Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/5626#discussion_r29024743
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
---
@@ -0,0 +1,184 @@
+/*
+ * 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 setFeatureSubsetStrategy(value: String): this.type =
+ super.setFeatureSubsetStrategy(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, getFeatureSubsetStrategy,
getSeed.toInt)
+ RandomForestClassificationModel.fromOld(oldModel, this, paramMap,
categoricalFeatures)
+ }
+}
+
+object RandomForestClassifier {
+ /** Accessor for supported impurity settings: entropy, gini */
+ final val supportedImpurities: Array[String] =
TreeClassifierParams.supportedImpurities
+
+ /** Accessor for supported featureSubsetStrategy settings: auto, all,
onethird, sqrt, log2 */
+ final val supportedFeatureSubsetStrategies: Array[String] =
+ RandomForestParams.supportedFeatureSubsetStrategies
+}
+
+/**
+ * :: AlphaComponent ::
+ *
+ * [[http://en.wikipedia.org/wiki/Random_forest Random Forest]] model for
classification.
+ * It supports both binary and multiclass labels, as well as both
continuous and categorical
+ * features.
+ * @param _trees Decision trees in the ensemble.
+ * Warning: These have null parents.
+ */
+@AlphaComponent
+final class RandomForestClassificationModel private[ml] (
+ override val parent: RandomForestClassifier,
+ override val fittingParamMap: ParamMap,
+ private val _trees: Array[DecisionTreeClassificationModel])
+ extends PredictionModel[Vector, RandomForestClassificationModel]
+ with TreeEnsembleModel with Serializable {
+
+ require(numTrees > 0, "RandomForestClassificationModel requires at least
1 tree.")
+
+ override def trees: Array[DecisionTreeModel] =
_trees.asInstanceOf[Array[DecisionTreeModel]]
+
+ // Note: We may add support for weights (based on tree performance)
later on.
+ lazy val _treeWeights: Array[Double] = Array.fill[Double](numTrees)(1.0)
--- End diff --
private
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
