<reponame>ptenteromano/Machine-Learning<filename>q3_adults.py<gh_stars>1-10
# <NAME>
# <NAME>
# <NAME>
# <NAME>

# 5/5/2019
# Big Data Programming

# Project 2 - Q3
# Logistic Regression

from sys import argv
from pyspark.sql import SparkSession
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.feature import OneHotEncoderEstimator, StringIndexer, OneHotEncoderModel
from pyspark.ml.feature import OneHotEncoder
from pyspark.ml import Pipeline

# set up the session 
spark = SparkSession\
    .builder\
    .appName("KMeansExample")\
    .getOrCreate()

# Taken from .names description file
col_names = ['Age','Workclass','FinalWeight','Education','EducationNum','MaritalStatus','Occupation','Relationship','Race',
         'Sex','CapitalGain','CapitalLoss','HoursPerWeek','NativeCountry','income']

# store file into a pyspark dataframe
df = spark.read.format("csv").load(argv[1], header='false', names=col_names, inferSchema="true")
df = df.toDF(*col_names)

# EducationNum works better than categorical 'Education'
df = df.drop('Education')
cols = df.columns

catColumns = [ item[0] for item in df.dtypes if item[1].startswith('string') ]
catColumns = [ c for c in catColumns if 'income' not in c ]

numColumns = [ item[0] for item in df.dtypes if not item[1].startswith('string') ]
catColVectors = [c + '_vector' for c in catColumns ]

# Change categorical values into numeric
indexers = [ StringIndexer(inputCol=column, outputCol=column+"_index") for column in catColumns ]
encoder = OneHotEncoderEstimator(
    inputCols=[c + "_index" for c in catColumns],
    outputCols=[c + "_vector" for c in catColumns]
    )

assembler = VectorAssembler(
    inputCols=encoder.getOutputCols() + numColumns,
    outputCol="features"
    )

label_stringIdx = StringIndexer(inputCol="income", outputCol="label")

pipeline = Pipeline(stages=indexers + [label_stringIdx, encoder, assembler])
encoded_df = pipeline.fit(df).transform(df)

selectedCols = ['label', 'features'] + cols
dataset = encoded_df.select(selectedCols)

# Randomly split data into training and test sets. set seed for reproducibility
(trainingData, testData) = dataset.randomSplit([0.7, 0.3], seed=100)
print(trainingData.count())
print(testData.count())

# fit model and train
lrModel = LogisticRegression().fit(encoded_df)
lr = LogisticRegression(labelCol="label", featuresCol="features", maxIter=10)
lrModel = lr.fit(trainingData)
predictions = lrModel.transform(testData)

trainingSummary = lrModel.summary

print("\n\tShowing Logistic Regression Results: \n")

accuracy = trainingSummary.accuracy
falsePositiveRate = trainingSummary.weightedFalsePositiveRate
truePositiveRate = trainingSummary.weightedTruePositiveRate
fMeasure = trainingSummary.weightedFMeasure()
precision = trainingSummary.weightedPrecision
recall = trainingSummary.weightedRecall
print("\tAccuracy: %s\n\tFalse PosRate: %s\n\tTrue PosRate: %s\n\tF-measure: %s\n\tPrecision: %s\n\tRecall: %s"
      % (accuracy, falsePositiveRate, truePositiveRate, fMeasure, precision, recall))



print('\nDone')

# <NAME>
# <NAME>
# <NAME>
# <NAME>

# 5/5/2019
# Big Data Programming

# Project 2 - Q3
# Logistic Regression

from sys import argv
from pyspark.sql import SparkSession
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.feature import OneHotEncoderEstimator, StringIndexer, OneHotEncoderModel
from pyspark.ml.feature import OneHotEncoder
from pyspark.ml import Pipeline

# set up the session 
spark = SparkSession\
    .builder\
    .appName("KMeansExample")\
    .getOrCreate()

# Taken from .names description file
col_names = ['Age','Workclass','FinalWeight','Education','EducationNum','MaritalStatus','Occupation','Relationship','Race',
         'Sex','CapitalGain','CapitalLoss','HoursPerWeek','NativeCountry','income']

# store file into a pyspark dataframe
df = spark.read.format("csv").load(argv[1], header='false', names=col_names, inferSchema="true")
df = df.toDF(*col_names)

# EducationNum works better than categorical 'Education'
df = df.drop('Education')
cols = df.columns

catColumns = [ item[0] for item in df.dtypes if item[1].startswith('string') ]
catColumns = [ c for c in catColumns if 'income' not in c ]

numColumns = [ item[0] for item in df.dtypes if not item[1].startswith('string') ]
catColVectors = [c + '_vector' for c in catColumns ]

# Change categorical values into numeric
indexers = [ StringIndexer(inputCol=column, outputCol=column+"_index") for column in catColumns ]
encoder = OneHotEncoderEstimator(
    inputCols=[c + "_index" for c in catColumns],
    outputCols=[c + "_vector" for c in catColumns]
    )

assembler = VectorAssembler(
    inputCols=encoder.getOutputCols() + numColumns,
    outputCol="features"
    )

label_stringIdx = StringIndexer(inputCol="income", outputCol="label")

pipeline = Pipeline(stages=indexers + [label_stringIdx, encoder, assembler])
encoded_df = pipeline.fit(df).transform(df)

selectedCols = ['label', 'features'] + cols
dataset = encoded_df.select(selectedCols)

# Randomly split data into training and test sets. set seed for reproducibility
(trainingData, testData) = dataset.randomSplit([0.7, 0.3], seed=100)
print(trainingData.count())
print(testData.count())

# fit model and train
lrModel = LogisticRegression().fit(encoded_df)
lr = LogisticRegression(labelCol="label", featuresCol="features", maxIter=10)
lrModel = lr.fit(trainingData)
predictions = lrModel.transform(testData)

trainingSummary = lrModel.summary

print("\n\tShowing Logistic Regression Results: \n")

accuracy = trainingSummary.accuracy
falsePositiveRate = trainingSummary.weightedFalsePositiveRate
truePositiveRate = trainingSummary.weightedTruePositiveRate
fMeasure = trainingSummary.weightedFMeasure()
precision = trainingSummary.weightedPrecision
recall = trainingSummary.weightedRecall
print("\tAccuracy: %s\n\tFalse PosRate: %s\n\tTrue PosRate: %s\n\tF-measure: %s\n\tPrecision: %s\n\tRecall: %s"
      % (accuracy, falsePositiveRate, truePositiveRate, fMeasure, precision, recall))



print('\nDone')
# <NAME> # <NAME> # <NAME> # <NAME> # 5/5/2019 # Big Data Programming # Project 2 - Q3 # Logistic Regression # set up the session # Taken from .names description file # store file into a pyspark dataframe # EducationNum works better than categorical 'Education' # Change categorical values into numeric # Randomly split data into training and test sets. set seed for reproducibility # fit model and train