Logistic Regression using PySpark Python

In this tutorial series, we are going to cover Logistic Regression using Pyspark. Logistic Regression is one of the basic ways to perform classification (don’t be confused by the word “regression”). Logistic Regression is a classification method. Some examples of classification are:

• Spam detection
• Disease Diagnosis

Loading Dataframe

We will be using the data for Titanic where I have columns PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, and Embarked. We have to predict whether the passenger will survive or not using the Logistic Regression machine learning model. To get started, open a new notebook and follow the steps mentioned in the below code:

Python3

# Starting the Spark Session
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('Titanic').getOrCreate()

# Reading the data
df = spark.read.csv('Titanic.csv',inferSchema=True, header=True)

# Showing the data
df.show()

Output:

Showing the data

df.printSchema()

Schema of the data

df.columns

Columns in the data

Removing NULL Values Columns

The next step includes removing the data having null values as shown in the above picture. We do not need the columns PassengerId, Name, Ticket, and Cabin as they are not required to train and test the model.

Python3

# Selecting the columns which are required
# to train and test the model.
rm_columns = df.select(['Survived','Pclass',
                       'Sex','Age','SibSp',
                       'Parch','Fare','Embarked'])

# Drops the data having null values
result = rm_columns.na.drop()

# Again showing the data
result.show()

Output:

Convert String Column to Ordinal Columns 

The next task is to convert the string columns (Sex and Embarked) to integral columns as without doing this, we cannot vectorize the data using VectorAssembler. 

Python3

# Importing the required libraries
from pyspark.ml.feature import VectorAssembler, StringIndexer, OneHotEncoder

# Converting the Sex Column
sexIdx = StringIndexer(inputCol='Sex',
                               outputCol='SexIndex')
sexEncode = OneHotEncoder(inputCol='SexIndex',
                               outputCol='SexVec')

# Converting the Embarked Column
embarkIdx = StringIndexer(inputCol='Embarked',
                               outputCol='EmbarkIndex')
embarkEncode = OneHotEncoder(inputCol='EmbarkIndex',
                               outputCol='EmbarkVec')

# Vectorizing the data into a new column "features"
# which will be our input/features class
assembler = VectorAssembler(inputCols=['Pclass',
                                       'SexVec','Age',
                                       'SibSp','Parch',
                                       'Fare','EmbarkVec'],
                                    outputCol='features')

Now we need Pipeline to stack the tasks one by one and import and call the Logistic Regression Model.

Python3

# Importing Pipeline and Model
from pyspark.ml import Pipeline
from pyspark.ml.classification import LogisticRegression

log_reg = LogisticRegression(featuresCol='features',
                             labelCol='Survived')

# Creating the pipeline
pipe = Pipeline(stages=[sexIdx, embarkIdx,
                            sexEncode, embarkEncode,
                            assembler, log_reg])

After pipelining the tasks, we will split the data into training data and testing data to train and test the model.

Python3

# Splitting the data into train and test
train_data, test_data = my_final_data.randomSplit([0.7, .3])

# Fitting the model on training data
fit_model = pipeline.fit(train_data)

# Storing the results on test data
results = fit_model.transform(test_data)

# Showing the results
results.show()

Output:

data.show()

Model evaluation using ROC-AUC

The results will add extra columns rawPrediction, probability, and prediction because we are transforming the results on our data. After getting the results, we will now find the AUC(Area under the ROC Curve) which will give the efficiency of the model. For this, we will use BinaryClassificationEvaluator as shown:

Python3

# Importing the evaluator
from pyspark.ml.evaluation import BinaryClassificationEvaluator

# Calling the evaluator
res = BinaryClassificationEvaluator
        (rawPredictionCol='prediction',labelCol='Survived')

# Evaluating the AUC on results
ROC_AUC = res.evaluate(results)

Output:

Note: In general, an AUC value above 0.7 is considered good, but it’s important to compare the value to the expected performance of the problem and the data to determine if it’s actually good.

ROC_AUC