huaxingao commented on a change in pull request #27322: [SPARK-26111][ML][WIP] Support F-value between label/feature for continuous distribution feature selection URL: https://github.com/apache/spark/pull/27322#discussion_r377214317
########## File path: mllib/src/main/scala/org/apache/spark/ml/feature/FRegressionSelector.scala ########## @@ -0,0 +1,357 @@ +/* + * 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.{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.FRegressionTest +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 [[FRegressionSelector]] and [[FRegressionSelectorModel]]. + * TODO: put all these params in shared.scala + * TODO: Not include fdr and fwe for now. Need to check if these two are applicable!!! + */ +private[feature] trait FRegressionSelectorParams 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 statistics value descending. + * 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 highest 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 selector type of the FRegressionSelector. + * Supported options: "numTopFeatures" (default), "percentile", "fpr". + * @group param + */ + @Since("3.1.0") + final val selectorType = new Param[String](this, "selectorType", + "The selector type of the FRegressionSelector. " + + "Supported options: numTopFeatures, percentile, fpr") + + /** @group getParam */ + @Since("3.1.0") + def getSelectorType: String = $(selectorType) +} + +/** + * Regression F-value Selector + * This feature selector is for regressions where features are continuous and labels are continuous. + * ANOVA F-value Classification Selector is for when features are continuous and labels are + * categorical. + * Currently, Chi-Squared is for categorical features and categorical labels + * The selector supports different selection methods: `numTopFeatures`, `percentile`, `fpr` + * - `numTopFeatures` chooses a fixed number of top features according to a fRegression 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. + * + * By default, the selection method is `numTopFeatures`, with the default number of top features + * set to 50. + */ +@Since("3.1.0") +final class FRegressionSelector @Since("3.1.0") (@Since("3.1.0") override val uid: String) + extends Estimator[FRegressionSelectorModel] with FRegressionSelectorParams + with DefaultParamsWritable { + + @Since("3.1.0") + def this() = this(Identifiable.randomUID("FRegressionSelector")) + + /** @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 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[_]): FRegressionSelectorModel = { + 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 FTestResult = FRegressionTest.test_regression(dataset, getFeaturesCol, getLabelCol) + .zipWithIndex + val features = $(selectorType) match { + case "numTopFeatures" => + FTestResult + .sortBy { case (res, _) => res.pValue } + .take(getNumTopFeatures) + case "percentile" => + FTestResult + .sortBy { case (res, _) => res.pValue } + .take((FTestResult.length * getPercentile).toInt) + case "fpr" => + FTestResult + .filter { case (res, _) => res.pValue < getFpr } + case errorType => + throw new IllegalStateException(s"Unknown FRegressionSelector Type: $errorType") + } + val indices = features.map { case (_, index) => index } + copyValues(new FRegressionSelectorModel(uid, indices).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): FRegressionSelector = defaultCopy(extra) +} + +@Since("3.1.0") +object FRegressionSelector extends DefaultParamsReadable[FRegressionSelector] { + + @Since("3.1.0") + override def load(path: String): FRegressionSelector = super.load(path) +} + +/** + * Model fitted by [[FRegressionSelector]]. + */ +@Since("3.1.0") +final class FRegressionSelectorModel private[ml] ( + @Since("3.1.0") override val uid: String, + val selectedFeatures: Array[Int]) + extends Model[FRegressionSelectorModel] with FRegressionSelectorParams with MLWritable { + + private val filterIndices = selectedFeatures.sorted + /** @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) + + @Since("3.1.0") + override def transform(dataset: Dataset[_]): DataFrame = { + val outputSchema = transformSchema(dataset.schema, logging = true) + + val newSize = selectedFeatures.length + val func = { vector: Vector => + vector match { + case SparseVector(_, indices, values) => + val (newIndices, newValues) = compressSparse(indices, values) + Vectors.sparse(newSize, newIndices, newValues) + case DenseVector(values) => + Vectors.dense(compressDense(values)) + case other => + throw new UnsupportedOperationException( + s"Only sparse and dense vectors are supported but got ${other.getClass}.") + } + } + + val transformer = udf(func) + dataset.withColumn($(outputCol), transformer(col($(featuresCol))), + outputSchema($(outputCol)).metadata) + } + + @Since("3.1.0") + override def transformSchema(schema: StructType): StructType = { + SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT) + val newField = prepOutputField(schema) + SchemaUtils.appendColumn(schema, newField) + } + + /** + * Prepare the output column field, including per-feature metadata. + */ + private def prepOutputField(schema: StructType): StructField = { + val selector = selectedFeatures.toSet + val origAttrGroup = AttributeGroup.fromStructField(schema($(featuresCol))) + val featureAttributes: Array[Attribute] = if (origAttrGroup.attributes.nonEmpty) { + origAttrGroup.attributes.get.zipWithIndex.filter(x => selector.contains(x._2)).map(_._1) + } else { + Array.fill[Attribute](selector.size)(NominalAttribute.defaultAttr) + } + val newAttributeGroup = new AttributeGroup($(outputCol), featureAttributes) + newAttributeGroup.toStructField() + } + + @Since("3.1.0") + override def copy(extra: ParamMap): FRegressionSelectorModel = { + val copied = new FRegressionSelectorModel(uid, selectedFeatures) + copyValues(copied, extra).setParent(parent) + } + + @Since("3.1.0") + override def write: MLWriter = null // new FRegressionSelectorModelWriter(this) + + @Since("3.1.0") + override def toString: String = { + s"FRegressionSelectorModel: uid=$uid, numSelectedFeatures=${selectedFeatures.length}" + } + + private[spark] def compressSparse(indices: Array[Int], Review comment: Sorry I am not sure what you want me to do for this one and the next one. 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