[GitHub] [spark] zhengruifeng commented on a change in pull request #27322: [SPARK-26111][ML][WIP] Support F-value between label/feature for continuous distribution feature selection

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zhengruifeng commented on a change in pull request #27322: [SPARK-26111][ML][WIP] Support F-value between label/feature for continuous distribution feature selection
URL: https://github.com/apache/spark/pull/27322#discussion_r373762290
 
 

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 File path: mllib/src/main/scala/org/apache/spark/ml/stat/FRegressionTest.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.{Vector, VectorUDT}
+import org.apache.spark.ml.util.SchemaUtils
+import org.apache.spark.mllib.stat.{Statistics => OldStatistics}
+import org.apache.spark.sql.Dataset
+import org.apache.spark.sql.functions.col
+
+
+/**
+ * F-Regression Test
+ */
+@Since("3.1.0")
+object FRegressionTest {
+
+  case class FRegressionTestResult(
+      pValue: Double,
+      degreesOfFreedom: Int,
+      fValue: Double)
+
+  /**
+   * @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 FRegressionTestResult for every feature against the label.
+   */
+  @Since("3.1.0")
+  def test_regression(dataset: Dataset[_], featuresCol: String, labelCol: String):
+    Array[FRegressionTestResult] = {
+
+    val spark = dataset.sparkSession
+    import spark.implicits._
+
+    SchemaUtils.checkColumnType(dataset.schema, featuresCol, new VectorUDT)
+    SchemaUtils.checkNumericType(dataset.schema, labelCol)
+    val rdd = dataset.select(col(labelCol).cast("double"), col(featuresCol)).as[(Double, Vector)]
+      .rdd.map { case (label, features) => LabeledPoint(label, features) }
+
+    val numOfFeatures = rdd.first().features.size
+    val numOfSamples = rdd.count()
+    val degreeOfFreedom = numOfSamples.toInt - 2
+
+    var fTestResultArray = new Array[FRegressionTestResult](numOfFeatures)
+    val labels = rdd.map(d => d.label)
+    for (i <- 0 until numOfFeatures) {
 
 Review comment:
   compute each col at once?
   This should be inefficient, I guess only one pass is needed.

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