[GitHub] [spark] srowen commented on a change in pull request #27895: [SPARK-31138][ML] Add ANOVA Selector for continuous features and categorical labels

[GitBox <gi...@apache.org>]

srowen commented on a change in pull request #27895: [SPARK-31138][ML] Add ANOVA Selector for continuous features and categorical labels
URL: https://github.com/apache/spark/pull/27895#discussion_r392700075
 
 

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 File path: mllib/src/main/scala/org/apache/spark/ml/feature/ANOVASelector.scala
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+/*
+ * 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.stat.{ANOVATest, 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}
+
+
+/**
+ * ANOVA F-value Classification selector, which selects continuous features to use for predicting a
+ * categorical 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 classification
+ *     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 ANOVASelector @Since("3.1.0")(@Since("3.1.0") override val uid: String)
+  extends Estimator[ANOVASelectorModel] with FValueSelectorParams
+    with DefaultParamsWritable {
+
+  @Since("3.1.0")
+  def this() = this(Identifiable.randomUID("ANOVASelector"))
+
+  /** @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[_]): ANOVASelectorModel = {
+    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 = ANOVATest.testClassification(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 }
+    copyValues(new ANOVASelectorModel(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): ANOVASelector = defaultCopy(extra)
+}
+
+@Since("3.1.0")
+object ANOVASelector extends DefaultParamsReadable[ANOVASelector] {
+
+  @Since("3.1.0")
+  override def load(path: String): ANOVASelector = super.load(path)
+}
+
+/**
+ * Model fitted by [[ANOVASelector]].
+ */
+@Since("3.1.0")
+class ANOVASelectorModel private[ml](
+    @Since("3.1.0") override val uid: String,
+    @Since("3.1.0") val selectedFeatures: Array[Int])
+  extends Model[ANOVASelectorModel] with FValueSelectorParams 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)
+
+  @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(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)
+  }
+
+  @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($(outputCol), featureAttributes)
+    newAttributeGroup.toStructField()
+  }
+
+  @Since("3.1.0")
+  override def copy(extra: ParamMap): ANOVASelectorModel = {
+    val copied = new ANOVASelectorModel(uid, selectedFeatures)
+      .setParent(parent)
+    copyValues(copied, extra)
+  }
+
+  @Since("3.1.0")
+  override def write: MLWriter = new ANOVASelectorModel.ANOVASelectorModelWriter(this)
+
+  @Since("3.1.0")
+  override def toString: String = {
+    s"ANOVASelectorModel: uid=$uid, numSelectedFeatures=${selectedFeatures.length}"
+  }
+
+  private[spark] def compressSparse(
+      indices: Array[Int],
+      values: Array[Double]): (Array[Int], Array[Double]) = {
+    val newValues = new ArrayBuilder.ofDouble
+    val newIndices = new ArrayBuilder.ofInt
+    var i = 0
+    var j = 0
+    var indicesIdx = 0
 
 Review comment:
   Nit: Can these just be `val` inside the while loop? or else I'm missing why they have to be outside

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