Repository: spark Updated Branches: refs/heads/master cb2d2e158 -> 13db11cb0
[SPARK-10061] [DOC] ML ensemble docs User guide for spark.ml GBTs and Random Forests. The examples are copied from the decision tree guide and modified to run. I caught some issues I had somehow missed in the tree guide as well. I have run all examples, including Java ones. (Of course, I thought I had previously as well...) CC: mengxr manishamde yanboliang Author: Joseph K. Bradley <jos...@databricks.com> Closes #8369 from jkbradley/ml-ensemble-docs. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/13db11cb Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/13db11cb Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/13db11cb Branch: refs/heads/master Commit: 13db11cb08eb90eb0ea3402c9fe0270aa282f247 Parents: cb2d2e1 Author: Joseph K. Bradley <jos...@databricks.com> Authored: Mon Aug 24 15:38:54 2015 -0700 Committer: Xiangrui Meng <m...@databricks.com> Committed: Mon Aug 24 15:38:54 2015 -0700 ---------------------------------------------------------------------- docs/ml-decision-tree.md | 75 ++-- docs/ml-ensembles.md | 952 +++++++++++++++++++++++++++++++++++++++++- 2 files changed, 976 insertions(+), 51 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/13db11cb/docs/ml-decision-tree.md ---------------------------------------------------------------------- diff --git a/docs/ml-decision-tree.md b/docs/ml-decision-tree.md index 958c6f5..542819e 100644 --- a/docs/ml-decision-tree.md +++ b/docs/ml-decision-tree.md @@ -30,7 +30,7 @@ The Pipelines API for Decision Trees offers a bit more functionality than the or Ensembles of trees (Random Forests and Gradient-Boosted Trees) are described in the [Ensembles guide](ml-ensembles.html). -# Inputs and Outputs (Predictions) +# Inputs and Outputs We list the input and output (prediction) column types here. All output columns are optional; to exclude an output column, set its corresponding Param to an empty string. @@ -234,7 +234,7 @@ IndexToString labelConverter = new IndexToString() // Chain indexers and tree in a Pipeline Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[]{labelIndexer, featureIndexer, dt, labelConverter}); + .setStages(new PipelineStage[] {labelIndexer, featureIndexer, dt, labelConverter}); // Train model. This also runs the indexers. PipelineModel model = pipeline.fit(trainingData); @@ -315,10 +315,13 @@ print treeModel # summary only ## Regression +The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set. +We use a feature transformer to index categorical features, adding metadata to the `DataFrame` which the Decision Tree algorithm can recognize. + <div class="codetabs"> <div data-lang="scala" markdown="1"> -More details on parameters can be found in the [Scala API documentation](api/scala/index.html#org.apache.spark.ml.classification.DecisionTreeClassifier). +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 @@ -347,7 +350,7 @@ val dt = new DecisionTreeRegressor() .setLabelCol("label") .setFeaturesCol("indexedFeatures") -// Chain indexers and tree in a Pipeline +// Chain indexer and tree in a Pipeline val pipeline = new Pipeline() .setStages(Array(featureIndexer, dt)) @@ -365,9 +368,7 @@ val evaluator = new RegressionEvaluator() .setLabelCol("label") .setPredictionCol("prediction") .setMetricName("rmse") -// We negate the RMSE value since RegressionEvalutor returns negated RMSE -// (since evaluation metrics are meant to be maximized by CrossValidator). -val rmse = - evaluator.evaluate(predictions) +val rmse = evaluator.evaluate(predictions) println("Root Mean Squared Error (RMSE) on test data = " + rmse) val treeModel = model.stages(1).asInstanceOf[DecisionTreeRegressionModel] @@ -377,14 +378,15 @@ println("Learned regression tree model:\n" + treeModel.toDebugString) <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). +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.*; +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; @@ -396,17 +398,12 @@ import org.apache.spark.sql.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. +// Set maxCategories so features with > 4 distinct values are treated as continuous. VectorIndexerModel featureIndexer = new VectorIndexer() .setInputCol("features") .setOutputCol("indexedFeatures") - .setMaxCategories(4) // features with > 4 distinct values are treated as continuous + .setMaxCategories(4) .fit(data); // Split the data into training and test sets (30% held out for testing) @@ -416,61 +413,49 @@ DataFrame testData = splits[1]; // Train a DecisionTree model. DecisionTreeRegressor dt = new DecisionTreeRegressor() - .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 +// Chain indexer and tree in a Pipeline Pipeline pipeline = new Pipeline() - .setStages(new PipelineStage[]{labelIndexer, featureIndexer, dt, labelConverter}); + .setStages(new PipelineStage[] {featureIndexer, dt}); -// Train model. This also runs the indexers. +// 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("predictedLabel", "label", "features").show(5); +predictions.select("label", "features").show(5); // Select (prediction, true label) and compute test error RegressionEvaluator evaluator = new RegressionEvaluator() - .setLabelCol("indexedLabel") + .setLabelCol("label") .setPredictionCol("prediction") .setMetricName("rmse"); -// We negate the RMSE value since RegressionEvalutor returns negated RMSE -// (since evaluation metrics are meant to be maximized by CrossValidator). -double rmse = - evaluator.evaluate(predictions); +double rmse = evaluator.evaluate(predictions); System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse); DecisionTreeRegressionModel treeModel = - (DecisionTreeRegressionModel)(model.stages()[2]); + (DecisionTreeRegressionModel)(model.stages()[1]); System.out.println("Learned regression tree model:\n" + treeModel.toDebugString()); {% endhighlight %} </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). +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 StringIndexer, VectorIndexer +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() -# 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 =\ @@ -480,26 +465,24 @@ featureIndexer =\ (trainingData, testData) = data.randomSplit([0.7, 0.3]) # Train a DecisionTree model. -dt = DecisionTreeRegressor(labelCol="indexedLabel", featuresCol="indexedFeatures") +dt = DecisionTreeRegressor(featuresCol="indexedFeatures") -# Chain indexers and tree in a Pipeline -pipeline = Pipeline(stages=[labelIndexer, featureIndexer, dt]) +# Chain indexer and tree in a Pipeline +pipeline = Pipeline(stages=[featureIndexer, dt]) -# Train model. This also runs the indexers. +# 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", "indexedLabel", "features").show(5) +predictions.select("prediction", "label", "features").show(5) # Select (prediction, true label) and compute test error evaluator = RegressionEvaluator( - labelCol="indexedLabel", predictionCol="prediction", metricName="rmse") -# We negate the RMSE value since RegressionEvalutor returns negated RMSE -# (since evaluation metrics are meant to be maximized by CrossValidator). -rmse = -evaluator.evaluate(predictions) + 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] http://git-wip-us.apache.org/repos/asf/spark/blob/13db11cb/docs/ml-ensembles.md ---------------------------------------------------------------------- diff --git a/docs/ml-ensembles.md b/docs/ml-ensembles.md index 9ff50e9..6274990 100644 --- a/docs/ml-ensembles.md +++ b/docs/ml-ensembles.md @@ -11,11 +11,947 @@ displayTitle: <a href="ml-guide.html">ML</a> - Ensembles An [ensemble method](http://en.wikipedia.org/wiki/Ensemble_learning) is a learning algorithm which creates a model composed of a set of other base models. -The Pipelines API supports the following ensemble algorithms: [`OneVsRest`](api/scala/index.html#org.apache.spark.ml.classifier.OneVsRest) -## OneVsRest +## Tree Ensembles -[OneVsRest](http://en.wikipedia.org/wiki/Multiclass_classification#One-vs.-rest) is an example of a machine learning reduction for performing multiclass classification given a base classifier that can perform binary classification efficiently. +The Pipelines API supports two major tree ensemble algorithms: [Random Forests](http://en.wikipedia.org/wiki/Random_forest) and [Gradient-Boosted Trees (GBTs)](http://en.wikipedia.org/wiki/Gradient_boosting). +Both use [MLlib decision trees](ml-decision-tree.html) as their base models. + +Users can find more information about ensemble algorithms in the [MLlib Ensemble guide](mllib-ensembles.html). In this section, we demonstrate the Pipelines API for ensembles. + +The main differences between this API and the [original MLlib ensembles API](mllib-ensembles.html) are: +* support for ML Pipelines +* separation of classification vs. regression +* use of DataFrame metadata to distinguish continuous and categorical features +* a bit more functionality for random forests: estimates of feature importance, as well as the predicted probability of each class (a.k.a. class conditional probabilities) for classification. + +### Random Forests + +[Random forests](http://en.wikipedia.org/wiki/Random_forest) +are ensembles of [decision trees](ml-decision-tree.html). +Random forests combine many decision trees in order to reduce the risk of overfitting. +MLlib supports random forests for binary and multiclass classification and for regression, +using both continuous and categorical features. + +This section gives examples of using random forests with the Pipelines API. +For more information on the algorithm, please see the [main MLlib docs on random forests](mllib-ensembles.html). + +#### Inputs and Outputs + +We list the input and output (prediction) column types here. +All output columns are optional; to exclude an output column, set its corresponding Param to an empty string. + +##### Input Columns + +<table class="table"> + <thead> + <tr> + <th align="left">Param name</th> + <th align="left">Type(s)</th> + <th align="left">Default</th> + <th align="left">Description</th> + </tr> + </thead> + <tbody> + <tr> + <td>labelCol</td> + <td>Double</td> + <td>"label"</td> + <td>Label to predict</td> + </tr> + <tr> + <td>featuresCol</td> + <td>Vector</td> + <td>"features"</td> + <td>Feature vector</td> + </tr> + </tbody> +</table> + +##### Output Columns (Predictions) + +<table class="table"> + <thead> + <tr> + <th align="left">Param name</th> + <th align="left">Type(s)</th> + <th align="left">Default</th> + <th align="left">Description</th> + <th align="left">Notes</th> + </tr> + </thead> + <tbody> + <tr> + <td>predictionCol</td> + <td>Double</td> + <td>"prediction"</td> + <td>Predicted label</td> + <td></td> + </tr> + <tr> + <td>rawPredictionCol</td> + <td>Vector</td> + <td>"rawPrediction"</td> + <td>Vector of length # classes, with the counts of training instance labels at the tree node which makes the prediction</td> + <td>Classification only</td> + </tr> + <tr> + <td>probabilityCol</td> + <td>Vector</td> + <td>"probability"</td> + <td>Vector of length # classes equal to rawPrediction normalized to a multinomial distribution</td> + <td>Classification only</td> + </tr> + </tbody> +</table> + +#### Example: Classification + +The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set. +We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the `DataFrame` which the tree-based algorithms can recognize. + +<div class="codetabs"> +<div data-lang="scala" markdown="1"> + +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 +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. +// 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 %} +</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.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. +// 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 %} +</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 +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. +# 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 %} +</div> +</div> + +#### Example: Regression + +The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set. +We use a feature transformer to index categorical features, adding metadata to the `DataFrame` which the tree-based algorithms can recognize. + +<div class="codetabs"> +<div data-lang="scala" markdown="1"> + +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 +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. +// 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 %} +</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.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 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 %} +</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 +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. +# 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 %} +</div> +</div> + +### Gradient-Boosted Trees (GBTs) + +[Gradient-Boosted Trees (GBTs)](http://en.wikipedia.org/wiki/Gradient_boosting) +are ensembles of [decision trees](ml-decision-tree.html). +GBTs iteratively train decision trees in order to minimize a loss function. +MLlib supports GBTs for binary classification and for regression, +using both continuous and categorical features. + +This section gives examples of using GBTs with the Pipelines API. +For more information on the algorithm, please see the [main MLlib docs on GBTs](mllib-ensembles.html). + +#### Inputs and Outputs + +We list the input and output (prediction) column types here. +All output columns are optional; to exclude an output column, set its corresponding Param to an empty string. + +##### Input Columns + +<table class="table"> + <thead> + <tr> + <th align="left">Param name</th> + <th align="left">Type(s)</th> + <th align="left">Default</th> + <th align="left">Description</th> + </tr> + </thead> + <tbody> + <tr> + <td>labelCol</td> + <td>Double</td> + <td>"label"</td> + <td>Label to predict</td> + </tr> + <tr> + <td>featuresCol</td> + <td>Vector</td> + <td>"features"</td> + <td>Feature vector</td> + </tr> + </tbody> +</table> + +Note that `GBTClassifier` currently only supports binary labels. + +##### Output Columns (Predictions) + +<table class="table"> + <thead> + <tr> + <th align="left">Param name</th> + <th align="left">Type(s)</th> + <th align="left">Default</th> + <th align="left">Description</th> + <th align="left">Notes</th> + </tr> + </thead> + <tbody> + <tr> + <td>predictionCol</td> + <td>Double</td> + <td>"prediction"</td> + <td>Predicted label</td> + <td></td> + </tr> + </tbody> +</table> + +In the future, `GBTClassifier` will also output columns for `rawPrediction` and `probability`, just as `RandomForestClassifier` does. + +#### Example: Classification + +The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set. +We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the `DataFrame` which the tree-based algorithms can recognize. + +<div class="codetabs"> +<div data-lang="scala" markdown="1"> + +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 +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. +// 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 %} +</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.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. +// 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 %} +</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 +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. +# 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 %} +</div> +</div> + +#### Example: Regression + +Note: For this example dataset, `GBTRegressor` actually only needs 1 iteration, but that will not +be true in general. + +<div class="codetabs"> +<div data-lang="scala" markdown="1"> + +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 +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. +// 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 %} +</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.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 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 %} +</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 +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. +# 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 %} +</div> +</div> + + +## One-vs-Rest (a.k.a. One-vs-All) + +[OneVsRest](http://en.wikipedia.org/wiki/Multiclass_classification#One-vs.-rest) is an example of a machine learning reduction for performing multiclass classification given a base classifier that can perform binary classification efficiently. It is also known as "One-vs-All." `OneVsRest` is implemented as an `Estimator`. For the base classifier it takes instances of `Classifier` and creates a binary classification problem for each of the k classes. The classifier for class i is trained to predict whether the label is i or not, distinguishing class i from all other classes. @@ -28,6 +964,9 @@ The example below demonstrates how to load the <div class="codetabs"> <div data-lang="scala" markdown="1"> + +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 @@ -64,9 +1003,12 @@ println("label\tfpr\n") } {% endhighlight %} </div> + <div data-lang="java" markdown="1"> -{% highlight java %} +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; @@ -88,7 +1030,7 @@ RDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_multiclass_classification_data.txt"); DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class); -DataFrame[] splits = dataFrame.randomSplit(new double[]{0.7, 0.3}, 12345); +DataFrame[] splits = dataFrame.randomSplit(new double[] {0.7, 0.3}, 12345); DataFrame train = splits[0]; DataFrame test = splits[1]; --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
