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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 org.apache.spark.{SparkContext, SparkFunSuite}
import org.apache.spark.ml.attribute.{Attribute, NominalAttribute}
import org.apache.spark.ml.util.DefaultReadWriteTest
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.sql.{Row, SQLContext}
class QuantileDiscretizerSuite
extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
import org.apache.spark.ml.feature.QuantileDiscretizerSuite._
test("Test quantile discretizer") {
checkDiscretizedData(sc,
Array[Double](1, 2, 3, 3, 3, 3, 3, 3, 3),
10,
Array[Double](1, 2, 3, 3, 3, 3, 3, 3, 3),
Array("-Infinity, 1.0", "1.0, 2.0", "2.0, 3.0", "3.0, Infinity"))
checkDiscretizedData(sc,
Array[Double](1, 2, 3, 3, 3, 3, 3, 3, 3),
4,
Array[Double](1, 2, 3, 3, 3, 3, 3, 3, 3),
Array("-Infinity, 1.0", "1.0, 2.0", "2.0, 3.0", "3.0, Infinity"))
checkDiscretizedData(sc,
Array[Double](1, 2, 3, 3, 3, 3, 3, 3, 3),
3,
Array[Double](0, 1, 2, 2, 2, 2, 2, 2, 2),
Array("-Infinity, 2.0", "2.0, 3.0", "3.0, Infinity"))
checkDiscretizedData(sc,
Array[Double](1, 2, 3, 3, 3, 3, 3, 3, 3),
2,
Array[Double](0, 1, 1, 1, 1, 1, 1, 1, 1),
Array("-Infinity, 2.0", "2.0, Infinity"))
}
test("Test getting splits") {
val splitTestPoints = Array(
Array[Double]() -> Array(Double.NegativeInfinity, 0, Double.PositiveInfinity),
Array(Double.NegativeInfinity) -> Array(Double.NegativeInfinity, 0, Double.PositiveInfinity),
Array(Double.PositiveInfinity) -> Array(Double.NegativeInfinity, 0, Double.PositiveInfinity),
Array(Double.NegativeInfinity, Double.PositiveInfinity)
-> Array(Double.NegativeInfinity, 0, Double.PositiveInfinity),
Array(0.0) -> Array(Double.NegativeInfinity, 0, Double.PositiveInfinity),
Array(1.0) -> Array(Double.NegativeInfinity, 1, Double.PositiveInfinity),
Array(0.0, 1.0) -> Array(Double.NegativeInfinity, 0, 1, Double.PositiveInfinity)
)
for ((ori, res) <- splitTestPoints) {
assert(QuantileDiscretizer.getSplits(ori) === res, "Returned splits are invalid.")
}
}
test("Test splits on dataset larger than minSamplesRequired") {
val sqlCtx = SQLContext.getOrCreate(sc)
import sqlCtx.implicits._
val datasetSize = QuantileDiscretizer.minSamplesRequired + 1
val numBuckets = 5
val df = sc.parallelize((1.0 to datasetSize by 1.0).map(Tuple1.apply)).toDF("input")
val discretizer = new QuantileDiscretizer()
.setInputCol("input")
.setOutputCol("result")
.setNumBuckets(numBuckets)
.setSeed(1)
val result = discretizer.fit(df).transform(df)
val observedNumBuckets = result.select("result").distinct.count
assert(observedNumBuckets === numBuckets,
"Observed number of buckets does not equal expected number of buckets.")
}
test("read/write") {
val t = new QuantileDiscretizer()
.setInputCol("myInputCol")
.setOutputCol("myOutputCol")
.setNumBuckets(6)
testDefaultReadWrite(t)
}
}
private object QuantileDiscretizerSuite extends SparkFunSuite {
def checkDiscretizedData(
sc: SparkContext,
data: Array[Double],
numBucket: Int,
expectedResult: Array[Double],
expectedAttrs: Array[String]): Unit = {
val sqlCtx = SQLContext.getOrCreate(sc)
import sqlCtx.implicits._
val df = sc.parallelize(data.map(Tuple1.apply)).toDF("input")
val discretizer = new QuantileDiscretizer().setInputCol("input").setOutputCol("result")
.setNumBuckets(numBucket).setSeed(1)
val model = discretizer.fit(df)
assert(model.hasParent)
val result = model.transform(df)
val transformedFeatures = result.select("result").collect()
.map { case Row(transformedFeature: Double) => transformedFeature }
val transformedAttrs = Attribute.fromStructField(result.schema("result"))
.asInstanceOf[NominalAttribute].values.get
assert(transformedFeatures === expectedResult,
"Transformed features do not equal expected features.")
assert(transformedAttrs === expectedAttrs,
"Transformed attributes do not equal expected attributes.")
}
}
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