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[SPARK-19591][ML][MLlib] Add sample weights to decision trees #16722

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2d86cea
add weights to dt
sethah Jan 27, 2017
7dc1437
dt tests passing
sethah Jan 27, 2017
2729a63
typo
sethah Jan 28, 2017
2112720
mima
sethah Jan 28, 2017
159c5a6
test failure
sethah Jan 31, 2017
1db8494
address some review
sethah Feb 1, 2017
48b1258
address review
sethah Feb 6, 2017
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add weights to dt
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@sethah
sethah committed Jan 27, 2017
commit 2d86cea640634a205e378bddee0b01780d019ea2
46 changes: 34 additions & 12 deletions mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
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Expand Up @@ -22,18 +22,21 @@ import org.json4s.{DefaultFormats, JObject}
import org.json4s.JsonDSL._

import org.apache.spark.annotation.Since
import org.apache.spark.ml.feature.LabeledPoint
import org.apache.spark.ml.feature.{Instance, LabeledPoint}
import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.param.shared.HasWeightCol
import org.apache.spark.ml.tree._
import org.apache.spark.ml.tree.{DecisionTreeModel, Node, TreeClassifierParams}
import org.apache.spark.ml.tree.DecisionTreeModelReadWrite._
import org.apache.spark.ml.tree.impl.RandomForest
import org.apache.spark.ml.util._
import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeModel}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.Dataset

import org.apache.spark.sql.{Dataset, Row}
import org.apache.spark.sql.functions.{col, lit}
import org.apache.spark.sql.types.DoubleType

/**
* Decision tree learning algorithm (http://en.wikipedia.org/wiki/Decision_tree_learning)
Expand All @@ -45,7 +48,7 @@ import org.apache.spark.sql.Dataset
class DecisionTreeClassifier @Since("1.4.0") (
@Since("1.4.0") override val uid: String)
extends ProbabilisticClassifier[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel]
with DecisionTreeClassifierParams with DefaultParamsWritable {
with DecisionTreeClassifierParams with HasWeightCol with DefaultParamsWritable {
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@imatiach-msft imatiach-msft Jan 31, 2017

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was there a specific reason not to put this on the DecisionTreeClassifierParams.
it looks like the other classifiers that have this are:
LinearSVC
LogisticRegression
NaïveBayes
and regressors:
GeneralizedLinearRegression
IsotonicRegression
LinearRegression
and all have it on the params, not on the class.
However, I do agree with you that it really makes no sense for the model to have this settable, although it may be useful for users to get the information on the model.


@Since("1.4.0")
def this() = this(Identifiable.randomUID("dtc"))
Expand All @@ -65,6 +68,9 @@ class DecisionTreeClassifier @Since("1.4.0") (
override def setMinInstancesPerNode(value: Int): this.type = set(minInstancesPerNode, value)

/** @group setParam */
@Since("2.2.0")
def setMinWeightFractionPerNode(value: Double): this.type = set(minWeightFractionPerNode, value)

@Since("1.4.0")
override def setMinInfoGain(value: Double): this.type = set(minInfoGain, value)

Expand Down Expand Up @@ -96,6 +102,16 @@ class DecisionTreeClassifier @Since("1.4.0") (
@Since("1.6.0")
override def setSeed(value: Long): this.type = set(seed, value)

/**
* Sets the value of param [[weightCol]].
* If this is not set or empty, we treat all instance weights as 1.0.
* Default is not set, so all instances have weight one.
*
* @group setParam
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it looks like by removing this method call you are removing some valuable validation logic (that exists in the base class).
specifically, this is the logic:
require(numClasses > 0, s"Classifier (in extractLabeledPoints) found numClasses =" +
s" $numClasses, but requires numClasses > 0.")

require(label % 1 == 0 && label >= 0 && label < numClasses, s"Classifier was given" +
s" dataset with invalid label $label. Labels must be integers in range" +
s" [0, $numClasses).")

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@sethah sethah Feb 1, 2017

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Good catch. Actually this problem exists elsewhere (LogisticRegression, e.g.) What to do you think about adding it back manually here and then addressing the larger issue in a separate JIRA?

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@imatiach-msft imatiach-msft Feb 2, 2017

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I would say that's fine if it was only in one place, but I also see this pattern in DecisionTreeRegressor.scala, it seems like we should be able to refactor this part out

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@sethah sethah Feb 6, 2017

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For regressors, extractLabeledPoints doesn't do any extra checking. The larger issue is that we are manually "extracting instances" but we have convenience methods for labeled points. Since correcting it now, in this PR, likely means implementing the framework to correct it everywhere - which is a larger and orthogonal change, I think we could just add the check manually to the classifier, then create a JIRA that addresses consolidating these, probably by adding extractInstances methods analogous their labeled point counterparts. This PR is large enough as is, without having to think about adding that method, then implementing it in all the other algos that manually extract instances, IMO.

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@imatiach-msft imatiach-msft Feb 15, 2017 •
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sounds reasonable, thanks for the explanation.

*/
@Since("2.2.0")
def setWeightCol(value: String): this.type = set(weightCol, value)

override protected def train(dataset: Dataset[_]): DecisionTreeClassificationModel = {
val categoricalFeatures: Map[Int, Int] =
MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
Expand All @@ -106,14 +122,18 @@ class DecisionTreeClassifier @Since("1.4.0") (
".train() called with non-matching numClasses and thresholds.length." +
s" numClasses=$numClasses, but thresholds has length ${$(thresholds).length}")
}

val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, numClasses)
val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))
val instances =
dataset.select(col($(labelCol)).cast(DoubleType), w, col($(featuresCol))).rdd.map {
case Row(label: Double, weight: Double, features: Vector) =>
Instance(label, weight, features)
}
val strategy = getOldStrategy(categoricalFeatures, numClasses)

val instr = Instrumentation.create(this, oldDataset)
val instr = Instrumentation.create(this, instances)
instr.logParams(params: _*)

val trees = RandomForest.run(oldDataset, strategy, numTrees = 1, featureSubsetStrategy = "all",
val trees = RandomForest.run(instances, strategy, numTrees = 1, featureSubsetStrategy = "all",
seed = $(seed), instr = Some(instr), parentUID = Some(uid))

val m = trees.head.asInstanceOf[DecisionTreeClassificationModel]
Expand All @@ -124,11 +144,12 @@ class DecisionTreeClassifier @Since("1.4.0") (
/** (private[ml]) Train a decision tree on an RDD */
private[ml] def train(data: RDD[LabeledPoint],
oldStrategy: OldStrategy): DecisionTreeClassificationModel = {
val instr = Instrumentation.create(this, data)
instr.logParams(params: _*)

val trees = RandomForest.run(data, oldStrategy, numTrees = 1, featureSubsetStrategy = "all",
seed = 0L, instr = Some(instr), parentUID = Some(uid))
val instances = data.map {lp => Instance(lp.label, 1.0, lp.features)}
val instr = Instrumentation.create(this, instances)
instr.logParams(params: _*)
val trees = RandomForest.run(instances, oldStrategy, numTrees = 1,
featureSubsetStrategy = "all", seed = 0L, instr = Some(instr), parentUID = Some(uid))

val m = trees.head.asInstanceOf[DecisionTreeClassificationModel]
instr.logSuccess(m)
Expand Down Expand Up @@ -176,6 +197,7 @@ class DecisionTreeClassificationModel private[ml] (

/**
* Construct a decision tree classification model.
*
* @param rootNode Root node of tree, with other nodes attached.
*/
private[ml] def this(rootNode: Node, numFeatures: Int, numClasses: Int) =
Expand Down
21 changes: 12 additions & 9 deletions mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
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Expand Up @@ -21,19 +21,20 @@ import org.json4s.{DefaultFormats, JObject}
import org.json4s.JsonDSL._

import org.apache.spark.annotation.Since
import org.apache.spark.ml.feature.LabeledPoint
import org.apache.spark.ml.feature.Instance
import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.tree._
import org.apache.spark.ml.tree.{RandomForestParams, TreeClassifierParams, TreeEnsembleModel}
import org.apache.spark.ml.tree.impl.RandomForest
import org.apache.spark.ml.util._
import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
import org.apache.spark.ml.util.DefaultParamsReader.Metadata
import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Dataset}
import org.apache.spark.sql.functions._

import org.apache.spark.sql.{DataFrame, Dataset, Row}
import org.apache.spark.sql.functions.{col, udf}
import org.apache.spark.sql.types.DoubleType

/**
* <a href="http://en.wikipedia.org/wiki/Random_forest">Random Forest</a> learning algorithm for
Expand Down Expand Up @@ -126,20 +127,22 @@ class RandomForestClassifier @Since("1.4.0") (
s" numClasses=$numClasses, but thresholds has length ${$(thresholds).length}")
}

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@imatiach-msft imatiach-msft Jan 31, 2017

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same as above, it looks like some validation logic is missing here

val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, numClasses)
val instances = dataset.select(col($(labelCol)).cast(DoubleType), col($(featuresCol))).rdd.map {
case Row(label: Double, features: Vector) => Instance(label, 1.0, features)
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it looks like we aren't getting the weight column here; not sure why this file needed to be changed

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@sethah sethah Feb 1, 2017

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We have to pass in RDD[Instance] to RandomForest.run. I changed this back to use extractLabeledPoints

}
val strategy =
super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, getOldImpurity)

val instr = Instrumentation.create(this, oldDataset)
val instr = Instrumentation.create(this, instances)
instr.logParams(labelCol, featuresCol, predictionCol, probabilityCol, rawPredictionCol,
impurity, numTrees, featureSubsetStrategy, maxDepth, maxBins, maxMemoryInMB, minInfoGain,
minInstancesPerNode, seed, subsamplingRate, thresholds, cacheNodeIds, checkpointInterval)

val trees = RandomForest
.run(oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed, Some(instr))
.run(instances, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed, Some(instr))
.map(_.asInstanceOf[DecisionTreeClassificationModel])

val numFeatures = oldDataset.first().features.size
val numFeatures = instances.first().features.size
val m = new RandomForestClassificationModel(trees, numFeatures, numClasses)
instr.logSuccess(m)
m
Expand Down
2 changes: 1 addition & 1 deletion mllib/src/main/scala/org/apache/spark/ml/feature/Instance.scala
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Expand Up @@ -26,4 +26,4 @@ import org.apache.spark.ml.linalg.Vector
* @param weight The weight of this instance.
* @param features The vector of features for this data point.
*/
private[ml] case class Instance(label: Double, weight: Double, features: Vector)
private[spark] case class Instance(label: Double, weight: Double, features: Vector)
7 changes: 7 additions & 0 deletions mllib/src/main/scala/org/apache/spark/ml/feature/LabeledPoint.scala
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Expand Up @@ -35,4 +35,11 @@ case class LabeledPoint(@Since("2.0.0") label: Double, @Since("2.0.0") features:
override def toString: String = {
s"($label,$features)"
}

private[spark] def toInstance: Instance = toInstance(1.0)
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this is kind of a nit pick, and optional, but I would usually refactor out magic numbers like 1.0 as something like "defaultWeight" and reuse it elsewhere, but it's not really necessary in this case since it probably won't ever change

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@sethah sethah Feb 6, 2017

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Actually, I'd prefer to remove the no arg function and be explicit everywhere. That way there is no ambiguity or unintended effects if someone changes the default value. Sound ok?


private[spark] def toInstance(weight: Double): Instance = {
Instance(label, weight, features)
}

}
40 changes: 32 additions & 8 deletions mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
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Expand Up @@ -23,8 +23,9 @@ import org.json4s.JsonDSL._

import org.apache.spark.annotation.Since
import org.apache.spark.ml.{PredictionModel, Predictor}
import org.apache.spark.ml.feature.LabeledPoint
import org.apache.spark.ml.feature.{Instance, LabeledPoint}
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.ml.param.shared.HasWeightCol
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.tree._
import org.apache.spark.ml.tree.DecisionTreeModelReadWrite._
Expand All @@ -33,8 +34,10 @@ import org.apache.spark.ml.util._
import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeModel}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Dataset}
import org.apache.spark.sql.{DataFrame, Dataset, Row}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.functions.{col, lit}
import org.apache.spark.sql.types.DoubleType


/**
Expand All @@ -45,7 +48,7 @@ import org.apache.spark.sql.functions._
@Since("1.4.0")
class DecisionTreeRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: String)
extends Predictor[Vector, DecisionTreeRegressor, DecisionTreeRegressionModel]
with DecisionTreeRegressorParams with DefaultParamsWritable {
with DecisionTreeRegressorParams with DefaultParamsWritable with HasWeightCol {

@Since("1.4.0")
def this() = this(Identifiable.randomUID("dtr"))
Expand All @@ -64,6 +67,9 @@ class DecisionTreeRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: S
override def setMinInstancesPerNode(value: Int): this.type = set(minInstancesPerNode, value)

/** @group setParam */
@Since("2.2.0")
def setMinWeightFractionPerNode(value: Double): this.type = set(minWeightFractionPerNode, value)

@Since("1.4.0")
override def setMinInfoGain(value: Double): this.type = set(minInfoGain, value)

Expand Down Expand Up @@ -99,16 +105,31 @@ class DecisionTreeRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: S
@Since("2.0.0")
def setVarianceCol(value: String): this.type = set(varianceCol, value)

/**
* Sets the value of param [[weightCol]].
* If this is not set or empty, we treat all instance weights as 1.0.
* Default is not set, so all instances have weight one.
*
* @group setParam
*/
@Since("2.2.0")
def setWeightCol(value: String): this.type = set(weightCol, value)

override protected def train(dataset: Dataset[_]): DecisionTreeRegressionModel = {
val categoricalFeatures: Map[Int, Int] =
MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))
val instances =
dataset.select(col($(labelCol)).cast(DoubleType), w, col($(featuresCol))).rdd.map {
case Row(label: Double, weight: Double, features: Vector) =>
Instance(label, weight, features)
}
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@imatiach-msft imatiach-msft Feb 2, 2017

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the code above looks the same as the classifier, can we refactor somehow:

val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol)) 
val instances = 
  dataset.select(col($(labelCol)).cast(DoubleType), w, col($(featuresCol))).rdd.map { 
    case Row(label: Double, weight: Double, features: Vector) => 
      Instance(label, weight, features)

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@imatiach-msft imatiach-msft Feb 15, 2017 •
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update: it sounds like you are going to create a separate JIRA for refactoring this code, that is reasonable to me.

val strategy = getOldStrategy(categoricalFeatures)

val instr = Instrumentation.create(this, oldDataset)
val instr = Instrumentation.create(this, instances)
instr.logParams(params: _*)

val trees = RandomForest.run(oldDataset, strategy, numTrees = 1, featureSubsetStrategy = "all",
val trees = RandomForest.run(instances, strategy, numTrees = 1, featureSubsetStrategy = "all",
seed = $(seed), instr = Some(instr), parentUID = Some(uid))

val m = trees.head.asInstanceOf[DecisionTreeRegressionModel]
Expand All @@ -122,8 +143,9 @@ class DecisionTreeRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: S
val instr = Instrumentation.create(this, data)
instr.logParams(params: _*)

val trees = RandomForest.run(data, oldStrategy, numTrees = 1, featureSubsetStrategy = "all",
seed = $(seed), instr = Some(instr), parentUID = Some(uid))
val instances = data.map {lp => Instance(lp.label, 1.0, lp.features)}
val trees = RandomForest.run(instances, oldStrategy, numTrees = 1,
featureSubsetStrategy = "all", seed = $(seed), instr = Some(instr), parentUID = Some(uid))

val m = trees.head.asInstanceOf[DecisionTreeRegressionModel]
instr.logSuccess(m)
Expand Down Expand Up @@ -153,6 +175,7 @@ object DecisionTreeRegressor extends DefaultParamsReadable[DecisionTreeRegressor
* <a href="http://en.wikipedia.org/wiki/Decision_tree_learning">
* Decision tree (Wikipedia)</a> model for regression.
* It supports both continuous and categorical features.
*
* @param rootNode Root of the decision tree
*/
@Since("1.4.0")
Expand All @@ -171,6 +194,7 @@ class DecisionTreeRegressionModel private[ml] (

/**
* Construct a decision tree regression model.
*
* @param rootNode Root node of tree, with other nodes attached.
*/
private[ml] def this(rootNode: Node, numFeatures: Int) =
Expand Down
22 changes: 11 additions & 11 deletions mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
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Expand Up @@ -22,7 +22,7 @@ import org.json4s.JsonDSL._

import org.apache.spark.annotation.Since
import org.apache.spark.ml.{PredictionModel, Predictor}
import org.apache.spark.ml.feature.LabeledPoint
import org.apache.spark.ml.feature.Instance
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.tree._
Expand All @@ -31,10 +31,9 @@ import org.apache.spark.ml.util._
import org.apache.spark.ml.util.DefaultParamsReader.Metadata
import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Dataset}
import org.apache.spark.sql.functions._

import org.apache.spark.sql.{DataFrame, Dataset, Row}
import org.apache.spark.sql.functions.{col, udf}
import org.apache.spark.sql.types.DoubleType

/**
* <a href="http://en.wikipedia.org/wiki/Random_forest">Random Forest</a>
Expand Down Expand Up @@ -65,7 +64,6 @@ class RandomForestRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: S
@Since("1.4.0")
override def setMinInstancesPerNode(value: Int): this.type = set(minInstancesPerNode, value)

/** @group setParam */
@Since("1.4.0")
override def setMinInfoGain(value: Double): this.type = set(minInfoGain, value)

Expand Down Expand Up @@ -117,20 +115,22 @@ class RandomForestRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: S
override protected def train(dataset: Dataset[_]): RandomForestRegressionModel = {
val categoricalFeatures: Map[Int, Int] =
MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)

val instances = dataset.select(col($(labelCol)).cast(DoubleType), col($(featuresCol))).rdd.map {
case Row(label: Double, features: Vector) => Instance(label, 1.0, features)
}
val strategy =
super.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, getOldImpurity)

val instr = Instrumentation.create(this, oldDataset)
val instr = Instrumentation.create(this, instances)
instr.logParams(labelCol, featuresCol, predictionCol, impurity, numTrees,
featureSubsetStrategy, maxDepth, maxBins, maxMemoryInMB, minInfoGain,
minInstancesPerNode, seed, subsamplingRate, cacheNodeIds, checkpointInterval)

val trees = RandomForest
.run(oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed, Some(instr))
.run(instances, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed, Some(instr))
.map(_.asInstanceOf[DecisionTreeRegressionModel])

val numFeatures = oldDataset.first().features.size
val numFeatures = instances.first().features.size
val m = new RandomForestRegressionModel(trees, numFeatures)
instr.logSuccess(m)
m
Expand Down
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