[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.

docs/ml-ann.md

@@ -48,76 +48,15 @@ MLPC employes backpropagation for learning the model. We use logistic loss funct
 <div class="codetabs">
 
 <div data-lang="scala" markdown="1">
-
-{% highlight scala %}
-import org.apache.spark.ml.classification.MultilayerPerceptronClassifier
-import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-import org.apache.spark.mllib.util.MLUtils
-import org.apache.spark.sql.Row
-
-// Load training data
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_multiclass_classification_data.txt").toDF()
-// Split the data into train and test
-val splits = data.randomSplit(Array(0.6, 0.4), seed = 1234L)
-val train = splits(0)
-val 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)
-val layers = Array[Int](4, 5, 4, 3)
-// create the trainer and set its parameters
-val trainer = new MultilayerPerceptronClassifier()
-  .setLayers(layers)
-  .setBlockSize(128)
-  .setSeed(1234L)
-  .setMaxIter(100)
-// train the model
-val model = trainer.fit(train)
-// compute precision on the test set
-val result = model.transform(test)
-val predictionAndLabels = result.select("prediction", "label")
-val evaluator = new MulticlassClassificationEvaluator()
-  .setMetricName("precision")
-println("Precision:" + evaluator.evaluate(predictionAndLabels))
-{% endhighlight %}
-
+{% include_example scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala %}
 </div>
 
 <div data-lang="java" markdown="1">
+{% include_example java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java %}
+</div>
 
-{% highlight java %}
-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;
-
-// Load training data
-String path = "data/mllib/sample_multiclass_classification_data.txt";
-JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD();
-DataFrame dataFrame = sqlContext.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));
-{% endhighlight %}
+<div data-lang="python" markdown="1">
+{% include_example python/ml/multilayer_perceptron_classification.py %}
 </div>
 
 </div>

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.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();
+  }
+}

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

+#
+# 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.
+#
+
+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
+from pyspark.mllib.util import MLUtils
+# $example off$
+
+if __name__ == "__main__":
+
+    sc = SparkContext(appName="multilayer_perceptron_classification_example")
+    sqlContext = SQLContext(sc)
+
+    # $example on$
+    # Load training data
+    data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_multiclass_classification_data.txt")\
+        .toDF()
+    # 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()

examples/src/main/scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala

+/*
+ * 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.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.ml
+
+import org.apache.spark.{SparkContext, SparkConf}
+import org.apache.spark.sql.SQLContext
+// $example on$
+import org.apache.spark.ml.classification.MultilayerPerceptronClassifier
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
+import org.apache.spark.mllib.util.MLUtils
+// $example off$
+
+/**
+ * An example for Multilayer Perceptron Classification.
+ */
+object MultilayerPerceptronClassifierExample {
+
+  def main(args: Array[String]): Unit = {
+    val conf = new SparkConf().setAppName("MultilayerPerceptronClassifierExample")
+    val sc = new SparkContext(conf)
+    val sqlContext = new SQLContext(sc)
+    import sqlContext.implicits._
+
+    // $example on$
+    // Load training data
+    val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_multiclass_classification_data.txt")
+      .toDF()
+    // Split the data into train and test
+    val splits = data.randomSplit(Array(0.6, 0.4), seed = 1234L)
+    val train = splits(0)
+    val 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)
+    val layers = Array[Int](4, 5, 4, 3)
+    // create the trainer and set its parameters
+    val trainer = new MultilayerPerceptronClassifier()
+      .setLayers(layers)
+      .setBlockSize(128)
+      .setSeed(1234L)
+      .setMaxIter(100)
+    // train the model
+    val model = trainer.fit(train)
+    // compute precision on the test set
+    val result = model.transform(test)
+    val predictionAndLabels = result.select("prediction", "label")
+    val evaluator = new MulticlassClassificationEvaluator()
+      .setMetricName("precision")
+    println("Precision:" + evaluator.evaluate(predictionAndLabels))
+    // $example off$
+
+    sc.stop()
+  }
+}
+// scalastyle:off println