mengxr commented on a change in pull request #31160: URL: https://github.com/apache/spark/pull/31160#discussion_r556971591
########## File path: mllib/src/main/scala/org/apache/spark/ml/feature/UnivariateFeatureSelector.scala ########## @@ -0,0 +1,500 @@ +/* + * 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("3.1.0") + final val featureType = new Param[String](this, "featureType", + "Feature type. Supported options: categorical, continuous.", + ParamValidators.inArray(Array("categorical", "continuous"))) + + /** @group getParam */ + @Since("3.1.0") + def getFeatureType: String = $(featureType) + + /** + * The label type. + * Supported options: "categorical", "continuous" + * @group param + */ + @Since("3.1.0") + final val labelType = new Param[String](this, "labelType", + "Label type. Supported options: categorical, continuous.", + ParamValidators.inArray(Array("categorical", "continuous"))) + + /** @group getParam */ + @Since("3.1.0") + def getLabelType: String = $(labelType) + + /** + * 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", Review comment: Shall we make 2 params instead of one for each selection method? * `selectionMode`: numTopFeatures, percentile, fpr, fdr, fwe * `selectionThreshold`: double value for all Or we can just call them "mode" and "threshold". Slightly prefer the full name, but no strong preference. See also: https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.GenericUnivariateSelect.html#sklearn.feature_selection.GenericUnivariateSelect ########## File path: mllib/src/main/scala/org/apache/spark/ml/feature/UnivariateFeatureSelector.scala ########## @@ -0,0 +1,500 @@ +/* + * 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("3.1.0") + final val featureType = new Param[String](this, "featureType", + "Feature type. Supported options: categorical, continuous.", + ParamValidators.inArray(Array("categorical", "continuous"))) + + /** @group getParam */ + @Since("3.1.0") + def getFeatureType: String = $(featureType) + + /** + * The label type. + * Supported options: "categorical", "continuous" + * @group param + */ + @Since("3.1.0") + final val labelType = new Param[String](this, "labelType", + "Label type. Supported options: categorical, continuous.", + ParamValidators.inArray(Array("categorical", "continuous"))) + + /** @group getParam */ + @Since("3.1.0") + def getLabelType: String = $(labelType) + + /** + * 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"))) + + /** @group getParam */ + @Since("3.1.0") + def getSelectorType: String = $(selectorType) + + /** + * 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)) + + /** @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)) + + /** @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)) + + /** @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)) + + /** @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)) + + /** @group getParam */ + def getFwe: Double = $(fwe) + + setDefault(numTopFeatures -> 50, percentile -> 0.1, fpr -> 0.05, fdr -> 0.05, fwe -> 0.05, + selectorType -> "numTopFeatures") +} + +/** + * 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 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 <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 method is `numTopFeatures`, with the default number of top features + * set to 50. + */ +@Since("3.1.0") +final class UnivariateFeatureSelector @Since("3.1.0")(@Since("3.1.0") override val uid: String) + extends Estimator[UnivariateFeatureSelectorModel] with UnivariateFeatureSelectorParams + with DefaultParamsWritable { + + @Since("3.1.0") + def this() = this(Identifiable.randomUID("UnivariateFeatureSelector")) + + /** @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) + + /** @group setParam */ + @Since("3.1.0") + def setFeatureType(value: String): this.type = set(featureType, value) + + /** @group setParam */ + @Since("3.1.0") + def setLabelType(value: String): this.type = set(labelType, value) + + @Since("3.1.0") + 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 = $(selectorType) match { + case "numTopFeatures" => + getTopIndices($(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 IllegalArgumentException(s"Unknown Selector Type: $errorType") + } + + copyValues(new UnivariateFeatureSelectorModel(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): UnivariateFeatureSelector = defaultCopy(extra) +} + +@Since("3.1.0") +object UnivariateFeatureSelector extends DefaultParamsReadable[UnivariateFeatureSelector] { + + @Since("3.1.0") + override def load(path: String): UnivariateFeatureSelector = super.load(path) +} + +/** + * Model fitted by [[UnivariateFeatureSelectorModel]]. + */ +@Since("3.1.0") +class UnivariateFeatureSelectorModel private[ml]( + @Since("3.1.0") override val uid: String, + @Since("3.1.0") val selectedFeatures: Array[Int]) + extends Model[UnivariateFeatureSelectorModel] with UnivariateFeatureSelectorParams + with MLWritable { + + 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) + + protected def isNumericAttribute = true + + @Since("3.1.0") + override def transform(dataset: Dataset[_]): DataFrame = { + val outputSchema = transformSchema(dataset.schema, logging = true) + + UnivariateFeatureSelectorModel + .transform(dataset, selectedFeatures, outputSchema, $(outputCol), $(featuresCol)) + } + + @Since("3.1.0") + override def transformSchema(schema: StructType): StructType = { + SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT) + val newField = + UnivariateFeatureSelectorModel + .prepOutputField(schema, selectedFeatures, $(outputCol), $(featuresCol), isNumericAttribute) + SchemaUtils.appendColumn(schema, newField) + } + + @Since("3.1.0") + override def copy(extra: ParamMap): UnivariateFeatureSelectorModel = { + val copied = new UnivariateFeatureSelectorModel(uid, selectedFeatures) + .setParent(parent) + copyValues(copied, extra) + } + + @Since("3.1.0") + override def write: MLWriter = + new UnivariateFeatureSelectorModel.UnivariateFeatureSelectorModelWriter(this) + + @Since("3.1.0") + override def toString: String = { + s"UnivariateFeatureSelectorModel: uid=$uid, numSelectedFeatures=${selectedFeatures.length}" + } +} + +@Since("3.1.0") +object UnivariateFeatureSelectorModel extends MLReadable[UnivariateFeatureSelectorModel] { + + @Since("3.1.0") + override def read: MLReader[UnivariateFeatureSelectorModel] = + new UnivariateFeatureSelectorModelReader + + @Since("3.1.0") + override def load(path: String): UnivariateFeatureSelectorModel = super.load(path) + + private[UnivariateFeatureSelectorModel] class UnivariateFeatureSelectorModelWriter( + instance: UnivariateFeatureSelectorModel) extends MLWriter { + + private case class Data(selectedFeatures: Seq[Int]) + + override protected def saveImpl(path: String): Unit = { + DefaultParamsWriter.saveMetadata(instance, path, sc) + val data = Data(instance.selectedFeatures.toSeq) + val dataPath = new Path(path, "data").toString + sparkSession.createDataFrame(Seq(data)).repartition(1).write.parquet(dataPath) + } + } + + private class UnivariateFeatureSelectorModelReader + extends MLReader[UnivariateFeatureSelectorModel] { + + /** Checked against metadata when loading model */ + private val className = classOf[UnivariateFeatureSelectorModel].getName + + override def load(path: String): UnivariateFeatureSelectorModel = { + val metadata = DefaultParamsReader.loadMetadata(path, sc, className) + val dataPath = new Path(path, "data").toString + val data = sparkSession.read.parquet(dataPath) + .select("selectedFeatures").head() + val selectedFeatures = data.getAs[Seq[Int]](0).toArray + val model = new UnivariateFeatureSelectorModel(metadata.uid, selectedFeatures) + metadata.getAndSetParams(model) + model + } + } + + def transform( Review comment: private? ########## File path: mllib/src/main/scala/org/apache/spark/ml/feature/UnivariateFeatureSelector.scala ########## @@ -0,0 +1,500 @@ +/* + * 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("3.1.0") + final val featureType = new Param[String](this, "featureType", + "Feature type. Supported options: categorical, continuous.", + ParamValidators.inArray(Array("categorical", "continuous"))) + + /** @group getParam */ + @Since("3.1.0") + def getFeatureType: String = $(featureType) + + /** + * The label type. + * Supported options: "categorical", "continuous" + * @group param + */ + @Since("3.1.0") + final val labelType = new Param[String](this, "labelType", + "Label type. Supported options: categorical, continuous.", + ParamValidators.inArray(Array("categorical", "continuous"))) + + /** @group getParam */ + @Since("3.1.0") + def getLabelType: String = $(labelType) + + /** + * 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"))) + + /** @group getParam */ + @Since("3.1.0") + def getSelectorType: String = $(selectorType) + + /** + * 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)) + + /** @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)) + + /** @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)) + + /** @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)) + + /** @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)) + + /** @group getParam */ + def getFwe: Double = $(fwe) + + setDefault(numTopFeatures -> 50, percentile -> 0.1, fpr -> 0.05, fdr -> 0.05, fwe -> 0.05, + selectorType -> "numTopFeatures") +} + +/** + * 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 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 <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 method is `numTopFeatures`, with the default number of top features + * set to 50. + */ +@Since("3.1.0") +final class UnivariateFeatureSelector @Since("3.1.0")(@Since("3.1.0") override val uid: String) + extends Estimator[UnivariateFeatureSelectorModel] with UnivariateFeatureSelectorParams + with DefaultParamsWritable { + + @Since("3.1.0") + def this() = this(Identifiable.randomUID("UnivariateFeatureSelector")) + + /** @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) + + /** @group setParam */ + @Since("3.1.0") + def setFeatureType(value: String): this.type = set(featureType, value) + + /** @group setParam */ + @Since("3.1.0") + def setLabelType(value: String): this.type = set(labelType, value) + + @Since("3.1.0") + 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 = $(selectorType) match { + case "numTopFeatures" => + getTopIndices($(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 IllegalArgumentException(s"Unknown Selector Type: $errorType") + } + + copyValues(new UnivariateFeatureSelectorModel(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): UnivariateFeatureSelector = defaultCopy(extra) +} + +@Since("3.1.0") +object UnivariateFeatureSelector extends DefaultParamsReadable[UnivariateFeatureSelector] { + + @Since("3.1.0") + override def load(path: String): UnivariateFeatureSelector = super.load(path) +} + +/** + * Model fitted by [[UnivariateFeatureSelectorModel]]. + */ +@Since("3.1.0") +class UnivariateFeatureSelectorModel private[ml]( + @Since("3.1.0") override val uid: String, + @Since("3.1.0") val selectedFeatures: Array[Int]) + extends Model[UnivariateFeatureSelectorModel] with UnivariateFeatureSelectorParams + with MLWritable { + + 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) + + protected def isNumericAttribute = true + + @Since("3.1.0") + override def transform(dataset: Dataset[_]): DataFrame = { + val outputSchema = transformSchema(dataset.schema, logging = true) + + UnivariateFeatureSelectorModel + .transform(dataset, selectedFeatures, outputSchema, $(outputCol), $(featuresCol)) + } + + @Since("3.1.0") + override def transformSchema(schema: StructType): StructType = { + SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT) + val newField = + UnivariateFeatureSelectorModel + .prepOutputField(schema, selectedFeatures, $(outputCol), $(featuresCol), isNumericAttribute) + SchemaUtils.appendColumn(schema, newField) + } + + @Since("3.1.0") + override def copy(extra: ParamMap): UnivariateFeatureSelectorModel = { + val copied = new UnivariateFeatureSelectorModel(uid, selectedFeatures) + .setParent(parent) + copyValues(copied, extra) + } + + @Since("3.1.0") + override def write: MLWriter = + new UnivariateFeatureSelectorModel.UnivariateFeatureSelectorModelWriter(this) + + @Since("3.1.0") + override def toString: String = { + s"UnivariateFeatureSelectorModel: uid=$uid, numSelectedFeatures=${selectedFeatures.length}" + } +} + +@Since("3.1.0") +object UnivariateFeatureSelectorModel extends MLReadable[UnivariateFeatureSelectorModel] { + + @Since("3.1.0") + override def read: MLReader[UnivariateFeatureSelectorModel] = + new UnivariateFeatureSelectorModelReader + + @Since("3.1.0") + override def load(path: String): UnivariateFeatureSelectorModel = super.load(path) + + private[UnivariateFeatureSelectorModel] class UnivariateFeatureSelectorModelWriter( + instance: UnivariateFeatureSelectorModel) extends MLWriter { + + private case class Data(selectedFeatures: Seq[Int]) + + override protected def saveImpl(path: String): Unit = { + DefaultParamsWriter.saveMetadata(instance, path, sc) + val data = Data(instance.selectedFeatures.toSeq) + val dataPath = new Path(path, "data").toString + sparkSession.createDataFrame(Seq(data)).repartition(1).write.parquet(dataPath) + } + } + + private class UnivariateFeatureSelectorModelReader + extends MLReader[UnivariateFeatureSelectorModel] { + + /** Checked against metadata when loading model */ + private val className = classOf[UnivariateFeatureSelectorModel].getName + + override def load(path: String): UnivariateFeatureSelectorModel = { + val metadata = DefaultParamsReader.loadMetadata(path, sc, className) + val dataPath = new Path(path, "data").toString + val data = sparkSession.read.parquet(dataPath) + .select("selectedFeatures").head() + val selectedFeatures = data.getAs[Seq[Int]](0).toArray + val model = new UnivariateFeatureSelectorModel(metadata.uid, selectedFeatures) + metadata.getAndSetParams(model) + model + } + } + + 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. + */ + def prepOutputField( + schema: StructType, + selectedFeatures: Array[Int], + outputCol: String, + featuresCol: String, + isNumericAttribute: Boolean): 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 { + if (isNumericAttribute) { + Array.fill[Attribute](selector.size)(NumericAttribute.defaultAttr) + } else { + Array.fill[Attribute](selector.size)(NominalAttribute.defaultAttr) + } + } + val newAttributeGroup = new AttributeGroup(outputCol, featureAttributes) + newAttributeGroup.toStructField() + } + + def compressSparse( Review comment: private? ########## File path: mllib/src/main/scala/org/apache/spark/ml/feature/UnivariateFeatureSelector.scala ########## @@ -0,0 +1,500 @@ +/* + * 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("3.1.0") + final val featureType = new Param[String](this, "featureType", + "Feature type. Supported options: categorical, continuous.", + ParamValidators.inArray(Array("categorical", "continuous"))) + + /** @group getParam */ + @Since("3.1.0") + def getFeatureType: String = $(featureType) + + /** + * The label type. + * Supported options: "categorical", "continuous" + * @group param + */ + @Since("3.1.0") + final val labelType = new Param[String](this, "labelType", + "Label type. Supported options: categorical, continuous.", + ParamValidators.inArray(Array("categorical", "continuous"))) + + /** @group getParam */ + @Since("3.1.0") + def getLabelType: String = $(labelType) + + /** + * 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"))) + + /** @group getParam */ + @Since("3.1.0") + def getSelectorType: String = $(selectorType) + + /** + * 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)) + + /** @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)) + + /** @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)) + + /** @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)) + + /** @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)) + + /** @group getParam */ + def getFwe: Double = $(fwe) + + setDefault(numTopFeatures -> 50, percentile -> 0.1, fpr -> 0.05, fdr -> 0.05, fwe -> 0.05, + selectorType -> "numTopFeatures") +} + +/** + * 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 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 <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 method is `numTopFeatures`, with the default number of top features + * set to 50. + */ +@Since("3.1.0") +final class UnivariateFeatureSelector @Since("3.1.0")(@Since("3.1.0") override val uid: String) + extends Estimator[UnivariateFeatureSelectorModel] with UnivariateFeatureSelectorParams + with DefaultParamsWritable { + + @Since("3.1.0") + def this() = this(Identifiable.randomUID("UnivariateFeatureSelector")) + + /** @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) + + /** @group setParam */ + @Since("3.1.0") + def setFeatureType(value: String): this.type = set(featureType, value) + + /** @group setParam */ + @Since("3.1.0") + def setLabelType(value: String): this.type = set(labelType, value) + + @Since("3.1.0") + 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 = $(selectorType) match { + case "numTopFeatures" => + getTopIndices($(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 IllegalArgumentException(s"Unknown Selector Type: $errorType") + } + + copyValues(new UnivariateFeatureSelectorModel(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): UnivariateFeatureSelector = defaultCopy(extra) +} + +@Since("3.1.0") +object UnivariateFeatureSelector extends DefaultParamsReadable[UnivariateFeatureSelector] { + + @Since("3.1.0") + override def load(path: String): UnivariateFeatureSelector = super.load(path) +} + +/** + * Model fitted by [[UnivariateFeatureSelectorModel]]. + */ +@Since("3.1.0") +class UnivariateFeatureSelectorModel private[ml]( + @Since("3.1.0") override val uid: String, + @Since("3.1.0") val selectedFeatures: Array[Int]) + extends Model[UnivariateFeatureSelectorModel] with UnivariateFeatureSelectorParams + with MLWritable { + + 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) + + protected def isNumericAttribute = true + + @Since("3.1.0") + override def transform(dataset: Dataset[_]): DataFrame = { + val outputSchema = transformSchema(dataset.schema, logging = true) + + UnivariateFeatureSelectorModel + .transform(dataset, selectedFeatures, outputSchema, $(outputCol), $(featuresCol)) + } + + @Since("3.1.0") + override def transformSchema(schema: StructType): StructType = { + SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT) + val newField = + UnivariateFeatureSelectorModel + .prepOutputField(schema, selectedFeatures, $(outputCol), $(featuresCol), isNumericAttribute) + SchemaUtils.appendColumn(schema, newField) + } + + @Since("3.1.0") + override def copy(extra: ParamMap): UnivariateFeatureSelectorModel = { + val copied = new UnivariateFeatureSelectorModel(uid, selectedFeatures) + .setParent(parent) + copyValues(copied, extra) + } + + @Since("3.1.0") + override def write: MLWriter = + new UnivariateFeatureSelectorModel.UnivariateFeatureSelectorModelWriter(this) + + @Since("3.1.0") + override def toString: String = { + s"UnivariateFeatureSelectorModel: uid=$uid, numSelectedFeatures=${selectedFeatures.length}" + } +} + +@Since("3.1.0") +object UnivariateFeatureSelectorModel extends MLReadable[UnivariateFeatureSelectorModel] { + + @Since("3.1.0") + override def read: MLReader[UnivariateFeatureSelectorModel] = + new UnivariateFeatureSelectorModelReader + + @Since("3.1.0") + override def load(path: String): UnivariateFeatureSelectorModel = super.load(path) + + private[UnivariateFeatureSelectorModel] class UnivariateFeatureSelectorModelWriter( + instance: UnivariateFeatureSelectorModel) extends MLWriter { + + private case class Data(selectedFeatures: Seq[Int]) + + override protected def saveImpl(path: String): Unit = { + DefaultParamsWriter.saveMetadata(instance, path, sc) + val data = Data(instance.selectedFeatures.toSeq) + val dataPath = new Path(path, "data").toString + sparkSession.createDataFrame(Seq(data)).repartition(1).write.parquet(dataPath) + } + } + + private class UnivariateFeatureSelectorModelReader + extends MLReader[UnivariateFeatureSelectorModel] { + + /** Checked against metadata when loading model */ + private val className = classOf[UnivariateFeatureSelectorModel].getName + + override def load(path: String): UnivariateFeatureSelectorModel = { + val metadata = DefaultParamsReader.loadMetadata(path, sc, className) + val dataPath = new Path(path, "data").toString + val data = sparkSession.read.parquet(dataPath) + .select("selectedFeatures").head() + val selectedFeatures = data.getAs[Seq[Int]](0).toArray + val model = new UnivariateFeatureSelectorModel(metadata.uid, selectedFeatures) + metadata.getAndSetParams(model) + model + } + } + + 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. + */ + def prepOutputField( 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