diff --git a/docs/ml-ann.md b/docs/ml-ann.md index d5ddd92af1e96..6e763e8f41568 100644 --- a/docs/ml-ann.md +++ b/docs/ml-ann.md @@ -48,76 +48,15 @@ MLPC employes backpropagation for learning the model. We use logistic loss funct
- -{% 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 %}
+{% include_example java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java %} +
-{% 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 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 %} +
+{% include_example python/ml/multilayer_perceptron_classification.py %}
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 new file mode 100644 index 0000000000000..f48e1339c5007 --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java @@ -0,0 +1,74 @@ +/* + * 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 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(); + } +} diff --git a/examples/src/main/python/ml/multilayer_perceptron_classification.py b/examples/src/main/python/ml/multilayer_perceptron_classification.py new file mode 100644 index 0000000000000..d8ef9f39e3fa5 --- /dev/null +++ b/examples/src/main/python/ml/multilayer_perceptron_classification.py @@ -0,0 +1,56 @@ +# +# 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() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala new file mode 100644 index 0000000000000..99d5f35b5a565 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala @@ -0,0 +1,71 @@ +/* + * 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