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Posted to reviews@spark.apache.org by imatiach-msft <gi...@git.apache.org> on 2018/06/25 05:00:28 UTC
[GitHub] spark pull request #21632: [SPARK-19591][ML][MLlib] Add sample weights to de...
GitHub user imatiach-msft opened a pull request:
https://github.com/apache/spark/pull/21632
[SPARK-19591][ML][MLlib] Add sample weights to decision trees
This is updated PR https://github.com/apache/spark/pull/16722
What changes were proposed in this pull request?
This patch adds support for sample weights to DecisionTreeRegressor and DecisionTreeClassifier.
Note: This patch does not add support for sample weights to RandomForest. As discussed in the JIRA, we would like to add sample weights into the bagging process. This patch is large enough as is, and there are some additional considerations to be made for random forests. Since the machinery introduced here needs to be present regardless, I have opted to leave random forests for a follow up pr.
How was this patch tested?
The algorithms are tested to ensure that:
Arbitrary scaling of constant weights has no effect
Outliers with small weights do not affect the learned model
Oversampling and weighting are equivalent
Unit tests are also added to test other smaller components.
Summary of changes
Impurity aggregators now store weighted sufficient statistics. They also store a raw count, however, since this is needed to use minInstancesPerNode.
Impurity aggregators now also hold the raw count.
This patch maintains the meaning of minInstancesPerNode, in that the parameter still corresponds to raw, unweighted counts. It also adds a new parameter minWeightFractionPerNode which requires that nodes must contain at least minWeightFractionPerNode * weightedNumExamples total weight.
This patch modifies findSplitsForContinuousFeatures to use weighted sums. Unit tests are added.
TreePoint is modified to hold a sample weight
BaggedPoint is modified from:
private[spark] class BaggedPoint[Datum](val datum: Datum, val subsampleWeights: Array[Double]) extends Serializable
to
private[spark] class BaggedPoint[Datum](
val datum: Datum,
val subsampleCounts: Array[Int],
val sampleWeight: Double) extends Serializable
We do not simply multiply the counts by the weight and store that because we need the raw counts and the weight in order to use both minInstancesPerNode and minWeightPerNode
Note: many of the changed files are due simply to using Instance instead of LabeledPoint
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/imatiach-msft/spark ilmat/sample-weights
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/21632.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #21632
----
commit b5278e5a54156c14b6a8bdd3256f18e1ff3b4128
Author: Ilya Matiach <il...@...>
Date: 2017-01-27T16:38:36Z
add weights to dt
----
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[GitHub] spark issue #21632: [SPARK-19591][ML][MLlib] Add sample weights to decision ...
Posted by imatiach-msft <gi...@git.apache.org>.
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jenkins retest this please
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cc also @jkbradley
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jenkins retest this please
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@holdenk @jkbradley ping... could you please take a look at this PR? Thank you!
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[GitHub] spark pull request #21632: [SPARK-19591][ML][MLlib] Add sample weights to de...
Posted by imatiach-msft <gi...@git.apache.org>.
Github user imatiach-msft commented on a diff in the pull request:
https://github.com/apache/spark/pull/21632#discussion_r209839752
--- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala ---
@@ -97,28 +103,48 @@ 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
+ */
+ @Since("2.2.0")
--- End diff --
need to change this version to 2.4.0
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jenkins retest this pretty please :)
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--- Diff: mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala ---
@@ -159,6 +169,31 @@ class DecisionTreeRegressorSuite extends MLTest with DefaultReadWriteTest {
}
}
+ test("training with sample weights") {
+ val df = linearRegressionData
+ val numClasses = 0
+ val testParams = Seq(5, 10)
+ for (maxDepth <- testParams) {
+ val estimator = new DecisionTreeRegressor()
+ .setImpurity("variance")
+ .setMaxDepth(maxDepth)
+ .setMaxBins(100)
+ .setMinWeightFractionPerNode(0.05)
+ .setSeed(123)
+ // TODO: This doesn't look correct, need to debug to figure out what is wrong with tolerance
--- End diff --
fixed after some debugging
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--- Diff: mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala ---
@@ -65,6 +68,9 @@ 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")
--- End diff --
need to change this version to 2.4.0
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--- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala ---
@@ -66,6 +69,9 @@ class DecisionTreeClassifier @Since("1.4.0") (
override def setMinInstancesPerNode(value: Int): this.type = set(minInstancesPerNode, value)
/** @group setParam */
+ @Since("2.2.0")
--- End diff --
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retest this please
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jenkins retest this please
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jenkins retest this please
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--- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala ---
@@ -97,28 +103,48 @@ 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
+ */
+ @Since("2.2.0")
--- End diff --
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--- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala ---
@@ -66,6 +69,9 @@ class DecisionTreeClassifier @Since("1.4.0") (
override def setMinInstancesPerNode(value: Int): this.type = set(minInstancesPerNode, value)
/** @group setParam */
+ @Since("2.2.0")
--- End diff --
need to change this version to 2.4.0
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--- Diff: mllib/src/test/scala/org/apache/spark/ml/tree/impl/RandomForestSuite.scala ---
@@ -700,6 +722,82 @@ class RandomForestSuite extends SparkFunSuite with MLlibTestSparkContext {
assert(unprunedTree.numNodes === 5)
assert(RandomForestSuite.getSumLeafCounters(List(prunedTree.rootNode)) === arr.size)
}
+
+ test("weights at arbitrary scale") {
+ val arr = EnsembleTestHelper.generateOrderedLabeledPoints(3, 10)
+ val rddWithUnitWeights = sc.parallelize(arr.map(_.asML.toInstance(1.0)))
+ val rddWithSmallWeights = rddWithUnitWeights.map { inst =>
+ Instance(inst.label, 0.001, inst.features)
+ }
+ val rddWithBigWeights = rddWithUnitWeights.map { inst =>
+ Instance(inst.label, 1000, inst.features)
+ }
+ val strategy = new OldStrategy(OldAlgo.Classification, Gini, 3, 2)
+ val unitWeightTrees = RandomForest.run(rddWithUnitWeights, strategy, 3, "all", 42L, None)
+
+ val smallWeightTrees = RandomForest.run(rddWithSmallWeights, strategy, 3, "all", 42L, None)
+ unitWeightTrees.zip(smallWeightTrees).foreach { case (unitTree, smallWeightTree) =>
+ TreeTests.checkEqual(unitTree, smallWeightTree)
+ }
+
+ val bigWeightTrees = RandomForest.run(rddWithBigWeights, strategy, 3, "all", 42L, None)
+ unitWeightTrees.zip(bigWeightTrees).foreach { case (unitTree, bigWeightTree) =>
+ TreeTests.checkEqual(unitTree, bigWeightTree)
+ }
+ }
+
+ test("minWeightFraction and minInstancesPerNode") {
+ val data = Array(
+ Instance(0.0, 1.0, Vectors.dense(0.0)),
+ Instance(0.0, 1.0, Vectors.dense(0.0)),
+ Instance(0.0, 1.0, Vectors.dense(0.0)),
+ Instance(0.0, 1.0, Vectors.dense(0.0)),
+ Instance(1.0, 0.1, Vectors.dense(1.0))
+ )
+ val rdd = sc.parallelize(data)
+ val strategy = new OldStrategy(OldAlgo.Classification, Gini, 3, 2,
+ minWeightFractionPerNode = 0.5)
+ val Array(tree1) = RandomForest.run(rdd, strategy, 1, "all", 42L, None)
+ assert(tree1.depth == 0)
+
+ strategy.minWeightFractionPerNode = 0.0
+ val Array(tree2) = RandomForest.run(rdd, strategy, 1, "all", 42L, None)
+ assert(tree2.depth == 1)
+
+ strategy.minInstancesPerNode = 2
+ val Array(tree3) = RandomForest.run(rdd, strategy, 1, "all", 42L, None)
+ assert(tree3.depth == 0)
+
+ strategy.minInstancesPerNode = 1
+ val Array(tree4) = RandomForest.run(rdd, strategy, 1, "all", 42L, None)
+ assert(tree4.depth == 1)
+ }
+
+ test("extremely unbalanced weighting with bagging") {
+ /*
+ This test verifies that sample weights are taken into account during the
+ bagging process, instead of applied afterwards. If sample weights were applied
+ after the sampling is done, then some of the trees would not contain the heavily
+ weighted example. Here, we verify that all trees predict the correct value.
+ */
+ val data = Array(
+ Instance(0.0, 1.0, Vectors.dense(0.0)),
+ Instance(0.0, 1.0, Vectors.dense(0.0)),
+ Instance(0.0, 1.0, Vectors.dense(0.0)),
+ Instance(0.0, 1.0, Vectors.dense(0.0)),
+ Instance(1.0, 1e6, Vectors.dense(1.0))
+ )
+ val rdd = sc.parallelize(data)
+ val strategy = new OldStrategy(OldAlgo.Classification, Gini, 3, 2)
+ val trees = RandomForest.run(rdd, strategy, 10, "all", 42L, None)
+ val features = Vectors.dense(1.0)
+ trees.foreach { tree =>
+ val predict = tree.rootNode.predictImpl(features).prediction
+ // TODO: need to investigate why this went to 0 for some trees
--- End diff --
need to investigate this test
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jenkins retest this please
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looks like a random test failure with hive client suite (not related to the PR), I'll try updating to latest master and rebuild...
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looks like a random failure
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[GitHub] spark pull request #21632: [SPARK-19591][ML][MLlib] Add sample weights to de...
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https://github.com/apache/spark/pull/21632#discussion_r218657039
--- Diff: mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala ---
@@ -65,6 +68,9 @@ 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")
--- End diff --
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Github user imatiach-msft commented on a diff in the pull request:
https://github.com/apache/spark/pull/21632#discussion_r220413827
--- Diff: mllib/src/test/scala/org/apache/spark/ml/tree/impl/RandomForestSuite.scala ---
@@ -700,6 +722,82 @@ class RandomForestSuite extends SparkFunSuite with MLlibTestSparkContext {
assert(unprunedTree.numNodes === 5)
assert(RandomForestSuite.getSumLeafCounters(List(prunedTree.rootNode)) === arr.size)
}
+
+ test("weights at arbitrary scale") {
+ val arr = EnsembleTestHelper.generateOrderedLabeledPoints(3, 10)
+ val rddWithUnitWeights = sc.parallelize(arr.map(_.asML.toInstance(1.0)))
+ val rddWithSmallWeights = rddWithUnitWeights.map { inst =>
+ Instance(inst.label, 0.001, inst.features)
+ }
+ val rddWithBigWeights = rddWithUnitWeights.map { inst =>
+ Instance(inst.label, 1000, inst.features)
+ }
+ val strategy = new OldStrategy(OldAlgo.Classification, Gini, 3, 2)
+ val unitWeightTrees = RandomForest.run(rddWithUnitWeights, strategy, 3, "all", 42L, None)
+
+ val smallWeightTrees = RandomForest.run(rddWithSmallWeights, strategy, 3, "all", 42L, None)
+ unitWeightTrees.zip(smallWeightTrees).foreach { case (unitTree, smallWeightTree) =>
+ TreeTests.checkEqual(unitTree, smallWeightTree)
+ }
+
+ val bigWeightTrees = RandomForest.run(rddWithBigWeights, strategy, 3, "all", 42L, None)
+ unitWeightTrees.zip(bigWeightTrees).foreach { case (unitTree, bigWeightTree) =>
+ TreeTests.checkEqual(unitTree, bigWeightTree)
+ }
+ }
+
+ test("minWeightFraction and minInstancesPerNode") {
+ val data = Array(
+ Instance(0.0, 1.0, Vectors.dense(0.0)),
+ Instance(0.0, 1.0, Vectors.dense(0.0)),
+ Instance(0.0, 1.0, Vectors.dense(0.0)),
+ Instance(0.0, 1.0, Vectors.dense(0.0)),
+ Instance(1.0, 0.1, Vectors.dense(1.0))
+ )
+ val rdd = sc.parallelize(data)
+ val strategy = new OldStrategy(OldAlgo.Classification, Gini, 3, 2,
+ minWeightFractionPerNode = 0.5)
+ val Array(tree1) = RandomForest.run(rdd, strategy, 1, "all", 42L, None)
+ assert(tree1.depth == 0)
+
+ strategy.minWeightFractionPerNode = 0.0
+ val Array(tree2) = RandomForest.run(rdd, strategy, 1, "all", 42L, None)
+ assert(tree2.depth == 1)
+
+ strategy.minInstancesPerNode = 2
+ val Array(tree3) = RandomForest.run(rdd, strategy, 1, "all", 42L, None)
+ assert(tree3.depth == 0)
+
+ strategy.minInstancesPerNode = 1
+ val Array(tree4) = RandomForest.run(rdd, strategy, 1, "all", 42L, None)
+ assert(tree4.depth == 1)
+ }
+
+ test("extremely unbalanced weighting with bagging") {
+ /*
+ This test verifies that sample weights are taken into account during the
+ bagging process, instead of applied afterwards. If sample weights were applied
+ after the sampling is done, then some of the trees would not contain the heavily
+ weighted example. Here, we verify that all trees predict the correct value.
+ */
+ val data = Array(
+ Instance(0.0, 1.0, Vectors.dense(0.0)),
+ Instance(0.0, 1.0, Vectors.dense(0.0)),
+ Instance(0.0, 1.0, Vectors.dense(0.0)),
+ Instance(0.0, 1.0, Vectors.dense(0.0)),
+ Instance(1.0, 1e6, Vectors.dense(1.0))
+ )
+ val rdd = sc.parallelize(data)
+ val strategy = new OldStrategy(OldAlgo.Classification, Gini, 3, 2)
+ val trees = RandomForest.run(rdd, strategy, 10, "all", 42L, None)
+ val features = Vectors.dense(1.0)
+ trees.foreach { tree =>
+ val predict = tree.rootNode.predictImpl(features).prediction
+ // TODO: need to investigate why this went to 0 for some trees
--- End diff --
investigated - this was added by mistake, in original PR it was added in and then removed, it got re-added during the big merge to latest code
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Github user imatiach-msft commented on a diff in the pull request:
https://github.com/apache/spark/pull/21632#discussion_r209840017
--- Diff: mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala ---
@@ -159,6 +169,31 @@ class DecisionTreeRegressorSuite extends MLTest with DefaultReadWriteTest {
}
}
+ test("training with sample weights") {
+ val df = linearRegressionData
+ val numClasses = 0
+ val testParams = Seq(5, 10)
+ for (maxDepth <- testParams) {
+ val estimator = new DecisionTreeRegressor()
+ .setImpurity("variance")
+ .setMaxDepth(maxDepth)
+ .setMaxBins(100)
+ .setMinWeightFractionPerNode(0.05)
+ .setSeed(123)
+ // TODO: This doesn't look correct, need to debug to figure out what is wrong with tolerance
--- End diff --
need to debug this
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[GitHub] spark issue #21632: [SPARK-19591][ML][MLlib] Add sample weights to decision ...
Posted by jianpan <gi...@git.apache.org>.
Github user jianpan commented on the issue:
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Is there an ETA when this will be merged to master? Allowing sample weights is an important enhancements. Thanks.
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Posted by imatiach-msft <gi...@git.apache.org>.
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@holdenk @sethah I've updated the PR to latest master (hopefully all of the tests still pass :) )
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@holdenk @sethah @HyukjinKwon @jkbradley I've fixed all known issues in the PR and did some small cleanup, could you please review this PR? Thank you!
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[GitHub] spark issue #21632: [SPARK-19591][ML][MLlib] Add sample weights to decision ...
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@holdenk @sethah @HyukjinKwon @jkbradley ping... could you please take another look at this PR? I've updated it to latest master again. Thank you!
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Jenkins retest this please
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[GitHub] spark issue #21632: [SPARK-19591][ML][MLlib] Add sample weights to decision ...
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@holdenk @sethah @HyukjinKwon I have a successful build, I need to look into 2-3 wacky test results that changed since when @sethah opened his PR (see comments in my PR). In the mean time, would anyone be able to review the PR - are there any comments from the previous PR that were still not resolved and need to be made?
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