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JIRA issue [here](https://issues.apache.org/jira/browse/SPARK-5893). One thing to make clear, the `buckets` parameter, which is an array of `Double`, performs as split points. Say, ```scala buckets = Array(-0.5, 0.0, 0.5) ``` splits the real number into 4 ranges, (-inf, -0.5], (-0.5, 0.0], (0.0, 0.5], (0.5, +inf), which is encoded as 0, 1, 2, 3. Author: Xusen Yin <yinxusen@gmail.com> Author: Joseph K. Bradley <joseph@databricks.com> Closes #5980 from yinxusen/SPARK-5893 and squashes the following commits: dc8c843 [Xusen Yin] Merge pull request #4 from jkbradley/yinxusen-SPARK-5893 1ca973a [Joseph K. Bradley] one more bucketizer test 34f124a [Joseph K. Bradley] Removed lowerInclusive, upperInclusive params from Bucketizer, and used splits instead. eacfcfa [Xusen Yin] change ML attribute from splits into buckets c3cc770 [Xusen Yin] add more unit test for binary search 3a16cc2 [Xusen Yin] refine comments and names ac77859 [Xusen Yin] fix style error fb30d79 [Xusen Yin] fix and test binary search 2466322 [Xusen Yin] refactor Bucketizer 11fb00a [Xusen Yin] change it into an Estimator 998bc87 [Xusen Yin] check buckets 4024cf1 [Xusen Yin] add test suite 5fe190e [Xusen Yin] add bucketizer
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mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.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. | ||
*/ | ||
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package org.apache.spark.ml.feature | ||
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import org.apache.spark.annotation.AlphaComponent | ||
import org.apache.spark.ml.attribute.NominalAttribute | ||
import org.apache.spark.ml.param._ | ||
import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol} | ||
import org.apache.spark.ml.util.SchemaUtils | ||
import org.apache.spark.ml.{Estimator, Model} | ||
import org.apache.spark.sql._ | ||
import org.apache.spark.sql.functions._ | ||
import org.apache.spark.sql.types.{DoubleType, StructField, StructType} | ||
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/** | ||
* :: AlphaComponent :: | ||
* `Bucketizer` maps a column of continuous features to a column of feature buckets. | ||
*/ | ||
@AlphaComponent | ||
final class Bucketizer private[ml] (override val parent: Estimator[Bucketizer]) | ||
extends Model[Bucketizer] with HasInputCol with HasOutputCol { | ||
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def this() = this(null) | ||
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/** | ||
* Parameter for mapping continuous features into buckets. With n splits, there are n+1 buckets. | ||
* A bucket defined by splits x,y holds values in the range [x,y). Splits should be strictly | ||
* increasing. Values at -inf, inf must be explicitly provided to cover all Double values; | ||
* otherwise, values outside the splits specified will be treated as errors. | ||
* @group param | ||
*/ | ||
val splits: Param[Array[Double]] = new Param[Array[Double]](this, "splits", | ||
"Split points for mapping continuous features into buckets. With n splits, there are n+1 " + | ||
"buckets. A bucket defined by splits x,y holds values in the range [x,y). The splits " + | ||
"should be strictly increasing. Values at -inf, inf must be explicitly provided to cover" + | ||
" all Double values; otherwise, values outside the splits specified will be treated as" + | ||
" errors.", | ||
Bucketizer.checkSplits) | ||
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/** @group getParam */ | ||
def getSplits: Array[Double] = $(splits) | ||
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/** @group setParam */ | ||
def setSplits(value: Array[Double]): this.type = set(splits, value) | ||
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/** @group setParam */ | ||
def setInputCol(value: String): this.type = set(inputCol, value) | ||
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/** @group setParam */ | ||
def setOutputCol(value: String): this.type = set(outputCol, value) | ||
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override def transform(dataset: DataFrame): DataFrame = { | ||
transformSchema(dataset.schema) | ||
val bucketizer = udf { feature: Double => | ||
Bucketizer.binarySearchForBuckets($(splits), feature) | ||
} | ||
val newCol = bucketizer(dataset($(inputCol))) | ||
val newField = prepOutputField(dataset.schema) | ||
dataset.withColumn($(outputCol), newCol.as($(outputCol), newField.metadata)) | ||
} | ||
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private def prepOutputField(schema: StructType): StructField = { | ||
val buckets = $(splits).sliding(2).map(bucket => bucket.mkString(", ")).toArray | ||
val attr = new NominalAttribute(name = Some($(outputCol)), isOrdinal = Some(true), | ||
values = Some(buckets)) | ||
attr.toStructField() | ||
} | ||
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override def transformSchema(schema: StructType): StructType = { | ||
SchemaUtils.checkColumnType(schema, $(inputCol), DoubleType) | ||
SchemaUtils.appendColumn(schema, prepOutputField(schema)) | ||
} | ||
} | ||
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private[feature] object Bucketizer { | ||
/** We require splits to be of length >= 3 and to be in strictly increasing order. */ | ||
def checkSplits(splits: Array[Double]): Boolean = { | ||
if (splits.length < 3) { | ||
false | ||
} else { | ||
var i = 0 | ||
while (i < splits.length - 1) { | ||
if (splits(i) >= splits(i + 1)) return false | ||
i += 1 | ||
} | ||
true | ||
} | ||
} | ||
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/** | ||
* Binary searching in several buckets to place each data point. | ||
* @throws RuntimeException if a feature is < splits.head or >= splits.last | ||
*/ | ||
def binarySearchForBuckets( | ||
splits: Array[Double], | ||
feature: Double): Double = { | ||
// Check bounds. We make an exception for +inf so that it can exist in some bin. | ||
if ((feature < splits.head) || (feature >= splits.last && feature != Double.PositiveInfinity)) { | ||
throw new RuntimeException(s"Feature value $feature out of Bucketizer bounds" + | ||
s" [${splits.head}, ${splits.last}). Check your features, or loosen " + | ||
s"the lower/upper bound constraints.") | ||
} | ||
var left = 0 | ||
var right = splits.length - 2 | ||
while (left < right) { | ||
val mid = (left + right) / 2 | ||
val split = splits(mid + 1) | ||
if (feature < split) { | ||
right = mid | ||
} else { | ||
left = mid + 1 | ||
} | ||
} | ||
left | ||
} | ||
} |
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148
mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.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. | ||
*/ | ||
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package org.apache.spark.ml.feature | ||
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import scala.util.Random | ||
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import org.scalatest.FunSuite | ||
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import org.apache.spark.SparkException | ||
import org.apache.spark.mllib.linalg.Vectors | ||
import org.apache.spark.mllib.util.MLlibTestSparkContext | ||
import org.apache.spark.mllib.util.TestingUtils._ | ||
import org.apache.spark.sql.{DataFrame, Row, SQLContext} | ||
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class BucketizerSuite extends FunSuite with MLlibTestSparkContext { | ||
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@transient private var sqlContext: SQLContext = _ | ||
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override def beforeAll(): Unit = { | ||
super.beforeAll() | ||
sqlContext = new SQLContext(sc) | ||
} | ||
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test("Bucket continuous features, without -inf,inf") { | ||
// Check a set of valid feature values. | ||
val splits = Array(-0.5, 0.0, 0.5) | ||
val validData = Array(-0.5, -0.3, 0.0, 0.2) | ||
val expectedBuckets = Array(0.0, 0.0, 1.0, 1.0) | ||
val dataFrame: DataFrame = | ||
sqlContext.createDataFrame(validData.zip(expectedBuckets)).toDF("feature", "expected") | ||
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val bucketizer: Bucketizer = new Bucketizer() | ||
.setInputCol("feature") | ||
.setOutputCol("result") | ||
.setSplits(splits) | ||
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bucketizer.transform(dataFrame).select("result", "expected").collect().foreach { | ||
case Row(x: Double, y: Double) => | ||
assert(x === y, | ||
s"The feature value is not correct after bucketing. Expected $y but found $x") | ||
} | ||
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// Check for exceptions when using a set of invalid feature values. | ||
val invalidData1: Array[Double] = Array(-0.9) ++ validData | ||
val invalidData2 = Array(0.5) ++ validData | ||
val badDF1 = sqlContext.createDataFrame(invalidData1.zipWithIndex).toDF("feature", "idx") | ||
intercept[RuntimeException]{ | ||
bucketizer.transform(badDF1).collect() | ||
println("Invalid feature value -0.9 was not caught as an invalid feature!") | ||
} | ||
val badDF2 = sqlContext.createDataFrame(invalidData2.zipWithIndex).toDF("feature", "idx") | ||
intercept[RuntimeException]{ | ||
bucketizer.transform(badDF2).collect() | ||
println("Invalid feature value 0.5 was not caught as an invalid feature!") | ||
} | ||
} | ||
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test("Bucket continuous features, with -inf,inf") { | ||
val splits = Array(Double.NegativeInfinity, -0.5, 0.0, 0.5, Double.PositiveInfinity) | ||
val validData = Array(-0.9, -0.5, -0.3, 0.0, 0.2, 0.5, 0.9) | ||
val expectedBuckets = Array(0.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0) | ||
val dataFrame: DataFrame = | ||
sqlContext.createDataFrame(validData.zip(expectedBuckets)).toDF("feature", "expected") | ||
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val bucketizer: Bucketizer = new Bucketizer() | ||
.setInputCol("feature") | ||
.setOutputCol("result") | ||
.setSplits(splits) | ||
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bucketizer.transform(dataFrame).select("result", "expected").collect().foreach { | ||
case Row(x: Double, y: Double) => | ||
assert(x === y, | ||
s"The feature value is not correct after bucketing. Expected $y but found $x") | ||
} | ||
} | ||
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test("Binary search correctness on hand-picked examples") { | ||
import BucketizerSuite.checkBinarySearch | ||
// length 3, with -inf | ||
checkBinarySearch(Array(Double.NegativeInfinity, 0.0, 1.0)) | ||
// length 4 | ||
checkBinarySearch(Array(-1.0, -0.5, 0.0, 1.0)) | ||
// length 5 | ||
checkBinarySearch(Array(-1.0, -0.5, 0.0, 1.0, 1.5)) | ||
// length 3, with inf | ||
checkBinarySearch(Array(0.0, 1.0, Double.PositiveInfinity)) | ||
// length 3, with -inf and inf | ||
checkBinarySearch(Array(Double.NegativeInfinity, 1.0, Double.PositiveInfinity)) | ||
// length 4, with -inf and inf | ||
checkBinarySearch(Array(Double.NegativeInfinity, 0.0, 1.0, Double.PositiveInfinity)) | ||
} | ||
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test("Binary search correctness in contrast with linear search, on random data") { | ||
val data = Array.fill(100)(Random.nextDouble()) | ||
val splits: Array[Double] = Double.NegativeInfinity +: | ||
Array.fill(10)(Random.nextDouble()).sorted :+ Double.PositiveInfinity | ||
val bsResult = Vectors.dense(data.map(x => Bucketizer.binarySearchForBuckets(splits, x))) | ||
val lsResult = Vectors.dense(data.map(x => BucketizerSuite.linearSearchForBuckets(splits, x))) | ||
assert(bsResult ~== lsResult absTol 1e-5) | ||
} | ||
} | ||
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private object BucketizerSuite extends FunSuite { | ||
/** Brute force search for buckets. Bucket i is defined by the range [split(i), split(i+1)). */ | ||
def linearSearchForBuckets(splits: Array[Double], feature: Double): Double = { | ||
require(feature >= splits.head) | ||
var i = 0 | ||
while (i < splits.length - 1) { | ||
if (feature < splits(i + 1)) return i | ||
i += 1 | ||
} | ||
throw new RuntimeException( | ||
s"linearSearchForBuckets failed to find bucket for feature value $feature") | ||
} | ||
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/** Check all values in splits, plus values between all splits. */ | ||
def checkBinarySearch(splits: Array[Double]): Unit = { | ||
def testFeature(feature: Double, expectedBucket: Double): Unit = { | ||
assert(Bucketizer.binarySearchForBuckets(splits, feature) === expectedBucket, | ||
s"Expected feature value $feature to be in bucket $expectedBucket with splits:" + | ||
s" ${splits.mkString(", ")}") | ||
} | ||
var i = 0 | ||
while (i < splits.length - 1) { | ||
testFeature(splits(i), i) // Split i should fall in bucket i. | ||
testFeature((splits(i) + splits(i + 1)) / 2, i) // Value between splits i,i+1 should be in i. | ||
i += 1 | ||
} | ||
if (splits.last === Double.PositiveInfinity) { | ||
testFeature(Double.PositiveInfinity, splits.length - 2) | ||
} | ||
} | ||
} |