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[GitHub] [spark] huaxingao opened a new pull request #27882:
[SPARK-31127][ML] Add abstract Selector
huaxingao opened a new pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882
### What changes were proposed in this pull request?
Add abstract ```Selector```. Put the common code between ```ChiSqSelector``` and ```FValueSelector``` to ```Selector```.
### Why are the changes needed?
Code reuse.
### Does this PR introduce any user-facing change?
Yes.
Add ```pValues``` and ```statistics``` to ```SelectorModel```
### How was this patch tested?
New and existing tests
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Add abstract Selector
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URL: https://github.com/apache/spark/pull/27882#issuecomment-599376249
**[Test build #119834 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/119834/testReport)** for PR 27882 at commit [`eb841a5`](https://github.com/apache/spark/commit/eb841a5148a56a4e814148898408704f40dadcba).
* This patch **fails due to an unknown error code, -9**.
* This patch merges cleanly.
* This patch adds no public classes.
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Add abstract Selector
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[GitHub] [spark] zhengruifeng commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391396804
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/FValueSelector.scala
##########
@@ -154,106 +46,75 @@ private[feature] trait FValueSelectorParams extends Params
* set to 50.
*/
@Since("3.1.0")
-final class FValueSelector @Since("3.1.0") (override val uid: String)
- extends Estimator[FValueSelectorModel] with FValueSelectorParams
- with DefaultParamsWritable {
+final class FValueSelector @Since("3.1.0") (@Since("3.1.0") override val uid: String) extends
+ Selector[FValueSelectorModel] {
@Since("3.1.0")
def this() = this(Identifiable.randomUID("FValueSelector"))
/** @group setParam */
@Since("3.1.0")
- def setNumTopFeatures(value: Int): this.type = set(numTopFeatures, value)
+ override def setNumTopFeatures(value: Int): this.type = super.setNumTopFeatures(value)
/** @group setParam */
@Since("3.1.0")
- def setPercentile(value: Double): this.type = set(percentile, value)
+ override def setPercentile(value: Double): this.type = super.setPercentile(value)
/** @group setParam */
@Since("3.1.0")
- def setFpr(value: Double): this.type = set(fpr, value)
+ override def setFpr(value: Double): this.type = super.setFpr(value)
/** @group setParam */
@Since("3.1.0")
- def setFdr(value: Double): this.type = set(fdr, value)
+ override def setFdr(value: Double): this.type = super.setFdr(value)
/** @group setParam */
@Since("3.1.0")
- def setFwe(value: Double): this.type = set(fwe, value)
+ override def setFwe(value: Double): this.type = super.setFwe(value)
/** @group setParam */
@Since("3.1.0")
- def setSelectorType(value: String): this.type = set(selectorType, value)
+ override def setSelectorType(value: String): this.type = super.setSelectorType(value)
/** @group setParam */
@Since("3.1.0")
- def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+ override def setFeaturesCol(value: String): this.type = super.setFeaturesCol(value)
/** @group setParam */
@Since("3.1.0")
- def setOutputCol(value: String): this.type = set(outputCol, value)
+ override def setOutputCol(value: String): this.type = super.setOutputCol(value)
/** @group setParam */
@Since("3.1.0")
- def setLabelCol(value: String): this.type = set(labelCol, value)
+ override def setLabelCol(value: String): this.type = super.setLabelCol(value)
+ /**
+ * get the SelectionTestResult for every feature against the label
+ */
@Since("3.1.0")
- override def fit(dataset: Dataset[_]): FValueSelectorModel = {
- transformSchema(dataset.schema, logging = true)
- dataset.select(col($(labelCol)).cast(DoubleType), col($(featuresCol))).rdd.map {
- case Row(label: Double, features: Vector) =>
- LabeledPoint(label, features)
- }
-
- val testResult = FValueTest.testRegression(dataset, getFeaturesCol, getLabelCol)
- .zipWithIndex
- val features = $(selectorType) match {
- case "numTopFeatures" =>
- testResult
- .sortBy { case (res, _) => res.pValue }
- .take(getNumTopFeatures)
- case "percentile" =>
- testResult
- .sortBy { case (res, _) => res.pValue }
- .take((testResult.length * getPercentile).toInt)
- case "fpr" =>
- testResult
- .filter { case (res, _) => res.pValue < getFpr }
- case "fdr" =>
- // This uses the Benjamini-Hochberg procedure.
- // https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure
- val tempRes = testResult
- .sortBy { case (res, _) => res.pValue }
- val selected = tempRes
- .zipWithIndex
- .filter { case ((res, _), index) =>
- res.pValue <= getFdr * (index + 1) / testResult.length }
- if (selected.isEmpty) {
- Array.empty[(SelectionTestResult, Int)]
- } else {
- val maxIndex = selected.map(_._2).max
- tempRes.take(maxIndex + 1)
- }
- case "fwe" =>
- testResult
- .filter { case (res, _) => res.pValue < getFwe / testResult.length }
- case errorType =>
- throw new IllegalStateException(s"Unknown Selector Type: $errorType")
- }
- val indices = features.map { case (_, index) => index }
- copyValues(new FValueSelectorModel(uid, indices.sorted)
- .setParent(this))
+ protected[this] override def getSelectionTestResult(dataset: Dataset[_]):
Review comment:
ditto
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[WIP][SPARK-31127][ML] Add abstract Selector
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abstract Selector
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SparkQA commented on issue #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#issuecomment-597974692
**[Test build #119685 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/119685/testReport)** for PR 27882 at commit [`456e688`](https://github.com/apache/spark/commit/456e6883cb280f360bc0b556082761a97e8985ee).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:
* `final class FValueSelector @Since(\"3.1.0\") (@Since(\"3.1.0\") override val uid: String) extends`
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[GitHub] [spark] huaxingao commented on issue #27882: [WIP][SPARK-31127][ML]
Add abstract Selector
Posted by GitBox <gi...@apache.org>.
huaxingao commented on issue #27882: [WIP][SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#issuecomment-602155620
Please see https://github.com/apache/spark/pull/27978
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[GitHub] [spark] zhengruifeng commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391399664
##########
File path: mllib/src/main/scala/org/apache/spark/ml/stat/ChiSquareTest.scala
##########
@@ -77,31 +75,4 @@ object ChiSquareTest {
val statistics: Vector = Vectors.dense(testResults.map(_.statistic))
spark.createDataFrame(Seq(ChiSquareResult(pValues, degreesOfFreedom, statistics)))
}
-
- /**
- * @param dataset DataFrame of categorical labels and categorical features.
- * Real-valued features will be treated as categorical for each distinct value.
- * @param featuresCol Name of features column in dataset, of type `Vector` (`VectorUDT`)
- * @param labelCol Name of label column in dataset, of any numerical type
- * @return Array containing the SelectionTestResult for every feature against the label.
- */
- @Since("3.1.0")
Review comment:
Why removing this public method?
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[GitHub] [spark] zhengruifeng commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391397045
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/FValueSelector.scala
##########
@@ -344,57 +156,31 @@ class FValueSelectorModel private[ml](
@Since("3.1.0")
override def toString: String = {
- s"FValueSelectorModel: uid=$uid, numSelectedFeatures=${selectedFeatures.length}"
- }
-
- private[spark] def compressSparse(
- indices: Array[Int],
- values: Array[Double]): (Array[Int], Array[Double]) = {
- val newValues = new ArrayBuilder.ofDouble
- val newIndices = new ArrayBuilder.ofInt
- var i = 0
- var j = 0
- var indicesIdx = 0
- var filterIndicesIdx = 0
- while (i < indices.length && j < selectedFeatures.length) {
- indicesIdx = indices(i)
- filterIndicesIdx = selectedFeatures(j)
- if (indicesIdx == filterIndicesIdx) {
- newIndices += j
- newValues += values(i)
- j += 1
- i += 1
- } else {
- if (indicesIdx > filterIndicesIdx) {
- j += 1
- } else {
- i += 1
- }
- }
- }
- // TODO: Sparse representation might be ineffective if (newSize ~= newValues.size)
- (newIndices.result(), newValues.result())
+ s"FValueModel: uid=$uid, numSelectedFeatures=${selectedFeatures.length}"
}
}
@Since("3.1.0")
object FValueSelectorModel extends MLReadable[FValueSelectorModel] {
@Since("3.1.0")
- override def read: MLReader[FValueSelectorModel] =
- new FValueSelectorModelReader
+ override def read: MLReader[FValueSelectorModel] = new FValueSelectorModelReader
@Since("3.1.0")
override def load(path: String): FValueSelectorModel = super.load(path)
private[FValueSelectorModel] class FValueSelectorModelWriter(
- instance: FValueSelectorModel) extends MLWriter {
+ instance:
+ FValueSelectorModel) extends MLWriter {
Review comment:
lint
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[WIP][SPARK-31127][ML] Add abstract Selector
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[GitHub] [spark] huaxingao commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391957647
##########
File path: mllib/src/main/scala/org/apache/spark/ml/stat/SelectionTest.scala
##########
@@ -0,0 +1,133 @@
+/*
+ * 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.stat
+
+import org.apache.commons.math3.distribution.FDistribution
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.ml.linalg.{Vector, VectorUDT}
+import org.apache.spark.ml.util.SchemaUtils
+import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
+import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint}
+import org.apache.spark.mllib.stat.{Statistics => OldStatistics}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{Dataset, Row}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.DoubleType
+
+
+@Since("3.1.0")
+object SelectionTest {
Review comment:
I will delete this class
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[GitHub] [spark] zhengruifeng commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391398559
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/Selector.scala
##########
@@ -0,0 +1,379 @@
+/*
+ * 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.feature
+
+import scala.collection.mutable.ArrayBuilder
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.ml._
+import org.apache.spark.ml.attribute.{AttributeGroup, _}
+import org.apache.spark.ml.linalg._
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared._
+import org.apache.spark.ml.stat.SelectionTestResult
+import org.apache.spark.ml.util._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
+
+
+/**
+ * Params for [[Selector]] and [[SelectorModel]].
+ */
+private[feature] trait SelectorParams extends Params
+ with HasFeaturesCol with HasOutputCol with HasLabelCol {
+
+ /**
+ * Number of features that selector will select, ordered by ascending p-value. If the
+ * number of features is less than numTopFeatures, then this will select all features.
+ * Only applicable when selectorType = "numTopFeatures".
+ * The default value of numTopFeatures is 50.
+ *
+ * @group param
+ */
+ @Since("3.1.0")
+ final val numTopFeatures = new IntParam(this, "numTopFeatures",
+ "Number of features that selector will select, ordered by ascending p-value. If the" +
+ " number of features is < numTopFeatures, then this will select all features.",
+ ParamValidators.gtEq(1))
+ setDefault(numTopFeatures -> 50)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getNumTopFeatures: Int = $(numTopFeatures)
+
+ /**
+ * Percentile of features that selector will select, ordered by ascending p-value.
+ * Only applicable when selectorType = "percentile".
+ * Default value is 0.1.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val percentile = new DoubleParam(this, "percentile",
+ "Percentile of features that selector will select, ordered by ascending p-value.",
+ ParamValidators.inRange(0, 1))
+ setDefault(percentile -> 0.1)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getPercentile: Double = $(percentile)
+
+ /**
+ * The highest p-value for features to be kept.
+ * Only applicable when selectorType = "fpr".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fpr = new DoubleParam(this, "fpr", "The higest p-value for features to be kept.",
+ ParamValidators.inRange(0, 1))
+ setDefault(fpr -> 0.05)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getFpr: Double = $(fpr)
+
+ /**
+ * The upper bound of the expected false discovery rate.
+ * Only applicable when selectorType = "fdr".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fdr = new DoubleParam(this, "fdr",
+ "The upper bound of the expected false discovery rate.", ParamValidators.inRange(0, 1))
+ setDefault(fdr -> 0.05)
+
+ /** @group getParam */
+ def getFdr: Double = $(fdr)
+
+ /**
+ * The upper bound of the expected family-wise error rate.
+ * Only applicable when selectorType = "fwe".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fwe = new DoubleParam(this, "fwe",
+ "The upper bound of the expected family-wise error rate.", ParamValidators.inRange(0, 1))
+ setDefault(fwe -> 0.05)
+
+ /** @group getParam */
+ def getFwe: Double = $(fwe)
+
+ /**
+ * The selector type.
+ * Supported options: "numTopFeatures" (default), "percentile", "fpr", "fdr", "fwe"
+ * @group param
+ */
+ @Since("3.1.0")
+ final val selectorType = new Param[String](this, "selectorType",
+ "The selector type. Supported options: numTopFeatures, percentile, fpr, fdr, fwe",
+ ParamValidators.inArray(Array("numTopFeatures", "percentile", "fpr", "fdr",
+ "fwe")))
+ setDefault(selectorType -> "numTopFeatures")
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getSelectorType: String = $(selectorType)
+}
+
+/**
+ * Super class for all the feature selectors. The following selectors are supported:
+ * 1. Chi-Square Selector
+ * This feature selector is for categorical features and categorical labels.
+ * 2. ANOVA F-value Classification Selector
+ * This feature selector is for continuous features and categorical labels.
+ * 3. Regression F-value Selector
+ * This feature selector is for continuous features and continuous labels.
+ * The selector supports different selection methods: `numTopFeatures`, `percentile`, `fpr`,
+ * `fdr`, `fwe`.
+ * - `numTopFeatures` chooses a fixed number of top features according to a hypothesis.
+ * - `percentile` is similar but chooses a fraction of all features instead of a fixed number.
+ * - `fpr` chooses all features whose p-value are below a threshold, thus controlling the false
+ * positive rate of selection.
+ * - `fdr` uses the [Benjamini-Hochberg procedure]
+ * (https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure)
+ * to choose all features whose false discovery rate is below a threshold.
+ * - `fwe` chooses all features whose p-values are below a threshold. The threshold is scaled by
+ * 1/numFeatures, thus controlling the family-wise error rate of selection.
+ * By default, the selection method is `numTopFeatures`, with the default number of top features
+ * set to 50.
+ */
+@Since("3.1.0")
+private[ml] abstract class Selector[T <: SelectorModel[T]]
+ extends Estimator[T] with SelectorParams with DefaultParamsWritable {
+ self: Estimator[T] =>
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setNumTopFeatures(value: Int): this.type = set(numTopFeatures, value)
Review comment:
We may do not need to override these methods in subclasses?
what about just removing the since annotation like we did in shared params?
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[GitHub] [spark] SparkQA commented on issue #27882: [WIP][SPARK-31127][ML]
Add abstract Selector
Posted by GitBox <gi...@apache.org>.
SparkQA commented on issue #27882: [WIP][SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#issuecomment-599471695
**[Test build #119842 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/119842/testReport)** for PR 27882 at commit [`eb841a5`](https://github.com/apache/spark/commit/eb841a5148a56a4e814148898408704f40dadcba).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] [spark] huaxingao commented on issue #27882: [WIP][SPARK-31127][ML]
Add abstract Selector
Posted by GitBox <gi...@apache.org>.
huaxingao commented on issue #27882: [WIP][SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#issuecomment-599379096
retest this please
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[GitHub] [spark] huaxingao commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391957450
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/Selector.scala
##########
@@ -0,0 +1,379 @@
+/*
+ * 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.feature
+
+import scala.collection.mutable.ArrayBuilder
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.ml._
+import org.apache.spark.ml.attribute.{AttributeGroup, _}
+import org.apache.spark.ml.linalg._
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared._
+import org.apache.spark.ml.stat.SelectionTestResult
+import org.apache.spark.ml.util._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
+
+
+/**
+ * Params for [[Selector]] and [[SelectorModel]].
+ */
+private[feature] trait SelectorParams extends Params
+ with HasFeaturesCol with HasOutputCol with HasLabelCol {
+
+ /**
+ * Number of features that selector will select, ordered by ascending p-value. If the
+ * number of features is less than numTopFeatures, then this will select all features.
+ * Only applicable when selectorType = "numTopFeatures".
+ * The default value of numTopFeatures is 50.
+ *
+ * @group param
+ */
+ @Since("3.1.0")
+ final val numTopFeatures = new IntParam(this, "numTopFeatures",
+ "Number of features that selector will select, ordered by ascending p-value. If the" +
+ " number of features is < numTopFeatures, then this will select all features.",
+ ParamValidators.gtEq(1))
+ setDefault(numTopFeatures -> 50)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getNumTopFeatures: Int = $(numTopFeatures)
+
+ /**
+ * Percentile of features that selector will select, ordered by ascending p-value.
+ * Only applicable when selectorType = "percentile".
+ * Default value is 0.1.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val percentile = new DoubleParam(this, "percentile",
+ "Percentile of features that selector will select, ordered by ascending p-value.",
+ ParamValidators.inRange(0, 1))
+ setDefault(percentile -> 0.1)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getPercentile: Double = $(percentile)
+
+ /**
+ * The highest p-value for features to be kept.
+ * Only applicable when selectorType = "fpr".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fpr = new DoubleParam(this, "fpr", "The higest p-value for features to be kept.",
+ ParamValidators.inRange(0, 1))
+ setDefault(fpr -> 0.05)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getFpr: Double = $(fpr)
+
+ /**
+ * The upper bound of the expected false discovery rate.
+ * Only applicable when selectorType = "fdr".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fdr = new DoubleParam(this, "fdr",
+ "The upper bound of the expected false discovery rate.", ParamValidators.inRange(0, 1))
+ setDefault(fdr -> 0.05)
+
+ /** @group getParam */
+ def getFdr: Double = $(fdr)
+
+ /**
+ * The upper bound of the expected family-wise error rate.
+ * Only applicable when selectorType = "fwe".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fwe = new DoubleParam(this, "fwe",
+ "The upper bound of the expected family-wise error rate.", ParamValidators.inRange(0, 1))
+ setDefault(fwe -> 0.05)
+
+ /** @group getParam */
+ def getFwe: Double = $(fwe)
+
+ /**
+ * The selector type.
+ * Supported options: "numTopFeatures" (default), "percentile", "fpr", "fdr", "fwe"
+ * @group param
+ */
+ @Since("3.1.0")
+ final val selectorType = new Param[String](this, "selectorType",
+ "The selector type. Supported options: numTopFeatures, percentile, fpr, fdr, fwe",
+ ParamValidators.inArray(Array("numTopFeatures", "percentile", "fpr", "fdr",
+ "fwe")))
+ setDefault(selectorType -> "numTopFeatures")
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getSelectorType: String = $(selectorType)
+}
+
+/**
+ * Super class for all the feature selectors. The following selectors are supported:
+ * 1. Chi-Square Selector
+ * This feature selector is for categorical features and categorical labels.
+ * 2. ANOVA F-value Classification Selector
+ * This feature selector is for continuous features and categorical labels.
+ * 3. Regression F-value Selector
+ * This feature selector is for continuous features and continuous labels.
+ * The selector supports different selection methods: `numTopFeatures`, `percentile`, `fpr`,
+ * `fdr`, `fwe`.
+ * - `numTopFeatures` chooses a fixed number of top features according to a hypothesis.
+ * - `percentile` is similar but chooses a fraction of all features instead of a fixed number.
+ * - `fpr` chooses all features whose p-value are below a threshold, thus controlling the false
+ * positive rate of selection.
+ * - `fdr` uses the [Benjamini-Hochberg procedure]
+ * (https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure)
+ * to choose all features whose false discovery rate is below a threshold.
+ * - `fwe` chooses all features whose p-values are below a threshold. The threshold is scaled by
+ * 1/numFeatures, thus controlling the family-wise error rate of selection.
+ * By default, the selection method is `numTopFeatures`, with the default number of top features
+ * set to 50.
+ */
+@Since("3.1.0")
+private[ml] abstract class Selector[T <: SelectorModel[T]]
+ extends Estimator[T] with SelectorParams with DefaultParamsWritable {
+ self: Estimator[T] =>
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setNumTopFeatures(value: Int): this.type = set(numTopFeatures, value)
Review comment:
I think I got an error if I don't override. I will double check.
Do you mean to remove since annotation for the params, and getXXX, but keep the annotation for setXXX? I think that's how the shared params handle since annotation.
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Add abstract Selector
Posted by GitBox <gi...@apache.org>.
SparkQA commented on issue #27882: [WIP][SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#issuecomment-599301772
**[Test build #119821 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/119821/testReport)** for PR 27882 at commit [`b4d30f9`](https://github.com/apache/spark/commit/b4d30f97a0d4bd40bc689f7d1fd961b8ce66d123).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] [spark] zhengruifeng commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391398684
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/Selector.scala
##########
@@ -0,0 +1,379 @@
+/*
+ * 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.feature
+
+import scala.collection.mutable.ArrayBuilder
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.ml._
+import org.apache.spark.ml.attribute.{AttributeGroup, _}
+import org.apache.spark.ml.linalg._
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared._
+import org.apache.spark.ml.stat.SelectionTestResult
+import org.apache.spark.ml.util._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
+
+
+/**
+ * Params for [[Selector]] and [[SelectorModel]].
+ */
+private[feature] trait SelectorParams extends Params
+ with HasFeaturesCol with HasOutputCol with HasLabelCol {
+
+ /**
+ * Number of features that selector will select, ordered by ascending p-value. If the
+ * number of features is less than numTopFeatures, then this will select all features.
+ * Only applicable when selectorType = "numTopFeatures".
+ * The default value of numTopFeatures is 50.
+ *
+ * @group param
+ */
+ @Since("3.1.0")
+ final val numTopFeatures = new IntParam(this, "numTopFeatures",
+ "Number of features that selector will select, ordered by ascending p-value. If the" +
+ " number of features is < numTopFeatures, then this will select all features.",
+ ParamValidators.gtEq(1))
+ setDefault(numTopFeatures -> 50)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getNumTopFeatures: Int = $(numTopFeatures)
+
+ /**
+ * Percentile of features that selector will select, ordered by ascending p-value.
+ * Only applicable when selectorType = "percentile".
+ * Default value is 0.1.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val percentile = new DoubleParam(this, "percentile",
+ "Percentile of features that selector will select, ordered by ascending p-value.",
+ ParamValidators.inRange(0, 1))
+ setDefault(percentile -> 0.1)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getPercentile: Double = $(percentile)
+
+ /**
+ * The highest p-value for features to be kept.
+ * Only applicable when selectorType = "fpr".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fpr = new DoubleParam(this, "fpr", "The higest p-value for features to be kept.",
+ ParamValidators.inRange(0, 1))
+ setDefault(fpr -> 0.05)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getFpr: Double = $(fpr)
+
+ /**
+ * The upper bound of the expected false discovery rate.
+ * Only applicable when selectorType = "fdr".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fdr = new DoubleParam(this, "fdr",
+ "The upper bound of the expected false discovery rate.", ParamValidators.inRange(0, 1))
+ setDefault(fdr -> 0.05)
+
+ /** @group getParam */
+ def getFdr: Double = $(fdr)
+
+ /**
+ * The upper bound of the expected family-wise error rate.
+ * Only applicable when selectorType = "fwe".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fwe = new DoubleParam(this, "fwe",
+ "The upper bound of the expected family-wise error rate.", ParamValidators.inRange(0, 1))
+ setDefault(fwe -> 0.05)
+
+ /** @group getParam */
+ def getFwe: Double = $(fwe)
+
+ /**
+ * The selector type.
+ * Supported options: "numTopFeatures" (default), "percentile", "fpr", "fdr", "fwe"
+ * @group param
+ */
+ @Since("3.1.0")
+ final val selectorType = new Param[String](this, "selectorType",
+ "The selector type. Supported options: numTopFeatures, percentile, fpr, fdr, fwe",
+ ParamValidators.inArray(Array("numTopFeatures", "percentile", "fpr", "fdr",
+ "fwe")))
+ setDefault(selectorType -> "numTopFeatures")
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getSelectorType: String = $(selectorType)
+}
+
+/**
+ * Super class for all the feature selectors. The following selectors are supported:
+ * 1. Chi-Square Selector
+ * This feature selector is for categorical features and categorical labels.
+ * 2. ANOVA F-value Classification Selector
+ * This feature selector is for continuous features and categorical labels.
+ * 3. Regression F-value Selector
+ * This feature selector is for continuous features and continuous labels.
+ * The selector supports different selection methods: `numTopFeatures`, `percentile`, `fpr`,
+ * `fdr`, `fwe`.
+ * - `numTopFeatures` chooses a fixed number of top features according to a hypothesis.
+ * - `percentile` is similar but chooses a fraction of all features instead of a fixed number.
+ * - `fpr` chooses all features whose p-value are below a threshold, thus controlling the false
+ * positive rate of selection.
+ * - `fdr` uses the [Benjamini-Hochberg procedure]
+ * (https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure)
+ * to choose all features whose false discovery rate is below a threshold.
+ * - `fwe` chooses all features whose p-values are below a threshold. The threshold is scaled by
+ * 1/numFeatures, thus controlling the family-wise error rate of selection.
+ * By default, the selection method is `numTopFeatures`, with the default number of top features
+ * set to 50.
+ */
+@Since("3.1.0")
+private[ml] abstract class Selector[T <: SelectorModel[T]]
+ extends Estimator[T] with SelectorParams with DefaultParamsWritable {
+ self: Estimator[T] =>
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setNumTopFeatures(value: Int): this.type = set(numTopFeatures, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setPercentile(value: Double): this.type = set(percentile, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setFpr(value: Double): this.type = set(fpr, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setFdr(value: Double): this.type = set(fdr, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setFwe(value: Double): this.type = set(fwe, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setSelectorType(value: String): this.type = set(selectorType, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setOutputCol(value: String): this.type = set(outputCol, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setLabelCol(value: String): this.type = set(labelCol, value)
+
+ /**
+ * get the SelectionTestResult for every feature against the label
+ */
+ @Since("3.1.0")
+ protected[this] def getSelectionTestResult(dataset: Dataset[_]): Array[SelectionTestResult]
+
+ /**
+ * Create a new instance of concrete SelectorModel.
+ * @param indices The indices of the selected features
+ * @param pValues The pValues of the selected features
+ * @param statistics The statistics of the selected features
+ * @return A new SelectorModel instance
+ */
+ @Since("3.1.0")
+ protected[this] def createSelectorModel(
Review comment:
ditto
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[GitHub] [spark] zhengruifeng commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391397568
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/Selector.scala
##########
@@ -0,0 +1,379 @@
+/*
+ * 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.feature
+
+import scala.collection.mutable.ArrayBuilder
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.ml._
+import org.apache.spark.ml.attribute.{AttributeGroup, _}
+import org.apache.spark.ml.linalg._
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared._
+import org.apache.spark.ml.stat.SelectionTestResult
+import org.apache.spark.ml.util._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
+
+
+/**
+ * Params for [[Selector]] and [[SelectorModel]].
+ */
+private[feature] trait SelectorParams extends Params
+ with HasFeaturesCol with HasOutputCol with HasLabelCol {
+
+ /**
+ * Number of features that selector will select, ordered by ascending p-value. If the
+ * number of features is less than numTopFeatures, then this will select all features.
+ * Only applicable when selectorType = "numTopFeatures".
+ * The default value of numTopFeatures is 50.
+ *
+ * @group param
+ */
+ @Since("3.1.0")
+ final val numTopFeatures = new IntParam(this, "numTopFeatures",
+ "Number of features that selector will select, ordered by ascending p-value. If the" +
+ " number of features is < numTopFeatures, then this will select all features.",
+ ParamValidators.gtEq(1))
+ setDefault(numTopFeatures -> 50)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getNumTopFeatures: Int = $(numTopFeatures)
+
+ /**
+ * Percentile of features that selector will select, ordered by ascending p-value.
+ * Only applicable when selectorType = "percentile".
+ * Default value is 0.1.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val percentile = new DoubleParam(this, "percentile",
+ "Percentile of features that selector will select, ordered by ascending p-value.",
+ ParamValidators.inRange(0, 1))
+ setDefault(percentile -> 0.1)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getPercentile: Double = $(percentile)
+
+ /**
+ * The highest p-value for features to be kept.
+ * Only applicable when selectorType = "fpr".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fpr = new DoubleParam(this, "fpr", "The higest p-value for features to be kept.",
+ ParamValidators.inRange(0, 1))
+ setDefault(fpr -> 0.05)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getFpr: Double = $(fpr)
+
+ /**
+ * The upper bound of the expected false discovery rate.
+ * Only applicable when selectorType = "fdr".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fdr = new DoubleParam(this, "fdr",
+ "The upper bound of the expected false discovery rate.", ParamValidators.inRange(0, 1))
+ setDefault(fdr -> 0.05)
+
+ /** @group getParam */
+ def getFdr: Double = $(fdr)
+
+ /**
+ * The upper bound of the expected family-wise error rate.
+ * Only applicable when selectorType = "fwe".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fwe = new DoubleParam(this, "fwe",
+ "The upper bound of the expected family-wise error rate.", ParamValidators.inRange(0, 1))
+ setDefault(fwe -> 0.05)
+
+ /** @group getParam */
+ def getFwe: Double = $(fwe)
+
+ /**
+ * The selector type.
+ * Supported options: "numTopFeatures" (default), "percentile", "fpr", "fdr", "fwe"
+ * @group param
+ */
+ @Since("3.1.0")
+ final val selectorType = new Param[String](this, "selectorType",
+ "The selector type. Supported options: numTopFeatures, percentile, fpr, fdr, fwe",
+ ParamValidators.inArray(Array("numTopFeatures", "percentile", "fpr", "fdr",
+ "fwe")))
+ setDefault(selectorType -> "numTopFeatures")
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getSelectorType: String = $(selectorType)
+}
+
+/**
+ * Super class for all the feature selectors. The following selectors are supported:
+ * 1. Chi-Square Selector
+ * This feature selector is for categorical features and categorical labels.
+ * 2. ANOVA F-value Classification Selector
Review comment:
`ANOVA F-value` is yet not implemented now, so what about implementing it first and then make such abstraction to reduce common code?
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[GitHub] [spark] zhengruifeng commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391395961
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/ChiSqSelector.scala
##########
@@ -153,102 +48,75 @@ private[feature] trait ChiSqSelectorParams extends Params
*/
@Since("1.6.0")
final class ChiSqSelector @Since("1.6.0") (@Since("1.6.0") override val uid: String)
- extends Estimator[ChiSqSelectorModel] with ChiSqSelectorParams with DefaultParamsWritable {
+ extends Selector[ChiSqSelectorModel] {
@Since("1.6.0")
def this() = this(Identifiable.randomUID("chiSqSelector"))
/** @group setParam */
@Since("1.6.0")
- def setNumTopFeatures(value: Int): this.type = set(numTopFeatures, value)
+ override def setNumTopFeatures(value: Int): this.type = super.setNumTopFeatures(value)
/** @group setParam */
@Since("2.1.0")
- def setPercentile(value: Double): this.type = set(percentile, value)
+ override def setPercentile(value: Double): this.type = super.setPercentile(value)
/** @group setParam */
@Since("2.1.0")
- def setFpr(value: Double): this.type = set(fpr, value)
+ override def setFpr(value: Double): this.type = super.setFpr(value)
/** @group setParam */
@Since("2.2.0")
- def setFdr(value: Double): this.type = set(fdr, value)
+ override def setFdr(value: Double): this.type = super.setFdr(value)
/** @group setParam */
@Since("2.2.0")
- def setFwe(value: Double): this.type = set(fwe, value)
+ override def setFwe(value: Double): this.type = super.setFwe(value)
/** @group setParam */
@Since("2.1.0")
- def setSelectorType(value: String): this.type = set(selectorType, value)
+ override def setSelectorType(value: String): this.type = super.setSelectorType(value)
/** @group setParam */
@Since("1.6.0")
- def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+ override def setFeaturesCol(value: String): this.type = super.setFeaturesCol(value)
/** @group setParam */
@Since("1.6.0")
- def setOutputCol(value: String): this.type = set(outputCol, value)
+ override def setOutputCol(value: String): this.type = super.setOutputCol(value)
/** @group setParam */
@Since("1.6.0")
- def setLabelCol(value: String): this.type = set(labelCol, value)
-
- @Since("2.0.0")
- override def fit(dataset: Dataset[_]): ChiSqSelectorModel = {
- transformSchema(dataset.schema, logging = true)
- dataset.select(col($(labelCol)).cast(DoubleType), col($(featuresCol))).rdd.map {
- case Row(label: Double, features: Vector) =>
- LabeledPoint(label, features)
- }
+ override def setLabelCol(value: String): this.type = super.setLabelCol(value)
- val testResult = ChiSquareTest.testChiSquare(dataset, getFeaturesCol, getLabelCol)
- .zipWithIndex
- val features = $(selectorType) match {
- case "numTopFeatures" =>
- testResult
- .sortBy { case (res, _) => res.pValue }
- .take(getNumTopFeatures)
- case "percentile" =>
- testResult
- .sortBy { case (res, _) => res.pValue }
- .take((testResult.length * getPercentile).toInt)
- case "fpr" =>
- testResult
- .filter { case (res, _) => res.pValue < getFpr }
- case "fdr" =>
- // This uses the Benjamini-Hochberg procedure.
- // https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure
- val tempRes = testResult
- .sortBy { case (res, _) => res.pValue }
- val selected = tempRes
- .zipWithIndex
- .filter { case ((res, _), index) =>
- res.pValue <= getFdr * (index + 1) / testResult.length }
- if (selected.isEmpty) {
- Array.empty[(SelectionTestResult, Int)]
- } else {
- val maxIndex = selected.map(_._2).max
- tempRes.take(maxIndex + 1)
- }
- case "fwe" =>
- testResult
- .filter { case (res, _) => res.pValue < getFwe / testResult.length }
- case errorType =>
- throw new IllegalStateException(s"Unknown Selector Type: $errorType")
- }
- val indices = features.map { case (_, index) => index }
- copyValues(new ChiSqSelectorModel(uid, indices.sorted)
- .setParent(this))
+ /**
+ * get the SelectionTestResult for every feature against the label
+ */
+ @Since("3.1.0")
+ protected[this] override def getSelectionTestResult(dataset: Dataset[_]):
+ Array[SelectionTestResult] = {
+ SelectionTest.chiSquareTest(dataset, getFeaturesCol, getLabelCol)
}
- @Since("1.6.0")
- override def transformSchema(schema: StructType): StructType = {
- SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT)
- SchemaUtils.checkNumericType(schema, $(labelCol))
- SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT)
+ /**
+ * Create a new instance of concrete SelectorModel.
+ * @param indices The indices of the selected features
+ * @param pValues The pValues of the selected features
+ * @param statistics The chi square statistic of the selected features
+ * @return A new SelectorModel instance
+ */
+ @Since("3.1.0")
+ protected[this] def createSelectorModel(
Review comment:
ditto
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[GitHub] [spark] huaxingao commented on issue #27882: [SPARK-31127][ML] Add
abstract Selector
Posted by GitBox <gi...@apache.org>.
huaxingao commented on issue #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#issuecomment-598488263
> ANOVA F-value is yet not implemented now, so what about implementing it first and then make such abstraction to reduce common code?
I submitted https://github.com/apache/spark/pull/27895 for ANOVASelector. I will mark this PR as WIP for now. After ANOVASelector is done, I will add ANOVA in this PR too.
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[GitHub] [spark] zhengruifeng commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391396309
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/ChiSqSelector.scala
##########
@@ -266,72 +134,27 @@ object ChiSqSelector extends DefaultParamsReadable[ChiSqSelector] {
@Since("1.6.0")
final class ChiSqSelectorModel private[ml] (
@Since("1.6.0") override val uid: String,
- @Since("3.1.0") val selectedFeatures: Array[Int])
- extends Model[ChiSqSelectorModel] with ChiSqSelectorParams with MLWritable {
+ @Since("3.1.0") override val selectedFeatures: Array[Int],
+ @Since("3.1.0") override val pValues: Array[Double],
+ @Since("3.1.0")override val statistic: Array[Double])
Review comment:
nit: space before `override`
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[GitHub] [spark] zhengruifeng commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391398635
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/Selector.scala
##########
@@ -0,0 +1,379 @@
+/*
+ * 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.feature
+
+import scala.collection.mutable.ArrayBuilder
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.ml._
+import org.apache.spark.ml.attribute.{AttributeGroup, _}
+import org.apache.spark.ml.linalg._
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared._
+import org.apache.spark.ml.stat.SelectionTestResult
+import org.apache.spark.ml.util._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
+
+
+/**
+ * Params for [[Selector]] and [[SelectorModel]].
+ */
+private[feature] trait SelectorParams extends Params
+ with HasFeaturesCol with HasOutputCol with HasLabelCol {
+
+ /**
+ * Number of features that selector will select, ordered by ascending p-value. If the
+ * number of features is less than numTopFeatures, then this will select all features.
+ * Only applicable when selectorType = "numTopFeatures".
+ * The default value of numTopFeatures is 50.
+ *
+ * @group param
+ */
+ @Since("3.1.0")
+ final val numTopFeatures = new IntParam(this, "numTopFeatures",
+ "Number of features that selector will select, ordered by ascending p-value. If the" +
+ " number of features is < numTopFeatures, then this will select all features.",
+ ParamValidators.gtEq(1))
+ setDefault(numTopFeatures -> 50)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getNumTopFeatures: Int = $(numTopFeatures)
+
+ /**
+ * Percentile of features that selector will select, ordered by ascending p-value.
+ * Only applicable when selectorType = "percentile".
+ * Default value is 0.1.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val percentile = new DoubleParam(this, "percentile",
+ "Percentile of features that selector will select, ordered by ascending p-value.",
+ ParamValidators.inRange(0, 1))
+ setDefault(percentile -> 0.1)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getPercentile: Double = $(percentile)
+
+ /**
+ * The highest p-value for features to be kept.
+ * Only applicable when selectorType = "fpr".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fpr = new DoubleParam(this, "fpr", "The higest p-value for features to be kept.",
+ ParamValidators.inRange(0, 1))
+ setDefault(fpr -> 0.05)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getFpr: Double = $(fpr)
+
+ /**
+ * The upper bound of the expected false discovery rate.
+ * Only applicable when selectorType = "fdr".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fdr = new DoubleParam(this, "fdr",
+ "The upper bound of the expected false discovery rate.", ParamValidators.inRange(0, 1))
+ setDefault(fdr -> 0.05)
+
+ /** @group getParam */
+ def getFdr: Double = $(fdr)
+
+ /**
+ * The upper bound of the expected family-wise error rate.
+ * Only applicable when selectorType = "fwe".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fwe = new DoubleParam(this, "fwe",
+ "The upper bound of the expected family-wise error rate.", ParamValidators.inRange(0, 1))
+ setDefault(fwe -> 0.05)
+
+ /** @group getParam */
+ def getFwe: Double = $(fwe)
+
+ /**
+ * The selector type.
+ * Supported options: "numTopFeatures" (default), "percentile", "fpr", "fdr", "fwe"
+ * @group param
+ */
+ @Since("3.1.0")
+ final val selectorType = new Param[String](this, "selectorType",
+ "The selector type. Supported options: numTopFeatures, percentile, fpr, fdr, fwe",
+ ParamValidators.inArray(Array("numTopFeatures", "percentile", "fpr", "fdr",
+ "fwe")))
+ setDefault(selectorType -> "numTopFeatures")
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getSelectorType: String = $(selectorType)
+}
+
+/**
+ * Super class for all the feature selectors. The following selectors are supported:
+ * 1. Chi-Square Selector
+ * This feature selector is for categorical features and categorical labels.
+ * 2. ANOVA F-value Classification Selector
+ * This feature selector is for continuous features and categorical labels.
+ * 3. Regression F-value Selector
+ * This feature selector is for continuous features and continuous labels.
+ * The selector supports different selection methods: `numTopFeatures`, `percentile`, `fpr`,
+ * `fdr`, `fwe`.
+ * - `numTopFeatures` chooses a fixed number of top features according to a hypothesis.
+ * - `percentile` is similar but chooses a fraction of all features instead of a fixed number.
+ * - `fpr` chooses all features whose p-value are below a threshold, thus controlling the false
+ * positive rate of selection.
+ * - `fdr` uses the [Benjamini-Hochberg procedure]
+ * (https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure)
+ * to choose all features whose false discovery rate is below a threshold.
+ * - `fwe` chooses all features whose p-values are below a threshold. The threshold is scaled by
+ * 1/numFeatures, thus controlling the family-wise error rate of selection.
+ * By default, the selection method is `numTopFeatures`, with the default number of top features
+ * set to 50.
+ */
+@Since("3.1.0")
+private[ml] abstract class Selector[T <: SelectorModel[T]]
+ extends Estimator[T] with SelectorParams with DefaultParamsWritable {
+ self: Estimator[T] =>
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setNumTopFeatures(value: Int): this.type = set(numTopFeatures, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setPercentile(value: Double): this.type = set(percentile, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setFpr(value: Double): this.type = set(fpr, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setFdr(value: Double): this.type = set(fdr, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setFwe(value: Double): this.type = set(fwe, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setSelectorType(value: String): this.type = set(selectorType, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setOutputCol(value: String): this.type = set(outputCol, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setLabelCol(value: String): this.type = set(labelCol, value)
+
+ /**
+ * get the SelectionTestResult for every feature against the label
+ */
+ @Since("3.1.0")
+ protected[this] def getSelectionTestResult(dataset: Dataset[_]): Array[SelectionTestResult]
Review comment:
ditto
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[GitHub] [spark] huaxingao closed pull request #27882:
[WIP][SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
huaxingao closed pull request #27882: [WIP][SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882
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[GitHub] [spark] zhengruifeng commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391398237
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/Selector.scala
##########
@@ -0,0 +1,379 @@
+/*
+ * 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.feature
+
+import scala.collection.mutable.ArrayBuilder
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.ml._
+import org.apache.spark.ml.attribute.{AttributeGroup, _}
+import org.apache.spark.ml.linalg._
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared._
+import org.apache.spark.ml.stat.SelectionTestResult
+import org.apache.spark.ml.util._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
+
+
+/**
+ * Params for [[Selector]] and [[SelectorModel]].
+ */
+private[feature] trait SelectorParams extends Params
+ with HasFeaturesCol with HasOutputCol with HasLabelCol {
+
+ /**
+ * Number of features that selector will select, ordered by ascending p-value. If the
+ * number of features is less than numTopFeatures, then this will select all features.
+ * Only applicable when selectorType = "numTopFeatures".
+ * The default value of numTopFeatures is 50.
+ *
+ * @group param
+ */
+ @Since("3.1.0")
+ final val numTopFeatures = new IntParam(this, "numTopFeatures",
+ "Number of features that selector will select, ordered by ascending p-value. If the" +
+ " number of features is < numTopFeatures, then this will select all features.",
+ ParamValidators.gtEq(1))
+ setDefault(numTopFeatures -> 50)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getNumTopFeatures: Int = $(numTopFeatures)
+
+ /**
+ * Percentile of features that selector will select, ordered by ascending p-value.
+ * Only applicable when selectorType = "percentile".
+ * Default value is 0.1.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val percentile = new DoubleParam(this, "percentile",
+ "Percentile of features that selector will select, ordered by ascending p-value.",
+ ParamValidators.inRange(0, 1))
+ setDefault(percentile -> 0.1)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getPercentile: Double = $(percentile)
+
+ /**
+ * The highest p-value for features to be kept.
+ * Only applicable when selectorType = "fpr".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fpr = new DoubleParam(this, "fpr", "The higest p-value for features to be kept.",
+ ParamValidators.inRange(0, 1))
+ setDefault(fpr -> 0.05)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getFpr: Double = $(fpr)
+
+ /**
+ * The upper bound of the expected false discovery rate.
+ * Only applicable when selectorType = "fdr".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fdr = new DoubleParam(this, "fdr",
+ "The upper bound of the expected false discovery rate.", ParamValidators.inRange(0, 1))
+ setDefault(fdr -> 0.05)
+
+ /** @group getParam */
+ def getFdr: Double = $(fdr)
+
+ /**
+ * The upper bound of the expected family-wise error rate.
+ * Only applicable when selectorType = "fwe".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fwe = new DoubleParam(this, "fwe",
+ "The upper bound of the expected family-wise error rate.", ParamValidators.inRange(0, 1))
+ setDefault(fwe -> 0.05)
+
+ /** @group getParam */
+ def getFwe: Double = $(fwe)
+
+ /**
+ * The selector type.
+ * Supported options: "numTopFeatures" (default), "percentile", "fpr", "fdr", "fwe"
+ * @group param
+ */
+ @Since("3.1.0")
+ final val selectorType = new Param[String](this, "selectorType",
+ "The selector type. Supported options: numTopFeatures, percentile, fpr, fdr, fwe",
+ ParamValidators.inArray(Array("numTopFeatures", "percentile", "fpr", "fdr",
+ "fwe")))
+ setDefault(selectorType -> "numTopFeatures")
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getSelectorType: String = $(selectorType)
+}
+
+/**
+ * Super class for all the feature selectors. The following selectors are supported:
+ * 1. Chi-Square Selector
+ * This feature selector is for categorical features and categorical labels.
+ * 2. ANOVA F-value Classification Selector
+ * This feature selector is for continuous features and categorical labels.
+ * 3. Regression F-value Selector
+ * This feature selector is for continuous features and continuous labels.
+ * The selector supports different selection methods: `numTopFeatures`, `percentile`, `fpr`,
+ * `fdr`, `fwe`.
+ * - `numTopFeatures` chooses a fixed number of top features according to a hypothesis.
+ * - `percentile` is similar but chooses a fraction of all features instead of a fixed number.
+ * - `fpr` chooses all features whose p-value are below a threshold, thus controlling the false
+ * positive rate of selection.
+ * - `fdr` uses the [Benjamini-Hochberg procedure]
+ * (https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure)
+ * to choose all features whose false discovery rate is below a threshold.
+ * - `fwe` chooses all features whose p-values are below a threshold. The threshold is scaled by
+ * 1/numFeatures, thus controlling the family-wise error rate of selection.
+ * By default, the selection method is `numTopFeatures`, with the default number of top features
+ * set to 50.
+ */
+@Since("3.1.0")
+private[ml] abstract class Selector[T <: SelectorModel[T]]
+ extends Estimator[T] with SelectorParams with DefaultParamsWritable {
+ self: Estimator[T] =>
Review comment:
`Selector` already extends `Estimator`, so do we really need this?
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[GitHub] [spark] huaxingao commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391957054
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/ChiSqSelector.scala
##########
@@ -153,102 +48,75 @@ private[feature] trait ChiSqSelectorParams extends Params
*/
@Since("1.6.0")
final class ChiSqSelector @Since("1.6.0") (@Since("1.6.0") override val uid: String)
- extends Estimator[ChiSqSelectorModel] with ChiSqSelectorParams with DefaultParamsWritable {
+ extends Selector[ChiSqSelectorModel] {
@Since("1.6.0")
def this() = this(Identifiable.randomUID("chiSqSelector"))
/** @group setParam */
@Since("1.6.0")
- def setNumTopFeatures(value: Int): this.type = set(numTopFeatures, value)
+ override def setNumTopFeatures(value: Int): this.type = super.setNumTopFeatures(value)
/** @group setParam */
@Since("2.1.0")
- def setPercentile(value: Double): this.type = set(percentile, value)
+ override def setPercentile(value: Double): this.type = super.setPercentile(value)
/** @group setParam */
@Since("2.1.0")
- def setFpr(value: Double): this.type = set(fpr, value)
+ override def setFpr(value: Double): this.type = super.setFpr(value)
/** @group setParam */
@Since("2.2.0")
- def setFdr(value: Double): this.type = set(fdr, value)
+ override def setFdr(value: Double): this.type = super.setFdr(value)
/** @group setParam */
@Since("2.2.0")
- def setFwe(value: Double): this.type = set(fwe, value)
+ override def setFwe(value: Double): this.type = super.setFwe(value)
/** @group setParam */
@Since("2.1.0")
- def setSelectorType(value: String): this.type = set(selectorType, value)
+ override def setSelectorType(value: String): this.type = super.setSelectorType(value)
/** @group setParam */
@Since("1.6.0")
- def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+ override def setFeaturesCol(value: String): this.type = super.setFeaturesCol(value)
/** @group setParam */
@Since("1.6.0")
- def setOutputCol(value: String): this.type = set(outputCol, value)
+ override def setOutputCol(value: String): this.type = super.setOutputCol(value)
/** @group setParam */
@Since("1.6.0")
- def setLabelCol(value: String): this.type = set(labelCol, value)
-
- @Since("2.0.0")
- override def fit(dataset: Dataset[_]): ChiSqSelectorModel = {
- transformSchema(dataset.schema, logging = true)
- dataset.select(col($(labelCol)).cast(DoubleType), col($(featuresCol))).rdd.map {
- case Row(label: Double, features: Vector) =>
- LabeledPoint(label, features)
- }
+ override def setLabelCol(value: String): this.type = super.setLabelCol(value)
- val testResult = ChiSquareTest.testChiSquare(dataset, getFeaturesCol, getLabelCol)
- .zipWithIndex
- val features = $(selectorType) match {
- case "numTopFeatures" =>
- testResult
- .sortBy { case (res, _) => res.pValue }
- .take(getNumTopFeatures)
- case "percentile" =>
- testResult
- .sortBy { case (res, _) => res.pValue }
- .take((testResult.length * getPercentile).toInt)
- case "fpr" =>
- testResult
- .filter { case (res, _) => res.pValue < getFpr }
- case "fdr" =>
- // This uses the Benjamini-Hochberg procedure.
- // https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure
- val tempRes = testResult
- .sortBy { case (res, _) => res.pValue }
- val selected = tempRes
- .zipWithIndex
- .filter { case ((res, _), index) =>
- res.pValue <= getFdr * (index + 1) / testResult.length }
- if (selected.isEmpty) {
- Array.empty[(SelectionTestResult, Int)]
- } else {
- val maxIndex = selected.map(_._2).max
- tempRes.take(maxIndex + 1)
- }
- case "fwe" =>
- testResult
- .filter { case (res, _) => res.pValue < getFwe / testResult.length }
- case errorType =>
- throw new IllegalStateException(s"Unknown Selector Type: $errorType")
- }
- val indices = features.map { case (_, index) => index }
- copyValues(new ChiSqSelectorModel(uid, indices.sorted)
- .setParent(this))
+ /**
+ * get the SelectionTestResult for every feature against the label
+ */
+ @Since("3.1.0")
+ protected[this] override def getSelectionTestResult(dataset: Dataset[_]):
+ Array[SelectionTestResult] = {
+ SelectionTest.chiSquareTest(dataset, getFeaturesCol, getLabelCol)
}
- @Since("1.6.0")
- override def transformSchema(schema: StructType): StructType = {
- SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT)
- SchemaUtils.checkNumericType(schema, $(labelCol))
- SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT)
+ /**
+ * Create a new instance of concrete SelectorModel.
+ * @param indices The indices of the selected features
+ * @param pValues The pValues of the selected features
+ * @param statistics The chi square statistic of the selected features
+ * @return A new SelectorModel instance
+ */
+ @Since("3.1.0")
+ protected[this] def createSelectorModel(
Review comment:
I am not sure if I can delete this method. I have problem doing the following in the end of ```Selector.fit```
```
copyValues(new SelectorModel(uid, indices, pValues, statistic).setParent(this))
```
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[GitHub] [spark] zhengruifeng commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391400190
##########
File path: mllib/src/main/scala/org/apache/spark/ml/stat/SelectionTest.scala
##########
@@ -0,0 +1,133 @@
+/*
+ * 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.stat
+
+import org.apache.commons.math3.distribution.FDistribution
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.ml.linalg.{Vector, VectorUDT}
+import org.apache.spark.ml.util.SchemaUtils
+import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
+import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint}
+import org.apache.spark.mllib.stat.{Statistics => OldStatistics}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{Dataset, Row}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.DoubleType
+
+
+@Since("3.1.0")
+object SelectionTest {
Review comment:
I am not sure whether we need this, since there already are `FValueTest` and `ChiSquareTest`
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Add abstract Selector
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* This patch **fails MiMa tests**.
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[GitHub] [spark] zhengruifeng commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391396858
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/FValueSelector.scala
##########
@@ -154,106 +46,75 @@ private[feature] trait FValueSelectorParams extends Params
* set to 50.
*/
@Since("3.1.0")
-final class FValueSelector @Since("3.1.0") (override val uid: String)
- extends Estimator[FValueSelectorModel] with FValueSelectorParams
- with DefaultParamsWritable {
+final class FValueSelector @Since("3.1.0") (@Since("3.1.0") override val uid: String) extends
+ Selector[FValueSelectorModel] {
@Since("3.1.0")
def this() = this(Identifiable.randomUID("FValueSelector"))
/** @group setParam */
@Since("3.1.0")
- def setNumTopFeatures(value: Int): this.type = set(numTopFeatures, value)
+ override def setNumTopFeatures(value: Int): this.type = super.setNumTopFeatures(value)
/** @group setParam */
@Since("3.1.0")
- def setPercentile(value: Double): this.type = set(percentile, value)
+ override def setPercentile(value: Double): this.type = super.setPercentile(value)
/** @group setParam */
@Since("3.1.0")
- def setFpr(value: Double): this.type = set(fpr, value)
+ override def setFpr(value: Double): this.type = super.setFpr(value)
/** @group setParam */
@Since("3.1.0")
- def setFdr(value: Double): this.type = set(fdr, value)
+ override def setFdr(value: Double): this.type = super.setFdr(value)
/** @group setParam */
@Since("3.1.0")
- def setFwe(value: Double): this.type = set(fwe, value)
+ override def setFwe(value: Double): this.type = super.setFwe(value)
/** @group setParam */
@Since("3.1.0")
- def setSelectorType(value: String): this.type = set(selectorType, value)
+ override def setSelectorType(value: String): this.type = super.setSelectorType(value)
/** @group setParam */
@Since("3.1.0")
- def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+ override def setFeaturesCol(value: String): this.type = super.setFeaturesCol(value)
/** @group setParam */
@Since("3.1.0")
- def setOutputCol(value: String): this.type = set(outputCol, value)
+ override def setOutputCol(value: String): this.type = super.setOutputCol(value)
/** @group setParam */
@Since("3.1.0")
- def setLabelCol(value: String): this.type = set(labelCol, value)
+ override def setLabelCol(value: String): this.type = super.setLabelCol(value)
+ /**
+ * get the SelectionTestResult for every feature against the label
+ */
@Since("3.1.0")
- override def fit(dataset: Dataset[_]): FValueSelectorModel = {
- transformSchema(dataset.schema, logging = true)
- dataset.select(col($(labelCol)).cast(DoubleType), col($(featuresCol))).rdd.map {
- case Row(label: Double, features: Vector) =>
- LabeledPoint(label, features)
- }
-
- val testResult = FValueTest.testRegression(dataset, getFeaturesCol, getLabelCol)
- .zipWithIndex
- val features = $(selectorType) match {
- case "numTopFeatures" =>
- testResult
- .sortBy { case (res, _) => res.pValue }
- .take(getNumTopFeatures)
- case "percentile" =>
- testResult
- .sortBy { case (res, _) => res.pValue }
- .take((testResult.length * getPercentile).toInt)
- case "fpr" =>
- testResult
- .filter { case (res, _) => res.pValue < getFpr }
- case "fdr" =>
- // This uses the Benjamini-Hochberg procedure.
- // https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure
- val tempRes = testResult
- .sortBy { case (res, _) => res.pValue }
- val selected = tempRes
- .zipWithIndex
- .filter { case ((res, _), index) =>
- res.pValue <= getFdr * (index + 1) / testResult.length }
- if (selected.isEmpty) {
- Array.empty[(SelectionTestResult, Int)]
- } else {
- val maxIndex = selected.map(_._2).max
- tempRes.take(maxIndex + 1)
- }
- case "fwe" =>
- testResult
- .filter { case (res, _) => res.pValue < getFwe / testResult.length }
- case errorType =>
- throw new IllegalStateException(s"Unknown Selector Type: $errorType")
- }
- val indices = features.map { case (_, index) => index }
- copyValues(new FValueSelectorModel(uid, indices.sorted)
- .setParent(this))
+ protected[this] override def getSelectionTestResult(dataset: Dataset[_]):
+ Array[SelectionTestResult] = {
+ SelectionTest.fValueTest(dataset, getFeaturesCol, getLabelCol)
}
+ /**
+ * Create a new instance of concrete SelectorModel.
+ * @param indices The indices of the selected features
+ * @param pValues The pValues of the selected features
+ * @param statistics The f value of the selected features
+ * @return A new SelectorModel instance
+ */
@Since("3.1.0")
- override def transformSchema(schema: StructType): StructType = {
- SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT)
- SchemaUtils.checkNumericType(schema, $(labelCol))
- SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT)
+ protected[this] def createSelectorModel(
Review comment:
ditto
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Add abstract Selector
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[GitHub] [spark] zhengruifeng commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391395690
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/ChiSqSelector.scala
##########
@@ -153,102 +48,75 @@ private[feature] trait ChiSqSelectorParams extends Params
*/
@Since("1.6.0")
final class ChiSqSelector @Since("1.6.0") (@Since("1.6.0") override val uid: String)
- extends Estimator[ChiSqSelectorModel] with ChiSqSelectorParams with DefaultParamsWritable {
+ extends Selector[ChiSqSelectorModel] {
@Since("1.6.0")
def this() = this(Identifiable.randomUID("chiSqSelector"))
/** @group setParam */
@Since("1.6.0")
- def setNumTopFeatures(value: Int): this.type = set(numTopFeatures, value)
+ override def setNumTopFeatures(value: Int): this.type = super.setNumTopFeatures(value)
/** @group setParam */
@Since("2.1.0")
- def setPercentile(value: Double): this.type = set(percentile, value)
+ override def setPercentile(value: Double): this.type = super.setPercentile(value)
/** @group setParam */
@Since("2.1.0")
- def setFpr(value: Double): this.type = set(fpr, value)
+ override def setFpr(value: Double): this.type = super.setFpr(value)
/** @group setParam */
@Since("2.2.0")
- def setFdr(value: Double): this.type = set(fdr, value)
+ override def setFdr(value: Double): this.type = super.setFdr(value)
/** @group setParam */
@Since("2.2.0")
- def setFwe(value: Double): this.type = set(fwe, value)
+ override def setFwe(value: Double): this.type = super.setFwe(value)
/** @group setParam */
@Since("2.1.0")
- def setSelectorType(value: String): this.type = set(selectorType, value)
+ override def setSelectorType(value: String): this.type = super.setSelectorType(value)
/** @group setParam */
@Since("1.6.0")
- def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+ override def setFeaturesCol(value: String): this.type = super.setFeaturesCol(value)
/** @group setParam */
@Since("1.6.0")
- def setOutputCol(value: String): this.type = set(outputCol, value)
+ override def setOutputCol(value: String): this.type = super.setOutputCol(value)
/** @group setParam */
@Since("1.6.0")
- def setLabelCol(value: String): this.type = set(labelCol, value)
-
- @Since("2.0.0")
- override def fit(dataset: Dataset[_]): ChiSqSelectorModel = {
- transformSchema(dataset.schema, logging = true)
- dataset.select(col($(labelCol)).cast(DoubleType), col($(featuresCol))).rdd.map {
- case Row(label: Double, features: Vector) =>
- LabeledPoint(label, features)
- }
+ override def setLabelCol(value: String): this.type = super.setLabelCol(value)
- val testResult = ChiSquareTest.testChiSquare(dataset, getFeaturesCol, getLabelCol)
- .zipWithIndex
- val features = $(selectorType) match {
- case "numTopFeatures" =>
- testResult
- .sortBy { case (res, _) => res.pValue }
- .take(getNumTopFeatures)
- case "percentile" =>
- testResult
- .sortBy { case (res, _) => res.pValue }
- .take((testResult.length * getPercentile).toInt)
- case "fpr" =>
- testResult
- .filter { case (res, _) => res.pValue < getFpr }
- case "fdr" =>
- // This uses the Benjamini-Hochberg procedure.
- // https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure
- val tempRes = testResult
- .sortBy { case (res, _) => res.pValue }
- val selected = tempRes
- .zipWithIndex
- .filter { case ((res, _), index) =>
- res.pValue <= getFdr * (index + 1) / testResult.length }
- if (selected.isEmpty) {
- Array.empty[(SelectionTestResult, Int)]
- } else {
- val maxIndex = selected.map(_._2).max
- tempRes.take(maxIndex + 1)
- }
- case "fwe" =>
- testResult
- .filter { case (res, _) => res.pValue < getFwe / testResult.length }
- case errorType =>
- throw new IllegalStateException(s"Unknown Selector Type: $errorType")
- }
- val indices = features.map { case (_, index) => index }
- copyValues(new ChiSqSelectorModel(uid, indices.sorted)
- .setParent(this))
+ /**
+ * get the SelectionTestResult for every feature against the label
+ */
+ @Since("3.1.0")
Review comment:
protected method do not need since annotation
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[GitHub] [spark] SparkQA commented on issue #27882: [WIP][SPARK-31127][ML]
Add abstract Selector
Posted by GitBox <gi...@apache.org>.
SparkQA commented on issue #27882: [WIP][SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#issuecomment-598514618
**[Test build #119731 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/119731/testReport)** for PR 27882 at commit [`1965424`](https://github.com/apache/spark/commit/1965424164d2c2ff7d4287ef3c88f2174f8a1dc2).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] [spark] zhengruifeng commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391396136
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/ChiSqSelector.scala
##########
@@ -153,102 +48,75 @@ private[feature] trait ChiSqSelectorParams extends Params
*/
@Since("1.6.0")
final class ChiSqSelector @Since("1.6.0") (@Since("1.6.0") override val uid: String)
- extends Estimator[ChiSqSelectorModel] with ChiSqSelectorParams with DefaultParamsWritable {
+ extends Selector[ChiSqSelectorModel] {
@Since("1.6.0")
def this() = this(Identifiable.randomUID("chiSqSelector"))
/** @group setParam */
@Since("1.6.0")
- def setNumTopFeatures(value: Int): this.type = set(numTopFeatures, value)
+ override def setNumTopFeatures(value: Int): this.type = super.setNumTopFeatures(value)
/** @group setParam */
@Since("2.1.0")
- def setPercentile(value: Double): this.type = set(percentile, value)
+ override def setPercentile(value: Double): this.type = super.setPercentile(value)
/** @group setParam */
@Since("2.1.0")
- def setFpr(value: Double): this.type = set(fpr, value)
+ override def setFpr(value: Double): this.type = super.setFpr(value)
/** @group setParam */
@Since("2.2.0")
- def setFdr(value: Double): this.type = set(fdr, value)
+ override def setFdr(value: Double): this.type = super.setFdr(value)
/** @group setParam */
@Since("2.2.0")
- def setFwe(value: Double): this.type = set(fwe, value)
+ override def setFwe(value: Double): this.type = super.setFwe(value)
/** @group setParam */
@Since("2.1.0")
- def setSelectorType(value: String): this.type = set(selectorType, value)
+ override def setSelectorType(value: String): this.type = super.setSelectorType(value)
/** @group setParam */
@Since("1.6.0")
- def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+ override def setFeaturesCol(value: String): this.type = super.setFeaturesCol(value)
/** @group setParam */
@Since("1.6.0")
- def setOutputCol(value: String): this.type = set(outputCol, value)
+ override def setOutputCol(value: String): this.type = super.setOutputCol(value)
/** @group setParam */
@Since("1.6.0")
- def setLabelCol(value: String): this.type = set(labelCol, value)
-
- @Since("2.0.0")
- override def fit(dataset: Dataset[_]): ChiSqSelectorModel = {
- transformSchema(dataset.schema, logging = true)
- dataset.select(col($(labelCol)).cast(DoubleType), col($(featuresCol))).rdd.map {
- case Row(label: Double, features: Vector) =>
- LabeledPoint(label, features)
- }
+ override def setLabelCol(value: String): this.type = super.setLabelCol(value)
- val testResult = ChiSquareTest.testChiSquare(dataset, getFeaturesCol, getLabelCol)
- .zipWithIndex
- val features = $(selectorType) match {
- case "numTopFeatures" =>
- testResult
- .sortBy { case (res, _) => res.pValue }
- .take(getNumTopFeatures)
- case "percentile" =>
- testResult
- .sortBy { case (res, _) => res.pValue }
- .take((testResult.length * getPercentile).toInt)
- case "fpr" =>
- testResult
- .filter { case (res, _) => res.pValue < getFpr }
- case "fdr" =>
- // This uses the Benjamini-Hochberg procedure.
- // https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure
- val tempRes = testResult
- .sortBy { case (res, _) => res.pValue }
- val selected = tempRes
- .zipWithIndex
- .filter { case ((res, _), index) =>
- res.pValue <= getFdr * (index + 1) / testResult.length }
- if (selected.isEmpty) {
- Array.empty[(SelectionTestResult, Int)]
- } else {
- val maxIndex = selected.map(_._2).max
- tempRes.take(maxIndex + 1)
- }
- case "fwe" =>
- testResult
- .filter { case (res, _) => res.pValue < getFwe / testResult.length }
- case errorType =>
- throw new IllegalStateException(s"Unknown Selector Type: $errorType")
- }
- val indices = features.map { case (_, index) => index }
- copyValues(new ChiSqSelectorModel(uid, indices.sorted)
- .setParent(this))
+ /**
+ * get the SelectionTestResult for every feature against the label
+ */
+ @Since("3.1.0")
+ protected[this] override def getSelectionTestResult(dataset: Dataset[_]):
+ Array[SelectionTestResult] = {
+ SelectionTest.chiSquareTest(dataset, getFeaturesCol, getLabelCol)
}
- @Since("1.6.0")
- override def transformSchema(schema: StructType): StructType = {
- SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT)
- SchemaUtils.checkNumericType(schema, $(labelCol))
- SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT)
+ /**
+ * Create a new instance of concrete SelectorModel.
+ * @param indices The indices of the selected features
+ * @param pValues The pValues of the selected features
+ * @param statistics The chi square statistic of the selected features
+ * @return A new SelectorModel instance
+ */
+ @Since("3.1.0")
+ protected[this] def createSelectorModel(
Review comment:
this method is just a constructor, so I think we do not need it.
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[GitHub] [spark] huaxingao commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391332980
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/Selector.scala
##########
@@ -0,0 +1,379 @@
+/*
+ * 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.feature
+
+import scala.collection.mutable.ArrayBuilder
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.ml._
+import org.apache.spark.ml.attribute.{AttributeGroup, _}
+import org.apache.spark.ml.linalg._
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared._
+import org.apache.spark.ml.stat.SelectionTestResult
+import org.apache.spark.ml.util._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
+
+
+/**
+ * Params for [[Selector]] and [[SelectorModel]].
+ */
+private[feature] trait SelectorParams extends Params
+ with HasFeaturesCol with HasOutputCol with HasLabelCol {
+
+ /**
+ * Number of features that selector will select, ordered by ascending p-value. If the
+ * number of features is less than numTopFeatures, then this will select all features.
+ * Only applicable when selectorType = "numTopFeatures".
+ * The default value of numTopFeatures is 50.
+ *
+ * @group param
+ */
+ @Since("3.1.0")
+ final val numTopFeatures = new IntParam(this, "numTopFeatures",
+ "Number of features that selector will select, ordered by ascending p-value. If the" +
+ " number of features is < numTopFeatures, then this will select all features.",
+ ParamValidators.gtEq(1))
+ setDefault(numTopFeatures -> 50)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getNumTopFeatures: Int = $(numTopFeatures)
+
+ /**
+ * Percentile of features that selector will select, ordered by ascending p-value.
+ * Only applicable when selectorType = "percentile".
+ * Default value is 0.1.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val percentile = new DoubleParam(this, "percentile",
+ "Percentile of features that selector will select, ordered by ascending p-value.",
+ ParamValidators.inRange(0, 1))
+ setDefault(percentile -> 0.1)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getPercentile: Double = $(percentile)
+
+ /**
+ * The highest p-value for features to be kept.
+ * Only applicable when selectorType = "fpr".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fpr = new DoubleParam(this, "fpr", "The higest p-value for features to be kept.",
+ ParamValidators.inRange(0, 1))
+ setDefault(fpr -> 0.05)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getFpr: Double = $(fpr)
+
+ /**
+ * The upper bound of the expected false discovery rate.
+ * Only applicable when selectorType = "fdr".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fdr = new DoubleParam(this, "fdr",
+ "The upper bound of the expected false discovery rate.", ParamValidators.inRange(0, 1))
+ setDefault(fdr -> 0.05)
+
+ /** @group getParam */
+ def getFdr: Double = $(fdr)
+
+ /**
+ * The upper bound of the expected family-wise error rate.
+ * Only applicable when selectorType = "fwe".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fwe = new DoubleParam(this, "fwe",
+ "The upper bound of the expected family-wise error rate.", ParamValidators.inRange(0, 1))
+ setDefault(fwe -> 0.05)
+
+ /** @group getParam */
+ def getFwe: Double = $(fwe)
+
+ /**
+ * The selector type.
+ * Supported options: "numTopFeatures" (default), "percentile", "fpr", "fdr", "fwe"
+ * @group param
+ */
+ @Since("3.1.0")
+ final val selectorType = new Param[String](this, "selectorType",
+ "The selector type. Supported options: numTopFeatures, percentile, fpr, fdr, fwe",
+ ParamValidators.inArray(Array("numTopFeatures", "percentile", "fpr", "fdr",
+ "fwe")))
+ setDefault(selectorType -> "numTopFeatures")
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getSelectorType: String = $(selectorType)
+}
+
+/**
+ * Super class for all the feature selectors. The following selectors are supported:
+ * 1. Chi-Square Selector
+ * This feature selector is for categorical features and categorical labels.
+ * 2. ANOVA F-value Classification Selector
+ * This feature selector is for continuous features and categorical labels.
+ * 3. Regression F-value Selector
+ * This feature selector is for continuous features and continuous labels.
+ * The selector supports different selection methods: `numTopFeatures`, `percentile`, `fpr`,
+ * `fdr`, `fwe`.
+ * - `numTopFeatures` chooses a fixed number of top features according to a hypothesis.
+ * - `percentile` is similar but chooses a fraction of all features instead of a fixed number.
+ * - `fpr` chooses all features whose p-value are below a threshold, thus controlling the false
+ * positive rate of selection.
+ * - `fdr` uses the [Benjamini-Hochberg procedure]
+ * (https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure)
+ * to choose all features whose false discovery rate is below a threshold.
+ * - `fwe` chooses all features whose p-values are below a threshold. The threshold is scaled by
+ * 1/numFeatures, thus controlling the family-wise error rate of selection.
+ * By default, the selection method is `numTopFeatures`, with the default number of top features
+ * set to 50.
+ */
+@Since("3.1.0")
+private[ml] abstract class Selector[T <: SelectorModel[T]]
+ extends Estimator[T] with SelectorParams with DefaultParamsWritable {
+ self: Estimator[T] =>
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setNumTopFeatures(value: Int): this.type = set(numTopFeatures, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setPercentile(value: Double): this.type = set(percentile, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setFpr(value: Double): this.type = set(fpr, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setFdr(value: Double): this.type = set(fdr, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setFwe(value: Double): this.type = set(fwe, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setSelectorType(value: String): this.type = set(selectorType, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setOutputCol(value: String): this.type = set(outputCol, value)
+
+ /** @group setParam */
+ @Since("3.1.0")
+ def setLabelCol(value: String): this.type = set(labelCol, value)
+
+ /**
+ * get the SelectionTestResult for every feature against the label
+ */
+ @Since("3.1.0")
+ protected[this] def getSelectionTestResult(dataset: Dataset[_]): Array[SelectionTestResult]
+
+ /**
+ * Create a new instance of concrete SelectorModel.
+ * @param indices The indices of the selected features
+ * @param pValues The pValues of the selected features
+ * @param statistics The statistics of the selected features
+ * @return A new SelectorModel instance
+ */
+ @Since("3.1.0")
+ protected[this] def createSelectorModel(
+ uid: String,
+ indices: Array[Int],
+ pValues: Array[Double],
+ statistics: Array[Double]): T
+
+ @Since("3.1.0")
+ override def fit(dataset: Dataset[_]): T = {
+ transformSchema(dataset.schema, logging = true)
+ val input: RDD[LabeledPoint] =
+ dataset.select(col($(labelCol)).cast(DoubleType), col($(featuresCol))).rdd.map {
+ case Row(label: Double, features: Vector) =>
+ LabeledPoint(label, features)
+ }
+
+ val testResult = getSelectionTestResult(dataset)
+ .zipWithIndex
+ val features = $(selectorType) match {
+ case "numTopFeatures" =>
+ testResult
+ .sortBy { case (res, _) => res.pValue }
+ .take(getNumTopFeatures)
+ case "percentile" =>
+ testResult
+ .sortBy { case (res, _) => res.pValue }
+ .take((testResult.length * getPercentile).toInt)
+ case "fpr" =>
+ testResult
+ .filter { case (res, _) => res.pValue < getFpr }
+ case "fdr" =>
+ // This uses the Benjamini-Hochberg procedure.
+ // https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure
+ val tempRes = testResult
+ .sortBy { case (res, _) => res.pValue }
+ val selected = tempRes
+ .zipWithIndex
+ .filter { case ((res, _), index) =>
+ res.pValue <= getFdr * (index + 1) / testResult.length }
+ if (selected.isEmpty) {
+ Array.empty[(SelectionTestResult, Int)]
+ } else {
+ val maxIndex = selected.map(_._2).max
+ tempRes.take(maxIndex + 1)
+ }
+ case "fwe" =>
+ testResult
+ .filter { case (res, _) => res.pValue < getFwe / testResult.length }
+ case errorType =>
+ throw new IllegalStateException(s"Unknown Selector Type: $errorType")
+ }
+ val indices = features.map { case (_, index) => index }
+ val pValues = features.map(_._1.pValue)
+ val statistic = features.map(_._1.statistic)
+ copyValues(createSelectorModel(uid, indices.sorted, pValues, statistic).setParent(this))
+ }
+
+ @Since("3.1.0")
+ override def transformSchema(schema: StructType): StructType = {
+ SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT)
+ SchemaUtils.checkNumericType(schema, $(labelCol))
+ SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT)
+ }
+
+ @Since("3.1.0")
+ override def copy(extra: ParamMap): Selector[T] = defaultCopy(extra)
+}
+
+/**
+ * Model fitted by [[Selector]].
+ */
+@Since("3.1.0")
+private[ml] abstract class SelectorModel[T <: SelectorModel[T]] (
+ @Since("3.1.0") val uid: String,
+ @Since("3.1.0") val selectedFeatures: Array[Int],
+ @Since("3.1.0") val pValues: Array[Double],
+ @Since("3.1.0") val statistic: Array[Double])
Review comment:
```pValues``` and ```statistic``` are useful information. I will put these in model.
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[GitHub] [spark] SparkQA commented on issue #27882: [WIP][SPARK-31127][ML]
Add abstract Selector
Posted by GitBox <gi...@apache.org>.
SparkQA commented on issue #27882: [WIP][SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#issuecomment-599289509
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[WIP][SPARK-31127][ML] Add abstract Selector
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[GitHub] [spark] SparkQA commented on issue #27882: [WIP][SPARK-31127][ML]
Add abstract Selector
Posted by GitBox <gi...@apache.org>.
SparkQA commented on issue #27882: [WIP][SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#issuecomment-599326224
**[Test build #119823 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/119823/testReport)** for PR 27882 at commit [`b219f0a`](https://github.com/apache/spark/commit/b219f0af6713c77b162db50b72f0b42d2c420818).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.
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Add abstract Selector
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AmplabJenkins commented on issue #27882: [SPARK-31127][ML] Add abstract Selector
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[GitHub] [spark] huaxingao commented on a change in pull request #27882:
[SPARK-31127][ML] Add abstract Selector
Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391957230
##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/Selector.scala
##########
@@ -0,0 +1,379 @@
+/*
+ * 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.feature
+
+import scala.collection.mutable.ArrayBuilder
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.ml._
+import org.apache.spark.ml.attribute.{AttributeGroup, _}
+import org.apache.spark.ml.linalg._
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared._
+import org.apache.spark.ml.stat.SelectionTestResult
+import org.apache.spark.ml.util._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
+
+
+/**
+ * Params for [[Selector]] and [[SelectorModel]].
+ */
+private[feature] trait SelectorParams extends Params
+ with HasFeaturesCol with HasOutputCol with HasLabelCol {
+
+ /**
+ * Number of features that selector will select, ordered by ascending p-value. If the
+ * number of features is less than numTopFeatures, then this will select all features.
+ * Only applicable when selectorType = "numTopFeatures".
+ * The default value of numTopFeatures is 50.
+ *
+ * @group param
+ */
+ @Since("3.1.0")
+ final val numTopFeatures = new IntParam(this, "numTopFeatures",
+ "Number of features that selector will select, ordered by ascending p-value. If the" +
+ " number of features is < numTopFeatures, then this will select all features.",
+ ParamValidators.gtEq(1))
+ setDefault(numTopFeatures -> 50)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getNumTopFeatures: Int = $(numTopFeatures)
+
+ /**
+ * Percentile of features that selector will select, ordered by ascending p-value.
+ * Only applicable when selectorType = "percentile".
+ * Default value is 0.1.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val percentile = new DoubleParam(this, "percentile",
+ "Percentile of features that selector will select, ordered by ascending p-value.",
+ ParamValidators.inRange(0, 1))
+ setDefault(percentile -> 0.1)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getPercentile: Double = $(percentile)
+
+ /**
+ * The highest p-value for features to be kept.
+ * Only applicable when selectorType = "fpr".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fpr = new DoubleParam(this, "fpr", "The higest p-value for features to be kept.",
+ ParamValidators.inRange(0, 1))
+ setDefault(fpr -> 0.05)
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getFpr: Double = $(fpr)
+
+ /**
+ * The upper bound of the expected false discovery rate.
+ * Only applicable when selectorType = "fdr".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fdr = new DoubleParam(this, "fdr",
+ "The upper bound of the expected false discovery rate.", ParamValidators.inRange(0, 1))
+ setDefault(fdr -> 0.05)
+
+ /** @group getParam */
+ def getFdr: Double = $(fdr)
+
+ /**
+ * The upper bound of the expected family-wise error rate.
+ * Only applicable when selectorType = "fwe".
+ * Default value is 0.05.
+ * @group param
+ */
+ @Since("3.1.0")
+ final val fwe = new DoubleParam(this, "fwe",
+ "The upper bound of the expected family-wise error rate.", ParamValidators.inRange(0, 1))
+ setDefault(fwe -> 0.05)
+
+ /** @group getParam */
+ def getFwe: Double = $(fwe)
+
+ /**
+ * The selector type.
+ * Supported options: "numTopFeatures" (default), "percentile", "fpr", "fdr", "fwe"
+ * @group param
+ */
+ @Since("3.1.0")
+ final val selectorType = new Param[String](this, "selectorType",
+ "The selector type. Supported options: numTopFeatures, percentile, fpr, fdr, fwe",
+ ParamValidators.inArray(Array("numTopFeatures", "percentile", "fpr", "fdr",
+ "fwe")))
+ setDefault(selectorType -> "numTopFeatures")
+
+ /** @group getParam */
+ @Since("3.1.0")
+ def getSelectorType: String = $(selectorType)
+}
+
+/**
+ * Super class for all the feature selectors. The following selectors are supported:
+ * 1. Chi-Square Selector
+ * This feature selector is for categorical features and categorical labels.
+ * 2. ANOVA F-value Classification Selector
+ * This feature selector is for continuous features and categorical labels.
+ * 3. Regression F-value Selector
+ * This feature selector is for continuous features and continuous labels.
+ * The selector supports different selection methods: `numTopFeatures`, `percentile`, `fpr`,
+ * `fdr`, `fwe`.
+ * - `numTopFeatures` chooses a fixed number of top features according to a hypothesis.
+ * - `percentile` is similar but chooses a fraction of all features instead of a fixed number.
+ * - `fpr` chooses all features whose p-value are below a threshold, thus controlling the false
+ * positive rate of selection.
+ * - `fdr` uses the [Benjamini-Hochberg procedure]
+ * (https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure)
+ * to choose all features whose false discovery rate is below a threshold.
+ * - `fwe` chooses all features whose p-values are below a threshold. The threshold is scaled by
+ * 1/numFeatures, thus controlling the family-wise error rate of selection.
+ * By default, the selection method is `numTopFeatures`, with the default number of top features
+ * set to 50.
+ */
+@Since("3.1.0")
+private[ml] abstract class Selector[T <: SelectorModel[T]]
+ extends Estimator[T] with SelectorParams with DefaultParamsWritable {
+ self: Estimator[T] =>
Review comment:
Will delete
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