huaxingao commented on a change in pull request #27679: [SPARK-30776][ML] Support FValueSelector for continuous features and continuous labels URL: https://github.com/apache/spark/pull/27679#discussion_r387202438
########## File path: mllib/src/main/scala/org/apache/spark/ml/feature/FValueSelector.scala ########## @@ -0,0 +1,432 @@ +/* + * 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.hadoop.fs.Path + +import org.apache.spark.annotation.Since +import org.apache.spark.ml._ +import org.apache.spark.ml.attribute._ +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.{FValueTest, SelectionTestResult} +import org.apache.spark.ml.util._ +import org.apache.spark.sql._ +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types.{DoubleType, StructField, StructType} + + +/** + * Params for [[FValueSelector]] and [[FValueSelectorModel]]. + */ +private[feature] trait FValueSelectorParams 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 lowest 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 lowest 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) +} + +/** + * F Value Regression feature selector, which selects continuous features to use for predicting a + * continuous label. + * The selector supports different selection methods: `numTopFeatures`, `percentile`, `fpr`, + * `fdr`, `fwe`. + * - `numTopFeatures` chooses a fixed number of top features according to a F value regression + * test. + * - `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") +final class FValueSelector @Since("3.1.0") (override val uid: String) + extends Estimator[FValueSelectorModel] with FValueSelectorParams + with DefaultParamsWritable { + + @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) + + /** @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) + + @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 } + val pValues = features.map(_._1.pValue) + val statistic = features.map(_._1.statistic) + copyValues(new FValueSelectorModel(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): FValueSelector = defaultCopy(extra) +} + +@Since("3.1.0") +object FValueSelector extends DefaultParamsReadable[FValueSelector] { + + @Since("3.1.0") + override def load(path: String): FValueSelector = super.load(path) +} + +/** + * Model fitted by [[FValueSelector]]. + */ +@Since("3.1.0") +class FValueSelectorModel private[ml]( + override val uid: String, + val selectedFeatures: Array[Int], + val pValues: Array[Double], Review comment: I am OK either way, but if having statistics info here, I will need to add statistics info in ```ChiSqSelector``` too, so I can have a common Selector later. It might be easier not to have these for now. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
