[SPARK-11629][ML][PYSPARK][DOC] Python example code for Multilayer Perceptron Classification

Add Python example code for Multilayer Perceptron Classification, and make example code in user guide document testable. mengxr

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #9594 from yanboliang/spark-11629.

1 files changed, 74 insertions, 0 deletions

diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java
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+/*
+ * 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.api.java.JavaRDD;
+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.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+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";
+    JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(), path).toJavaRDD();
+    DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
+    // 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();
+  }
+}