[GitHub] [spark] srowen commented on a change in pull request #27623: [SPARK-30867][ML] Add FValueTest

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

srowen commented on a change in pull request #27623: [SPARK-30867][ML] Add FValueTest
URL: https://github.com/apache/spark/pull/27623#discussion_r382036161
 
 

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 File path: mllib/src/main/scala/org/apache/spark/ml/stat/FValueTest.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.linalg.{Vector, Vectors, VectorUDT}
+import org.apache.spark.ml.util.SchemaUtils
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.sql.functions._
+
+
+/**
+ * FValue test for continuous data.
+ */
+@Since("3.1.0")
+object FValueTest {
+
+  /** Used to construct output schema of tests */
+  private case class FValueResult(
+      pValues: Vector,
+      degreesOfFreedom: Array[Long],
+      fValues: Vector)
+
+  /**
+   * @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 DataFrame containing the test result for every feature against the label.
+   *         This DataFrame will contain a single Row with the following fields:
+   *          - `pValues: Vector`
+   *          - `degreesOfFreedom: Array[Int]`
+   *          - `fValues: Vector`
+   *         Each of these fields has one value per feature.
+   */
+  @Since("3.1.0")
+  def test(dataset: DataFrame, featuresCol: String, labelCol: String): DataFrame = {
+
+    val spark = dataset.sparkSession
+    import spark.implicits._
+
+    SchemaUtils.checkColumnType(dataset.schema, featuresCol, new VectorUDT)
+    SchemaUtils.checkNumericType(dataset.schema, labelCol)
+
+    val Row(xMeans: Vector, xStd: Vector, yMean: Double, yStd: Double, count: Long) = dataset
+      .select(Summarizer.metrics("mean", "std", "count").summary(col(featuresCol)).as("summary"),
+        avg(col(labelCol)).as("yMean"),
+        stddev(col(labelCol)).as("yStd"))
+      .select("summary.mean", "summary.std", "yMean", "yStd", "summary.count")
+      .first()
+
+    val labeledPointRdd = dataset.select(col("label").cast("double"), col("features"))
+      .as[(Double, Vector)].rdd
+
+    val numFeatures = xMeans.size
+    val numSamples = count
+    val degreesOfFreedom = numSamples - 2
+
+    // Use two pass equation Cov[X,Y] = E[(X - E[X]) * (Y - E[Y])] to compute covariance because
+    // one pass equation Cov[X,Y] = E[XY] - E[X]E[Y] is susceptible to catastrophic cancellation
+    // sumForCov = Sum(((Xi - Avg(X)) * ((Yi-Avg(Y)))
+    val sumForCov = labeledPointRdd.mapPartitions { iter =>
+      if (iter.hasNext) {
+        val array = Array.ofDim[Double](numFeatures)
+        while(iter.hasNext) {
+          val (label, features) = iter.next
+          val yDiff = label - yMean
+          if (yDiff != 0) {
+            features.iterator.zip(xMeans.iterator)
+              .foreach { case ((col, x), (_, xMean)) => array(col) += yDiff * (x - xMean) }
+          }
+        }
+        Iterator.single(array)
+      } else Iterator.empty
+    }.treeReduce { case (array1, array2) =>
+      var i = 0
+      while (i < numFeatures) {
+        array1(i) += array2(i)
+        i += 1
+      }
+      array1
+    }
+
+    val pValues = Array.ofDim[Double](numFeatures)
+    val degreesOfFreedoms = Array.fill(numFeatures)(degreesOfFreedom)
+    val fValues = Array.ofDim[Double](numFeatures)
+
 
 Review comment:
   Nit: blank line

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