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diff --git a/docs/ml-ensembles.md b/docs/ml-ensembles.md index ce15f5e646..f6c3c30d53 100644 --- a/docs/ml-ensembles.md +++ b/docs/ml-ensembles.md @@ -115,194 +115,21 @@ We use two feature transformers to prepare the data; these help index categories Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classification.RandomForestClassifier) for more details. -{% highlight scala %} -import org.apache.spark.ml.Pipeline -import org.apache.spark.ml.classification.RandomForestClassifier -import org.apache.spark.ml.classification.RandomForestClassificationModel -import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer} -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator - -// Load and parse the data file, converting it to a DataFrame. -val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") - -// 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. -// Set maxCategories so features with > 4 distinct values are treated 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 RandomForest model. -val rf = new RandomForestClassifier() - .setLabelCol("indexedLabel") - .setFeaturesCol("indexedFeatures") - .setNumTrees(10) - -// Convert indexed labels back to original labels. -val labelConverter = new IndexToString() - .setInputCol("prediction") - .setOutputCol("predictedLabel") - .setLabels(labelIndexer.labels) - -// Chain indexers and forest in a Pipeline -val pipeline = new Pipeline() - .setStages(Array(labelIndexer, featureIndexer, rf, 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 rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel] -println("Learned classification forest model:\n" + rfModel.toDebugString) -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala %} </div> <div data-lang="java" markdown="1"> Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/RandomForestClassifier.html) for more details. -{% 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.RandomForestClassifier; -import org.apache.spark.ml.classification.RandomForestClassificationModel; -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; -import org.apache.spark.ml.feature.*; -import org.apache.spark.sql.DataFrame; - -// Load and parse the data file, converting it to a DataFrame. -DataFrame data = sqlContext.read().format("libsvm") - .load("data/mllib/sample_libsvm_data.txt"); - -// 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. -// 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 RandomForest model. -RandomForestClassifier rf = new RandomForestClassifier() - .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 forest in a Pipeline -Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[] {labelIndexer, featureIndexer, rf, 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)); - -RandomForestClassificationModel rfModel = - (RandomForestClassificationModel)(model.stages()[2]); -System.out.println("Learned classification forest model:\n" + rfModel.toDebugString()); -{% endhighlight %} +{% include_example java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java %} </div> <div data-lang="python" markdown="1"> Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.classification.RandomForestClassifier) for more details. -{% highlight python %} -from pyspark.ml import Pipeline -from pyspark.ml.classification import RandomForestClassifier -from pyspark.ml.feature import StringIndexer, VectorIndexer -from pyspark.ml.evaluation import MulticlassClassificationEvaluator - -# Load and parse the data file, converting it to a DataFrame. -data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") - -# 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. -# Set 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 RandomForest model. -rf = RandomForestClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures") - -# Chain indexers and forest in a Pipeline -pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf]) - -# 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) - -rfModel = model.stages[2] -print rfModel # summary only -{% endhighlight %} +{% include_example python/ml/random_forest_classifier_example.py %} </div> </div> @@ -316,167 +143,21 @@ We use a feature transformer to index categorical features, adding metadata to t Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.regression.RandomForestRegressor) for more details. -{% highlight scala %} -import org.apache.spark.ml.Pipeline -import org.apache.spark.ml.regression.RandomForestRegressor -import org.apache.spark.ml.regression.RandomForestRegressionModel -import org.apache.spark.ml.feature.VectorIndexer -import org.apache.spark.ml.evaluation.RegressionEvaluator - -// Load and parse the data file, converting it to a DataFrame. -val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") - -// Automatically identify categorical features, and index them. -// Set maxCategories so features with > 4 distinct values are treated 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 RandomForest model. -val rf = new RandomForestRegressor() - .setLabelCol("label") - .setFeaturesCol("indexedFeatures") - -// Chain indexer and forest in a Pipeline -val pipeline = new Pipeline() - .setStages(Array(featureIndexer, rf)) - -// 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 rfModel = model.stages(1).asInstanceOf[RandomForestRegressionModel] -println("Learned regression forest model:\n" + rfModel.toDebugString) -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/ml/RandomForestRegressorExample.scala %} </div> <div data-lang="java" markdown="1"> Refer to the [Java API docs](api/java/org/apache/spark/ml/regression/RandomForestRegressor.html) for more details. -{% 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.RandomForestRegressionModel; -import org.apache.spark.ml.regression.RandomForestRegressor; -import org.apache.spark.sql.DataFrame; - -// Load and parse the data file, converting it to a DataFrame. -DataFrame data = sqlContext.read().format("libsvm") - .load("data/mllib/sample_libsvm_data.txt"); - -// 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 RandomForest model. -RandomForestRegressor rf = new RandomForestRegressor() - .setLabelCol("label") - .setFeaturesCol("indexedFeatures"); - -// Chain indexer and forest in a Pipeline -Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[] {featureIndexer, rf}); - -// 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("prediction", "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); - -RandomForestRegressionModel rfModel = - (RandomForestRegressionModel)(model.stages()[1]); -System.out.println("Learned regression forest model:\n" + rfModel.toDebugString()); -{% endhighlight %} +{% include_example java/org/apache/spark/examples/ml/JavaRandomForestRegressorExample.java %} </div> <div data-lang="python" markdown="1"> Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor) for more details. -{% highlight python %} -from pyspark.ml import Pipeline -from pyspark.ml.regression import RandomForestRegressor -from pyspark.ml.feature import VectorIndexer -from pyspark.ml.evaluation import RegressionEvaluator - -# Load and parse the data file, converting it to a DataFrame. -data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") - -# Automatically identify categorical features, and index them. -# Set 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 RandomForest model. -rf = RandomForestRegressor(featuresCol="indexedFeatures") - -# Chain indexer and forest in a Pipeline -pipeline = Pipeline(stages=[featureIndexer, rf]) - -# 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 - -rfModel = model.stages[1] -print rfModel # summary only -{% endhighlight %} +{% include_example python/ml/random_forest_regressor_example.py %} </div> </div> @@ -560,194 +241,21 @@ We use two feature transformers to prepare the data; these help index categories Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classification.GBTClassifier) for more details. -{% highlight scala %} -import org.apache.spark.ml.Pipeline -import org.apache.spark.ml.classification.GBTClassifier -import org.apache.spark.ml.classification.GBTClassificationModel -import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer} -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator - -// Load and parse the data file, converting it to a DataFrame. -val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") - -// 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. -// Set maxCategories so features with > 4 distinct values are treated 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 GBT model. -val gbt = new GBTClassifier() - .setLabelCol("indexedLabel") - .setFeaturesCol("indexedFeatures") - .setMaxIter(10) - -// Convert indexed labels back to original labels. -val labelConverter = new IndexToString() - .setInputCol("prediction") - .setOutputCol("predictedLabel") - .setLabels(labelIndexer.labels) - -// Chain indexers and GBT in a Pipeline -val pipeline = new Pipeline() - .setStages(Array(labelIndexer, featureIndexer, gbt, 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 gbtModel = model.stages(2).asInstanceOf[GBTClassificationModel] -println("Learned classification GBT model:\n" + gbtModel.toDebugString) -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala %} </div> <div data-lang="java" markdown="1"> Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/GBTClassifier.html) for more details. -{% 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.GBTClassifier; -import org.apache.spark.ml.classification.GBTClassificationModel; -import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; -import org.apache.spark.ml.feature.*; -import org.apache.spark.sql.DataFrame; - -// Load and parse the data file, converting it to a DataFrame. -DataFrame data sqlContext.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt"); - -// 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. -// 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 GBT model. -GBTClassifier gbt = new GBTClassifier() - .setLabelCol("indexedLabel") - .setFeaturesCol("indexedFeatures") - .setMaxIter(10); - -// Convert indexed labels back to original labels. -IndexToString labelConverter = new IndexToString() - .setInputCol("prediction") - .setOutputCol("predictedLabel") - .setLabels(labelIndexer.labels()); - -// Chain indexers and GBT in a Pipeline -Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[] {labelIndexer, featureIndexer, gbt, 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)); - -GBTClassificationModel gbtModel = - (GBTClassificationModel)(model.stages()[2]); -System.out.println("Learned classification GBT model:\n" + gbtModel.toDebugString()); -{% endhighlight %} +{% include_example java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java %} </div> <div data-lang="python" markdown="1"> Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.classification.GBTClassifier) for more details. -{% highlight python %} -from pyspark.ml import Pipeline -from pyspark.ml.classification import GBTClassifier -from pyspark.ml.feature import StringIndexer, VectorIndexer -from pyspark.ml.evaluation import MulticlassClassificationEvaluator - -# Load and parse the data file, converting it to a DataFrame. -data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") - -# 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. -# Set 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 GBT model. -gbt = GBTClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures", maxIter=10) - -# Chain indexers and GBT in a Pipeline -pipeline = Pipeline(stages=[labelIndexer, featureIndexer, gbt]) - -# 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) - -gbtModel = model.stages[2] -print gbtModel # summary only -{% endhighlight %} +{% include_example python/ml/gradient_boosted_tree_classifier_example.py %} </div> </div> @@ -761,168 +269,21 @@ be true in general. Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.regression.GBTRegressor) for more details. -{% highlight scala %} -import org.apache.spark.ml.Pipeline -import org.apache.spark.ml.regression.GBTRegressor -import org.apache.spark.ml.regression.GBTRegressionModel -import org.apache.spark.ml.feature.VectorIndexer -import org.apache.spark.ml.evaluation.RegressionEvaluator - -// Load and parse the data file, converting it to a DataFrame. -val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") - -// Automatically identify categorical features, and index them. -// Set maxCategories so features with > 4 distinct values are treated 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 GBT model. -val gbt = new GBTRegressor() - .setLabelCol("label") - .setFeaturesCol("indexedFeatures") - .setMaxIter(10) - -// Chain indexer and GBT in a Pipeline -val pipeline = new Pipeline() - .setStages(Array(featureIndexer, gbt)) - -// 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 gbtModel = model.stages(1).asInstanceOf[GBTRegressionModel] -println("Learned regression GBT model:\n" + gbtModel.toDebugString) -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/ml/GradientBoostedTreeRegressorExample.scala %} </div> <div data-lang="java" markdown="1"> Refer to the [Java API docs](api/java/org/apache/spark/ml/regression/GBTRegressor.html) for more details. -{% 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.GBTRegressionModel; -import org.apache.spark.ml.regression.GBTRegressor; -import org.apache.spark.sql.DataFrame; - -// Load and parse the data file, converting it to a DataFrame. -DataFrame data = sqlContext.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt"); - -// 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 GBT model. -GBTRegressor gbt = new GBTRegressor() - .setLabelCol("label") - .setFeaturesCol("indexedFeatures") - .setMaxIter(10); - -// Chain indexer and GBT in a Pipeline -Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[] {featureIndexer, gbt}); - -// 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("prediction", "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); - -GBTRegressionModel gbtModel = - (GBTRegressionModel)(model.stages()[1]); -System.out.println("Learned regression GBT model:\n" + gbtModel.toDebugString()); -{% endhighlight %} +{% include_example java/org/apache/spark/examples/ml/JavaGradientBoostedTreeRegressorExample.java %} </div> <div data-lang="python" markdown="1"> Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.regression.GBTRegressor) for more details. -{% highlight python %} -from pyspark.ml import Pipeline -from pyspark.ml.regression import GBTRegressor -from pyspark.ml.feature import VectorIndexer -from pyspark.ml.evaluation import RegressionEvaluator - -# Load and parse the data file, converting it to a DataFrame. -data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") - -# Automatically identify categorical features, and index them. -# Set 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 GBT model. -gbt = GBTRegressor(featuresCol="indexedFeatures", maxIter=10) - -# Chain indexer and GBT in a Pipeline -pipeline = Pipeline(stages=[featureIndexer, gbt]) - -# 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 - -gbtModel = model.stages[1] -print gbtModel # summary only -{% endhighlight %} +{% include_example python/ml/gradient_boosted_tree_regressor_example.py %} </div> </div> @@ -945,100 +306,13 @@ The example below demonstrates how to load the Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classifier.OneVsRest) for more details. -{% highlight scala %} -import org.apache.spark.ml.classification.{LogisticRegression, OneVsRest} -import org.apache.spark.mllib.evaluation.MulticlassMetrics -import org.apache.spark.sql.{Row, SQLContext} - -val sqlContext = new SQLContext(sc) - -// parse data into dataframe -val data = sqlContext.read.format("libsvm") - .load("data/mllib/sample_multiclass_classification_data.txt") -val Array(train, test) = data.randomSplit(Array(0.7, 0.3)) - -// instantiate multiclass learner and train -val ovr = new OneVsRest().setClassifier(new LogisticRegression) - -val ovrModel = ovr.fit(train) - -// score model on test data -val predictions = ovrModel.transform(test).select("prediction", "label") -val predictionsAndLabels = predictions.map {case Row(p: Double, l: Double) => (p, l)} - -// compute confusion matrix -val metrics = new MulticlassMetrics(predictionsAndLabels) -println(metrics.confusionMatrix) - -// the Iris DataSet has three classes -val numClasses = 3 - -println("label\tfpr\n") -(0 until numClasses).foreach { index => - val label = index.toDouble - println(label + "\t" + metrics.falsePositiveRate(label)) -} -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/ml/OneVsRestExample.scala %} </div> <div data-lang="java" markdown="1"> Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/OneVsRest.html) for more details. -{% highlight java %} -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.ml.classification.LogisticRegression; -import org.apache.spark.ml.classification.OneVsRest; -import org.apache.spark.ml.classification.OneVsRestModel; -import org.apache.spark.mllib.evaluation.MulticlassMetrics; -import org.apache.spark.mllib.linalg.Matrix; -import org.apache.spark.sql.DataFrame; -import org.apache.spark.sql.SQLContext; - -SparkConf conf = new SparkConf().setAppName("JavaOneVsRestExample"); -JavaSparkContext jsc = new JavaSparkContext(conf); -SQLContext jsql = new SQLContext(jsc); - -DataFrame dataFrame = sqlContext.read().format("libsvm") - .load("data/mllib/sample_multiclass_classification_data.txt"); - -DataFrame[] splits = dataFrame.randomSplit(new double[] {0.7, 0.3}, 12345); -DataFrame train = splits[0]; -DataFrame test = splits[1]; - -// instantiate the One Vs Rest Classifier -OneVsRest ovr = new OneVsRest().setClassifier(new LogisticRegression()); - -// train the multiclass model -OneVsRestModel ovrModel = ovr.fit(train.cache()); - -// score the model on test data -DataFrame predictions = ovrModel - .transform(test) - .select("prediction", "label"); - -// obtain metrics -MulticlassMetrics metrics = new MulticlassMetrics(predictions); -Matrix confusionMatrix = metrics.confusionMatrix(); - -// output the Confusion Matrix -System.out.println("Confusion Matrix"); -System.out.println(confusionMatrix); - -// compute the false positive rate per label -System.out.println(); -System.out.println("label\tfpr\n"); - -// the Iris DataSet has three classes -int numClasses = 3; -for (int index = 0; index < numClasses; index++) { - double label = (double) index; - System.out.print(label); - System.out.print("\t"); - System.out.print(metrics.falsePositiveRate(label)); - System.out.println(); -} -{% endhighlight %} +{% include_example java/org/apache/spark/examples/ml/JavaOneVsRestExample.java %} </div> </div> |