[GitHub] [spark] zhengruifeng commented on a change in pull request #27527: [SPARK-30776][ML] Support FValueRegressionSelector for continuous features and continuous labels

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

zhengruifeng commented on a change in pull request #27527: [SPARK-30776][ML] Support FValueRegressionSelector for continuous features and continuous labels
URL: https://github.com/apache/spark/pull/27527#discussion_r379288066
 
 

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 File path: mllib/src/main/scala/org/apache/spark/ml/stat/SelectionTest.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.stat
+
+import org.apache.commons.math3.distribution.FDistribution
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.{DenseVector, Vector, VectorUDT}
+import org.apache.spark.ml.util.SchemaUtils
+import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
+import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint}
+import org.apache.spark.mllib.stat.{Statistics => OldStatistics}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{Dataset, Row}
+import org.apache.spark.sql.functions.col
+import org.apache.spark.sql.types.DoubleType
+
+
+@Since("3.1.0")
+object SelectionTest {
+
+  /**
+   * @param dataset  DataFrame of categorical labels and categorical features.
+   *                 Real-valued features will be treated as categorical for each distinct value.
+   * @param featuresCol  Name of features column in dataset, of type `Vector` (`VectorUDT`)
+   * @param labelCol  Name of label column in dataset, of any numerical type
+   * @return Array containing the SelectionTestResult for every feature against the label.
+   */
+  @Since("3.1.0")
+  def chiSquareTest(dataset: Dataset[_], featuresCol: String, labelCol: String):
+  Array[SelectionTestResult] = {
+
+    val spark = dataset.sparkSession
+
+    SchemaUtils.checkColumnType(dataset.schema, featuresCol, new VectorUDT)
+    SchemaUtils.checkNumericType(dataset.schema, labelCol)
+    val input: RDD[OldLabeledPoint] =
+      dataset.select(col(labelCol).cast(DoubleType), col(featuresCol)).rdd
+        .map {
+        case Row(label: Double, features: Vector) =>
+          OldLabeledPoint(label, OldVectors.fromML(features))
+      }
+    val chiTestResult = OldStatistics.chiSqTest(input)
+    var chiTestResultArray = new Array[SelectionTestResult](chiTestResult.length)
+    for (i <- 0 until chiTestResult.length) {
+      chiTestResultArray(i) = new ChiSqTestResult(chiTestResult(i).pValue,
+        chiTestResult(i).degreesOfFreedom, chiTestResult(i).statistic)
+    }
+    chiTestResultArray
+  }
+
+  /**
+   * @param dataset  DataFrame of continuous labels and continuous features.
+   * @param featuresCol  Name of features column in dataset, of type `Vector` (`VectorUDT`)
+   * @param labelCol  Name of label column in dataset, of any numerical type
+   * @return Array containing the SelectionTestResult for every feature against the label.
+   */
+  @Since("3.1.0")
+  def fValueRegressionTest(dataset: Dataset[_], featuresCol: String, labelCol: String):
+    Array[SelectionTestResult] = {
+
+    val spark = dataset.sparkSession
+    import spark.implicits._
+
+    SchemaUtils.checkColumnType(dataset.schema, featuresCol, new VectorUDT)
+    SchemaUtils.checkNumericType(dataset.schema, labelCol)
+
+    val yMean = dataset.select(col(labelCol)).as[Double].rdd.stats().mean
+
+    val stats = dataset
+      .select(Summarizer.metrics("mean", "std").summary(col("features")).as("summary"))
+    val xMeans = stats.select("summary.mean").rdd.collect()(0).get(0).asInstanceOf[DenseVector]
+      .toArray
+    val xStdev = stats.select("summary.std").rdd.collect()(0).get(0).asInstanceOf[DenseVector]
+      .toArray
+
+    val labeledPointRdd = dataset.select(col("label").cast("double"), col("features"))
+      .as[(Double, Vector)]
+      .rdd.map { case (label, features) => LabeledPoint(label, features) }
+
+    val numOfFeatures = labeledPointRdd.first().features.size
+    val numOfSamples = labeledPointRdd.count()
+    val degreeOfFreedom = numOfSamples.toInt - 2
+    var fTestResultArray = new Array[SelectionTestResult](numOfFeatures)
+
+    labeledPointRdd.flatMap { case LabeledPoint(label, features) =>
+      features.iterator.map { case (col, value) =>
+        (col, (value - xMeans(col.toInt), (label - yMean)))
+      }
+    }.aggregateByKey[(Double, Double)]((0.0, 0.0))(
+      seqOp = {
 
 Review comment:
   I suggest to use `E(XY)-E(X)E(Y)` instead, then only one pass is needed.

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