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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.feature
import scala.collection.mutable
import org.apache.spark.annotation.{Experimental, Since}
import org.apache.spark.internal.Logging
import org.apache.spark.ml._
import org.apache.spark.ml.attribute.NominalAttribute
import org.apache.spark.ml.param.{IntParam, _}
import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol, HasSeed}
import org.apache.spark.ml.util._
import org.apache.spark.sql.{DataFrame, Row}
import org.apache.spark.sql.types.{DoubleType, StructType}
import org.apache.spark.util.random.XORShiftRandom
/**
* Params for [[QuantileDiscretizer]].
*/
private[feature] trait QuantileDiscretizerBase extends Params
with HasInputCol with HasOutputCol with HasSeed {
/**
* 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 with DefaultParamsWritable {
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)
/** @group setParam */
def setSeed(value: Long): this.type = set(seed, 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), $(seed))
.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.setParent(this))
}
override def copy(extra: ParamMap): QuantileDiscretizer = defaultCopy(extra)
}
@Since("1.6.0")
object QuantileDiscretizer extends DefaultParamsReadable[QuantileDiscretizer] with Logging {
/**
* Minimum number of samples required for finding splits, regardless of number of bins. If
* the dataset has fewer rows than this value, the entire dataset will be used.
*/
private[spark] val minSamplesRequired: Int = 10000
/**
* Sampling from the given dataset to collect quantile statistics.
*/
private[feature] def getSampledInput(dataset: DataFrame, numBins: Int, seed: Long): 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, minSamplesRequired)
val fraction = math.min(requiredSamples.toDouble / totalSamples, 1.0)
dataset.sample(withReplacement = false, fraction, new XORShiftRandom(seed).nextInt()).collect()
}
/**
* Compute split points with respect to the sample distribution.
*/
private[feature]
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()
}
}
/**
* 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.
*/
private[feature] def getSplits(candidates: Array[Double]): Array[Double] = {
val effectiveValues = if (candidates.nonEmpty) {
if (candidates.head == Double.NegativeInfinity
&& candidates.last == Double.PositiveInfinity) {
candidates.drop(1).dropRight(1)
} else if (candidates.head == Double.NegativeInfinity) {
candidates.drop(1)
} else if (candidates.last == Double.PositiveInfinity) {
candidates.dropRight(1)
} else {
candidates
}
} else {
candidates
}
if (effectiveValues.isEmpty) {
Array(Double.NegativeInfinity, 0, Double.PositiveInfinity)
} else {
Array(Double.NegativeInfinity) ++ effectiveValues ++ Array(Double.PositiveInfinity)
}
}
@Since("1.6.0")
override def load(path: String): QuantileDiscretizer = super.load(path)
}
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