Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/5626#discussion_r28987606
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
---
@@ -0,0 +1,180 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.classification
+
+import scala.collection.mutable
+
+import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor}
+import org.apache.spark.ml.impl.tree._
+import org.apache.spark.ml.param.{Params, ParamMap}
+import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel}
+import org.apache.spark.ml.util.MetadataUtils
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.tree.{RandomForest => OldRandomForest}
+import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo,
Strategy => OldStrategy}
+import org.apache.spark.mllib.tree.model.{RandomForestModel =>
OldRandomForestModel}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.DataFrame
+
+
+/**
+ * :: AlphaComponent ::
+ *
+ * [[http://en.wikipedia.org/wiki/Random_forest Random Forest]] learning
algorithm for
+ * classification.
+ * It supports both binary and multiclass labels, as well as both
continuous and categorical
+ * features.
+ */
+@AlphaComponent
+final class RandomForestClassifier
+ extends Predictor[Vector, RandomForestClassifier,
RandomForestClassificationModel]
+ with RandomForestParams with TreeClassifierParams {
+
+ // Override parameter setters from parent trait for Java API
compatibility.
+
+ // Parameters from TreeClassifierParams:
+
+ override def setMaxDepth(value: Int): this.type =
super.setMaxDepth(value)
+
+ override def setMaxBins(value: Int): this.type = super.setMaxBins(value)
+
+ override def setMinInstancesPerNode(value: Int): this.type =
+ super.setMinInstancesPerNode(value)
+
+ override def setMinInfoGain(value: Double): this.type =
super.setMinInfoGain(value)
+
+ override def setMaxMemoryInMB(value: Int): this.type =
super.setMaxMemoryInMB(value)
+
+ override def setCacheNodeIds(value: Boolean): this.type =
super.setCacheNodeIds(value)
+
+ override def setCheckpointInterval(value: Int): this.type =
super.setCheckpointInterval(value)
+
+ override def setImpurity(value: String): this.type =
super.setImpurity(value)
+
+ // Parameters from TreeEnsembleParams:
+
+ override def setSubsamplingRate(value: Double): this.type =
super.setSubsamplingRate(value)
+
+ override def setSeed(value: Long): this.type = super.setSeed(value)
+
+ // Parameters from RandomForestParams:
+
+ override def setNumTrees(value: Int): this.type =
super.setNumTrees(value)
+
+ override def setFeaturesPerNode(value: String): this.type =
super.setFeaturesPerNode(value)
+
+ override protected def train(
+ dataset: DataFrame,
+ paramMap: ParamMap): RandomForestClassificationModel = {
+ val categoricalFeatures: Map[Int, Int] =
+
MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol)))
+ val numClasses: Int =
MetadataUtils.getNumClasses(dataset.schema(paramMap(labelCol))) match {
+ case Some(n: Int) => n
+ case None => throw new
IllegalArgumentException("RandomForestClassifier was given input" +
+ s" with invalid label column, without the number of classes
specified.")
+ // TODO: Automatically index labels.
+ }
+ val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset,
paramMap)
+ val strategy =
+ super.getOldStrategy(categoricalFeatures, numClasses,
OldAlgo.Classification, getOldImpurity)
+ val oldModel = OldRandomForest.trainClassifier(
+ oldDataset, strategy, getNumTrees, getFeaturesPerNodeStr,
getSeed.toInt)
+ RandomForestClassificationModel.fromOld(oldModel, this, paramMap,
categoricalFeatures)
+ }
+}
+
+object RandomForestClassifier {
+ /** Accessor for supported impurity settings */
--- End diff --
Need to put available options in the JavaDoc.
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