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7 files changed, 527 insertions, 330 deletions
diff --git a/docs/ml-decision-tree.md b/docs/ml-decision-tree.md index 542819e93e..2bfac6f6c8 100644 --- a/docs/ml-decision-tree.md +++ b/docs/ml-decision-tree.md @@ -118,196 +118,24 @@ We use two feature transformers to prepare the data; these help index categories More details on parameters can be found in the [Scala API documentation](api/scala/index.html#org.apache.spark.ml.classification.DecisionTreeClassifier). -{% highlight scala %} -import org.apache.spark.ml.Pipeline -import org.apache.spark.ml.classification.DecisionTreeClassifier -import org.apache.spark.ml.classification.DecisionTreeClassificationModel -import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer} -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator -import org.apache.spark.mllib.util.MLUtils - -// Load and parse the data file, converting it to a DataFrame. -val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() - -// Index labels, adding metadata to the label column. -// Fit on whole dataset to include all labels in index. -val labelIndexer = new StringIndexer() - .setInputCol("label") - .setOutputCol("indexedLabel") - .fit(data) -// Automatically identify categorical features, and index them. -val featureIndexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexedFeatures") - .setMaxCategories(4) // features with > 4 distinct values are treated as continuous - .fit(data) - -// Split the data into training and test sets (30% held out for testing) -val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3)) - -// Train a DecisionTree model. -val dt = new DecisionTreeClassifier() - .setLabelCol("indexedLabel") - .setFeaturesCol("indexedFeatures") - -// Convert indexed labels back to original labels. -val labelConverter = new IndexToString() - .setInputCol("prediction") - .setOutputCol("predictedLabel") - .setLabels(labelIndexer.labels) - -// Chain indexers and tree in a Pipeline -val pipeline = new Pipeline() - .setStages(Array(labelIndexer, featureIndexer, dt, labelConverter)) - -// Train model. This also runs the indexers. -val model = pipeline.fit(trainingData) - -// Make predictions. -val predictions = model.transform(testData) - -// Select example rows to display. -predictions.select("predictedLabel", "label", "features").show(5) - -// Select (prediction, true label) and compute test error -val evaluator = new MulticlassClassificationEvaluator() - .setLabelCol("indexedLabel") - .setPredictionCol("prediction") - .setMetricName("precision") -val accuracy = evaluator.evaluate(predictions) -println("Test Error = " + (1.0 - accuracy)) - -val treeModel = model.stages(2).asInstanceOf[DecisionTreeClassificationModel] -println("Learned classification tree model:\n" + treeModel.toDebugString) -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala %} + </div> <div data-lang="java" markdown="1"> More details on parameters can be found in the [Java API documentation](api/java/org/apache/spark/ml/classification/DecisionTreeClassifier.html). -{% highlight java %} -import org.apache.spark.ml.Pipeline; -import org.apache.spark.ml.PipelineModel; -import org.apache.spark.ml.PipelineStage; -import org.apache.spark.ml.classification.DecisionTreeClassifier; -import org.apache.spark.ml.classification.DecisionTreeClassificationModel; -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; -import org.apache.spark.ml.feature.*; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.util.MLUtils; -import org.apache.spark.rdd.RDD; -import org.apache.spark.sql.DataFrame; - -// Load and parse the data file, converting it to a DataFrame. -RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt"); -DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class); - -// Index labels, adding metadata to the label column. -// Fit on whole dataset to include all labels in index. -StringIndexerModel labelIndexer = new StringIndexer() - .setInputCol("label") - .setOutputCol("indexedLabel") - .fit(data); -// Automatically identify categorical features, and index them. -VectorIndexerModel featureIndexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexedFeatures") - .setMaxCategories(4) // features with > 4 distinct values are treated as continuous - .fit(data); - -// Split the data into training and test sets (30% held out for testing) -DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3}); -DataFrame trainingData = splits[0]; -DataFrame testData = splits[1]; - -// Train a DecisionTree model. -DecisionTreeClassifier dt = new DecisionTreeClassifier() - .setLabelCol("indexedLabel") - .setFeaturesCol("indexedFeatures"); - -// Convert indexed labels back to original labels. -IndexToString labelConverter = new IndexToString() - .setInputCol("prediction") - .setOutputCol("predictedLabel") - .setLabels(labelIndexer.labels()); - -// Chain indexers and tree in a Pipeline -Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[] {labelIndexer, featureIndexer, dt, labelConverter}); - -// Train model. This also runs the indexers. -PipelineModel model = pipeline.fit(trainingData); - -// Make predictions. -DataFrame predictions = model.transform(testData); - -// Select example rows to display. -predictions.select("predictedLabel", "label", "features").show(5); - -// Select (prediction, true label) and compute test error -MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() - .setLabelCol("indexedLabel") - .setPredictionCol("prediction") - .setMetricName("precision"); -double accuracy = evaluator.evaluate(predictions); -System.out.println("Test Error = " + (1.0 - accuracy)); - -DecisionTreeClassificationModel treeModel = - (DecisionTreeClassificationModel)(model.stages()[2]); -System.out.println("Learned classification tree model:\n" + treeModel.toDebugString()); -{% endhighlight %} +{% include_example java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java %} + </div> <div data-lang="python" markdown="1"> More details on parameters can be found in the [Python API documentation](api/python/pyspark.ml.html#pyspark.ml.classification.DecisionTreeClassifier). -{% highlight python %} -from pyspark.ml import Pipeline -from pyspark.ml.classification import DecisionTreeClassifier -from pyspark.ml.feature import StringIndexer, VectorIndexer -from pyspark.ml.evaluation import MulticlassClassificationEvaluator -from pyspark.mllib.util import MLUtils - -# Load and parse the data file, converting it to a DataFrame. -data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() - -# Index labels, adding metadata to the label column. -# Fit on whole dataset to include all labels in index. -labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data) -# Automatically identify categorical features, and index them. -# We specify maxCategories so features with > 4 distinct values are treated as continuous. -featureIndexer =\ - VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) - -# Split the data into training and test sets (30% held out for testing) -(trainingData, testData) = data.randomSplit([0.7, 0.3]) - -# Train a DecisionTree model. -dt = DecisionTreeClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures") - -# Chain indexers and tree in a Pipeline -pipeline = Pipeline(stages=[labelIndexer, featureIndexer, dt]) - -# Train model. This also runs the indexers. -model = pipeline.fit(trainingData) - -# Make predictions. -predictions = model.transform(testData) - -# Select example rows to display. -predictions.select("prediction", "indexedLabel", "features").show(5) - -# Select (prediction, true label) and compute test error -evaluator = MulticlassClassificationEvaluator( - labelCol="indexedLabel", predictionCol="prediction", metricName="precision") -accuracy = evaluator.evaluate(predictions) -print "Test Error = %g" % (1.0 - accuracy) +{% include_example python/ml/decision_tree_classification_example.py %} -treeModel = model.stages[2] -print treeModel # summary only -{% endhighlight %} </div> </div> @@ -323,171 +151,21 @@ We use a feature transformer to index categorical features, adding metadata to t More details on parameters can be found in the [Scala API documentation](api/scala/index.html#org.apache.spark.ml.regression.DecisionTreeRegressor). -{% highlight scala %} -import org.apache.spark.ml.Pipeline -import org.apache.spark.ml.regression.DecisionTreeRegressor -import org.apache.spark.ml.regression.DecisionTreeRegressionModel -import org.apache.spark.ml.feature.VectorIndexer -import org.apache.spark.ml.evaluation.RegressionEvaluator -import org.apache.spark.mllib.util.MLUtils - -// Load and parse the data file, converting it to a DataFrame. -val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() - -// Automatically identify categorical features, and index them. -// Here, we treat features with > 4 distinct values as continuous. -val featureIndexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexedFeatures") - .setMaxCategories(4) - .fit(data) - -// Split the data into training and test sets (30% held out for testing) -val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3)) - -// Train a DecisionTree model. -val dt = new DecisionTreeRegressor() - .setLabelCol("label") - .setFeaturesCol("indexedFeatures") - -// Chain indexer and tree in a Pipeline -val pipeline = new Pipeline() - .setStages(Array(featureIndexer, dt)) - -// Train model. This also runs the indexer. -val model = pipeline.fit(trainingData) - -// Make predictions. -val predictions = model.transform(testData) - -// Select example rows to display. -predictions.select("prediction", "label", "features").show(5) - -// Select (prediction, true label) and compute test error -val evaluator = new RegressionEvaluator() - .setLabelCol("label") - .setPredictionCol("prediction") - .setMetricName("rmse") -val rmse = evaluator.evaluate(predictions) -println("Root Mean Squared Error (RMSE) on test data = " + rmse) - -val treeModel = model.stages(1).asInstanceOf[DecisionTreeRegressionModel] -println("Learned regression tree model:\n" + treeModel.toDebugString) -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/ml/DecisionTreeRegressionExample.scala %} </div> <div data-lang="java" markdown="1"> More details on parameters can be found in the [Java API documentation](api/java/org/apache/spark/ml/regression/DecisionTreeRegressor.html). -{% highlight java %} -import org.apache.spark.ml.Pipeline; -import org.apache.spark.ml.PipelineModel; -import org.apache.spark.ml.PipelineStage; -import org.apache.spark.ml.evaluation.RegressionEvaluator; -import org.apache.spark.ml.feature.VectorIndexer; -import org.apache.spark.ml.feature.VectorIndexerModel; -import org.apache.spark.ml.regression.DecisionTreeRegressionModel; -import org.apache.spark.ml.regression.DecisionTreeRegressor; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.util.MLUtils; -import org.apache.spark.rdd.RDD; -import org.apache.spark.sql.DataFrame; - -// Load and parse the data file, converting it to a DataFrame. -RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt"); -DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class); - -// Automatically identify categorical features, and index them. -// Set maxCategories so features with > 4 distinct values are treated as continuous. -VectorIndexerModel featureIndexer = new VectorIndexer() - .setInputCol("features") - .setOutputCol("indexedFeatures") - .setMaxCategories(4) - .fit(data); - -// Split the data into training and test sets (30% held out for testing) -DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3}); -DataFrame trainingData = splits[0]; -DataFrame testData = splits[1]; - -// Train a DecisionTree model. -DecisionTreeRegressor dt = new DecisionTreeRegressor() - .setFeaturesCol("indexedFeatures"); - -// Chain indexer and tree in a Pipeline -Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[] {featureIndexer, dt}); - -// Train model. This also runs the indexer. -PipelineModel model = pipeline.fit(trainingData); - -// Make predictions. -DataFrame predictions = model.transform(testData); - -// Select example rows to display. -predictions.select("label", "features").show(5); - -// Select (prediction, true label) and compute test error -RegressionEvaluator evaluator = new RegressionEvaluator() - .setLabelCol("label") - .setPredictionCol("prediction") - .setMetricName("rmse"); -double rmse = evaluator.evaluate(predictions); -System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse); - -DecisionTreeRegressionModel treeModel = - (DecisionTreeRegressionModel)(model.stages()[1]); -System.out.println("Learned regression tree model:\n" + treeModel.toDebugString()); -{% endhighlight %} +{% include_example java/org/apache/spark/examples/ml/JavaDecisionTreeRegressionExample.java %} </div> <div data-lang="python" markdown="1"> More details on parameters can be found in the [Python API documentation](api/python/pyspark.ml.html#pyspark.ml.regression.DecisionTreeRegressor). -{% highlight python %} -from pyspark.ml import Pipeline -from pyspark.ml.regression import DecisionTreeRegressor -from pyspark.ml.feature import VectorIndexer -from pyspark.ml.evaluation import RegressionEvaluator -from pyspark.mllib.util import MLUtils - -# Load and parse the data file, converting it to a DataFrame. -data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() - -# Automatically identify categorical features, and index them. -# We specify maxCategories so features with > 4 distinct values are treated as continuous. -featureIndexer =\ - VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) - -# Split the data into training and test sets (30% held out for testing) -(trainingData, testData) = data.randomSplit([0.7, 0.3]) - -# Train a DecisionTree model. -dt = DecisionTreeRegressor(featuresCol="indexedFeatures") - -# Chain indexer and tree in a Pipeline -pipeline = Pipeline(stages=[featureIndexer, dt]) - -# Train model. This also runs the indexer. -model = pipeline.fit(trainingData) - -# Make predictions. -predictions = model.transform(testData) - -# Select example rows to display. -predictions.select("prediction", "label", "features").show(5) - -# Select (prediction, true label) and compute test error -evaluator = RegressionEvaluator( - labelCol="label", predictionCol="prediction", metricName="rmse") -rmse = evaluator.evaluate(predictions) -print "Root Mean Squared Error (RMSE) on test data = %g" % rmse - -treeModel = model.stages[1] -print treeModel # summary only -{% endhighlight %} +{% include_example python/ml/decision_tree_regression_example.py %} </div> </div> diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java new file mode 100644 index 0000000000..51c1730a8a --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java @@ -0,0 +1,103 @@ +/* + * 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; +// $example on$ +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.ml.Pipeline; +import org.apache.spark.ml.PipelineModel; +import org.apache.spark.ml.PipelineStage; +import org.apache.spark.ml.classification.DecisionTreeClassifier; +import org.apache.spark.ml.classification.DecisionTreeClassificationModel; +import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; +import org.apache.spark.ml.feature.*; +import org.apache.spark.mllib.regression.LabeledPoint; +import org.apache.spark.mllib.util.MLUtils; +import org.apache.spark.rdd.RDD; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.SQLContext; +// $example off$ + +public class JavaDecisionTreeClassificationExample { + public static void main(String[] args) { + SparkConf conf = new SparkConf().setAppName("JavaDecisionTreeClassificationExample"); + JavaSparkContext jsc = new JavaSparkContext(conf); + SQLContext sqlContext = new SQLContext(jsc); + + // $example on$ + // Load and parse the data file, converting it to a DataFrame. + RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt"); + DataFrame data = sqlContext.createDataFrame(rdd, LabeledPoint.class); + + // Index labels, adding metadata to the label column. + // Fit on whole dataset to include all labels in index. + StringIndexerModel labelIndexer = new StringIndexer() + .setInputCol("label") + .setOutputCol("indexedLabel") + .fit(data); + + // Automatically identify categorical features, and index them. + VectorIndexerModel featureIndexer = new VectorIndexer() + .setInputCol("features") + .setOutputCol("indexedFeatures") + .setMaxCategories(4) // features with > 4 distinct values are treated as continuous + .fit(data); + + // Split the data into training and test sets (30% held out for testing) + DataFrame[] splits = data.randomSplit(new double[]{0.7, 0.3}); + DataFrame trainingData = splits[0]; + DataFrame testData = splits[1]; + + // Train a DecisionTree model. + DecisionTreeClassifier dt = new DecisionTreeClassifier() + .setLabelCol("indexedLabel") + .setFeaturesCol("indexedFeatures"); + + // Convert indexed labels back to original labels. + IndexToString labelConverter = new IndexToString() + .setInputCol("prediction") + .setOutputCol("predictedLabel") + .setLabels(labelIndexer.labels()); + + // Chain indexers and tree in a Pipeline + Pipeline pipeline = new Pipeline() + .setStages(new PipelineStage[]{labelIndexer, featureIndexer, dt, labelConverter}); + + // Train model. This also runs the indexers. + PipelineModel model = pipeline.fit(trainingData); + + // Make predictions. + DataFrame predictions = model.transform(testData); + + // Select example rows to display. + predictions.select("predictedLabel", "label", "features").show(5); + + // Select (prediction, true label) and compute test error + MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() + .setLabelCol("indexedLabel") + .setPredictionCol("prediction") + .setMetricName("precision"); + double accuracy = evaluator.evaluate(predictions); + System.out.println("Test Error = " + (1.0 - accuracy)); + + DecisionTreeClassificationModel treeModel = + (DecisionTreeClassificationModel) (model.stages()[2]); + System.out.println("Learned classification tree model:\n" + treeModel.toDebugString()); + // $example off$ + } +} diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeRegressionExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeRegressionExample.java new file mode 100644 index 0000000000..a4098a4233 --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDecisionTreeRegressionExample.java @@ -0,0 +1,90 @@ +/* + * 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; +// $example on$ +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.ml.Pipeline; +import org.apache.spark.ml.PipelineModel; +import org.apache.spark.ml.PipelineStage; +import org.apache.spark.ml.evaluation.RegressionEvaluator; +import org.apache.spark.ml.feature.VectorIndexer; +import org.apache.spark.ml.feature.VectorIndexerModel; +import org.apache.spark.ml.regression.DecisionTreeRegressionModel; +import org.apache.spark.ml.regression.DecisionTreeRegressor; +import org.apache.spark.mllib.regression.LabeledPoint; +import org.apache.spark.mllib.util.MLUtils; +import org.apache.spark.rdd.RDD; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.SQLContext; +// $example off$ + +public class JavaDecisionTreeRegressionExample { + public static void main(String[] args) { + SparkConf conf = new SparkConf().setAppName("JavaDecisionTreeRegressionExample"); + JavaSparkContext jsc = new JavaSparkContext(conf); + SQLContext sqlContext = new SQLContext(jsc); + // $example on$ + // Load and parse the data file, converting it to a DataFrame. + RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt"); + DataFrame data = sqlContext.createDataFrame(rdd, LabeledPoint.class); + + // Automatically identify categorical features, and index them. + // Set maxCategories so features with > 4 distinct values are treated as continuous. + VectorIndexerModel featureIndexer = new VectorIndexer() + .setInputCol("features") + .setOutputCol("indexedFeatures") + .setMaxCategories(4) + .fit(data); + + // Split the data into training and test sets (30% held out for testing) + DataFrame[] splits = data.randomSplit(new double[]{0.7, 0.3}); + DataFrame trainingData = splits[0]; + DataFrame testData = splits[1]; + + // Train a DecisionTree model. + DecisionTreeRegressor dt = new DecisionTreeRegressor() + .setFeaturesCol("indexedFeatures"); + + // Chain indexer and tree in a Pipeline + Pipeline pipeline = new Pipeline() + .setStages(new PipelineStage[]{featureIndexer, dt}); + + // Train model. This also runs the indexer. + PipelineModel model = pipeline.fit(trainingData); + + // Make predictions. + DataFrame predictions = model.transform(testData); + + // Select example rows to display. + predictions.select("label", "features").show(5); + + // Select (prediction, true label) and compute test error + RegressionEvaluator evaluator = new RegressionEvaluator() + .setLabelCol("label") + .setPredictionCol("prediction") + .setMetricName("rmse"); + double rmse = evaluator.evaluate(predictions); + System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse); + + DecisionTreeRegressionModel treeModel = + (DecisionTreeRegressionModel) (model.stages()[1]); + System.out.println("Learned regression tree model:\n" + treeModel.toDebugString()); + // $example off$ + } +} diff --git a/examples/src/main/python/ml/decision_tree_classification_example.py b/examples/src/main/python/ml/decision_tree_classification_example.py new file mode 100644 index 0000000000..0af92050e3 --- /dev/null +++ b/examples/src/main/python/ml/decision_tree_classification_example.py @@ -0,0 +1,77 @@ +# +# 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. +# + +""" +Decision Tree Classification Example. +""" +from __future__ import print_function + +import sys + +# $example on$ +from pyspark import SparkContext, SQLContext +from pyspark.ml import Pipeline +from pyspark.ml.classification import DecisionTreeClassifier +from pyspark.ml.feature import StringIndexer, VectorIndexer +from pyspark.ml.evaluation import MulticlassClassificationEvaluator +from pyspark.mllib.util import MLUtils +# $example off$ + +if __name__ == "__main__": + sc = SparkContext(appName="decision_tree_classification_example") + sqlContext = SQLContext(sc) + + # $example on$ + # Load and parse the data file, converting it to a DataFrame. + data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() + + # Index labels, adding metadata to the label column. + # Fit on whole dataset to include all labels in index. + labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data) + # Automatically identify categorical features, and index them. + # We specify maxCategories so features with > 4 distinct values are treated as continuous. + featureIndexer =\ + VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) + + # Split the data into training and test sets (30% held out for testing) + (trainingData, testData) = data.randomSplit([0.7, 0.3]) + + # Train a DecisionTree model. + dt = DecisionTreeClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures") + + # Chain indexers and tree in a Pipeline + pipeline = Pipeline(stages=[labelIndexer, featureIndexer, dt]) + + # Train model. This also runs the indexers. + model = pipeline.fit(trainingData) + + # Make predictions. + predictions = model.transform(testData) + + # Select example rows to display. + predictions.select("prediction", "indexedLabel", "features").show(5) + + # Select (prediction, true label) and compute test error + evaluator = MulticlassClassificationEvaluator( + labelCol="indexedLabel", predictionCol="prediction", metricName="precision") + accuracy = evaluator.evaluate(predictions) + print("Test Error = %g " % (1.0 - accuracy)) + + treeModel = model.stages[2] + # summary only + print(treeModel) + # $example off$ diff --git a/examples/src/main/python/ml/decision_tree_regression_example.py b/examples/src/main/python/ml/decision_tree_regression_example.py new file mode 100644 index 0000000000..3857aed538 --- /dev/null +++ b/examples/src/main/python/ml/decision_tree_regression_example.py @@ -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. +# + +""" +Decision Tree Regression Example. +""" +from __future__ import print_function + +import sys + +from pyspark import SparkContext, SQLContext +# $example on$ +from pyspark.ml import Pipeline +from pyspark.ml.regression import DecisionTreeRegressor +from pyspark.ml.feature import VectorIndexer +from pyspark.ml.evaluation import RegressionEvaluator +from pyspark.mllib.util import MLUtils +# $example off$ + +if __name__ == "__main__": + sc = SparkContext(appName="decision_tree_classification_example") + sqlContext = SQLContext(sc) + + # $example on$ + # Load and parse the data file, converting it to a DataFrame. + data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() + + # Automatically identify categorical features, and index them. + # We specify maxCategories so features with > 4 distinct values are treated as continuous. + featureIndexer =\ + VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) + + # Split the data into training and test sets (30% held out for testing) + (trainingData, testData) = data.randomSplit([0.7, 0.3]) + + # Train a DecisionTree model. + dt = DecisionTreeRegressor(featuresCol="indexedFeatures") + + # Chain indexer and tree in a Pipeline + pipeline = Pipeline(stages=[featureIndexer, dt]) + + # Train model. This also runs the indexer. + model = pipeline.fit(trainingData) + + # Make predictions. + predictions = model.transform(testData) + + # Select example rows to display. + predictions.select("prediction", "label", "features").show(5) + + # Select (prediction, true label) and compute test error + evaluator = RegressionEvaluator( + labelCol="label", predictionCol="prediction", metricName="rmse") + rmse = evaluator.evaluate(predictions) + print("Root Mean Squared Error (RMSE) on test data = %g" % rmse) + + treeModel = model.stages[1] + # summary only + print(treeModel) + # $example off$ diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala new file mode 100644 index 0000000000..a24a344f1b --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala @@ -0,0 +1,94 @@ +/* + * 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.sql.SQLContext +import org.apache.spark.{SparkContext, SparkConf} +// $example on$ +import org.apache.spark.ml.Pipeline +import org.apache.spark.ml.classification.DecisionTreeClassifier +import org.apache.spark.ml.classification.DecisionTreeClassificationModel +import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer} +import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator +import org.apache.spark.mllib.util.MLUtils +// $example off$ + +object DecisionTreeClassificationExample { + def main(args: Array[String]): Unit = { + val conf = new SparkConf().setAppName("DecisionTreeClassificationExample") + val sc = new SparkContext(conf) + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + // $example on$ + // Load and parse the data file, converting it to a DataFrame. + val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() + + // Index labels, adding metadata to the label column. + // Fit on whole dataset to include all labels in index. + val labelIndexer = new StringIndexer() + .setInputCol("label") + .setOutputCol("indexedLabel") + .fit(data) + // Automatically identify categorical features, and index them. + val featureIndexer = new VectorIndexer() + .setInputCol("features") + .setOutputCol("indexedFeatures") + .setMaxCategories(4) // features with > 4 distinct values are treated as continuous + .fit(data) + + // Split the data into training and test sets (30% held out for testing) + val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3)) + + // Train a DecisionTree model. + val dt = new DecisionTreeClassifier() + .setLabelCol("indexedLabel") + .setFeaturesCol("indexedFeatures") + + // Convert indexed labels back to original labels. + val labelConverter = new IndexToString() + .setInputCol("prediction") + .setOutputCol("predictedLabel") + .setLabels(labelIndexer.labels) + + // Chain indexers and tree in a Pipeline + val pipeline = new Pipeline() + .setStages(Array(labelIndexer, featureIndexer, dt, labelConverter)) + + // Train model. This also runs the indexers. + val model = pipeline.fit(trainingData) + + // Make predictions. + val predictions = model.transform(testData) + + // Select example rows to display. + predictions.select("predictedLabel", "label", "features").show(5) + + // Select (prediction, true label) and compute test error + val evaluator = new MulticlassClassificationEvaluator() + .setLabelCol("indexedLabel") + .setPredictionCol("prediction") + .setMetricName("precision") + val accuracy = evaluator.evaluate(predictions) + println("Test Error = " + (1.0 - accuracy)) + + val treeModel = model.stages(2).asInstanceOf[DecisionTreeClassificationModel] + println("Learned classification tree model:\n" + treeModel.toDebugString) + // $example off$ + } +} diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeRegressionExample.scala new file mode 100644 index 0000000000..64cd986129 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeRegressionExample.scala @@ -0,0 +1,81 @@ +/* + * 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.sql.SQLContext +import org.apache.spark.{SparkContext, SparkConf} +// $example on$ +import org.apache.spark.ml.Pipeline +import org.apache.spark.ml.regression.DecisionTreeRegressor +import org.apache.spark.ml.regression.DecisionTreeRegressionModel +import org.apache.spark.ml.feature.VectorIndexer +import org.apache.spark.ml.evaluation.RegressionEvaluator +import org.apache.spark.mllib.util.MLUtils +// $example off$ +object DecisionTreeRegressionExample { + def main(args: Array[String]): Unit = { + val conf = new SparkConf().setAppName("DecisionTreeRegressionExample") + val sc = new SparkContext(conf) + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + // $example on$ + // Load and parse the data file, converting it to a DataFrame. + val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() + + // Automatically identify categorical features, and index them. + // Here, we treat features with > 4 distinct values as continuous. + val featureIndexer = new VectorIndexer() + .setInputCol("features") + .setOutputCol("indexedFeatures") + .setMaxCategories(4) + .fit(data) + + // Split the data into training and test sets (30% held out for testing) + val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3)) + + // Train a DecisionTree model. + val dt = new DecisionTreeRegressor() + .setLabelCol("label") + .setFeaturesCol("indexedFeatures") + + // Chain indexer and tree in a Pipeline + val pipeline = new Pipeline() + .setStages(Array(featureIndexer, dt)) + + // Train model. This also runs the indexer. + val model = pipeline.fit(trainingData) + + // Make predictions. + val predictions = model.transform(testData) + + // Select example rows to display. + predictions.select("prediction", "label", "features").show(5) + + // Select (prediction, true label) and compute test error + val evaluator = new RegressionEvaluator() + .setLabelCol("label") + .setPredictionCol("prediction") + .setMetricName("rmse") + val rmse = evaluator.evaluate(predictions) + println("Root Mean Squared Error (RMSE) on test data = " + rmse) + + val treeModel = model.stages(1).asInstanceOf[DecisionTreeRegressionModel] + println("Learned regression tree model:\n" + treeModel.toDebugString) + // $example off$ + } +} |