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From imatiach-msft <...@git.apache.org>
Subject [GitHub] spark pull request #17110: [SPARK-19635][ML] DataFrame-based API for chi squ...
Date Wed, 01 Mar 2017 23:05:37 GMT
Github user imatiach-msft commented on a diff in the pull request:

    https://github.com/apache/spark/pull/17110#discussion_r103813169
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/stat/ChiSquare.scala ---
    @@ -0,0 +1,81 @@
    +/*
    + * 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.spark.annotation.{Experimental, Since}
    +import org.apache.spark.ml.linalg.{Vector, Vectors, 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.sql.DataFrame
    +import org.apache.spark.sql.functions.col
    +
    +
    +/**
    + * :: Experimental ::
    + *
    + * Chi-square hypothesis testing for categorical data.
    + *
    + * See <a href="http://en.wikipedia.org/wiki/Chi-squared_test">Wikipedia</a>
for more information
    + * on the Chi-squared test.
    + */
    +@Experimental
    +@Since("2.2.0")
    +object ChiSquare {
    +
    +  /** Used to construct output schema of tests */
    +  private case class ChiSquareResult(
    +      pValues: Vector,
    +      degreesOfFreedom: Array[Int],
    +      statistics: Vector)
    +
    +  /**
    +   * Conduct Pearson's independence test for every feature against the label across the
input RDD.
    +   * For each feature, the (feature, label) pairs are converted into a contingency matrix
for which
    +   * the Chi-squared statistic is computed. All label and feature values must be categorical.
    +   *
    +   * The null hypothesis is that the occurrence of the outcomes is statistically independent.
    +   *
    +   * @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 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]`
    +   *          - `statistics: Vector`
    +   *         Each of these fields has one value per feature.
    +   */
    +  @Since("2.2.0")
    +  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 rdd = dataset.select(col(labelCol).cast("double"), col(featuresCol)).as[(Double,
Vector)]
    +      .rdd.map { case (label, features) => OldLabeledPoint(label, OldVectors.fromML(features))
}
    +    val testResults = OldStatistics.chiSqTest(rdd)
    --- End diff --
    
    it would be nice to optimize this in the future -- since we have schema, if the label
and features have been converted to categorical, we can get the unique values right away instead
of having to re-generate the maps for distinct labels and features


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