[GitHub] [spark] huaxingao commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector

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

huaxingao commented on a change in pull request #27882: [SPARK-31127][ML] Add abstract Selector
URL: https://github.com/apache/spark/pull/27882#discussion_r391332980
 
 

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 File path: mllib/src/main/scala/org/apache/spark/ml/feature/Selector.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.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.SelectionTestResult
+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 [[Selector]] and [[SelectorModel]].
+ */
+private[feature] trait SelectorParams 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 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 all the feature selectors. The following selectors are supported:
+ * 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 {
+  self: Estimator[T] =>
+
+  /** @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)
+
+  /**
+   * get the SelectionTestResult for every feature against the label
+   */
+  @Since("3.1.0")
+  protected[this] def getSelectionTestResult(dataset: Dataset[_]): Array[SelectionTestResult]
+
+  /**
+   * Create a new instance of concrete SelectorModel.
+   * @param indices The indices of the selected features
+   * @param pValues The pValues of the selected features
+   * @param statistics The statistics of the selected features
+   * @return A new SelectorModel instance
+   */
+  @Since("3.1.0")
+  protected[this] def createSelectorModel(
+      uid: String,
+      indices: Array[Int],
+      pValues: Array[Double],
+      statistics: Array[Double]): T
+
+  @Since("3.1.0")
+  override def fit(dataset: Dataset[_]): T = {
+    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 testResult = getSelectionTestResult(dataset)
+      .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 }
+    val pValues = features.map(_._1.pValue)
+    val statistic = features.map(_._1.statistic)
+    copyValues(createSelectorModel(uid, indices.sorted, pValues, statistic).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],
+    @Since("3.1.0") val pValues: Array[Double],
+    @Since("3.1.0") val statistic: Array[Double])
 
 Review comment:
   ```pValues``` and ```statistic``` are useful information. I will put these in model. 

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