2JSbt68ybTG\nKEzRh2yxrDbiU2MRI3HMuHbtmoKjUZ6JXMIeCrH7RXyKmJweHR2lyXgQ6ejowK5du4TYpOewKEe+\nKtj0/ESsfIZNcl67dg1vv/02BgcHZTtPpFNdXY0PPviA2YhanGvEs2UxYbEZlZAWi7krcoR4aGgI\nu3fvVnBEyuD1erF58+Zx1cvWIG0yqBWpWxEAdHV1KTQSZbl69arw2qTnUBhC8YE1QXyud3V1YXR0\ndIp3E1IOHTrECBkX5xqRFyZOMUkZNqYdxtDQEDZu3KjgiAhCXVRXVzNxuLlAEcFjdHSUEQql2bQo\nTgu/34+RQbYwh0Rh46mvr2c2shap0CXsPjq9FjklKUJ8+/btqC22kIvr169jx44dQmzSc1gTZNf8\n1Jx4LHmkiDn22WefoampKajnjSROnTqFyspKIU6I0eA7i0KXKyGnsJlz+vRppvB3nT38BNahIC6Z\nzcNH6++Uw+EQXnNc6ObmMTYTyh4sEGKv14tNmzYxubxwpq2tbVyh2/eWxIZNXkNurD6iMJpLzR6v\n18vsOfvC8zxcLlcIR0QQBBG9RNeOIzElvra3Ty4ID2FBMOA4Dun5YgVOW1tbVDtT1dTUML3ky7KN\nSAyxbbDvZJxEYcHB4/Hgo48+Yio0VheboZe5TU72nGSs/u48aLTiY6impga//OUvUV9fL+u5IpG6\nujq8++67zH1pQbYRzy0JL3epuctzEBsvbqqcOnWKsYGPBi5dusTcX+3pBiwIoTuUWoixsf/maBSF\nDQ4OMi3K7OmGkLbMsEi+A57no9a5z1+cTifTet2k5/B4aXhtqM9fmcs4slZVVTGbaAQRzUhFYToN\nkJsUnRsjxHiqqqoYoVB5vims5uH36b/HbionJiZO8s7oxbdIcG6Guh1s8uazzvfSogPCPxobG8cV\nYj1kt8BkCH46vWBBOuaUZwmx2+3Ge++9R3P0GXD37l1s27ZNiDUc8NIyK4z60G2D6A2sKGx4eDhk\n5w43pK3q4i0acgmbhAjRHgWMVBSWnaAL6XWdX5qG1Jx4Ib5582ZEdJdxu93YsGEDUxj4kN2sahF8\nsDHHsjlKevbOnubmZuYZWJ4XOtdOgiAIgoVEYQSAsb7O169fF2J7ukG1dvihQro55vF4olqEJN3s\nBIAHQ+wSBgAxPi4ynZ2dIR9DNLB7925GoJKdoMOaOcGpgs0oSMSa78+HVrKA7+npwZtvvon9+/cz\niVdCpLGxEW+//TbTbnNuhgEvlMdCE2YbURqtBmUPFQoxz/PYuXOngiMKLW63m6k612qApxZGpyBb\no9VAI2m1F43W4VVVVcx9L9QbjiYLez5Kes2MQ4cOMVWNj5fGICbMWq9rtJpxrZ23bNlC1ZpE1OP1\nepmNp7xkPfQqbwtLhA5pqyutBmEr6vcVhaWmpk7yzuiE53lGFJYep0WcJbQFcv6SmG6FKUac15Eo\nbHZ0dnbi7bffZuZDC7ONWFUUuk3yhWvykZorChB6enqwfv36qC5anQ6Px4OPP/6Y+d4enmdBVkJo\nRd06n/aR0bi+nQkdHR1MC755mcaozIfMBN5LqrCenh5mfyYUrSOlcByHRQ8XMp0vdu/eHfb35B07\ndjDXYUGKHg/PDf3ej5rQaDjo9OJ9PFqd+eSgpqaGiVcVmZGdEN37zgRBEEoRXjsWRNA4evQoE68t\nCa4VejhgS2Inv9FqE9vY2MhshszPNCApxC5hAGCy6BnxUDSL9IJFZWUlIwA06Di8uMwaVKea1Jx4\nrHtpIeNQ4/V6sXPnTrz11lvo7e0N2rnDke7ubrzzzjtMQrE4VY+XllmhCaGjkJyk5ycgLU9MdN+4\ncSNqru+jR4+iu7tbiFcUmpEQo+5NpmAxMuiG1yMmOePj46d4d2Qi3XDUaYGi1BCLwmLY8zmdzpCe\nPxzp7e3FiRMnhDgxRoMlueEpCohPjUXhwgwh7u7uHlcUQBDRRmNjIyOQDfV9mVAvbrebcZGzpxtg\nCYFzUDDo7RoUXut0uqicg01FZ2cnszYpSVf/fYDjOGQWio5vt2/fJrG/n3R2duIPf/gD8/+tIEWP\n75eH1pmb03BY+cxcxMSJQrS6ujocOXIkZGMIN44ePcoUOuYkBq/QcSp8RWHkFDYxly9fZuL55BI2\nKV1t7H08ISFhkndGLr4t6ApSQv/7Yku0oKhMXDd3dHTgwoULIR+HXDQ0NDDPFLOew/MhftapFWkb\nYBKFzR7pminWpEGyNTrz3gRBEGogPLNWhKzwPI8bN24IcV6SDtmJ1BZDp2MnKJHSI95fDh8+zMRK\nJFOAscRmjE1MhN27d0+RcUQqzc3N2LBhA/N7/t1FMSERqCSkWfHYXyxB1pxk5nhNTQ1+9atfMe3U\nopnh4WG88847jFAjP1mPH6y0QRfGrhUcx8G+LEeIeZ5nRBaRitfrZQTZZj2HB6NYkD3Yx1ZOJyUl\nKTQSZeB5nklwFibrZW/bOx1GMzv3o83D6Tl48CDj2rhuriVsBboAULo6j/k9OHLkCLkaEFGNb8u4\ncBCDEKGhsbGRcYUI59+Nbskmc3Z2NjQaShNKuX37NhMXh4k4NL2AbQN669YthUYSfrS3t+ONN95g\ninfSbFq8uiK4BXOTYTDp8MBzrMP67t27o7ZwdSqcTif27t0rxAYdhxeWWhVxVNfp2HspOfBOjPTe\nZDVpkEXuMZPS3tgjvDabzcjJyZni3ZHJzZs3hddaDRT7fbEvy4ZWco0fO3ZMkXEECs/z+Oqrr5hj\nzy2Jhc1Moh0A0EpyctLWmsTMGRkZYa7bohQ9CQ4JgiAUhLI9BO7cucNs/NkzwtPhQG68PrbM0Zgc\nHRoaYloNFKTokRliy3UpekmlHW1Sykdvby/effddJklVnm/CwpzQtUUwGHVY+YwdSx4thkYrXmt9\nfX34wx/+gAMHDkStMBMYExBt2LCBsfNOsWrx6kprRLQxSs6yMe6MZ86cCXv79emoqalhnPBWzzHD\nHKYOE3LQ3c4KkBITEyd5Z2TS3t7OzMXykkP/rNX4bFxE+jUYKG63G2fPnhXipFht2LYOu4/BpMO8\nVblCPDw8jIsXLyo4ovDH7Xbjvffewz//8z/jP/7jP8aJjAh1U1lZKbxOtmoVcUsm1Il0cwMA8pLC\ns6jO7RpFb9eAEBcUFCg4GnXS2NgovNZwQHp8eAgWkjJtTFxfX6/QSMKLlpYWvPHGG8w6LSFGg79c\nbYNRr9xazZZkwYI1+ULsdrvx2WefMa3nCWDnzp2MI9fDcy2KOXFr9ex5SRQ2MdKC31SblsQCkzA8\n6MLdRvH/ld1uh1YbffNS6fwrK0GnWD7UFGNA3vw0IW5sbGTmC+HCjRs3mOJEe7oB8zLDO6chJ9K9\nwWjcF5QDh8PB5BaLUsNzzUQQBBEp0NOMGNfXuUCBjUg14hphKwB0uvBI/snJlStXmEqI8rzQiYQm\nQkeiMNm5v1nZ0yNWnBUk6/FsWUzIx8JxHAoXpuPR1xbBmigKhHiex44dO/Dee+9hcHBwik+IXE6c\nOIGqqiohjjFy+ItVNpgUTEzLCcdxKFiQLsSDg4O4c+eOgiMKPlIxCQAsyonuxEvjdbHS3WKxID09\nfYp3Rx6+bRDylRCF+TgfkChsaqqrq5kWAquLzYq4EMhN3txUZr516tQpBUcT/pw9exapXIX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LkKjir4qEUoEgoc55uZZEqkuxX4isLu9EZH2+Z7Hf04uuUqXMPiv3fRokV47rnnFByV+lBqv2Cg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\ncq0OF5NfYSqVAEYgNP9KXa8kD4WBbWrCRDJuWObPnz/t+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wGOXN3E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\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jv2SD9NwZ+AUpx06MiLnKuhHN+wamN8qz+yngO8CvgG9X0rPW/nqQzAb0IDmweA2wJXCA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\nd8/eVt5MAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 336,
"width": 1218
}
},
"output_type": "display_data"
}
],
"source": [
"g = sns.factorplot(x=\"workclass\", y=\"education_num\",\n",
" hue=\"income\", col=\"sex\",\n",
" data=train_df.select('workclass', 'sex', 'education_num', 'income').toPandas(), kind=\"violin\", split=True,\n",
" size=4, aspect=2, palette=\"Spectral\")\n",
"g.set_xticklabels(rotation=45);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this dataset we have lots of categorical features. Categorical features are mainly of two types. If the values in the Categorical Variables have a natural ordering then we call them as 'ordinal' e.g. high. medium, low. However, if the values of the categorical attribute do not possess any ordering then they are called 'nominal' such as country code. If the categorical attribute is 'ordinal' then we can use LabelEncoding to convert them into integer numbers and to give them a sense of ordering. However, if they are 'nominal' we would do OneHotEncoding so that there is no inherent ordering.\n",
"\n",
"In this dataset we can safely assume that all the categorical attributes are nominal in nature."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Data Preprocessing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**sample the training set:**"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" age | \n",
" workclass | \n",
" fnlgwt | \n",
" education | \n",
" education_num | \n",
" marital_status | \n",
" occupation | \n",
" relationship | \n",
" race | \n",
" sex | \n",
" capital_gain | \n",
" capital_loss | \n",
" hours_per_week | \n",
" native_country | \n",
" income | \n",
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" Black | \n",
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" Jamaica | \n",
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" Married-civ-spouse | \n",
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" Husband | \n",
" White | \n",
" Male | \n",
" 15024.0 | \n",
" 0.0 | \n",
" 60.0 | \n",
" United-States | \n",
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\n",
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" 2 | \n",
" 53 | \n",
" Private | \n",
" 95647.0 | \n",
" 9th | \n",
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" Husband | \n",
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" United-States | \n",
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" 40.0 | \n",
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" \n",
" 4 | \n",
" 20 | \n",
" ? | \n",
" 214635.0 | \n",
" Some-college | \n",
" 10.0 | \n",
" Never-married | \n",
" ? | \n",
" Own-child | \n",
" White | \n",
" Male | \n",
" 0.0 | \n",
" 0.0 | \n",
" 24.0 | \n",
" United-States | \n",
" <=50K | \n",
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\n",
" \n",
" 5 | \n",
" 35 | \n",
" Private | \n",
" 54576.0 | \n",
" HS-grad | \n",
" 9.0 | \n",
" Married-civ-spouse | \n",
" Machine-op-inspct | \n",
" Husband | \n",
" White | \n",
" Male | \n",
" 0.0 | \n",
" 0.0 | \n",
" 40.0 | \n",
" United-States | \n",
" <=50K | \n",
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\n",
" \n",
" 6 | \n",
" 17 | \n",
" ? | \n",
" 80077.0 | \n",
" 11th | \n",
" 7.0 | \n",
" Never-married | \n",
" ? | \n",
" Own-child | \n",
" White | \n",
" Female | \n",
" 0.0 | \n",
" 0.0 | \n",
" 20.0 | \n",
" United-States | \n",
" <=50K | \n",
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" \n",
" 7 | \n",
" 45 | \n",
" Private | \n",
" 185385.0 | \n",
" HS-grad | \n",
" 9.0 | \n",
" Married-civ-spouse | \n",
" Craft-repair | \n",
" Husband | \n",
" White | \n",
" Male | \n",
" 0.0 | \n",
" 0.0 | \n",
" 47.0 | \n",
" United-States | \n",
" >50K | \n",
"
\n",
" \n",
" 8 | \n",
" 30 | \n",
" Private | \n",
" 236770.0 | \n",
" HS-grad | \n",
" 9.0 | \n",
" Married-civ-spouse | \n",
" Craft-repair | \n",
" Husband | \n",
" White | \n",
" Male | \n",
" 0.0 | \n",
" 0.0 | \n",
" 40.0 | \n",
" United-States | \n",
" <=50K | \n",
"
\n",
" \n",
" 9 | \n",
" 32 | \n",
" Private | \n",
" 509350.0 | \n",
" Some-college | \n",
" 10.0 | \n",
" Married-civ-spouse | \n",
" Handlers-cleaners | \n",
" Husband | \n",
" White | \n",
" Male | \n",
" 0.0 | \n",
" 0.0 | \n",
" 50.0 | \n",
" Canada | \n",
" >50K | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" age workclass fnlgwt education education_num \\\n",
"0 49 Private 160187.0 9th 5.0 \n",
"1 44 Private 198282.0 Bachelors 13.0 \n",
"2 53 Private 95647.0 9th 5.0 \n",
"3 24 Private 388093.0 Bachelors 13.0 \n",
"4 20 ? 214635.0 Some-college 10.0 \n",
"5 35 Private 54576.0 HS-grad 9.0 \n",
"6 17 ? 80077.0 11th 7.0 \n",
"7 45 Private 185385.0 HS-grad 9.0 \n",
"8 30 Private 236770.0 HS-grad 9.0 \n",
"9 32 Private 509350.0 Some-college 10.0 \n",
"\n",
" marital_status occupation relationship race sex \\\n",
"0 Married-spouse-absent Other-service Not-in-family Black Female \n",
"1 Married-civ-spouse Exec-managerial Husband White Male \n",
"2 Married-civ-spouse Handlers-cleaners Husband White Male \n",
"3 Never-married Exec-managerial Not-in-family Black Male \n",
"4 Never-married ? Own-child White Male \n",
"5 Married-civ-spouse Machine-op-inspct Husband White Male \n",
"6 Never-married ? Own-child White Female \n",
"7 Married-civ-spouse Craft-repair Husband White Male \n",
"8 Married-civ-spouse Craft-repair Husband White Male \n",
"9 Married-civ-spouse Handlers-cleaners Husband White Male \n",
"\n",
" capital_gain capital_loss hours_per_week native_country income \n",
"0 0.0 0.0 16.0 Jamaica <=50K \n",
"1 15024.0 0.0 60.0 United-States >50K \n",
"2 0.0 0.0 50.0 United-States <=50K \n",
"3 0.0 0.0 40.0 United-States <=50K \n",
"4 0.0 0.0 24.0 United-States <=50K \n",
"5 0.0 0.0 40.0 United-States <=50K \n",
"6 0.0 0.0 20.0 United-States <=50K \n",
"7 0.0 0.0 47.0 United-States >50K \n",
"8 0.0 0.0 40.0 United-States <=50K \n",
"9 0.0 0.0 50.0 Canada >50K "
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sample the training data\n",
"train_df.sample(withReplacement=False, fraction=0.01, seed=rnd_seed).limit(10).toPandas()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**sample the testing set:**"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
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" marital_status | \n",
" occupation | \n",
" relationship | \n",
" race | \n",
" sex | \n",
" capital_gain | \n",
" capital_loss | \n",
" hours_per_week | \n",
" native_country | \n",
" income | \n",
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" 33 | \n",
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" Some-college | \n",
" 10.0 | \n",
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" Adm-clerical | \n",
" Unmarried | \n",
" Black | \n",
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" 0.0 | \n",
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" 35.0 | \n",
" United-States | \n",
" <=50K | \n",
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" 2 | \n",
" 26 | \n",
" Private | \n",
" 206721.0 | \n",
" HS-grad | \n",
" 9.0 | \n",
" Never-married | \n",
" Handlers-cleaners | \n",
" Unmarried | \n",
" White | \n",
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" 40.0 | \n",
" United-States | \n",
" <=50K | \n",
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" 3 | \n",
" 38 | \n",
" ? | \n",
" 48976.0 | \n",
" HS-grad | \n",
" 9.0 | \n",
" Married-civ-spouse | \n",
" ? | \n",
" Wife | \n",
" White | \n",
" Female | \n",
" 0.0 | \n",
" 1887.0 | \n",
" 10.0 | \n",
" United-States | \n",
" >50K | \n",
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" 4 | \n",
" 31 | \n",
" Private | \n",
" 213339.0 | \n",
" HS-grad | \n",
" 9.0 | \n",
" Separated | \n",
" Tech-support | \n",
" Not-in-family | \n",
" White | \n",
" Female | \n",
" 0.0 | \n",
" 0.0 | \n",
" 40.0 | \n",
" United-States | \n",
" <=50K | \n",
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" 5 | \n",
" 39 | \n",
" Self-emp-not-inc | \n",
" 199753.0 | \n",
" Bachelors | \n",
" 13.0 | \n",
" Married-civ-spouse | \n",
" Other-service | \n",
" Husband | \n",
" White | \n",
" Male | \n",
" 0.0 | \n",
" 0.0 | \n",
" 60.0 | \n",
" United-States | \n",
" <=50K | \n",
"
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" 6 | \n",
" 47 | \n",
" Private | \n",
" 34307.0 | \n",
" Some-college | \n",
" 10.0 | \n",
" Separated | \n",
" Other-service | \n",
" Not-in-family | \n",
" White | \n",
" Female | \n",
" 0.0 | \n",
" 0.0 | \n",
" 35.0 | \n",
" United-States | \n",
" <=50K | \n",
"
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" \n",
" 7 | \n",
" 31 | \n",
" Private | \n",
" 25610.0 | \n",
" Preschool | \n",
" 1.0 | \n",
" Never-married | \n",
" Handlers-cleaners | \n",
" Not-in-family | \n",
" Amer-Indian-Eskimo | \n",
" Male | \n",
" 0.0 | \n",
" 0.0 | \n",
" 25.0 | \n",
" United-States | \n",
" <=50K | \n",
"
\n",
" \n",
" 8 | \n",
" 46 | \n",
" Self-emp-not-inc | \n",
" 481987.0 | \n",
" Some-college | \n",
" 10.0 | \n",
" Married-civ-spouse | \n",
" Prof-specialty | \n",
" Husband | \n",
" White | \n",
" Male | \n",
" 0.0 | \n",
" 0.0 | \n",
" 35.0 | \n",
" United-States | \n",
" <=50K | \n",
"
\n",
" \n",
" 9 | \n",
" 37 | \n",
" Private | \n",
" 112497.0 | \n",
" Bachelors | \n",
" 13.0 | \n",
" Married-spouse-absent | \n",
" Exec-managerial | \n",
" Unmarried | \n",
" White | \n",
" Male | \n",
" 4934.0 | \n",
" 0.0 | \n",
" 50.0 | \n",
" United-States | \n",
" >50K | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" age workclass fnlgwt education education_num \\\n",
"0 29 ? 227026.0 HS-grad 9.0 \n",
"1 33 Private 202191.0 Some-college 10.0 \n",
"2 26 Private 206721.0 HS-grad 9.0 \n",
"3 38 ? 48976.0 HS-grad 9.0 \n",
"4 31 Private 213339.0 HS-grad 9.0 \n",
"5 39 Self-emp-not-inc 199753.0 Bachelors 13.0 \n",
"6 47 Private 34307.0 Some-college 10.0 \n",
"7 31 Private 25610.0 Preschool 1.0 \n",
"8 46 Self-emp-not-inc 481987.0 Some-college 10.0 \n",
"9 37 Private 112497.0 Bachelors 13.0 \n",
"\n",
" marital_status occupation relationship \\\n",
"0 Never-married ? Unmarried \n",
"1 Never-married Adm-clerical Unmarried \n",
"2 Never-married Handlers-cleaners Unmarried \n",
"3 Married-civ-spouse ? Wife \n",
"4 Separated Tech-support Not-in-family \n",
"5 Married-civ-spouse Other-service Husband \n",
"6 Separated Other-service Not-in-family \n",
"7 Never-married Handlers-cleaners Not-in-family \n",
"8 Married-civ-spouse Prof-specialty Husband \n",
"9 Married-spouse-absent Exec-managerial Unmarried \n",
"\n",
" race sex capital_gain capital_loss hours_per_week \\\n",
"0 Black Male 0.0 0.0 40.0 \n",
"1 Black Female 0.0 0.0 35.0 \n",
"2 White Male 0.0 0.0 40.0 \n",
"3 White Female 0.0 1887.0 10.0 \n",
"4 White Female 0.0 0.0 40.0 \n",
"5 White Male 0.0 0.0 60.0 \n",
"6 White Female 0.0 0.0 35.0 \n",
"7 Amer-Indian-Eskimo Male 0.0 0.0 25.0 \n",
"8 White Male 0.0 0.0 35.0 \n",
"9 White Male 4934.0 0.0 50.0 \n",
"\n",
" native_country income \n",
"0 United-States <=50K \n",
"1 United-States <=50K \n",
"2 United-States <=50K \n",
"3 United-States >50K \n",
"4 United-States <=50K \n",
"5 United-States <=50K \n",
"6 United-States <=50K \n",
"7 United-States <=50K \n",
"8 United-States <=50K \n",
"9 United-States >50K "
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sample the test data\n",
"test_df.sample(withReplacement=False, fraction=0.01, seed=rnd_seed).limit(10).toPandas()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> *We can see some rows have **'?'** which are missing values*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.1 Variance of Values - How many unique values per attribute:\n",
"\n",
"Let's count the distinct number of values in each feature. Low count of unique values in numerical features signifies that they are categorical. However, exceptions can be attributes such as zipcodes."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" education | \n",
" education_num | \n",
" marital_status | \n",
" occupation | \n",
" relationship | \n",
" race | \n",
" sex | \n",
" capital_gain | \n",
" capital_loss | \n",
" hours_per_week | \n",
" native_country | \n",
" income | \n",
"
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" 0 | \n",
" 73 | \n",
" 9 | \n",
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" 2 | \n",
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"
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"
"
],
"text/plain": [
" age workclass fnlgwt education education_num marital_status \\\n",
"0 73 9 21648 16 16 7 \n",
"\n",
" occupation relationship race sex capital_gain capital_loss \\\n",
"0 15 6 5 2 119 92 \n",
"\n",
" hours_per_week native_country income \n",
"0 94 42 2 "
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.select([F.countDistinct(col(c)).alias(c) for c in train_df.columns]).toPandas()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" age | \n",
" workclass | \n",
" fnlgwt | \n",
" education | \n",
" education_num | \n",
" marital_status | \n",
" occupation | \n",
" relationship | \n",
" race | \n",
" sex | \n",
" capital_gain | \n",
" capital_loss | \n",
" hours_per_week | \n",
" native_country | \n",
" income | \n",
"
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" 0 | \n",
" 73 | \n",
" 9 | \n",
" 12787 | \n",
" 16 | \n",
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" 2 | \n",
"
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" \n",
"
\n",
"
"
],
"text/plain": [
" age workclass fnlgwt education education_num marital_status \\\n",
"0 73 9 12787 16 16 7 \n",
"\n",
" occupation relationship race sex capital_gain capital_loss \\\n",
"0 15 6 5 2 113 82 \n",
"\n",
" hours_per_week native_country income \n",
"0 89 41 2 "
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_df.select([F.countDistinct(col(c)).alias(c) for c in test_df.columns]).toPandas()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see 4 unique values for income level because of the typo in test set. None of the other attributes show anomalous small set of unique values. It seems reasobale count of unique values for each feature."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.2 How many missing values per attribute:"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" age | \n",
" workclass | \n",
" fnlgwt | \n",
" education | \n",
" education_num | \n",
" marital_status | \n",
" occupation | \n",
" relationship | \n",
" race | \n",
" sex | \n",
" capital_gain | \n",
" capital_loss | \n",
" hours_per_week | \n",
" native_country | \n",
" income | \n",
"
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" \n",
" \n",
" \n",
" 0 | \n",
" 0 | \n",
" 1836 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 1843 | \n",
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"text/plain": [
" age workclass fnlgwt education education_num marital_status \\\n",
"0 0 1836 0 0 0 0 \n",
"\n",
" occupation relationship race sex capital_gain capital_loss \\\n",
"0 1843 0 0 0 0 0 \n",
"\n",
" hours_per_week native_country income \n",
"0 0 583 0 "
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# count how many missing values per column\n",
"train_df.select([F.count(F.when(col(c).contains('?'), c)).alias(c) for c in train_df.columns]).toPandas()\n",
"#train_df.select([F.count(F.when(F.isnan(c) | col(c).isNull(), c)).alias(c) for c in train_df.columns]).toPandas()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 274 | \n",
" 0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" age workclass fnlgwt education education_num marital_status \\\n",
"0 0 963 0 0 0 0 \n",
"\n",
" occupation relationship race sex capital_gain capital_loss \\\n",
"0 966 0 0 0 0 0 \n",
"\n",
" hours_per_week native_country income \n",
"0 0 274 0 "
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# count how many missing values per column\n",
"test_df.select([F.count(F.when(col(c).contains('?'), c)).alias(c) for c in test_df.columns]).toPandas()\n",
"#test_df.select([F.count(F.when(F.isnan(c) | col(c).isNull(), c)).alias(c) for c in test_df.columns]).toPandas()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Filling the missing values:**\n",
" \n",
"There are significant missing values and we need to come up with a smart strategy for that, treating them as an `unknown` category for now. Filling up with most frequent item in each attribute does not seem to be a valid choice for me especially for the `native_country` column."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_df = train_df.replace(to_replace='?', value='unknown', subset=['workclass', 'occupation', 'native_country'])"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"test_df = test_df.replace(to_replace='?', value='unknown', subset=['workclass', 'occupation', 'native_country'])"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# re-check all missing values have been handled\n",
"(train_df\n",
" .filter(col('workclass').contains('?') | col('occupation').contains('?') | col('native_country').contains('?'))\n",
").count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.3 Featurization: \n",
"\n",
"Check the unique values for each attribute and determine whether we need to feature engineer any one of them.\n",
"\n",
"**Check Workclass:**"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" workclass | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Self-emp-not-inc | \n",
"
\n",
" \n",
" 1 | \n",
" unknown | \n",
"
\n",
" \n",
" 2 | \n",
" Local-gov | \n",
"
\n",
" \n",
" 3 | \n",
" State-gov | \n",
"
\n",
" \n",
" 4 | \n",
" Private | \n",
"
\n",
" \n",
" 5 | \n",
" Without-pay | \n",
"
\n",
" \n",
" 6 | \n",
" Federal-gov | \n",
"
\n",
" \n",
" 7 | \n",
" Never-worked | \n",
"
\n",
" \n",
" 8 | \n",
" Self-emp-inc | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" workclass\n",
"0 Self-emp-not-inc\n",
"1 unknown\n",
"2 Local-gov\n",
"3 State-gov\n",
"4 Private\n",
"5 Without-pay\n",
"6 Federal-gov\n",
"7 Never-worked\n",
"8 Self-emp-inc"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.select([\"workclass\"]).distinct().toPandas()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Check Education & Education_Num:**"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" education | \n",
" education_num | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Preschool | \n",
" 1.0 | \n",
"
\n",
" \n",
" 1 | \n",
" 9th | \n",
" 5.0 | \n",
"
\n",
" \n",
" 2 | \n",
" Assoc-voc | \n",
" 11.0 | \n",
"
\n",
" \n",
" 3 | \n",
" Bachelors | \n",
" 13.0 | \n",
"
\n",
" \n",
" 4 | \n",
" 1st-4th | \n",
" 2.0 | \n",
"
\n",
" \n",
" 5 | \n",
" 7th-8th | \n",
" 4.0 | \n",
"
\n",
" \n",
" 6 | \n",
" 12th | \n",
" 8.0 | \n",
"
\n",
" \n",
" 7 | \n",
" 5th-6th | \n",
" 3.0 | \n",
"
\n",
" \n",
" 8 | \n",
" Doctorate | \n",
" 16.0 | \n",
"
\n",
" \n",
" 9 | \n",
" Prof-school | \n",
" 15.0 | \n",
"
\n",
" \n",
" 10 | \n",
" Assoc-acdm | \n",
" 12.0 | \n",
"
\n",
" \n",
" 11 | \n",
" Masters | \n",
" 14.0 | \n",
"
\n",
" \n",
" 12 | \n",
" 11th | \n",
" 7.0 | \n",
"
\n",
" \n",
" 13 | \n",
" HS-grad | \n",
" 9.0 | \n",
"
\n",
" \n",
" 14 | \n",
" Some-college | \n",
" 10.0 | \n",
"
\n",
" \n",
" 15 | \n",
" 10th | \n",
" 6.0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" education education_num\n",
"0 Preschool 1.0\n",
"1 9th 5.0\n",
"2 Assoc-voc 11.0\n",
"3 Bachelors 13.0\n",
"4 1st-4th 2.0\n",
"5 7th-8th 4.0\n",
"6 12th 8.0\n",
"7 5th-6th 3.0\n",
"8 Doctorate 16.0\n",
"9 Prof-school 15.0\n",
"10 Assoc-acdm 12.0\n",
"11 Masters 14.0\n",
"12 11th 7.0\n",
"13 HS-grad 9.0\n",
"14 Some-college 10.0\n",
"15 10th 6.0"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check the Education and Education Num \n",
"train_df.select([\"education\", \"education_num\"]).distinct().toPandas()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is a one-to-one mapping between Education and Education Num. We will drop Education."
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_df = train_df.drop('education')\n",
"test_df = test_df.drop('education')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Marital Status:**"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" marital_status | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Separated | \n",
"
\n",
" \n",
" 1 | \n",
" Never-married | \n",
"
\n",
" \n",
" 2 | \n",
" Married-spouse-absent | \n",
"
\n",
" \n",
" 3 | \n",
" Divorced | \n",
"
\n",
" \n",
" 4 | \n",
" Widowed | \n",
"
\n",
" \n",
" 5 | \n",
" Married-AF-spouse | \n",
"
\n",
" \n",
" 6 | \n",
" Married-civ-spouse | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" marital_status\n",
"0 Separated\n",
"1 Never-married\n",
"2 Married-spouse-absent\n",
"3 Divorced\n",
"4 Widowed\n",
"5 Married-AF-spouse\n",
"6 Married-civ-spouse"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.select([\"marital_status\"]).distinct().toPandas()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Occupation:**"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" occupation | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Sales | \n",
"
\n",
" \n",
" 1 | \n",
" Exec-managerial | \n",
"
\n",
" \n",
" 2 | \n",
" Prof-specialty | \n",
"
\n",
" \n",
" 3 | \n",
" Handlers-cleaners | \n",
"
\n",
" \n",
" 4 | \n",
" unknown | \n",
"
\n",
" \n",
" 5 | \n",
" Farming-fishing | \n",
"
\n",
" \n",
" 6 | \n",
" Craft-repair | \n",
"
\n",
" \n",
" 7 | \n",
" Transport-moving | \n",
"
\n",
" \n",
" 8 | \n",
" Priv-house-serv | \n",
"
\n",
" \n",
" 9 | \n",
" Protective-serv | \n",
"
\n",
" \n",
" 10 | \n",
" Other-service | \n",
"
\n",
" \n",
" 11 | \n",
" Tech-support | \n",
"
\n",
" \n",
" 12 | \n",
" Machine-op-inspct | \n",
"
\n",
" \n",
" 13 | \n",
" Armed-Forces | \n",
"
\n",
" \n",
" 14 | \n",
" Adm-clerical | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" occupation\n",
"0 Sales\n",
"1 Exec-managerial\n",
"2 Prof-specialty\n",
"3 Handlers-cleaners\n",
"4 unknown\n",
"5 Farming-fishing\n",
"6 Craft-repair\n",
"7 Transport-moving\n",
"8 Priv-house-serv\n",
"9 Protective-serv\n",
"10 Other-service\n",
"11 Tech-support\n",
"12 Machine-op-inspct\n",
"13 Armed-Forces\n",
"14 Adm-clerical"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.select([\"occupation\"]).distinct().toPandas()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Relationship:**"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" relationship | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Own-child | \n",
"
\n",
" \n",
" 1 | \n",
" Not-in-family | \n",
"
\n",
" \n",
" 2 | \n",
" Unmarried | \n",
"
\n",
" \n",
" 3 | \n",
" Wife | \n",
"
\n",
" \n",
" 4 | \n",
" Other-relative | \n",
"
\n",
" \n",
" 5 | \n",
" Husband | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" relationship\n",
"0 Own-child\n",
"1 Not-in-family\n",
"2 Unmarried\n",
"3 Wife\n",
"4 Other-relative\n",
"5 Husband"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.select([\"relationship\"]).distinct().toPandas()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Check Race:**"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" race | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Other | \n",
"
\n",
" \n",
" 1 | \n",
" Amer-Indian-Eskimo | \n",
"
\n",
" \n",
" 2 | \n",
" White | \n",
"
\n",
" \n",
" 3 | \n",
" Asian-Pac-Islander | \n",
"
\n",
" \n",
" 4 | \n",
" Black | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" race\n",
"0 Other\n",
"1 Amer-Indian-Eskimo\n",
"2 White\n",
"3 Asian-Pac-Islander\n",
"4 Black"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.select([\"race\"]).distinct().toPandas()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Sex:**"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sex | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Female | \n",
"
\n",
" \n",
" 1 | \n",
" Male | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" sex\n",
"0 Female\n",
"1 Male"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.select([\"sex\"]).distinct().toPandas()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Native Country:**"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" native_country | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Philippines | \n",
"
\n",
" \n",
" 1 | \n",
" Germany | \n",
"
\n",
" \n",
" 2 | \n",
" Cambodia | \n",
"
\n",
" \n",
" 3 | \n",
" France | \n",
"
\n",
" \n",
" 4 | \n",
" Greece | \n",
"
\n",
" \n",
" 5 | \n",
" unknown | \n",
"
\n",
" \n",
" 6 | \n",
" Taiwan | \n",
"
\n",
" \n",
" 7 | \n",
" Ecuador | \n",
"
\n",
" \n",
" 8 | \n",
" Nicaragua | \n",
"
\n",
" \n",
" 9 | \n",
" Hong | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" native_country\n",
"0 Philippines\n",
"1 Germany\n",
"2 Cambodia\n",
"3 France\n",
"4 Greece\n",
"5 unknown\n",
"6 Taiwan\n",
"7 Ecuador\n",
"8 Nicaragua\n",
"9 Hong"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.select([\"native_country\"]).distinct().limit(10).toPandas()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Preparing Features for Machine Learning\n",
"\n",
"We will build a logistic regression to predict the label of income based on the following features:\n",
"\n",
"+ Label: \n",
" - <=50K = 0\n",
" - \\>50K = 1\n",
"+ Features -> {age, workclass, education_num, marital_status, occupation, relationship, race, sex, capital_gain, capital_loss, hours_per_week, native_country}\n",
"\n",
"In order for the features to be used by a machine learning algorithm, they must be transformed and put into feature vectors, which are vectors of numbers representing the value for each feature."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Using the Spark ML Package\n",
"\n",
"The [ML package](http://spark.apache.org/docs/latest/ml-guide.html) is the newer library of machine learning routines. [Spark ML provides a uniform set of high-level APIs built on top of DataFrames](http://spark.apache.org/docs/latest/ml-pipeline.html#pipeline-components).\n",
"\n",
"![](assets/ml-pipeline.png)\n",
"\n",
"Image: https://mapr.com/blog/fast-data-processing-pipeline-predicting-flight-delays-using-apache-apis-pt-1/\n",
"\n",
"We will use an ML Pipeline to pass the data through transformers in order to extract the features and an estimator to produce the model.\n",
"\n",
"- `Transformer`: A Transformer is an algorithm which transforms one DataFrame into another DataFrame. We will use a transformer to get a DataFrame with a features vector column.\n",
"- `Estimator`: An Estimator is an algorithm which can be fit on a DataFrame to produce a Transformer. We will use a an estimator to train a model which can transform data to get predictions.\n",
"- `Pipeline`: A Pipeline chains multiple Transformers and Estimators together to specify a ML workflow."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Feature Extraction and Pipelining\n",
"\n",
"The ML package needs the label and feature vector to be added as columns to the input dataframe. We set up a pipeline to pass the data through transformers in order to extract the features and label. We use a StringIndexer to encode a string columns to a column of number indices. We use a OneHotEncoder to map a number indices column to a column of binary vectors, with at most a single one-value. Encoding categorical features allows machine learning algorithms to treat categorical features appropriately, improving performance. An example of StringIndexing and OneHotEncoding for 'race' is shown below:\n",
"\n",
"![](assets/example-category-encoding.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.1 Use a combination of StringIndexer and OneHotEncoder to encode Categorical columns:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Experiment: Let's StringIndex the 'race' column:**"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"col_name = \"race\""
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"race_indexer_model = StringIndexer(inputCol=col_name, outputCol=\"{0}_indexed\".format(col_name)).fit(train_df)\n",
"race_indexed_df = race_indexer_model.transform(train_df)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['White', 'Black', 'Asian-Pac-Islander', 'Amer-Indian-Eskimo', 'Other']"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check the encoded carrier values\n",
"race_indexer_model.labels"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+------------------+------------+\n",
"| race|race_indexed|\n",
"+------------------+------------+\n",
"| Other| 4.0|\n",
"| Black| 1.0|\n",
"|Amer-Indian-Eskimo| 3.0|\n",
"|Asian-Pac-Islander| 2.0|\n",
"| White| 0.0|\n",
"+------------------+------------+\n",
"\n"
]
}
],
"source": [
"# check the race code and index mapping \n",
"race_indexed_df.select(['race', 'race_indexed']).distinct().show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Experiment: Let's OneHotEncode the 'race' column:**\n",
"\n",
"`pyspark.ml.feature.OneHotEncoder` maps a column of category indices to a column of binary vectors, with at most a single one-value per row that indicates the input category index. For example with 5 categories, an input value of 2.0 would map to an output vector of [0.0, 0.0, 1.0, 0.0]. The last category is not included by default (configurable via dropLast=True) because it makes the vector entries sum up to one, and hence linearly dependent. So an input value of 4.0 maps to [0.0, 0.0, 0.0, 0.0]. Note that this is different from `scikit-learn's OneHotEncoder`, which keeps all categories. The output vectors are sparse. However, if we configure `dropLast=False`, then with 5 categories, an input value of 2.0 would map to an output vector of [0.0, 0.0, 1.0, 0.0, 0.0]."
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"race_encoder = OneHotEncoder(inputCol=\"{0}_indexed\".format(col_name), outputCol=\"{0}_encoded\".format(col_name), dropLast=False)\n",
"race_encoded_df = race_encoder.transform(race_indexed_df)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
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" | \n",
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" Divorced | \n",
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"text/plain": [
" age marital_status race race_indexed race_encoded \\\n",
"0 39 Never-married White 0.0 (1.0, 0.0, 0.0, 0.0, 0.0) \n",
"1 50 Married-civ-spouse White 0.0 (1.0, 0.0, 0.0, 0.0, 0.0) \n",
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"4 28 Married-civ-spouse Black 1.0 (0.0, 1.0, 0.0, 0.0, 0.0) \n",
"5 37 Married-civ-spouse White 0.0 (1.0, 0.0, 0.0, 0.0, 0.0) \n",
"6 49 Married-spouse-absent Black 1.0 (0.0, 1.0, 0.0, 0.0, 0.0) \n",
"7 52 Married-civ-spouse White 0.0 (1.0, 0.0, 0.0, 0.0, 0.0) \n",
"8 31 Never-married White 0.0 (1.0, 0.0, 0.0, 0.0, 0.0) \n",
"9 42 Married-civ-spouse White 0.0 (1.0, 0.0, 0.0, 0.0, 0.0) \n",
"\n",
" income \n",
"0 <=50K \n",
"1 <=50K \n",
"2 <=50K \n",
"3 <=50K \n",
"4 <=50K \n",
"5 <=50K \n",
"6 <=50K \n",
"7 >50K \n",
"8 >50K \n",
"9 >50K "
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(race_encoded_df\n",
" .select('age', 'marital_status', 'race', 'race_indexed', 'race_encoded', 'income')\n",
" .limit(10)\n",
" .toPandas())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: The column vector is a SparseVector: (5,[0],[1.0]) means there are 5 elements and the 0th position has a 1.0."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.2 Combine StringIndexer, OneHotEncoder, StandardScaler, VectorAssembler put features into a feature vector column:"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"assembled_train_df = train_df\n",
"assembled_test_df = test_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**OneHotEncode all categorical columns:**"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# categorical columns\n",
"categorical_columns = [\"workclass\", \"marital_status\", \"occupation\", \"relationship\", \"race\", \"sex\", \"native_country\"]\n",
"# numerial columns\n",
"numerical_columns = [\"age\", \"education_num\", \"capital_gain\", \"capital_loss\", \"hours_per_week\"]"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"string_indexer_models = []\n",
"one_hot_encoders = []\n",
"for col_name in categorical_columns:\n",
" # String Indexers will encode string categorical columns into a column of numeric indices\n",
" string_indexer_model = StringIndexer(inputCol=col_name, outputCol=\"{0}_indexed\".format(col_name)).fit(assembled_train_df)\n",
" assembled_train_df = string_indexer_model.transform(assembled_train_df)\n",
" assembled_test_df = string_indexer_model.transform(assembled_test_df)\n",
" \n",
" string_indexer_models.append(string_indexer_model)\n",
" \n",
" # OneHotEncoders map number indices column to column of binary vectors\n",
" one_hot_encoder = OneHotEncoder(inputCol=\"{0}_indexed\".format(col_name), outputCol=\"{0}_encoded\".format(col_name), dropLast=False)\n",
" assembled_train_df = one_hot_encoder.transform(assembled_train_df)\n",
" assembled_test_df = one_hot_encoder.transform(assembled_test_df)\n",
" \n",
" one_hot_encoders.append(one_hot_encoder)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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"text/plain": [
" age marital_status_indexed marital_status_encoded \\\n",
"0 39 1.0 (0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0) \n",
"1 50 0.0 (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) \n",
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"0 0.0 (1.0, 0.0, 0.0, 0.0, 0.0) <=50K \n",
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"2 0.0 (1.0, 0.0, 0.0, 0.0, 0.0) <=50K \n",
"3 1.0 (0.0, 1.0, 0.0, 0.0, 0.0) <=50K \n",
"4 1.0 (0.0, 1.0, 0.0, 0.0, 0.0) <=50K \n",
"5 0.0 (1.0, 0.0, 0.0, 0.0, 0.0) <=50K \n",
"6 1.0 (0.0, 1.0, 0.0, 0.0, 0.0) <=50K \n",
"7 0.0 (1.0, 0.0, 0.0, 0.0, 0.0) >50K \n",
"8 0.0 (1.0, 0.0, 0.0, 0.0, 0.0) >50K \n",
"9 0.0 (1.0, 0.0, 0.0, 0.0, 0.0) >50K "
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(assembled_train_df\n",
" .select('age', 'marital_status_indexed', 'marital_status_encoded', 'race_indexed', 'race_encoded', 'income')\n",
" .limit(10)\n",
" .toPandas())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**StandardScale all numerical columns:**\n",
"\n",
"Standardize all the numerical columns of the Spark DataFrame. `StandardScaler` expects all the numerical columns in a vectorized form. Hence we need a `VectorAssembler` to transform all the numerical features into a Vectorized feature column."
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Vectorize the numerical features first\n",
"scaler_vector_assembler = VectorAssembler(inputCols=numerical_columns, outputCol=\"numerical_features\")"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"assembled_train_df = scaler_vector_assembler.transform(assembled_train_df)\n",
"assembled_test_df = scaler_vector_assembler.transform(assembled_test_df)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
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" 8 | \n",
" [31.0, 14.0, 14084.0, 0.0, 50.0] | \n",
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" (1.0, 0.0, 0.0, 0.0, 0.0) | \n",
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" numerical_features marital_status_encoded \\\n",
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"2 [38.0, 9.0, 0.0, 0.0, 40.0] (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0) \n",
"3 [53.0, 7.0, 0.0, 0.0, 40.0] (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) \n",
"4 [28.0, 13.0, 0.0, 0.0, 40.0] (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) \n",
"5 [37.0, 14.0, 0.0, 0.0, 40.0] (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) \n",
"6 [49.0, 5.0, 0.0, 0.0, 16.0] (0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0) \n",
"7 [52.0, 9.0, 0.0, 0.0, 45.0] (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) \n",
"8 [31.0, 14.0, 14084.0, 0.0, 50.0] (0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0) \n",
"9 [42.0, 13.0, 5178.0, 0.0, 40.0] (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) \n",
"\n",
" race_encoded income \n",
"0 (1.0, 0.0, 0.0, 0.0, 0.0) <=50K \n",
"1 (1.0, 0.0, 0.0, 0.0, 0.0) <=50K \n",
"2 (1.0, 0.0, 0.0, 0.0, 0.0) <=50K \n",
"3 (0.0, 1.0, 0.0, 0.0, 0.0) <=50K \n",
"4 (0.0, 1.0, 0.0, 0.0, 0.0) <=50K \n",
"5 (1.0, 0.0, 0.0, 0.0, 0.0) <=50K \n",
"6 (0.0, 1.0, 0.0, 0.0, 0.0) <=50K \n",
"7 (1.0, 0.0, 0.0, 0.0, 0.0) >50K \n",
"8 (1.0, 0.0, 0.0, 0.0, 0.0) >50K \n",
"9 (1.0, 0.0, 0.0, 0.0, 0.0) >50K "
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(assembled_train_df\n",
" .select('numerical_features', 'marital_status_encoded', 'race_encoded', 'income')\n",
" .limit(10)\n",
" .toPandas())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Observe how all the numerical features have been clubbed as vectors into a single column."
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# create StandardScalerModel\n",
"standard_scaler_model = StandardScaler(withMean=True, inputCol='numerical_features', outputCol='numerical_features_scaled').fit(assembled_train_df)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# apply StandardScaler\n",
"assembled_train_df = standard_scaler_model.transform(assembled_train_df)\n",
"assembled_test_df = standard_scaler_model.transform(assembled_test_df)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
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"\n",
"
\n",
" \n",
" \n",
" | \n",
" numerical_features_scaled | \n",
" marital_status_encoded | \n",
" race_encoded | \n",
" income | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" [0.03067008638, 1.13472133885, 0.148450615588, -0.216656200028, -0.0354289029213] | \n",
" (0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0) | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0) | \n",
" <=50K | \n",
"
\n",
" \n",
" 1 | \n",
" [0.837096125788, 1.13472133885, -0.145918242817, -0.216656200028, -2.22211899816] | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0) | \n",
" <=50K | \n",
"
\n",
" \n",
" 2 | \n",
" [-0.042641371748, -0.420053173618, -0.145918242817, -0.216656200028, -0.0354289029213] | \n",
" (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0) | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0) | \n",
" <=50K | \n",
"
\n",
" \n",
" 3 | \n",
" [1.05703050017, -1.19744042985, -0.145918242817, -0.216656200028, -0.0354289029213] | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) | \n",
" (0.0, 1.0, 0.0, 0.0, 0.0) | \n",
" <=50K | \n",
"
\n",
" \n",
" 4 | \n",
" [-0.775755953028, 1.13472133885, -0.145918242817, -0.216656200028, -0.0354289029213] | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) | \n",
" (0.0, 1.0, 0.0, 0.0, 0.0) | \n",
" <=50K | \n",
"
\n",
" \n",
" 5 | \n",
" [-0.115952829876, 1.52341496696, -0.145918242817, -0.216656200028, -0.0354289029213] | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0) | \n",
" <=50K | \n",
"
\n",
" \n",
" 6 | \n",
" [0.76378466766, -1.97482768608, -0.145918242817, -0.216656200028, -1.97915343202] | \n",
" (0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0) | \n",
" (0.0, 1.0, 0.0, 0.0, 0.0) | \n",
" <=50K | \n",
"
\n",
" \n",
" 7 | \n",
" [0.983719042044, -0.420053173618, -0.145918242817, -0.216656200028, 0.369513707308] | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0) | \n",
" >50K | \n",
"
\n",
" \n",
" 8 | \n",
" [-0.555821578644, 1.52341496696, 1.76111533666, -0.216656200028, 0.774456317537] | \n",
" (0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0) | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0) | \n",
" >50K | \n",
"
\n",
" \n",
" 9 | \n",
" [0.250604460764, 1.13472133885, 0.55520500871, -0.216656200028, -0.0354289029213] | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0) | \n",
" >50K | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" numerical_features_scaled \\\n",
"0 [0.03067008638, 1.13472133885, 0.148450615588, -0.216656200028, -0.0354289029213] \n",
"1 [0.837096125788, 1.13472133885, -0.145918242817, -0.216656200028, -2.22211899816] \n",
"2 [-0.042641371748, -0.420053173618, -0.145918242817, -0.216656200028, -0.0354289029213] \n",
"3 [1.05703050017, -1.19744042985, -0.145918242817, -0.216656200028, -0.0354289029213] \n",
"4 [-0.775755953028, 1.13472133885, -0.145918242817, -0.216656200028, -0.0354289029213] \n",
"5 [-0.115952829876, 1.52341496696, -0.145918242817, -0.216656200028, -0.0354289029213] \n",
"6 [0.76378466766, -1.97482768608, -0.145918242817, -0.216656200028, -1.97915343202] \n",
"7 [0.983719042044, -0.420053173618, -0.145918242817, -0.216656200028, 0.369513707308] \n",
"8 [-0.555821578644, 1.52341496696, 1.76111533666, -0.216656200028, 0.774456317537] \n",
"9 [0.250604460764, 1.13472133885, 0.55520500871, -0.216656200028, -0.0354289029213] \n",
"\n",
" marital_status_encoded race_encoded income \n",
"0 (0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0) (1.0, 0.0, 0.0, 0.0, 0.0) <=50K \n",
"1 (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) (1.0, 0.0, 0.0, 0.0, 0.0) <=50K \n",
"2 (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0) (1.0, 0.0, 0.0, 0.0, 0.0) <=50K \n",
"3 (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) (0.0, 1.0, 0.0, 0.0, 0.0) <=50K \n",
"4 (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) (0.0, 1.0, 0.0, 0.0, 0.0) <=50K \n",
"5 (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) (1.0, 0.0, 0.0, 0.0, 0.0) <=50K \n",
"6 (0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0) (0.0, 1.0, 0.0, 0.0, 0.0) <=50K \n",
"7 (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) (1.0, 0.0, 0.0, 0.0, 0.0) >50K \n",
"8 (0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0) (1.0, 0.0, 0.0, 0.0, 0.0) >50K \n",
"9 (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) (1.0, 0.0, 0.0, 0.0, 0.0) >50K "
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assembled_train_df.select('numerical_features_scaled', 'marital_status_encoded', 'race_encoded', 'income').limit(10).toPandas()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> **Observation:** toPandas() method transforms the Sparse One Hot Encoded Vectors into DenseVectors for display."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Transform Income attribute into Binary Labels:**"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"income_indexer_model = StringIndexer(inputCol='income', outputCol='label').fit(assembled_train_df)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['<=50K', '>50K']"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"income_indexer_model.labels"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"assembled_train_df = income_indexer_model.transform(assembled_train_df)\n",
"assembled_test_df = income_indexer_model.transform(assembled_test_df)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" income | \n",
" label | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" <=50K | \n",
" 0.0 | \n",
"
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" \n",
" 1 | \n",
" >50K | \n",
" 1.0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" income label\n",
"0 <=50K 0.0\n",
"1 >50K 1.0"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check the income level and index mapping \n",
"assembled_train_df.select([\"income\", \"label\"]).distinct().toPandas()"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" numerical_features_scaled | \n",
" marital_status_encoded | \n",
" race_encoded | \n",
" income | \n",
" label | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
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" (0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0) | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0) | \n",
" <=50K | \n",
" 0.0 | \n",
"
\n",
" \n",
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" [0.837096125788, 1.13472133885, -0.145918242817, -0.216656200028, -2.22211899816] | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0) | \n",
" <=50K | \n",
" 0.0 | \n",
"
\n",
" \n",
" 2 | \n",
" [-0.042641371748, -0.420053173618, -0.145918242817, -0.216656200028, -0.0354289029213] | \n",
" (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0) | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0) | \n",
" <=50K | \n",
" 0.0 | \n",
"
\n",
" \n",
" 3 | \n",
" [1.05703050017, -1.19744042985, -0.145918242817, -0.216656200028, -0.0354289029213] | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) | \n",
" (0.0, 1.0, 0.0, 0.0, 0.0) | \n",
" <=50K | \n",
" 0.0 | \n",
"
\n",
" \n",
" 4 | \n",
" [-0.775755953028, 1.13472133885, -0.145918242817, -0.216656200028, -0.0354289029213] | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) | \n",
" (0.0, 1.0, 0.0, 0.0, 0.0) | \n",
" <=50K | \n",
" 0.0 | \n",
"
\n",
" \n",
" 5 | \n",
" [-0.115952829876, 1.52341496696, -0.145918242817, -0.216656200028, -0.0354289029213] | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0) | \n",
" <=50K | \n",
" 0.0 | \n",
"
\n",
" \n",
" 6 | \n",
" [0.76378466766, -1.97482768608, -0.145918242817, -0.216656200028, -1.97915343202] | \n",
" (0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0) | \n",
" (0.0, 1.0, 0.0, 0.0, 0.0) | \n",
" <=50K | \n",
" 0.0 | \n",
"
\n",
" \n",
" 7 | \n",
" [0.983719042044, -0.420053173618, -0.145918242817, -0.216656200028, 0.369513707308] | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0) | \n",
" >50K | \n",
" 1.0 | \n",
"
\n",
" \n",
" 8 | \n",
" [-0.555821578644, 1.52341496696, 1.76111533666, -0.216656200028, 0.774456317537] | \n",
" (0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0) | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0) | \n",
" >50K | \n",
" 1.0 | \n",
"
\n",
" \n",
" 9 | \n",
" [0.250604460764, 1.13472133885, 0.55520500871, -0.216656200028, -0.0354289029213] | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) | \n",
" (1.0, 0.0, 0.0, 0.0, 0.0) | \n",
" >50K | \n",
" 1.0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" numerical_features_scaled \\\n",
"0 [0.03067008638, 1.13472133885, 0.148450615588, -0.216656200028, -0.0354289029213] \n",
"1 [0.837096125788, 1.13472133885, -0.145918242817, -0.216656200028, -2.22211899816] \n",
"2 [-0.042641371748, -0.420053173618, -0.145918242817, -0.216656200028, -0.0354289029213] \n",
"3 [1.05703050017, -1.19744042985, -0.145918242817, -0.216656200028, -0.0354289029213] \n",
"4 [-0.775755953028, 1.13472133885, -0.145918242817, -0.216656200028, -0.0354289029213] \n",
"5 [-0.115952829876, 1.52341496696, -0.145918242817, -0.216656200028, -0.0354289029213] \n",
"6 [0.76378466766, -1.97482768608, -0.145918242817, -0.216656200028, -1.97915343202] \n",
"7 [0.983719042044, -0.420053173618, -0.145918242817, -0.216656200028, 0.369513707308] \n",
"8 [-0.555821578644, 1.52341496696, 1.76111533666, -0.216656200028, 0.774456317537] \n",
"9 [0.250604460764, 1.13472133885, 0.55520500871, -0.216656200028, -0.0354289029213] \n",
"\n",
" marital_status_encoded race_encoded income \\\n",
"0 (0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0) (1.0, 0.0, 0.0, 0.0, 0.0) <=50K \n",
"1 (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) (1.0, 0.0, 0.0, 0.0, 0.0) <=50K \n",
"2 (0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0) (1.0, 0.0, 0.0, 0.0, 0.0) <=50K \n",
"3 (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) (0.0, 1.0, 0.0, 0.0, 0.0) <=50K \n",
"4 (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) (0.0, 1.0, 0.0, 0.0, 0.0) <=50K \n",
"5 (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) (1.0, 0.0, 0.0, 0.0, 0.0) <=50K \n",
"6 (0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0) (0.0, 1.0, 0.0, 0.0, 0.0) <=50K \n",
"7 (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) (1.0, 0.0, 0.0, 0.0, 0.0) >50K \n",
"8 (0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0) (1.0, 0.0, 0.0, 0.0, 0.0) >50K \n",
"9 (1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) (1.0, 0.0, 0.0, 0.0, 0.0) >50K \n",
"\n",
" label \n",
"0 0.0 \n",
"1 0.0 \n",
"2 0.0 \n",
"3 0.0 \n",
"4 0.0 \n",
"5 0.0 \n",
"6 0.0 \n",
"7 1.0 \n",
"8 1.0 \n",
"9 1.0 "
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(assembled_train_df\n",
" .select('numerical_features_scaled', 'marital_status_encoded', 'race_encoded', 'income', 'label')\n",
" .limit(10)\n",
" .toPandas())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Assemble transformed features into one Feature Vector for Spark:**"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"feature_cols = [\"{0}_encoded\".format(col) for col in categorical_columns] + ['numerical_features_scaled']"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['workclass_encoded',\n",
" 'marital_status_encoded',\n",
" 'occupation_encoded',\n",
" 'relationship_encoded',\n",
" 'race_encoded',\n",
" 'sex_encoded',\n",
" 'native_country_encoded',\n",
" 'numerical_features_scaled']"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feature_cols"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# The VectorAssembler combines a given list of columns into a single feature vector column.\n",
"feature_assembler = VectorAssembler(inputCols=feature_cols, outputCol=\"features\")"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# cache the dataframe because we would be reusing this again and again\n",
"assembled_train_df = feature_assembler.transform(assembled_train_df).cache()\n",
"assembled_test_df = feature_assembler.transform(assembled_test_df).cache()"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['age',\n",
" 'workclass',\n",
" 'fnlgwt',\n",
" 'education_num',\n",
" 'marital_status',\n",
" 'occupation',\n",
" 'relationship',\n",
" 'race',\n",
" 'sex',\n",
" 'capital_gain',\n",
" 'capital_loss',\n",
" 'hours_per_week',\n",
" 'native_country',\n",
" 'income',\n",
" 'workclass_indexed',\n",
" 'workclass_encoded',\n",
" 'marital_status_indexed',\n",
" 'marital_status_encoded',\n",
" 'occupation_indexed',\n",
" 'occupation_encoded',\n",
" 'relationship_indexed',\n",
" 'relationship_encoded',\n",
" 'race_indexed',\n",
" 'race_encoded',\n",
" 'sex_indexed',\n",
" 'sex_encoded',\n",
" 'native_country_indexed',\n",
" 'native_country_encoded',\n",
" 'numerical_features',\n",
" 'numerical_features_scaled',\n",
" 'label',\n",
" 'features']"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assembled_train_df.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Result: SparseVector representation of the Feature Vectors**"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+----------------------------------------------------------------------------------------------------+-----+\n",
"| features|label|\n",
"+----------------------------------------------------------------------------------------------------+-----+\n",
"|(91,[4,10,19,32,37,42,44,86,87,88,89,90],[1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.03067008637999638,1.13472...| 0.0|\n",
"|(91,[1,9,18,31,37,42,44,86,87,88,89,90],[1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.8370961257882483,1.1347213...| 0.0|\n",
"|(91,[0,11,25,32,37,42,44,86,87,88,89,90],[1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.04264137174802653,-0.420...| 0.0|\n",
"|(91,[0,9,25,31,38,42,44,86,87,88,89,90],[1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.057030500172317,-1.1974404...| 0.0|\n",
"|(91,[0,9,16,35,38,43,53,86,87,88,89,90],[1.0,1.0,1.0,1.0,1.0,1.0,1.0,-0.7757559530282556,1.134721...| 0.0|\n",
"+----------------------------------------------------------------------------------------------------+-----+\n",
"\n"
]
}
],
"source": [
"assembled_train_df.select('features', 'label').limit(5).show(10,100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Class Imbalance\n",
"\n",
"Let's see the distribution of negative and positive classes in our training dataset."
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" income | \n",
" count | \n",
" %age | \n",
"
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" \n",
" \n",
" \n",
" 0 | \n",
" <=50K | \n",
" 24720 | \n",
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" 7841 | \n",
" 0.24 | \n",
"
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"
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"
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],
"text/plain": [
" income count %age\n",
"0 <=50K 24720 0.76\n",
"1 >50K 7841 0.24"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(assembled_train_df\n",
" .groupBy('income')\n",
" .count()\n",
" .withColumn('%age', F.round(col('count') / assembled_train_df.count(), 2))\n",
" .toPandas())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is quite a significant `class imbalance`. In order to ensure that our model is sensitive to the income levels, we can put the two sample types on the same footing. In this case we have 76% negatives (label == 0) and 24% positives (label == 1) and in the dataset, so theoretically we want to \"under-sample\" the negative class. The logistic loss objective function should treat the positive class (label == 1) with higher weight.\n",
"\n",
"https://stackoverflow.com/questions/33372838/dealing-with-unbalanced-datasets-in-spark-mllib\n",
"\n",
"### 7.1 Assign `class_weights` to class labels:\n",
"\n",
"We add a new column to the dataframe for each record in the dataset:"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"label1count = assembled_train_df.filter(col('income') == '>50K').count()\n",
"datacount = assembled_train_df.count()"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.76000000000000001"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weight_balance = np.round((datacount - label1count) / datacount, decimals=2)\n",
"weight_balance"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# we \"under sample\" the negative class\n",
"assembled_train_df = (assembled_train_df\n",
" .withColumn('class_weight', \n",
" F.when(col('income') == '>50K', F.lit(weight_balance)).otherwise(F.lit(1 - weight_balance)))\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
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],
"text/plain": [
" class_weight label\n",
"0 0.24 0.0\n",
"1 0.24 0.0\n",
"2 0.24 0.0\n",
"3 0.24 0.0\n",
"4 0.24 0.0\n",
"5 0.24 0.0\n",
"6 0.24 0.0\n",
"7 0.76 1.0\n",
"8 0.76 1.0\n",
"9 0.76 1.0\n",
"10 0.76 1.0\n",
"11 0.76 1.0\n",
"12 0.24 0.0\n",
"13 0.24 0.0\n",
"14 0.76 1.0\n",
"15 0.24 0.0\n",
"16 0.24 0.0\n",
"17 0.24 0.0\n",
"18 0.24 0.0\n",
"19 0.76 1.0"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assembled_train_df.select('class_weight', 'label').limit(20).toPandas()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will use this `class_weight` column when we define our `LogisticRegression` model."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Stratified Sampling\n",
"\n",
"We need to ensure that when we create the training and validation set the two different class labels are represented in the same proportion in the training and the validation set. This is known as `stratified sampling`. The data is split using the proportion of the class labels. \n",
"\n",
"The DataFrames `sampleBy()` function provides a way of specifying fractions of each sample type to be returned.\n",
"\n",
"We will split the training set into training and validation set in the ratio 0.75:0.25. So we will take 25% from each class to create the validation set and the rest 75% from each class would go in the training set."
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" income | \n",
" count | \n",
" %age | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" <=50K | \n",
" 24720 | \n",
" 0.76 | \n",
"
\n",
" \n",
" 1 | \n",
" >50K | \n",
" 7841 | \n",
" 0.24 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" income count %age\n",
"0 <=50K 24720 0.76\n",
"1 >50K 7841 0.24"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(assembled_train_df\n",
" .groupBy('income')\n",
" .count()\n",
" .withColumn('%age', F.round(col('count') / assembled_train_df.count(), 2))\n",
" .toPandas())"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# specify the exact fraction desired from each key as a dictionary\n",
"fractions = {0.0: 0.25, 1.0: 0.25}"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# create the validation set with 25% of the entire data and same distribution of income levels\n",
"assembled_validation_df = assembled_train_df.stat.sampleBy('label', fractions, seed=rnd_seed).cache()"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# subtract the validation set from the original training set to get 75% of the entire data \n",
"# and same distribution of income levels\n",
"assembled_train_df = assembled_train_df.subtract(assembled_validation_df).cache()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Recheck the distribution:**\n",
"\n",
"Check whether the training and validation set contain rows across are represented in the same proportion of income levels."
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" income | \n",
" count | \n",
" %age | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" <=50K | \n",
" 18414 | \n",
" 0.76 | \n",
"
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" \n",
" 1 | \n",
" >50K | \n",
" 5851 | \n",
" 0.24 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" income count %age\n",
"0 <=50K 18414 0.76\n",
"1 >50K 5851 0.24"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(assembled_train_df\n",
" .groupBy('income')\n",
" .count()\n",
" .withColumn('%age', F.round(col('count') / assembled_train_df.count(), 2))\n",
" .toPandas())"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" income | \n",
" count | \n",
" %age | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" <=50K | \n",
" 6284 | \n",
" 0.76 | \n",
"
\n",
" \n",
" 1 | \n",
" >50K | \n",
" 1988 | \n",
" 0.24 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" income count %age\n",
"0 <=50K 6284 0.76\n",
"1 >50K 1988 0.24"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(assembled_validation_df\n",
" .groupBy('income')\n",
" .count()\n",
" .withColumn('%age', F.round(col('count') / assembled_validation_df.count(), 2))\n",
" .toPandas())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9. Train a Logistic Regression\n",
"\n",
"We will create a LogisticRegression model with `class_weight` specified."
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# watch how we specify the class_weight using the weightCol feature\n",
"log_reg = LogisticRegression(featuresCol='features', labelCol='label', weightCol='class_weight', maxIter=20, family='binomial')"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"evaluator = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction', labelCol='label', metricName='areaUnderROC')"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = log_reg.fit(assembled_train_df)"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_preds = model.transform(assembled_validation_df)"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['age', 'workclass', 'fnlgwt', 'education_num', 'marital_status', 'occupation', 'relationship', 'race', 'sex', 'capital_gain', 'capital_loss', 'hours_per_week', 'native_country', 'income', 'workclass_indexed', 'workclass_encoded', 'marital_status_indexed', 'marital_status_encoded', 'occupation_indexed', 'occupation_encoded', 'relationship_indexed', 'relationship_encoded', 'race_indexed', 'race_encoded', 'sex_indexed', 'sex_encoded', 'native_country_indexed', 'native_country_encoded', 'numerical_features', 'numerical_features_scaled', 'label', 'features', 'class_weight', 'rawPrediction', 'probability', 'prediction']\n"
]
}
],
"source": [
"print(train_preds.columns)"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9127818710480433"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_areaUnderROC = evaluator.evaluate(train_preds)\n",
"train_areaUnderROC"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trainpredlbls = train_preds.select(\"prediction\", \"label\").cache()"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
" \n",
" | \n",
" prediction | \n",
" label | \n",
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"
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"
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"
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],
"text/plain": [
" prediction label\n",
"0 0.0 0.0\n",
"1 1.0 1.0\n",
"2 1.0 1.0\n",
"3 1.0 1.0\n",
"4 0.0 0.0\n",
"5 0.0 1.0\n",
"6 1.0 1.0\n",
"7 0.0 0.0\n",
"8 0.0 0.0\n",
"9 0.0 1.0"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trainpredlbls.limit(10).toPandas()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Training Accuracy:**"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def accuracy(predlbls):\n",
" counttotal = predlbls.count()\n",
" correct = predlbls.filter(col('label') == col(\"prediction\")).count()\n",
" wrong = predlbls.filter(col('label') != col(\"prediction\")).count()\n",
" ratioCorrect = float(correct)/counttotal\n",
" print(\"Correct: {0}, Wrong: {1}, Model Accuracy: {2}\".format(correct, wrong, np.round(ratioCorrect, 2)))"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Correct: 6750, Wrong: 1522, Model Accuracy: 0.82\n"
]
}
],
"source": [
"accuracy(trainpredlbls)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Training & Validation Accuracy: Using Spark 2.3.0 enhancements:**\n",
"\n",
"Spark 2.3.0 has introdcued several new methods in `BinaryLogisticRegressionSummary` which we can leverage to get various metrics."
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_summary = model.evaluate(assembled_train_df)\n",
"validation_summary = model.evaluate(assembled_validation_df)"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(pyspark.ml.classification.BinaryLogisticRegressionSummary,\n",
" pyspark.ml.classification.BinaryLogisticRegressionSummary)"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(train_summary), type(validation_summary)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training Accuracy : 0.8091901916340408\n",
"Validation Accuracy : 0.8160058027079303\n"
]
}
],
"source": [
"print('Training Accuracy :', train_summary.accuracy)\n",
"print('Validation Accuracy :', validation_summary.accuracy)"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.906075974495489"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"0.9128234156690619"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_summary.areaUnderROC\n",
"validation_summary.areaUnderROC"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.8684301521438451, 0.6941318327974276]"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"validation_summary.fMeasureByLabel(beta=1.0)"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.9506056018168054, 0.5779785809906292]"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"validation_summary.precisionByLabel"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.7993316359007002, 0.8687122736418511]"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"validation_summary.recallByLabel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Null Accuracy:**\n",
"\n",
"Our model should at least perform better than the `Null Accuracy`. Null Accuracy is defined as the accuracy we would have got if we would have blindly predicted the majority class of the training set as the label."
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_total = trainpredlbls.count()"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_label0count = trainpredlbls.filter(col(\"label\") == 0.0).count()"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_label1count = trainpredlbls.filter(col(\"label\") == 1.0).count()"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7596711798839458"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# If we would have predicted everything to be the majority label then what would have been the accuracy\n",
"max(train_label0count, train_label1count) / train_total"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> Observation: The model accuracy on the training set is 0.85 i.e. it is at least doing better than the NULL accuracy."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Test Accuracy:**"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"test_preds = model.transform(assembled_test_df)"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9030185356992586"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_areaUnderROC = evaluator.evaluate(test_preds)\n",
"test_areaUnderROC"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"testpredlbls = test_preds.select(\"prediction\", \"label\")"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Correct: 13122, Wrong: 3159, Model Accuracy: 0.81\n"
]
}
],
"source": [
"accuracy(testpredlbls)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Using Spark 2.3.0 enhancements:**"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"test_summary = model.evaluate(assembled_test_df)"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pyspark.ml.classification.BinaryLogisticRegressionSummary"
]
},
"execution_count": 132,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(test_summary)"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8059701492537313"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_summary.accuracy"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9029399680209164"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_summary.areaUnderROC"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.862274926973885, 0.6717922077922077]"
]
},
"execution_count": 135,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_summary.fMeasureByLabel(beta=1.0)"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.9416301656827271, 0.5594393493684028]"
]
},
"execution_count": 136,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_summary.precisionByLabel"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.7952553277040612, 0.8406136245449818]"
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_summary.recallByLabel"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" FPR | \n",
" TPR | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 1 | \n",
" 0.000080 | \n",
" 0.050962 | \n",
"
\n",
" \n",
" 2 | \n",
" 0.000161 | \n",
" 0.089444 | \n",
"
\n",
" \n",
" 3 | \n",
" 0.000885 | \n",
" 0.126105 | \n",
"
\n",
" \n",
" 4 | \n",
" 0.002091 | \n",
" 0.161206 | \n",
"
\n",
" \n",
" 5 | \n",
" 0.004021 | \n",
" 0.193968 | \n",
"
\n",
" \n",
" 6 | \n",
" 0.006112 | \n",
" 0.226989 | \n",
"
\n",
" \n",
" 7 | \n",
" 0.008203 | \n",
" 0.261050 | \n",
"
\n",
" \n",
" 8 | \n",
" 0.011017 | \n",
" 0.292512 | \n",
"
\n",
" \n",
" 9 | \n",
" 0.014395 | \n",
" 0.324493 | \n",
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"text/plain": [
" FPR TPR\n",
"0 0.000000 0.000000\n",
"1 0.000080 0.050962\n",
"2 0.000161 0.089444\n",
"3 0.000885 0.126105\n",
"4 0.002091 0.161206\n",
"5 0.004021 0.193968\n",
"6 0.006112 0.226989\n",
"7 0.008203 0.261050\n",
"8 0.011017 0.292512\n",
"9 0.014395 0.324493"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_summary.roc.limit(10).toPandas()"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_roc_pdf = train_summary.roc.toPandas()\n",
"validation_roc_pdf = validation_summary.roc.toPandas()\n",
"test_roc_pdf = test_summary.roc.toPandas()"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {},
"outputs": [
{
"data": {
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UuhNYBcwD5gKpwFO6rn/wIsoqAL4D3ABkAzXAi8C3dV1vHoz6CiGEEEIIEa8GqwX9a8Dn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\ngfw/+vPChwcdJakAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 263,
"width": 372
}
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(6,4))\n",
"plt.plot(train_roc_pdf['FPR'], train_roc_pdf['TPR'], lw=1, label='Train AUC = %0.2f' % (train_summary.areaUnderROC))\n",
"plt.plot(validation_roc_pdf['FPR'], validation_roc_pdf['TPR'], lw=1, label='Validation AUC = %0.2f' % (test_summary.areaUnderROC))\n",
"plt.plot(test_roc_pdf['FPR'], test_roc_pdf['TPR'], lw=1, label='Test AUC = %0.2f' % (validation_summary.areaUnderROC))\n",
"plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='NULL Accuracy')\n",
"plt.title('ROC AUC Curve')\n",
"plt.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 10. Setup a pipeline with feature transformers and model estimator\n",
"\n",
"Since, we have now trained a model after feature transformation, we are now in a position to set up a pipeline that will call feature extractors, transformers, assemblers, estimator in a chain automatically."
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# categorical columns\n",
"categorical_columns = [\"workclass\", \"marital_status\", \"occupation\", \"relationship\", \"race\", \"sex\", \"native_country\"]\n",
"# numerial columns\n",
"numerical_columns = [\"age\", \"education_num\", \"capital_gain\", \"capital_loss\", \"hours_per_week\"]"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# String Indexers will encode string categorical columns into a column of numeric indices\n",
"string_indexers = [StringIndexer(inputCol=col_name, outputCol=\"{0}_indexed\".format(col_name), handleInvalid='skip') for col_name in categorical_columns]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In our training set there is a single row where the `native_country` has value 'Holand-Netherlans'. That single will fall into a training or test fold during cross validation leading to Spark throwing an exception saying that it has encountered an unseen value. In order to skip that record during cross-validation we need to configure `handleInvalid='skip'`.\n",
"\n",
"More details here: http://spark.apache.org/docs/2.3.0/api/python/pyspark.ml.html#pyspark.ml.feature.StringIndexer"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# OneHotEncoders map number indices column to column of binary vectors\n",
"one_hot_encoders = [OneHotEncoder(inputCol=\"{0}_indexed\".format(col_name), outputCol=\"{0}_encoded\".format(col_name), dropLast=False) for col_name in categorical_columns]"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"scaler_vector_assembler = VectorAssembler(inputCols=numerical_columns, outputCol=\"numerical_features\")"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"standard_scaler = StandardScaler(withMean=True, inputCol='numerical_features', outputCol='numerical_features_scaled')"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"income_indexer = StringIndexer(inputCol='income', outputCol='label')"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"feature_cols = [\"{0}_encoded\".format(col) for col in categorical_columns] + ['numerical_features_scaled']"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['workclass_encoded',\n",
" 'marital_status_encoded',\n",
" 'occupation_encoded',\n",
" 'relationship_encoded',\n",
" 'race_encoded',\n",
" 'sex_encoded',\n",
" 'native_country_encoded',\n",
" 'numerical_features_scaled']"
]
},
"execution_count": 148,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feature_cols"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# The VectorAssembler combines a given list of columns into a single feature vector column.\n",
"feature_assembler = VectorAssembler(inputCols=feature_cols, outputCol=\"features\")"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"log_reg = LogisticRegression(featuresCol='features', labelCol='label', weightCol='class_weight', maxIter=20, family='binomial')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Assemble the pipeline:**"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[StringIndexer_492880a800b95ea8119e,\n",
" StringIndexer_464080636b53dfa4e597,\n",
" StringIndexer_424b81a8c7a59e5f5aba,\n",
" StringIndexer_45e39b2235e6957f74d7,\n",
" StringIndexer_4a8b8d8e9d52eb2aa17d,\n",
" StringIndexer_40ee835b624f551c2ee1,\n",
" StringIndexer_41398d95da8dc65bd9c8,\n",
" OneHotEncoder_4973918c918af3986164,\n",
" OneHotEncoder_4ecaa69d59f4695b2ba8,\n",
" OneHotEncoder_40d4958687354e951eb7,\n",
" OneHotEncoder_444195b4f6d6f3fa5d51,\n",
" OneHotEncoder_413c976fbef74af7ecdf,\n",
" OneHotEncoder_4836bed3a623d4ad42c7,\n",
" OneHotEncoder_437384bdd331db933a39,\n",
" VectorAssembler_45f197169aec013291b7,\n",
" StandardScaler_4d528f4f1c75958f9bc5,\n",
" StringIndexer_4083a3b050fd3ad394a6,\n",
" VectorAssembler_4ed58472c709978bc1cd,\n",
" LogisticRegression_41a2ada033060e643027]"
]
},
"execution_count": 151,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"steps = string_indexers + one_hot_encoders + [scaler_vector_assembler, standard_scaler, income_indexer, feature_assembler, log_reg]\n",
"steps"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pipeline = Pipeline(stages=steps)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 11. Set up a CrossValidator with the Parameters, a Logistic Regressor estimator and a BinaryClassificationEvaluator\n",
"\n",
"A common technique for model selection is `k-fold cross-validation`, where the data is randomly split into `k` partitions. Each partition is used once as the testing dataset, while the rest `k - 1` are used for the training. `k` Models are then generated using the training sets and evaluated with the testing sets, resulting in `k` model performance measurements. \n",
"\n",
"We can also provide a grid of paramater names and a set of values for each paramater that can be tried to see which set of parameter values of the logistic regression produce the best model. \n",
"\n",
"Spark ML supports `k-fold cross-validation` with a transformation/estimation pipeline to try out different combinations of parameters, using the `grid search` process. Here, we set up the parameters to test, and a cross-validation evaluator to construct a model selection workflow.\n",
"\n",
"We use a `ParamGridBuilder` to construct the parameter grid. We define an `BinaryClassificationEvaluator`, which will evaluate the model by comparing the test label column with the test prediction column. We use a `CrossValidator` with 5 folds for model selection.\n",
"\n",
"The model parameters leading to the highest performance metric produce the best model."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 11.1 Set up a Grid of Parameters to tune"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# set param grid to search through logistic regression's regularization parameter for best model\n",
"paramGrid = (ParamGridBuilder()\n",
" .addGrid(log_reg.regParam, [0.1, 0.01, 0.001])\n",
" .addGrid(log_reg.elasticNetParam, [0.0, 0.5, 1.0])\n",
" .build())"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"evaluator = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction', labelCol='label', metricName='areaUnderROC')"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Set up 5-fold cross validation with paramGrid\n",
"crossVal = CrossValidator(estimator=pipeline, evaluator=evaluator, estimatorParamMaps=paramGrid, numFolds=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The CrossValidator uses the Estimator Pipeline, the Parameter Grid, and the Classification Evaluator to fit the training data set and returns a model."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 11.2 Use CrossValidator Estimator to fit the training data set"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Add the `class_weight` column before we invoke the `cross_validator`:**"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# we \"under sample\" the negative class\n",
"train_df = (train_df.withColumn('class_weight', \n",
" F.when(col('income') == '>50K', F.lit(weight_balance)).otherwise(F.lit(1 - weight_balance))))"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {
"collapsed": true,
"scrolled": false
},
"outputs": [],
"source": [
"cvModel = crossVal.fit(train_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `CrossValidator` uses the `ParamGridBuilder` to iterate through the `regParam` parameter of the logistic regression and evaluate the models, repeating 5 times per parameter value for reliable results."
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[StringIndexer_492880a800b95ea8119e,\n",
" StringIndexer_464080636b53dfa4e597,\n",
" StringIndexer_424b81a8c7a59e5f5aba,\n",
" StringIndexer_45e39b2235e6957f74d7,\n",
" StringIndexer_4a8b8d8e9d52eb2aa17d,\n",
" StringIndexer_40ee835b624f551c2ee1,\n",
" StringIndexer_41398d95da8dc65bd9c8,\n",
" OneHotEncoder_4973918c918af3986164,\n",
" OneHotEncoder_4ecaa69d59f4695b2ba8,\n",
" OneHotEncoder_40d4958687354e951eb7,\n",
" OneHotEncoder_444195b4f6d6f3fa5d51,\n",
" OneHotEncoder_413c976fbef74af7ecdf,\n",
" OneHotEncoder_4836bed3a623d4ad42c7,\n",
" OneHotEncoder_437384bdd331db933a39,\n",
" VectorAssembler_45f197169aec013291b7,\n",
" StandardScaler_4d528f4f1c75958f9bc5,\n",
" StringIndexer_4083a3b050fd3ad394a6,\n",
" VectorAssembler_4ed58472c709978bc1cd,\n",
" LogisticRegression_41a2ada033060e643027]"
]
},
"execution_count": 158,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cvModel.bestModel.stages"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression_41a2ada033060e643027"
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the last stage of the best model is the best logistic regressor\n",
"cvModel.bestModel.stages[-1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 11.3 Get the Best Logistic Regression Model"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression_41a2ada033060e643027"
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"best_log_reg_model = cvModel.bestModel.stages[-1]\n",
"best_log_reg_model"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{Param(parent='LogisticRegression_41a2ada033060e643027', name='family', doc='The name of family which is a description of the label distribution to be used in the model. Supported options: auto, binomial, multinomial.'): 'binomial',\n",
" Param(parent='LogisticRegression_41a2ada033060e643027', name='rawPredictionCol', doc='raw prediction (a.k.a. confidence) column name'): 'rawPrediction',\n",
" Param(parent='LogisticRegression_41a2ada033060e643027', name='featuresCol', doc='features column name'): 'features',\n",
" Param(parent='LogisticRegression_41a2ada033060e643027', name='elasticNetParam', doc='the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty'): 1.0,\n",
" Param(parent='LogisticRegression_41a2ada033060e643027', name='fitIntercept', doc='whether to fit an intercept term'): True,\n",
" Param(parent='LogisticRegression_41a2ada033060e643027', name='labelCol', doc='label column name'): 'label',\n",
" Param(parent='LogisticRegression_41a2ada033060e643027', name='predictionCol', doc='prediction column name'): 'prediction',\n",
" Param(parent='LogisticRegression_41a2ada033060e643027', name='threshold', doc='threshold in binary classification prediction, in range [0, 1]'): 0.5,\n",
" Param(parent='LogisticRegression_41a2ada033060e643027', name='maxIter', doc='maximum number of iterations (>= 0)'): 20,\n",
" Param(parent='LogisticRegression_41a2ada033060e643027', name='standardization', doc='whether to standardize the training features before fitting the model'): True,\n",
" Param(parent='LogisticRegression_41a2ada033060e643027', name='weightCol', doc='weight column name. If this is not set or empty, we treat all instance weights as 1.0'): 'class_weight',\n",
" Param(parent='LogisticRegression_41a2ada033060e643027', name='aggregationDepth', doc='suggested depth for treeAggregate (>= 2)'): 2,\n",
" Param(parent='LogisticRegression_41a2ada033060e643027', name='regParam', doc='regularization parameter (>= 0)'): 0.001,\n",
" Param(parent='LogisticRegression_41a2ada033060e643027', name='probabilityCol', doc='Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities'): 'probability',\n",
" Param(parent='LogisticRegression_41a2ada033060e643027', name='tol', doc='the convergence tolerance for iterative algorithms (>= 0)'): 1e-06}"
]
},
"execution_count": 161,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Extracts the embedded default param values and user-supplied values and then merges them into a flat param map\n",
"# with ordering: default param values < user-supplied values < extra.\n",
"best_log_reg_model.extractParamMap()"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'regParam: regularization parameter (>= 0) (default: 0.0, current: 0.001)'"
]
},
"execution_count": 162,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"'elasticNetParam: the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty (default: 0.0, current: 1.0)'"
]
},
"execution_count": 162,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"'maxIter: maximum number of iterations (>= 0) (default: 100, current: 20)'"
]
},
"execution_count": 162,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"'tol: the convergence tolerance for iterative algorithms (>= 0) (default: 1e-06)'"
]
},
"execution_count": 162,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.\n",
"for param in ['regParam', 'elasticNetParam', 'maxIter', 'tol']:\n",
" best_log_reg_model.explainParam(param)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 11.4 Hyperparameter Heat Map\n",
"\n",
"Next, we perform a visualization of hyperparameter search. Specifically, we create a heat map where the brighter colors correspond to higher areaUnderROC values."
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"grid_score_param_records = []\n",
"for metric, param_map in zip(cvModel.avgMetrics, paramGrid):\n",
" grid_score_param_records.append(list(param_map.values()) + [metric])"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"grid_score_param_df = pd.DataFrame.from_records(grid_score_param_records, columns=['regParam', 'elasticNetParam', 'areaUnderROC'])"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {},
"outputs": [
{
"data": {
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"grid_score_param_df"
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{
"cell_type": "code",
"execution_count": 166,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"grid_score_param_df = pd.pivot_table(grid_score_param_df, index=['regParam', 'elasticNetParam'], values=['areaUnderROC'])"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {},
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{
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{
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"execution_count": 168,
"metadata": {},
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{
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" \n",
" \n",
" 0.001 | \n",
" 0.905587 | \n",
" 0.906075 | \n",
" 0.906433 | \n",
"
\n",
" \n",
" 0.010 | \n",
" 0.901424 | \n",
" 0.901224 | \n",
" 0.900640 | \n",
"
\n",
" \n",
" 0.100 | \n",
" 0.892122 | \n",
" 0.870795 | \n",
" 0.850399 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"elasticNetParam 0.0 0.5 1.0\n",
"regParam \n",
"0.001 0.905587 0.906075 0.906433\n",
"0.010 0.901424 0.901224 0.900640\n",
"0.100 0.892122 0.870795 0.850399"
]
},
"execution_count": 168,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid_score_param_df['areaUnderROC'].unstack()"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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DCAP53Jv3/MhkukuBdXYg9OIy2t3ntWHfCsZFREREpH12bejuHwDDCKNmnpg3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\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 277,
"width": 369
}
},
"output_type": "display_data"
}
],
"source": [
"# Compute the heat map\n",
"hmap = grid_score_param_df['areaUnderROC'].unstack()\n",
"\n",
"# Set up the matplotlib figure\n",
"plt.figure(figsize=(6,4))\n",
"plt.title('Heat Map of Grid Search Parameters')\n",
"sns.heatmap(hmap, square=False, annot=True, cmap='viridis', fmt='.4g', linewidths=1);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 12. Evaluate the best Logistic Regression Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Training Accuracy - Using Spark 2.3.0 enhancements:**"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cv_train_summary = best_log_reg_model.summary"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pyspark.ml.classification.BinaryLogisticRegressionTrainingSummary"
]
},
"execution_count": 171,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(cv_train_summary)"
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8108166211111453"
]
},
"execution_count": 172,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cv_train_summary.accuracy"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9077689352994321"
]
},
"execution_count": 173,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cv_train_summary.areaUnderROC"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.8651429572222953, 0.6831927586916272]"
]
},
"execution_count": 174,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cv_train_summary.fMeasureByLabel(beta=1.0)"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.9427903425899418, 0.5724381625441696]"
]
},
"execution_count": 175,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cv_train_summary.precisionByLabel"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.7993122977346279, 0.8470858308889172]"
]
},
"execution_count": 176,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cv_train_summary.recallByLabel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Confusion Matrix:**\n",
"\n",
"The Metrics classes have not been ported to `ml` package yet so we have to resort to RDD based `mllib` package."
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cv_train_preds = cvModel.transform(train_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Calculate Confusion Matrix Using MulticlassMetrics:**"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# we cache the predictions because we will be using it over and over again for several metrics calculation\n",
"cv_train_preds_labels = cv_train_preds.select('prediction', 'label').cache()"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"metrics = MulticlassMetrics(cv_train_preds_labels.rdd)\n",
"cv_train_cfm = metrics.confusionMatrix()"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[19759, 4961],\n",
" [ 1199, 6642]])"
]
},
"execution_count": 180,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cv_train_cfm.toArray().astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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qXv0sWA3z+5q1W8//vmYtmzO38NvqtTz63idcfupxDH38DkY9cy/3XHIWJBLc9sK7fJdP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\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 230,
"width": 260
}
},
"output_type": "display_data"
}
],
"source": [
"# using seaborn to plot confusion matrix\n",
"classes=[\"<=50K\",\">50K\"]\n",
"df_cm = pd.DataFrame(cv_train_cfm.toArray().astype(int), index=classes, columns=classes)\n",
"fig = plt.figure(figsize=(3,3))\n",
"ax = sns.heatmap(df_cm, annot=True, fmt=\"d\", cmap=plt.cm.Reds)\n",
"ax.yaxis.set_ticklabels(ax.yaxis.get_ticklabels(), rotation=0, ha='right')\n",
"ax.xaxis.set_ticklabels(ax.xaxis.get_ticklabels(), rotation=45, ha='right')\n",
"plt.xlabel('Predicted Values', )\n",
"plt.ylabel('Actual Values');"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# standard metrics\n",
"tn = cv_train_cfm[0, 0]\n",
"fp = cv_train_cfm[0, 1]\n",
"fn = cv_train_cfm[1, 0]\n",
"tp = cv_train_cfm[1, 1]"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(19759.0, 4961.0, 1199.0, 6642.0)"
]
},
"execution_count": 183,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tn, fp, fn, tp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also calculate the confusion matrix manually and validate our understanding with that values we got from `MulticlassMetrics`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Calculate Confusion Matrix Manually:**\n",
"\n",
"Below, we calculate some more metricse and validate our understanding with that values we got from MulticlassMetrics. The number of false/true positive and negative predictions is also useful:\n",
"\n",
"+ True positives are how often the model correctly predicted >50K.\n",
"+ False positives are how often the model incorrectly predicted >50K.\n",
"+ True negatives indicate how often the model correctly predicted <=50K.\n",
"+ False negatives indicate how often the model incorrectly predicted <=50K."
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tn = cv_train_preds_labels.filter(col('prediction') == 0.0).filter(col('label') == 0.0).count()"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fp = cv_train_preds_labels.filter(col('prediction') == 1.0).filter(col('label') == 0.0).count()"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fn = cv_train_preds_labels.filter(col('prediction') == 0.0).filter(col('label') == 1.0).count()"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tp = cv_train_preds_labels.filter(col('prediction') == 1.0).filter(col('label') == 1.0).count()"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(19759, 4961, 1199, 6642)"
]
},
"execution_count": 188,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Revalidate with MulticlassMetrics\n",
"tn, fp, fn, tp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 13. Test Predictions & Model Evaluation\n",
"\n",
"The actual performance of the model can be determined using the test data set that has not been used for any training or cross-validation activities.\n",
"\n",
"We transform the test DataFrame with the model pipeline, which will transform the features according to the pipeline, estimate and then return the label predictions in a column of a new DataFrame."
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pyspark.ml.tuning.CrossValidatorModel"
]
},
"execution_count": 189,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(cvModel)"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pyspark.ml.pipeline.PipelineModel"
]
},
"execution_count": 190,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(cvModel.bestModel)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We cannot use something like `test_summary = model.evaluate(test_df)` that we did in section 11 above. `cvModel` is an instance of `pyspark.ml.tuning.CrossValidatorModel` and the `cvModel.bestModel` is a an instance of `pyspark.ml.pipeline.PipelineModel`. They do not have an `evaluate` method. Quite likely so because they are designed to take an estimator and set of transformation stages; the transformation stages are applied on the raw input and then the estimator is called to evaluate on the transformed features."
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pyspark.ml.classification.LogisticRegressionModel"
]
},
"execution_count": 191,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(cvModel.bestModel.stages[-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Although `cvModel.bestModel.stages[-1])` is an instance of `pyspark.ml.classification.LogisticRegressionModel`, we cannot use something like `cvModel.bestModel.stages[-1]).evaluate(test_df)` either because the `test_df` need to be transformed first through the pipeline before it can be evaluated by the `cvModel.bestModel.stages[-1]` or the `LogisticRegressionModel`.\n",
"\n",
"Hence, we have to rely on the `cvModel.transform(test_df)` to generate our test predictions. We will later see another smarter way to generate the test predictions instead of the `cvModel.transform(test_df)`\n",
"\n",
"Once the `CrossValidatorModel` has already been fitted calling the `transform` method on it will call the transformation pipeline internally and finally call the transform method of the `best estimator` which is at the last stage of the pipeline."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 13.1 Generate Test Predictions:"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Make predictions on test set. cvModel uses the best model found (logregModel).\n",
"cv_test_preds = cvModel.transform(test_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Test Accuracy:**"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.904629220150728"
]
},
"execution_count": 193,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cv_test_areaUnderROC = evaluator.evaluate(cv_test_preds)\n",
"cv_test_areaUnderROC"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The evaluator will provide us with the score of the predictions. Quality of the predictions is measured by the area under the ROC curve. The area measures the ability of the test to correctly classify true positives from false positives. A random predictor would have .5 accuracy. The closer the value is to 1 the better its predictions are. In this case, the evaluation returns `roc_auc` of 0.9."
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# we cache the predictions because we will be using it over and over again for several metrics calculation\n",
"cv_test_preds_labels = cv_test_preds.select('prediction', 'label').cache()"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"metrics = MulticlassMetrics(cv_test_preds_labels.rdd)\n",
"cv_test_cfm = metrics.confusionMatrix()"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[9903, 2532],\n",
" [ 600, 3246]])"
]
},
"execution_count": 196,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cv_test_cfm.toArray().astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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m74lB3d3fAz4jGBboUuIPmoQCvoiIZJIjCAL420COmR1jZjeZ2TVmlmxzt8PC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\nvoiISB1QwBcREakD/wWHKRbCnf75ZQAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 230,
"width": 254
}
},
"output_type": "display_data"
}
],
"source": [
"# using seaborn to plot confusion matrix\n",
"classes=[\"<=50K\",\">50K\"]\n",
"df_cm = pd.DataFrame(cv_test_cfm.toArray().astype(int), index=classes, columns=classes)\n",
"fig = plt.figure(figsize=(3,3))\n",
"ax = sns.heatmap(df_cm, annot=True, fmt=\"d\")\n",
"ax.yaxis.set_ticklabels(ax.yaxis.get_ticklabels(), rotation=0, ha='right')\n",
"ax.xaxis.set_ticklabels(ax.xaxis.get_ticklabels(), rotation=45, ha='right')\n",
"plt.xlabel('Predicted Values', )\n",
"plt.ylabel('Actual Values');"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tn = cv_test_cfm[0, 0]\n",
"fp = cv_test_cfm[0, 1]\n",
"fn = cv_test_cfm[1, 0]\n",
"tp = cv_test_cfm[1, 1]"
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(9903.0, 2532.0, 600.0, 3246.0)"
]
},
"execution_count": 199,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tn, fp, fn, tp"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8076285240464345"
]
},
"execution_count": 200,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"metrics.accuracy"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.8634580172639289, 0.674563591022444]"
]
},
"execution_count": 201,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Equivalent to Spark 2.3.0 train_summary.fMeasureByLabel(beta=1.0)\n",
"[metrics.fMeasure(label=0.0, beta=1.0), metrics.fMeasure(label=1.0, beta=1.0)]"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.9428734647243645, 0.5617860851505712]"
]
},
"execution_count": 202,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Equivalent to Spark 2.3.0 train_summary.precisionByLabel\n",
"[metrics.precision(label=0.0), metrics.precision(label=1.0)]"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.7963811821471652, 0.84399375975039]"
]
},
"execution_count": 203,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Equivalent to Spark 2.3.0 train_summary.recallByLabel\n",
"[metrics.recall(label=0.0), metrics.recall(label=1.0)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 13.2 Creating Partial Pipelines"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Using SMART HACKS:**\n",
"\n",
"Instead of going through the `cvModel.transform(adult_test_df)` and subsequently using the evaluator we can be quite creative and smarter to build a `partialPipeline` using our existing pipeline stages. This is similar to chopping off a chunk of the terminal neural network layers as we do in `transfer learning`.\n",
"\n",
"We create a partialPipeline reusing our all the existing Pipeline stages except the last one i.e. we do not include the final LogisticRegression estimator in the pipeline."
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"best_log_reg_model = cvModel.bestModel.stages[-1]"
]
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pyspark.ml.classification.LogisticRegressionModel"
]
},
"execution_count": 205,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(best_log_reg_model)"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# chop off the estimator from the pipeline\n",
"last_stage = cvModel.bestModel.stages.pop()"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pyspark.ml.classification.LogisticRegressionModel"
]
},
"execution_count": 207,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(last_stage)"
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[StringIndexer_492880a800b95ea8119e,\n",
" StringIndexer_464080636b53dfa4e597,\n",
" StringIndexer_424b81a8c7a59e5f5aba,\n",
" StringIndexer_45e39b2235e6957f74d7,\n",
" StringIndexer_4a8b8d8e9d52eb2aa17d,\n",
" StringIndexer_40ee835b624f551c2ee1,\n",
" StringIndexer_41398d95da8dc65bd9c8,\n",
" OneHotEncoder_4973918c918af3986164,\n",
" OneHotEncoder_4ecaa69d59f4695b2ba8,\n",
" OneHotEncoder_40d4958687354e951eb7,\n",
" OneHotEncoder_444195b4f6d6f3fa5d51,\n",
" OneHotEncoder_413c976fbef74af7ecdf,\n",
" OneHotEncoder_4836bed3a623d4ad42c7,\n",
" OneHotEncoder_437384bdd331db933a39,\n",
" VectorAssembler_45f197169aec013291b7,\n",
" StandardScaler_4d528f4f1c75958f9bc5,\n",
" StringIndexer_4083a3b050fd3ad394a6,\n",
" VectorAssembler_4ed58472c709978bc1cd]"
]
},
"execution_count": 208,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check the stages after chopping off the last layer\n",
"cvModel.bestModel.stages"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Transform test data using the Partial Pipeline:**\n",
"\n",
"Once the partial Pipeline is setup we use it to transform the test DataFrame and then feed the transformed data to our `best LogisticRegression` estimator."
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# use the partial pipeline to transform the test set\n",
"prepped_test_df = cvModel.bestModel.transform(test_df)"
]
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['age',\n",
" 'workclass',\n",
" 'fnlgwt',\n",
" 'education_num',\n",
" 'marital_status',\n",
" 'occupation',\n",
" 'relationship',\n",
" 'race',\n",
" 'sex',\n",
" 'capital_gain',\n",
" 'capital_loss',\n",
" 'hours_per_week',\n",
" 'native_country',\n",
" 'income',\n",
" 'workclass_indexed',\n",
" 'marital_status_indexed',\n",
" 'occupation_indexed',\n",
" 'relationship_indexed',\n",
" 'race_indexed',\n",
" 'sex_indexed',\n",
" 'native_country_indexed',\n",
" 'workclass_encoded',\n",
" 'marital_status_encoded',\n",
" 'occupation_encoded',\n",
" 'relationship_encoded',\n",
" 'race_encoded',\n",
" 'sex_encoded',\n",
" 'native_country_encoded',\n",
" 'numerical_features',\n",
" 'numerical_features_scaled',\n",
" 'label',\n",
" 'features']"
]
},
"execution_count": 210,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prepped_test_df.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Pipeline has been successfully applied on the adult_test_df and the new columns `features` and `label` have been generated from the transformed feature columns `*_encoded` + `numerical_features_scaled`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Use the best model for prediction on the transformed test DataFrame:**"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# evaluate the test set now using exclusively the last stage of the original pipeline\n",
"#cv_test_summary = best_log_reg_model.evaluate(prepped_test_df) # this works same as line below\n",
"cv_test_summary = last_stage.evaluate(prepped_test_df)"
]
},
{
"cell_type": "code",
"execution_count": 212,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pyspark.ml.classification.BinaryLogisticRegressionSummary"
]
},
"execution_count": 212,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(cv_test_summary)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compare with the scenario when we were training with `cross-validation`. The `train_summary` was an instance of `BinaryLogisticRegressionTrainingSummary` and now we are getting `BinaryLogisticRegressionSummary`.\n",
"\n",
"For more clarity refer to: \n",
"http://spark.apache.org/docs/2.3.0/api/python/pyspark.ml.html#pyspark.ml.classification.BinaryLogisticRegressionSummary \n",
"http://spark.apache.org/docs/2.3.0/api/python/pyspark.ml.html#pyspark.ml.classification.BinaryLogisticRegressionTrainingSummary"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" **Test Accuracy and Spark 2.3.0 enhancements:**"
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9045944161851718"
]
},
"execution_count": 213,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cv_test_summary.areaUnderROC"
]
},
{
"cell_type": "code",
"execution_count": 214,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.8634580172639289, 0.674563591022444]"
]
},
"execution_count": 214,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cv_test_summary.fMeasureByLabel(beta=1.0)"
]
},
{
"cell_type": "code",
"execution_count": 215,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.9428734647243645, 0.5617860851505712]"
]
},
"execution_count": 215,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cv_test_summary.precisionByLabel"
]
},
{
"cell_type": "code",
"execution_count": 216,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.7963811821471652, 0.84399375975039]"
]
},
"execution_count": 216,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cv_test_summary.recallByLabel"
]
},
{
"cell_type": "code",
"execution_count": 217,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" FPR | \n",
" TPR | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 1 | \n",
" 0.000080 | \n",
" 0.039782 | \n",
"
\n",
" \n",
" 2 | \n",
" 0.000161 | \n",
" 0.078523 | \n",
"
\n",
" \n",
" 3 | \n",
" 0.000885 | \n",
" 0.115445 | \n",
"
\n",
" \n",
" 4 | \n",
" 0.001769 | \n",
" 0.151586 | \n",
"
\n",
" \n",
" 5 | \n",
" 0.003619 | \n",
" 0.185127 | \n",
"
\n",
" \n",
" 6 | \n",
" 0.005871 | \n",
" 0.217889 | \n",
"
\n",
" \n",
" 7 | \n",
" 0.007479 | \n",
" 0.254030 | \n",
"
\n",
" \n",
" 8 | \n",
" 0.010696 | \n",
" 0.284191 | \n",
"
\n",
" \n",
" 9 | \n",
" 0.013510 | \n",
" 0.318253 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" FPR TPR\n",
"0 0.000000 0.000000\n",
"1 0.000080 0.039782\n",
"2 0.000161 0.078523\n",
"3 0.000885 0.115445\n",
"4 0.001769 0.151586\n",
"5 0.003619 0.185127\n",
"6 0.005871 0.217889\n",
"7 0.007479 0.254030\n",
"8 0.010696 0.284191\n",
"9 0.013510 0.318253"
]
},
"execution_count": 217,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cv_test_summary.roc.limit(10).toPandas()"
]
},
{
"cell_type": "code",
"execution_count": 218,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cv_train_roc_pdf = cv_train_summary.roc.toPandas() # we got this while we trained cv\n",
"cv_test_roc_pdf = cv_test_summary.roc.toPandas()"
]
},
{
"cell_type": "code",
"execution_count": 219,
"metadata": {},
"outputs": [
{
"data": {
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A523bbuyPekVEREREUlV/9aD/JfAJ3IB+9nYbMcZMBqqADwHbgC8Dx4BPA5uNMQV3XqqI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\nAF1EREREJID8f9JNWTa2PZj5AAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 263,
"width": 372
}
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(6,4))\n",
"plt.plot(cv_train_roc_pdf['FPR'], cv_train_roc_pdf['TPR'], lw=1, label='Train AUC = %0.2f' % (cv_train_summary.areaUnderROC))\n",
"plt.plot(cv_test_roc_pdf['FPR'], cv_test_roc_pdf['TPR'], lw=1, label='Test AUC = %0.2f' % (cv_test_summary.areaUnderROC))\n",
"plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='NULL Accuracy')\n",
"plt.title('ROC AUC Curve')\n",
"plt.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 14. Save & Load the Model\n",
"\n",
"We can now save our fitted Pipeline for later use with streaming events. This saves both the feature extraction stage and the best logistic regression model chosen by model tuning.\n",
"\n",
"Before we save the Pipeline we need to add back the last layer that we chopped off.\n",
"\n",
"### 14.1 Save the Model to the file system for later use"
]
},
{
"cell_type": "code",
"execution_count": 220,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pyspark.ml.pipeline.PipelineModel"
]
},
"execution_count": 220,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(cvModel.bestModel)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Add the Chopped off Last Layer to the Pipeline again:**"
]
},
{
"cell_type": "code",
"execution_count": 221,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cvModel.bestModel.stages.append(last_stage)"
]
},
{
"cell_type": "code",
"execution_count": 222,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression_41a2ada033060e643027"
]
},
"execution_count": 222,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cvModel.bestModel.stages[-1]"
]
},
{
"cell_type": "code",
"execution_count": 223,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"MODEL_PATH = \"model/census_pipeline_model\""
]
},
{
"cell_type": "code",
"execution_count": 224,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cvModel.bestModel.write().overwrite().save(MODEL_PATH)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The result of saving the pipeline model is a JSON file for metadata and a Parquet for model data. We can re-load the model with the load command; the original and the re-loaded models should be same (we will check that too):\n",
"\n",
"![s](assets/model-diretory-structure.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 14.2 Reload the Model from the file system for use in a different context"
]
},
{
"cell_type": "code",
"execution_count": 225,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"same_pipeline_model = PipelineModel.load(MODEL_PATH)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Re-check with our best logistic regression model and this deserialized model's last stage to be same or not:**"
]
},
{
"cell_type": "code",
"execution_count": 226,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'regParam: regularization parameter (>= 0) (current: 0.001)'"
]
},
"execution_count": 226,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"'elasticNetParam: the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty (current: 1.0)'"
]
},
"execution_count": 226,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"'maxIter: maximum number of iterations (>= 0) (current: 20)'"
]
},
"execution_count": 226,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"'tol: the convergence tolerance for iterative algorithms (>= 0) (current: 1e-06)'"
]
},
"execution_count": 226,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.\n",
"for param in ['regParam', 'elasticNetParam', 'maxIter', 'tol']:\n",
" same_pipeline_model.stages[-1].explainParam(param)"
]
},
{
"cell_type": "code",
"execution_count": 227,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'regParam: regularization parameter (>= 0) (default: 0.0, current: 0.001)'"
]
},
"execution_count": 227,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"'elasticNetParam: the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty (default: 0.0, current: 1.0)'"
]
},
"execution_count": 227,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"'maxIter: maximum number of iterations (>= 0) (default: 100, current: 20)'"
]
},
"execution_count": 227,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"'tol: the convergence tolerance for iterative algorithms (>= 0) (default: 1e-06)'"
]
},
"execution_count": 227,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.\n",
"for param in ['regParam', 'elasticNetParam', 'maxIter', 'tol']:\n",
" best_log_reg_model.explainParam(param)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> The `'regParam'`, `elasticNetParam` and `'maxIter'` were provided by us and we see they are the same as the best model that resulted out of cross validation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Generate Predictions from the deserialized model:**"
]
},
{
"cell_type": "code",
"execution_count": 228,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"same_test_preds = same_pipeline_model.transform(test_df)"
]
},
{
"cell_type": "code",
"execution_count": 229,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"binary_classif_evaluator = BinaryClassificationEvaluator(labelCol=\"label\", metricName=\"areaUnderROC\")"
]
},
{
"cell_type": "code",
"execution_count": 230,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"areaUnderROC: 0.904629220150728\n"
]
}
],
"source": [
"areaUnderROC = binary_classif_evaluator.evaluate(same_test_preds)\n",
"print(\"areaUnderROC: {0}\".format(areaUnderROC))"
]
},
{
"cell_type": "code",
"execution_count": 231,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"spark.stop()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## End Notes:\n",
"\n",
"In this notebook, we tried to predict a person's income is above or below $50K/yr based on given features such as workclass, number of years of education, occupation, relationship, marital status, hours worked per week, race, sex etc. Seems like a simple Binary Classification task in scikit-learn and pandas; but the catch is here we did that entirely in PySpark and built a comprehensive near real-life or production grade Machine Learning worklow.\n",
"\n",
"We used a Logistic Regression here. The goal of the notebook was not to get too fancy with the choice of the ML Algorithms but it was more on how can you achieve or at least try to achieve what you could do using scikit-learn and pandas. This work covered - EDA, custom udf, cleaning, basic missing values treatment, data variance per feature, stratified sampling (custom implementation), class weights for imbalanced class distribution (custom code), onehotencoding, standard scaling, vector assembling, label encoding, grid search with cross validation, pipeline, partial pipelines (custom code), binary and multi class evaluators and new metrics introduced in Spark 2.3.0, auc_roc, roc curves, model serialization and deserialization.\n",
"\n",
"We also saw despite the inability to plot visualizations using Spark how can we leverage the `toPandas()` method to convert the aggregated DataFrames or results into Pandas DataFrame for visualization and analysis. \n",
"\n",
"Going forward you can try other classification models. Also, try to convert this notebook into Scala, that would give you more exposure. In Scala, there is an `UnaryTransformer` class that you can use to generate the custom `class-weight` or `logarithm` of the `age` feature and plug that as a step into the `Pipeline`. In Python, its a bit tricky to do that because in order to use that we have to develop the code for the `UnaryTransformer` into Java or Scala first and then include that in the class path and then use that in Python."
]
}
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