This section provides Python for Spark scripting examples.

import spss.pyspark.runtime
from pyspark.sql.types import *

cxt = spss.pyspark.runtime.getContext() 

if  cxt.isComputeDataModelOnly():   
        _schema = cxt.getSparkInputSchema()   
        cxt.setSparkOutputSchema(_schema)
else:   
        _structType = cxt.getSparkInputSchema()
        df = cxt.getSparkInputData()   
        _newDF = df.sample(False, 0.01, 1)
        cxt.setSparkOutputData(_newDF)

from pyspark.context import SparkContext
from pyspark.sql.context import SQLContext
from pyspark.sql import Row
from pyspark.mllib.regression import
LabeledPoint,LinearRegressionWithSGD, LinearRegressionModel
from pyspark.mllib.linalg import DenseVector
import numpy
import json

import spss.pyspark.runtime
from spss.pyspark.exceptions import ASContextException

ascontext = spss.pyspark.runtime.getContext()
sc = ascontext.getSparkContext()
df = ascontext.getSparkInputData()

# field settings and algorithm parameters
# replace target_field, predictor_fields, and num iterations with your actual values!

target = #'target_field'
predictors = [#predictor_fields]
num_iterations = #num iterations
prediction_field = "$LR-" + target

# save linear regression model to a filesystem path

def save(model, sc, path):
        data =
sc.parallelize([json.dumps({"intercept":model.intercept,"weights":model.weights.tolist()})])
        data.saveAsTextFile(path)

# print model details to stdout

def dump(model,predictors):   
        print(prediction_field+" = " + str(model.intercept))   
        weights = model.weights.tolist()
        for i in range(0,len(predictors)):        
                print("\t+ "+predictors[i]+"*"+ str(weights[i]))

# check that required fields exist in the input data

input_field_names = [ty[0] for ty in df.dtypes[:]]
if target not in input_field_names:
        raise ASContextException("target field "+target+" not found") for predictor in predictors:
        if predictor not in input_field_names:        
                raise ASContextException("predictor field "+predictor+" not found")

# define map function to convert from dataframe Row objects to mllib LabeledPoint 

def row2LabeledPoint(target,predictors,row):
        pvals = []
        for predictor in predictors:        
                pval = getattr(row,predictor)        
                pvals.append(float(pval))
        tval = getattr(row,target)   
        return LabeledPoint(float(tval),DenseVector(pvals))

# convert dataframe to an RDD containing LabeledPoint

training_points = df.rdd.map(lambda row:
row2LabeledPoint(target,predictors,row))

# build the model 

model = LinearRegressionWithSGD.train(training_points,num_iterations,intercept=True) 

# write a text description of the model to stdout

dump(model,predictors)

# save the model to the filesystem and store into the output model content

modelpath = ascontext.createTemporaryFolder()
save(model,sc,modelpath)
ascontext.setModelContentFromPath("model",modelpath)