examples/src/main/python/ml/multilayer_perceptron_classification.py

#
# Licensed to the Apache Software Foundation (ASF) under one or more
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# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
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#    http://www.apache.org/licenses/LICENSE-2.0
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# $example on$
from pyspark.ml.classification import MultilayerPerceptronClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
# $example off$
from pyspark.sql import SparkSession

if __name__ == "__main__":
    spark = SparkSession\
        .builder.appName("multilayer_perceptron_classification_example").getOrCreate()

    # $example on$
    # Load training data
    data = spark.read.format("libsvm")\
        .load("data/mllib/sample_multiclass_classification_data.txt")

    # Split the data into train and test
    splits = data.randomSplit([0.6, 0.4], 1234)
    train = splits[0]
    test = splits[1]

    # specify layers for the neural network:
    # input layer of size 4 (features), two intermediate of size 5 and 4
    # and output of size 3 (classes)
    layers = [4, 5, 4, 3]

    # create the trainer and set its parameters
    trainer = MultilayerPerceptronClassifier(maxIter=100, layers=layers, blockSize=128, seed=1234)

    # train the model
    model = trainer.fit(train)

    # compute accuracy on the test set
    result = model.transform(test)
    predictionAndLabels = result.select("prediction", "label")
    evaluator = MulticlassClassificationEvaluator(metricName="accuracy")
    print("Test set accuracy = " + str(evaluator.evaluate(predictionAndLabels)))
    # $example off$

    spark.stop()