Github user manishamde commented on a diff in the pull request:
https://github.com/apache/spark/pull/2607#discussion_r19570259
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoosting.scala ---
@@ -0,0 +1,433 @@
+/*
+ * 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.mllib.tree
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.api.java.JavaRDD
+import org.apache.spark.mllib.tree.configuration.BoostingStrategy
+import org.apache.spark.Logging
+import org.apache.spark.mllib.tree.impl.TimeTracker
+import org.apache.spark.mllib.tree.loss.Losses
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.tree.model.{WeightedEnsembleModel,
DecisionTreeModel}
+import org.apache.spark.mllib.tree.configuration.Algo._
+import org.apache.spark.storage.StorageLevel
+import
org.apache.spark.mllib.tree.configuration.EnsembleCombiningStrategy.Sum
+
+/**
+ * :: Experimental ::
+ * A class that implements gradient boosting for regression problems.
+ * @param boostingStrategy Parameters for the gradient boosting algorithm
+ */
+@Experimental
+class GradientBoosting (
+ private val boostingStrategy: BoostingStrategy) extends Serializable
with Logging {
+
+ /**
+ * Method to train a gradient boosting model
+ * @param input Training dataset: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]].
+ * @return GradientBoostingModel that can be used for prediction
+ */
+ def train(input: RDD[LabeledPoint]): WeightedEnsembleModel = {
+ val algo = boostingStrategy.algo
+ algo match {
+ case Regression => GradientBoosting.boost(input, boostingStrategy)
+ case Classification =>
+ val remappedInput = input.map(x => new LabeledPoint((x.label * 2)
- 1, x.features))
+ GradientBoosting.boost(remappedInput, boostingStrategy)
+ case _ =>
+ throw new IllegalArgumentException(s"$algo is not supported by the
gradient boosting.")
+ }
+ }
+
+}
+
+
+object GradientBoosting extends Logging {
+
+ /**
+ * Method to train a gradient boosting model.
+ *
+ * Note: Using
[[org.apache.spark.mllib.tree.GradientBoosting#trainRegressor]]
+ * is recommended to clearly specify regression.
+ * Using
[[org.apache.spark.mllib.tree.GradientBoosting#trainClassifier]]
+ * is recommended to clearly specify regression.
+ *
+ * @param input Training dataset: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]].
+ * For classification, labels should take values {0, 1,
..., numClasses-1}.
+ * For regression, labels are real numbers.
+ * @param boostingStrategy Configuration options for the boosting
algorithm.
+ * @return GradientBoostingModel that can be used for prediction
+ */
+ def train(
+ input: RDD[LabeledPoint],
+ boostingStrategy: BoostingStrategy): WeightedEnsembleModel = {
+ new GradientBoosting(boostingStrategy).train(input)
+ }
+
+ /**
+ * Method to train a gradient boosting regression model.
+ *
+ * @param input Training dataset: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]].
+ * For classification, labels should take values {0, 1,
..., numClasses-1}.
+ * For regression, labels are real numbers.
+ * @param numEstimators Number of estimators used in boosting stages. In
other words,
+ * number of boosting iterations performed.
+ * @param loss Loss function used for minimization during gradient
boosting.
+ * @param maxDepth Maximum depth of the tree.
+ * E.g., depth 0 means 1 leaf node; depth 1 means 1
internal node + 2 leaf nodes.
+ * @param learningRate Learning rate for shrinking the contribution of
each estimator. The
+ * learning rate should be between in the interval
(0, 1]
+ * @param subsample Fraction of the training data used for learning the
decision tree.
+ * @param checkpointPeriod Checkpointing the dataset in memory to avoid
long lineage chains.
+ * @param categoricalFeaturesInfo A map storing information about the
categorical variables and
+ * the number of discrete values they
take. For example,
+ * an entry (n -> k) implies the feature
n is categorical with k
+ * categories 0, 1, 2, ... , k-1. It's
important to note that
+ * features are zero-indexed.
+ * @return GradientBoostingModel that can be used for prediction
+ */
+ def trainRegressor(
+ input: RDD[LabeledPoint],
+ numEstimators: Int,
+ loss: String,
+ maxDepth: Int,
+ learningRate: Double,
+ subsample: Double,
+ checkpointPeriod: Int,
+ categoricalFeaturesInfo: Map[Int, Int]): WeightedEnsembleModel = {
+ val lossType = Losses.fromString(loss)
+ val boostingStrategy = new BoostingStrategy(Regression, numEstimators,
lossType,
+ maxDepth, learningRate, subsample, checkpointPeriod, 2,
+ categoricalFeaturesInfo = categoricalFeaturesInfo)
+ new GradientBoosting(boostingStrategy).train(input)
+ }
+
+
+ /**
+ * Method to train a gradient boosting binary classification model.
+ *
+ * @param input Training dataset: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]].
+ * For classification, labels should take values {0, 1,
..., numClasses-1}.
+ * For regression, labels are real numbers.
+ * @param numEstimators Number of estimators used in boosting stages. In
other words,
+ * number of boosting iterations performed.
+ * @param loss Loss function used for minimization during gradient
boosting.
+ * @param maxDepth Maximum depth of the tree.
+ * E.g., depth 0 means 1 leaf node; depth 1 means 1
internal node + 2 leaf nodes.
+ * @param learningRate Learning rate for shrinking the contribution of
each estimator. The
+ * learning rate should be between in the interval
(0, 1]
+ * @param subsample Fraction of the training data used for learning the
decision tree.
+ * @param checkpointPeriod Checkpointing the dataset in memory to avoid
long lineage chains.
+ * @param numClassesForClassification Number of classes for
classification.
+ * (Ignored for regression.)
+ * Default value is 2 (binary
classification).
+ * @param categoricalFeaturesInfo A map storing information about the
categorical variables and
+ * the number of discrete values they
take. For example,
+ * an entry (n -> k) implies the feature
n is categorical with k
+ * categories 0, 1, 2, ... , k-1. It's
important to note that
+ * features are zero-indexed.
+ * @return GradientBoostingModel that can be used for prediction
+ */
+ def trainClassifier(
+ input: RDD[LabeledPoint],
+ numEstimators: Int,
+ loss: String,
+ maxDepth: Int,
+ learningRate: Double,
+ subsample: Double,
+ checkpointPeriod: Int,
+ numClassesForClassification: Int,
+ categoricalFeaturesInfo: Map[Int, Int]): WeightedEnsembleModel = {
+ val lossType = Losses.fromString(loss)
+ val boostingStrategy = new BoostingStrategy(Regression, numEstimators,
lossType,
+ maxDepth, learningRate, subsample, checkpointPeriod,
numClassesForClassification,
+ categoricalFeaturesInfo = categoricalFeaturesInfo)
+ new GradientBoosting(boostingStrategy).train(input)
+ }
+
+ /**
+ * Java-friendly API for
[[org.apache.spark.mllib.tree.GradientBoosting#trainRegressor]]
+ *
+ * @param input Training dataset: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]].
+ * For classification, labels should take values {0, 1,
..., numClasses-1}.
+ * For regression, labels are real numbers.
+ * @param numEstimators Number of estimators used in boosting stages. In
other words,
+ * number of boosting iterations performed.
+ * @param loss Loss function used for minimization during gradient
boosting.
+ * @param maxDepth Maximum depth of the tree.
+ * E.g., depth 0 means 1 leaf node; depth 1 means 1
internal node + 2 leaf nodes.
+ * @param learningRate Learning rate for shrinking the contribution of
each estimator. The
+ * learning rate should be between in the interval
(0, 1]
+ * @param subsample Fraction of the training data used for learning the
decision tree.
+ * @param checkpointPeriod Checkpointing the dataset in memory to avoid
long lineage chains.
+ * @param categoricalFeaturesInfo A map storing information about the
categorical variables and
+ * the number of discrete values they
take. For example,
+ * an entry (n -> k) implies the feature
n is categorical with k
+ * categories 0, 1, 2, ... , k-1. It's
important to note that
+ * features are zero-indexed.
+ * @return GradientBoostingModel that can be used for prediction
+ */
+ def trainRegressor(
+ input: JavaRDD[LabeledPoint],
+ numEstimators: Int,
+ loss: String,
+ maxDepth: Int,
+ learningRate: Double,
+ subsample: Double,
+ checkpointPeriod: Int,
+ categoricalFeaturesInfo: java.util.Map[java.lang.Integer,
java.lang.Integer])
+ : WeightedEnsembleModel = {
+ val lossType = Losses.fromString(loss)
+ val boostingStrategy = new BoostingStrategy(Regression, numEstimators,
lossType,
+ maxDepth, learningRate, subsample, checkpointPeriod, 2,
categoricalFeaturesInfo =
+ categoricalFeaturesInfo.asInstanceOf[java.util.Map[Int,
Int]].asScala.toMap)
+ new GradientBoosting(boostingStrategy).train(input)
+ }
+
+ /**
+ * Java-friendly API for
[[org.apache.spark.mllib.tree.GradientBoosting#trainClassifier]]
+ *
+ * @param input Training dataset: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]].
+ * For classification, labels should take values {0, 1,
..., numClasses-1}.
+ * For regression, labels are real numbers.
+ * @param numEstimators Number of estimators used in boosting stages. In
other words,
+ * number of boosting iterations performed.
+ * @param loss Loss function used for minimization during gradient
boosting.
+ * @param maxDepth Maximum depth of the tree.
+ * E.g., depth 0 means 1 leaf node; depth 1 means 1
internal node + 2 leaf nodes.
+ * @param learningRate Learning rate for shrinking the contribution of
each estimator. The
+ * learning rate should be between in the interval
(0, 1]
+ * @param subsample Fraction of the training data used for learning the
decision tree.
+ * @param checkpointPeriod Checkpointing the dataset in memory to avoid
long lineage chains.
+ * @param numClassesForClassification Number of classes for
classification.
+ * (Ignored for regression.)
+ * Default value is 2 (binary
classification).
+ * @param categoricalFeaturesInfo A map storing information about the
categorical variables and
+ * the number of discrete values they
take. For example,
+ * an entry (n -> k) implies the feature
n is categorical with k
+ * categories 0, 1, 2, ... , k-1. It's
important to note that
+ * features are zero-indexed.
+ * @return GradientBoostingModel that can be used for prediction
+ */
+ def trainClassifier(
+ input: JavaRDD[LabeledPoint],
+ numEstimators: Int,
+ loss: String,
+ maxDepth: Int,
+ learningRate: Double,
+ subsample: Double,
+ checkpointPeriod: Int,
+ numClassesForClassification: Int,
+ categoricalFeaturesInfo: java.util.Map[java.lang.Integer,
java.lang.Integer])
+ : WeightedEnsembleModel = {
+ val lossType = Losses.fromString(loss)
+ val boostingStrategy = new BoostingStrategy(Regression, numEstimators,
lossType,
+ maxDepth, learningRate, subsample, checkpointPeriod,
+ numClassesForClassification, categoricalFeaturesInfo =
+ categoricalFeaturesInfo.asInstanceOf[java.util.Map[Int,
Int]].asScala.toMap)
+ new GradientBoosting(boostingStrategy).train(input)
+ }
+
+ /**
+ * Method to train a gradient boosting regression model.
+ *
+ * @param input Training dataset: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]].
+ * For classification, labels should take values {0, 1,
..., numClasses-1}.
+ * For regression, labels are real numbers.
+ * @param numEstimators Number of estimators used in boosting stages. In
other words,
+ * number of boosting iterations performed.
+ * @param loss Loss function used for minimization during gradient
boosting.
+ * @param maxDepth Maximum depth of the tree.
+ * E.g., depth 0 means 1 leaf node; depth 1 means 1
internal node + 2 leaf nodes.
+ * @param learningRate Learning rate for shrinking the contribution of
each estimator. The
+ * learning rate should be between in the interval
(0, 1]
+ * @param subsample Fraction of the training data used for learning the
decision tree.
+ * @return GradientBoostingModel that can be used for prediction
+ */
+ def trainRegressor(
+ input: RDD[LabeledPoint],
+ numEstimators: Int,
+ loss: String,
+ maxDepth: Int,
+ learningRate: Double,
+ subsample: Double): WeightedEnsembleModel = {
+ val lossType = Losses.fromString(loss)
+ val boostingStrategy = new BoostingStrategy(Regression, numEstimators,
lossType,
+ maxDepth, learningRate, subsample)
+ new GradientBoosting(boostingStrategy).train(input)
+ }
+
+ /**
+ * Method to train a gradient boosting binary classification model.
+ *
+ * @param input Training dataset: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]].
+ * For classification, labels should take values {0, 1,
..., numClasses-1}.
+ * For regression, labels are real numbers.
+ * @param numEstimators Number of estimators used in boosting stages. In
other words,
+ * number of boosting iterations performed.
+ * @param loss Loss function used for minimization during gradient
boosting.
+ * @param maxDepth Maximum depth of the tree.
+ * E.g., depth 0 means 1 leaf node; depth 1 means 1
internal node + 2 leaf nodes.
+ * @param learningRate Learning rate for shrinking the contribution of
each estimator. The
+ * learning rate should be between in the interval
(0, 1]
+ * @param subsample Fraction of the training data used for learning the
decision tree.
+ * @return GradientBoostingModel that can be used for prediction
+ */
+ def trainClassifier(
+ input: RDD[LabeledPoint],
+ numEstimators: Int,
+ loss: String,
+ maxDepth: Int,
+ learningRate: Double,
+ subsample: Double): WeightedEnsembleModel = {
+ val lossType = Losses.fromString(loss)
+ val boostingStrategy = new BoostingStrategy(Regression, numEstimators,
lossType,
+ maxDepth, learningRate, subsample)
+ new GradientBoosting(boostingStrategy).train(input)
+ }
+
+ /**
+ * Method to train a gradient boosting regression model.
+ *
+ * @param input Training dataset: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]].
+ * For classification, labels should take values {0, 1,
..., numClasses-1}.
+ * For regression, labels are real numbers.
+ * @param boostingStrategy Configuration options for the boosting
algorithm.
+ * @return GradientBoostingModel that can be used for prediction
+ */
+ def trainRegressor(
+ input: RDD[LabeledPoint],
+ boostingStrategy: BoostingStrategy): WeightedEnsembleModel = {
+ val algo = boostingStrategy.algo
+ require(algo == Regression, s"Only Regression algo supported. Provided
algo is $algo.")
+ new GradientBoosting(boostingStrategy).train(input)
+ }
+
+ /**
+ * Method to train a gradient boosting classification model.
+ *
+ * @param input Training dataset: RDD of
[[org.apache.spark.mllib.regression.LabeledPoint]].
+ * For classification, labels should take values {0, 1,
..., numClasses-1}.
+ * For regression, labels are real numbers.
+ * @param boostingStrategy Configuration options for the boosting
algorithm.
+ * @return GradientBoostingModel that can be used for prediction
+ */
+ def trainClassifier(
+ input: RDD[LabeledPoint],
+ boostingStrategy: BoostingStrategy): WeightedEnsembleModel = {
+ val algo = boostingStrategy.algo
+ require(algo == Classification, s"Only Classification algo supported.
Provided algo is $algo.")
+ new GradientBoosting(boostingStrategy).train(input)
+ }
+
+ /**
+ * Internal method for performing regression using trees as base
learners.
+ * @param input training dataset
+ * @param boostingStrategy boosting parameters
+ * @return
+ */
+ private def boost(
+ input: RDD[LabeledPoint],
+ boostingStrategy: BoostingStrategy): WeightedEnsembleModel = {
+
+ val timer = new TimeTracker()
+
+ timer.start("total")
+ timer.start("init")
+
+
+ // Initialize gradient boosting parameters
+ val numEstimators = boostingStrategy.numEstimators
+ val baseLearners = new Array[DecisionTreeModel](numEstimators)
+ val baseLearnerWeights = new Array[Double](numEstimators)
+ val loss = boostingStrategy.loss
+ val learningRate = boostingStrategy.learningRate
+ val checkpointingPeriod = boostingStrategy.checkpointPeriod
+ val strategy = boostingStrategy.strategy
+
+ // Cache input
+ input.persist(StorageLevel.MEMORY_AND_DISK)
+ // Dataset reference for keeping track of last cached dataset in
memory.
+ var lastCachedData = input
+ // Dataset reference for noting dataset marked for unpersisting.
+ var unpersistData = lastCachedData
+
+ timer.stop("init")
+
+ logDebug("##########")
+ logDebug("Building tree 0")
+ logDebug("##########")
+ var data = input
+
+ // 1. Initialize tree
+ timer.start("building tree 0")
+ val firstModel = new DecisionTree(strategy).train(data)
+ timer.stop("building tree 0")
+ baseLearners(0) = firstModel
+ baseLearnerWeights(0) = 1.0
+ logDebug("error of tree = " + loss.computeError(firstModel, data))
+
+ // psuedo-residual for second iteration
+ data = data.map(point => LabeledPoint(loss.lossGradient(firstModel,
point,
+ learningRate), point.features))
+
+ var m = 1
+ while (m < numEstimators) {
+ timer.start(s"building tree $m")
+ logDebug("###################################################")
+ logDebug("Gradient boosting tree iteration " + m)
+ logDebug("###################################################")
+ val model = new DecisionTree(strategy).train(data)
--- End diff --
Correct. That's the big disadvantage of not using the internal format. It
won't affect other algos as much since there is no discretization.
We have a few options:
1. Keep the implementation as is and inform the user about memory
requirements.
2. Persisting RDD[TreePoint] is essential since we perform multiple passes
on it during each tree construction and reading RDD[LabeledPoint] from disk
every time.
3. Persisting RDD[LabeledPoint] and not caching RDD[TreePoint] during tree
construction leading to repeated LabeledPoint -> TreePoint conversions for each
NodeGroup.
Thoughts? cc: @jkbradley
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