zhengruifeng commented on a change in pull request #27978: URL: https://github.com/apache/spark/pull/27978#discussion_r417048353
########## File path: mllib/src/main/scala/org/apache/spark/ml/feature/Selector.scala ########## @@ -0,0 +1,391 @@ +/* + * 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.util._ +import org.apache.spark.sql.{DataFrame, Dataset} +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types.{StructField, StructType} + + +/** + * Params for [[Selector]] and [[SelectorModel]]. + */ +private[feature] trait SelectorParams extends Params + with HasFeaturesCol with HasLabelCol with HasOutputCol { + + /** + * 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 feature selectors. + * 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 { + + /** @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 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 setLabelCol(value: String): this.type = set(labelCol, value) + + /** + * get the SelectionTestResult for every feature against the label + */ + protected[this] def getSelectionTestResult(df: DataFrame): DataFrame + + /** + * Create a new instance of concrete SelectorModel. + * @param indices The indices of the selected features + * @return A new SelectorModel instance + */ + protected[this] def createSelectorModel( + uid: String, + indices: Array[Int]): T + + @Since("3.1.0") + override def fit(dataset: Dataset[_]): T = { + transformSchema(dataset.schema, logging = true) + val spark = dataset.sparkSession + import spark.implicits._ + + val numFeatures = MetadataUtils.getNumFeatures(dataset, $(featuresCol)) + val resultDF = getSelectionTestResult(dataset.toDF) + + def getTopIndices(k: Int): Array[Int] = { + resultDF.sort("pValue", "featureIndex") + .select("featureIndex") + .limit(k) + .as[Int] + .collect() + } + + val indices = $(selectorType) match { + case "numTopFeatures" => + getTopIndices($(numTopFeatures)) + case "percentile" => + getTopIndices((numFeatures * getPercentile).toInt) + case "fpr" => + resultDF.select("featureIndex") + .where(col("pValue") < $(fpr)) + .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 = $(fdr) / 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) + if (maxIndex >= 0) { + getTopIndices(maxIndex + 1) + } else Array.emptyIntArray + case "fwe" => + resultDF.select("featureIndex") + .where(col("pValue") < $(fwe) / numFeatures) + .as[Int].collect() + case errorType => + throw new IllegalStateException(s"Unknown Selector Type: $errorType") + } + + copyValues(createSelectorModel(uid, indices.sorted) + .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]) + extends Model[T] with SelectorParams with MLWritable { + self: T => + + if (selectedFeatures.length >= 2) { + require(selectedFeatures.sliding(2).forall(l => l(0) < l(1)), + "Index should be strictly increasing.") + } + + /** @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) + + SelectorModel.transform(dataset, selectedFeatures, outputSchema, $(outputCol), $(featuresCol)) + } + + @Since("3.1.0") + override def transformSchema(schema: StructType): StructType = { + SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT) + val newField = + SelectorModel.prepOutputField(schema, selectedFeatures, $(outputCol), $(featuresCol)) + SchemaUtils.appendColumn(schema, newField) + } +} + +object SelectorModel { + + private[ml] def transform( + dataset: Dataset[_], + selectedFeatures: Array[Int], + outputSchema: StructType, + outputCol: String, + featuresCol: String): DataFrame = { + val newSize = selectedFeatures.length + val func = { vector: Vector => + vector match { + case SparseVector(_, indices, values) => + val (newIndices, newValues) = + compressSparse(indices, values, selectedFeatures) + Vectors.sparse(newSize, newIndices, newValues) + case DenseVector(values) => + Vectors.dense(selectedFeatures.map(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) + } + + /** + * Prepare the output column field, including per-feature metadata. + */ + private[ml] def prepOutputField( + schema: StructType, + selectedFeatures: Array[Int], + outputCol: String, + featuresCol: String): 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) Review comment: I guess we need to change this place: ``` Array.fill[Attribute](selector.size)(NominalAttribute.defaultAttr) ``` this comes from `ChiSqSelector`, which treat each column as `Nominal`. Since `selector`s now support numerical columns, like `FValue`/`ANOVA`, so we may need change it to: 1, only attach output vector length by method `SchemaUtils.updateAttributeGroupSize`, you can refer to the it usage in GMM; 2, add a new method to let each subclass tell the features's type ---------------------------------------------------------------- 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: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
