zhengruifeng commented on a change in pull request #31160: URL: https://github.com/apache/spark/pull/31160#discussion_r557272412
########## File path: mllib/src/main/scala/org/apache/spark/ml/feature/UnivariateFeatureSelector.scala ########## @@ -0,0 +1,421 @@ +/* + * 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.{Estimator, Model} +import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NominalAttribute, NumericAttribute} +import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors, VectorUDT} +import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasLabelCol, HasOutputCol} +import org.apache.spark.ml.stat.{ANOVATest, ChiSquareTest, FValueTest} +import org.apache.spark.ml.util._ +import org.apache.spark.sql.{DataFrame, Dataset} +import org.apache.spark.sql.functions.{col, udf} +import org.apache.spark.sql.types.{StructField, StructType} + + +/** + * Params for [[UnivariateFeatureSelector]] and [[UnivariateFeatureSelectorModel]]. + */ +private[feature] trait UnivariateFeatureSelectorParams extends Params + with HasFeaturesCol with HasLabelCol with HasOutputCol { + + /** + * The feature type. + * Supported options: "categorical", "continuous" + * @group param + */ + @Since("3.1.1") + final val featureType = new Param[String](this, "featureType", + "Feature type. Supported options: categorical, continuous.", + ParamValidators.inArray(Array("categorical", "continuous"))) + + /** @group getParam */ + @Since("3.1.1") + def getFeatureType: String = $(featureType) + + /** + * The label type. + * Supported options: "categorical", "continuous" + * @group param + */ + @Since("3.1.1") + final val labelType = new Param[String](this, "labelType", + "Label type. Supported options: categorical, continuous.", + ParamValidators.inArray(Array("categorical", "continuous"))) + + /** @group getParam */ + @Since("3.1.1") + def getLabelType: String = $(labelType) + + /** + * The selection mode. + * Supported options: "numTopFeatures" (default), "percentile", "fpr", "fdr", "fwe" + * @group param + */ + @Since("3.1.1") + final val selectionMode = new Param[String](this, "selectionMode", + "The selection mode. Supported options: numTopFeatures, percentile, fpr, fdr, fwe", + ParamValidators.inArray(Array("numTopFeatures", "percentile", "fpr", "fdr", + "fwe"))) + + /** @group getParam */ + @Since("3.1.1") + def getSelectionMode: String = $(selectionMode) + + /** + * The upper bound of the features that selector will select. + * @group param + */ + @Since("3.1.1") + final val selectionThreshold = new DoubleParam(this, "selectionThreshold", + "The upper bound of the features that selector will select.") + + /** @group getParam */ + def getSelectionThreshold: Double = $(selectionThreshold) + + setDefault(selectionMode -> "numTopFeatures", selectionThreshold -> 50) +} + +/** + * The user can set `featureType` and labelType`, and Spark will pick the score function based on + * the specified `featureType` and labelType`. + * The following combination of `featureType` and `labelType` are supported: + * - `featureType` `categorical` and `labelType` `categorical`: Spark uses chi2. + * - `featureType` `continuous` and `labelType` `categorical`: Spark uses f_classif. + * - `featureType` `continuous` and `labelType` `continuous`: Spark uses f_regression. + * + * The `UnivariateFeatureSelector` supports different selection modes: `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 <a href= + * "https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure";> + * Benjamini-Hochberg procedure</a> + * 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 mode is `numTopFeatures`, with the default selectionThreshold + * sets to 50. + */ +@Since("3.1.1") +final class UnivariateFeatureSelector @Since("3.1.1")(@Since("3.1.1") override val uid: String) + extends Estimator[UnivariateFeatureSelectorModel] with UnivariateFeatureSelectorParams + with DefaultParamsWritable { + + @Since("3.1.1") + def this() = this(Identifiable.randomUID("UnivariateFeatureSelector")) + + /** @group setParam */ + @Since("3.1.1") + def setSelectionMode(value: String): this.type = set(selectionMode, value) + + /** @group setParam */ + @Since("3.1.1") + def setSelectionThreshold(value: Double): this.type = set(selectionThreshold, value) + + /** @group setParam */ + @Since("3.1.1") + def setFeatureType(value: String): this.type = set(featureType, value) + + /** @group setParam */ + @Since("3.1.1") + def setLabelType(value: String): this.type = set(labelType, value) + + /** @group setParam */ + @Since("3.1.1") + def setFeaturesCol(value: String): this.type = set(featuresCol, value) + + /** @group setParam */ + @Since("3.1.1") + def setOutputCol(value: String): this.type = set(outputCol, value) + + /** @group setParam */ + @Since("3.1.1") + def setLabelCol(value: String): this.type = set(labelCol, value) + + @Since("3.1.1") + override def fit(dataset: Dataset[_]): UnivariateFeatureSelectorModel = { + transformSchema(dataset.schema, logging = true) + val spark = dataset.sparkSession + import spark.implicits._ + + val numFeatures = MetadataUtils.getNumFeatures(dataset, $(featuresCol)) + + require(isSet(featureType) && isSet(labelType), "featureType and labelType need to be set") + val resultDF = ($(featureType), $(labelType)) match { + case ("categorical", "categorical") => + ChiSquareTest.test(dataset.toDF, getFeaturesCol, getLabelCol, true) + case ("continuous", "categorical") => + ANOVATest.test(dataset.toDF, getFeaturesCol, getLabelCol, true) + case ("continuous", "continuous") => + FValueTest.test(dataset.toDF, getFeaturesCol, getLabelCol, true) + case _ => + throw new IllegalArgumentException(s"Unsupported combination:" + + s" featureType=${$(featureType)}, labelType=${$(labelType)}") + } + + def getTopIndices(k: Int): Array[Int] = { + resultDF.sort("pValue", "featureIndex") + .select("featureIndex") + .limit(k) + .as[Int] + .collect() + } + + val indices = $(selectionMode) match { + case "numTopFeatures" => + getTopIndices($(selectionThreshold).toInt) + case "percentile" => + getTopIndices((numFeatures * $(selectionThreshold)).toInt) + case "fpr" => + resultDF.select("featureIndex") + .where(col("pValue") < $(selectionThreshold)) + .as[Int].collect() + case "fdr" => + // This uses the Benjamini-Hochberg procedure. + // https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure + val f = $(selectionThreshold) / numFeatures + val maxIndex = resultDF.sort("pValue", "featureIndex") + .select("pValue") + .as[Double].rdd + .zipWithIndex + .flatMap { case (pValue, index) => + if (pValue <= f * (index + 1)) { + Iterator.single(index.toInt) + } else Iterator.empty + }.fold(-1)(math.max) Review comment: this rdd maybe empty: ``` scala> sc.range(0, 0).max java.lang.UnsupportedOperationException: empty collection at org.apache.spark.rdd.RDD.$anonfun$reduce$4(RDD.scala:1096) at scala.Option.getOrElse(Option.scala:189) at org.apache.spark.rdd.RDD.$anonfun$reduce$1(RDD.scala:1096) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:388) at org.apache.spark.rdd.RDD.reduce(RDD.scala:1076) at org.apache.spark.rdd.RDD.$anonfun$max$1(RDD.scala:1511) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:388) at org.apache.spark.rdd.RDD.max(RDD.scala:1511) ... 47 elided ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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