#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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#

from __future__ import print_function

from pyspark import SparkContext
from pyspark.sql import SQLContext
# $example on$
from pyspark.ml.classification import MultilayerPerceptronClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
# $example off$

if __name__ == "__main__":

    sc = SparkContext(appName="multilayer_perceptron_classification_example")
    sqlContext = SQLContext(sc)

    # $example on$
    # Load training data
    data = sqlContext.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 precision on the test set
    result = model.transform(test)
    predictionAndLabels = result.select("prediction", "label")
    evaluator = MulticlassClassificationEvaluator(metricName="precision")
    print("Precision:" + str(evaluator.evaluate(predictionAndLabels)))
    # $example off$

    sc.stop()