Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/5779#discussion_r40962335
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala ---
@@ -0,0 +1,178 @@
+/*
+ * 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.feature
+
+import org.apache.spark.Logging
+
+import scala.collection.mutable
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml._
+import org.apache.spark.ml.attribute.NominalAttribute
+import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
+import org.apache.spark.ml.param.{IntParam, _}
+import org.apache.spark.ml.util._
+import org.apache.spark.sql.types.{DoubleType, StructType}
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.util.random.XORShiftRandom
+
+/**
+ * Params for [[QuantileDiscretizer]].
+ */
+private[feature] trait QuantileDiscretizerBase extends Params with
HasInputCol with HasOutputCol {
+
+ /**
+ * Maximum number of buckets (quantiles, or categories) into which data
points are grouped. Must
+ * be >= 2.
+ * default: 2
+ * @group param
+ */
+ val numBuckets = new IntParam(this, "numBuckets", "Maximum number of
buckets (quantiles, or " +
+ "categories) into which data points are grouped. Must be >= 2.",
+ ParamValidators.gtEq(2))
+ setDefault(numBuckets -> 2)
+
+ /** @group getParam */
+ def getNumBuckets: Int = getOrDefault(numBuckets)
+}
+
+/**
+ * :: Experimental ::
+ * `QuantileDiscretizer` takes a column with continuous features and
outputs a column with binned
+ * categorical features. The bin ranges are chosen by taking a sample of
the data and dividing it
+ * into roughly equal parts. The lower and upper bin bounds will be
-Infinity and +Infinity,
+ * covering all real values. This attempts to find numBuckets partitions
based on a sample of data,
+ * but it may find fewer depending on the data sample values.
+ */
+@Experimental
+final class QuantileDiscretizer(override val uid: String)
+ extends Estimator[Bucketizer] with QuantileDiscretizerBase {
+
+ def this() = this(Identifiable.randomUID("quantileDiscretizer"))
+
+ /** @group setParam */
+ def setNumBuckets(value: Int): this.type = set(numBuckets, value)
+
+ /** @group setParam */
+ def setInputCol(value: String): this.type = set(inputCol, value)
+
+ /** @group setParam */
+ def setOutputCol(value: String): this.type = set(outputCol, value)
+
+ override def transformSchema(schema: StructType): StructType = {
+ SchemaUtils.checkColumnType(schema, $(inputCol), DoubleType)
+ val inputFields = schema.fields
+ require(inputFields.forall(_.name != $(outputCol)),
+ s"Output column ${$(outputCol)} already exists.")
+ val attr = NominalAttribute.defaultAttr.withName($(outputCol))
+ val outputFields = inputFields :+ attr.toStructField()
+ StructType(outputFields)
+ }
+
+ override def fit(dataset: DataFrame): Bucketizer = {
+ val samples =
QuantileDiscretizer.getSampledInput(dataset.select($(inputCol)), $(numBuckets))
+ .map { case Row(feature: Double) => feature }
+ val candidates = QuantileDiscretizer.findSplitCandidates(samples,
$(numBuckets) - 1)
+ val splits = QuantileDiscretizer.getSplits(candidates)
+ val bucketizer = new Bucketizer(uid).setSplits(splits)
+ copyValues(bucketizer)
+ }
+
+ override def copy(extra: ParamMap): QuantileDiscretizer =
defaultCopy(extra)
+}
+
+private[feature] object QuantileDiscretizer extends Logging {
+ /**
+ * Sampling from the given dataset to collect quantile statistics.
+ */
+ def getSampledInput(dataset: DataFrame, numBins: Int): Array[Row] = {
+ val totalSamples = dataset.count()
+ require(totalSamples > 0,
+ "QuantileDiscretizer requires non-empty input dataset but was given
an empty input.")
+ val requiredSamples = math.max(numBins * numBins, 10000)
+ val fraction = math.min(requiredSamples / dataset.count(), 1.0)
+ dataset.sample(withReplacement = false, fraction, new
XORShiftRandom().nextInt()).collect()
+ }
+
+ /**
+ * Compute split points with respect to the sample distribution.
+ */
+ def findSplitCandidates(samples: Array[Double], numSplits: Int):
Array[Double] = {
+ val valueCountMap = samples.foldLeft(Map.empty[Double, Int]) { (m, x)
=>
+ m + ((x, m.getOrElse(x, 0) + 1))
+ }
+ val valueCounts = valueCountMap.toSeq.sortBy(_._1).toArray ++
Array((Double.MaxValue, 1))
+ val possibleSplits = valueCounts.length - 1
+ if (possibleSplits <= numSplits) {
+ valueCounts.dropRight(1).map(_._1)
+ } else {
+ val stride: Double = math.ceil(samples.length.toDouble / (numSplits
+ 1))
+ val splitsBuilder = mutable.ArrayBuilder.make[Double]
+ var index = 1
+ // currentCount: sum of counts of values that have been visited
+ var currentCount = valueCounts(0)._2
+ // targetCount: target value for `currentCount`. If `currentCount`
is closest value to
+ // `targetCount`, then current value is a split threshold. After
finding a split threshold,
+ // `targetCount` is added by stride.
+ var targetCount = stride
+ while (index < valueCounts.length) {
+ val previousCount = currentCount
+ currentCount += valueCounts(index)._2
+ val previousGap = math.abs(previousCount - targetCount)
+ val currentGap = math.abs(currentCount - targetCount)
+ // If adding count of current value to currentCount makes the gap
between currentCount and
+ // targetCount smaller, previous value is a split threshold.
+ if (previousGap < currentGap) {
+ splitsBuilder += valueCounts(index - 1)._1
+ targetCount += stride
+ }
+ index += 1
+ }
+ splitsBuilder.result()
+ }
+ }
+
+ /**
+ * Regulate split candidates to effective splits, such as adding
positive/negative infinity in
+ * both sides, or using default split value in case of only
ineffectiveness split candidates are
--- End diff --
Rephrase:
"Adjust split candidates to proper splits by: adding positive/negative
infinity to both sides as needed, and adding a default split value of 0 if no
good candidates are found."
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