Pyspark – رگرسیون خطی با استفاده از آپاچی MLlib

import pyspark 
from pyspark.sql import SparkSession 
#SparkSession is now the entry point of Spark 
#SparkSession can also be construed as gateway to spark libraries 

#create instance of spark class 
spark=SparkSession.builder.appName('housing_price_model').getOrCreate() 

#create spark dataframe of input csv file 
df=spark.read.csv('D:\python coding\pyspark_tutorial\Linear regression\cruise_ship_info.csv'
				,inferSchema=True,header=True) 
df.show(10) 

#prints structure of dataframe along with datatype 
df.printSchema() 

#In our predictive model, below are the columns 
df.columns 

#columns identified as features are as below: 
#['Cruise_line','Age','Tonnage','passengers','length','cabins','passenger_density'] 
#to work on the features, spark MLlib expects every value to be in numeric form 
#feature 'Cruise_line is string datatype 
#using StringIndexer, string type will be typecast to numeric datatype 
#import library strinindexer for typecasting 

from pyspark.ml.feature import StringIndexer 
indexer=StringIndexer(inputCol='Cruise_line',outputCol='cruise_cat') 
indexed=indexer.fit(df).transform(df) 

#above code will convert string to numeric feature and create a new dataframe 
#new dataframe contains a new feature 'cruise_cat' and can be used further 
#feature cruise_cat is now vectorized and can be used to fed to model 
for item in indexed.head(5): 
	print(item) 
	print('\n') 

from pyspark.ml.linalg import Vectors 
from pyspark.ml.feature import VectorAssembler 
#creating vectors from features 
#Apache MLlib takes input if vector form 
assembler=VectorAssembler(inputCols=['Age', 
'Tonnage', 
'passengers', 
'length', 
'cabins', 
'passenger_density', 
'cruise_cat'],outputCol='features') 
output=assembler.transform(indexed) 
output.select('features','crew').show(5) 
#output as below 

#final data consist of features and label which is crew. 
final_data=output.select('features','crew') 
#splitting data into train and test 
train_data,test_data=final_data.randomSplit([0.7,0.3]) 
train_data.describe().show() 

test_data.describe().show() 

#import LinearRegression library 
from pyspark.ml.regression import LinearRegression 
#creating an object of class LinearRegression 
#object takes features and label as input arguments 
ship_lr=LinearRegression(featuresCol='features',labelCol='crew') 
#pass train_data to train model 
trained_ship_model=ship_lr.fit(train_data) 
#evaluating model trained for Rsquared error 
ship_results=trained_ship_model.evaluate(train_data) 

print('Rsquared Error :',ship_results.r2) 
#R2 value shows accuracy of model is 92% 
#model accuracy is very good and can be use for predictive analysis 

#testing Model on unlabeled data 
#create unlabeled data from test_data 
#testing model on unlabeled data 
unlabeled_data=test_data.select('features') 
unlabeled_data.show(5) 

predictions=trained_ship_model.transform(unlabeled_data) 
predictions.show() 
#below are the results of output from test data