spark-gpu-master/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java
/*
 * 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
 * limitations under the License.
 */

package org.apache.spark.examples.ml;

// $example on$
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.SQLContext;
import org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel;
import org.apache.spark.ml.classification.MultilayerPerceptronClassifier;
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
import org.apache.spark.sql.DataFrame;
// $example off$

/**
 * An example for Multilayer Perceptron Classification.
 */
public class JavaMultilayerPerceptronClassifierExample {

  public static void main(String[] args) {
    SparkConf conf = new SparkConf().setAppName("JavaMultilayerPerceptronClassifierExample");
    JavaSparkContext jsc = new JavaSparkContext(conf);
    SQLContext jsql = new SQLContext(jsc);

    // $example on$
    // Load training data
    String path = "data/mllib/sample_multiclass_classification_data.txt";
    DataFrame dataFrame = jsql.read().format("libsvm").load(path);
    // Split the data into train and test
    DataFrame[] splits = dataFrame.randomSplit(new double[]{0.6, 0.4}, 1234L);
    DataFrame train = splits[0];
    DataFrame 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)
    int[] layers = new int[] {4, 5, 4, 3};
    // create the trainer and set its parameters
    MultilayerPerceptronClassifier trainer = new MultilayerPerceptronClassifier()
      .setLayers(layers)
      .setBlockSize(128)
      .setSeed(1234L)
      .setMaxIter(100);
    // train the model
    MultilayerPerceptronClassificationModel model = trainer.fit(train);
    // compute precision on the test set
    DataFrame result = model.transform(test);
    DataFrame predictionAndLabels = result.select("prediction", "label");
    MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
      .setMetricName("precision");
    System.out.println("Precision = " + evaluator.evaluate(predictionAndLabels));
    // $example off$

    jsc.stop();
  }
}
