srowen 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_r379595713
########## File path: mllib/src/main/scala/org/apache/spark/ml/stat/SelectionTest.scala ########## @@ -0,0 +1,132 @@ +/* + * 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: Does this lead to numerical instability? i don't know either way, just know this has been an issue at times. Maybe it's 'worth it' here. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
