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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.util.Random
import org.apache.spark.{SparkException, SparkFunSuite}
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.ml.param.ParamsSuite
import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
import org.apache.spark.ml.util.TestingUtils._
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.sql.{DataFrame, Row}
class BucketizerSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
test("params") {
ParamsSuite.checkParams(new Bucketizer)
}
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 =
spark.createDataFrame(validData.zip(expectedBuckets)).toDF("feature", "expected")
val bucketizer: Bucketizer = new Bucketizer()
.setInputCol("feature")
.setOutputCol("result")
.setSplits(splits)
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")
}
// Check for exceptions when using a set of invalid feature values.
val invalidData1: Array[Double] = Array(-0.9) ++ validData
val invalidData2 = Array(0.51) ++ validData
val badDF1 = spark.createDataFrame(invalidData1.zipWithIndex).toDF("feature", "idx")
withClue("Invalid feature value -0.9 was not caught as an invalid feature!") {
intercept[SparkException] {
bucketizer.transform(badDF1).collect()
}
}
val badDF2 = spark.createDataFrame(invalidData2.zipWithIndex).toDF("feature", "idx")
withClue("Invalid feature value 0.51 was not caught as an invalid feature!") {
intercept[SparkException] {
bucketizer.transform(badDF2).collect()
}
}
}
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 =
spark.createDataFrame(validData.zip(expectedBuckets)).toDF("feature", "expected")
val bucketizer: Bucketizer = new Bucketizer()
.setInputCol("feature")
.setOutputCol("result")
.setSplits(splits)
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")
}
}
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))
}
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)
}
test("read/write") {
val t = new Bucketizer()
.setInputCol("myInputCol")
.setOutputCol("myOutputCol")
.setSplits(Array(0.1, 0.8, 0.9))
testDefaultReadWrite(t)
}
}
private object BucketizerSuite extends SparkFunSuite {
/** 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
val n = splits.length - 1
while (i < n) {
if (feature < splits(i + 1)) return i
i += 1
}
throw new RuntimeException(
s"linearSearchForBuckets failed to find bucket for feature value $feature")
}
/** 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
val n = splits.length - 1
while (i < n) {
// Split i should fall in bucket i.
testFeature(splits(i), i)
// Value between splits i,i+1 should be in i, which is also true if the (i+1)-th split is inf.
testFeature((splits(i) + splits(i + 1)) / 2, i)
i += 1
}
}
}
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