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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.scalatest.exceptions.TestFailedException
import org.apache.spark.SparkFunSuite
import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
import org.apache.spark.ml.param.ParamsSuite
import org.apache.spark.ml.util.DefaultReadWriteTest
import org.apache.spark.ml.util.TestingUtils._
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.sql.Row
class PolynomialExpansionSuite
extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
test("params") {
ParamsSuite.checkParams(new PolynomialExpansion)
}
private val data = Array(
Vectors.sparse(3, Seq((0, -2.0), (1, 2.3))),
Vectors.dense(-2.0, 2.3),
Vectors.dense(0.0, 0.0, 0.0),
Vectors.dense(0.6, -1.1, -3.0),
Vectors.sparse(3, Seq())
)
private val twoDegreeExpansion: Array[Vector] = Array(
Vectors.sparse(9, Array(0, 1, 2, 3, 4), Array(-2.0, 4.0, 2.3, -4.6, 5.29)),
Vectors.dense(-2.0, 4.0, 2.3, -4.6, 5.29),
Vectors.dense(new Array[Double](9)),
Vectors.dense(0.6, 0.36, -1.1, -0.66, 1.21, -3.0, -1.8, 3.3, 9.0),
Vectors.sparse(9, Array.empty, Array.empty))
private val threeDegreeExpansion: Array[Vector] = Array(
Vectors.sparse(19, Array(0, 1, 2, 3, 4, 5, 6, 7, 8),
Array(-2.0, 4.0, -8.0, 2.3, -4.6, 9.2, 5.29, -10.58, 12.17)),
Vectors.dense(-2.0, 4.0, -8.0, 2.3, -4.6, 9.2, 5.29, -10.58, 12.17),
Vectors.dense(new Array[Double](19)),
Vectors.dense(0.6, 0.36, 0.216, -1.1, -0.66, -0.396, 1.21, 0.726, -1.331, -3.0, -1.8,
-1.08, 3.3, 1.98, -3.63, 9.0, 5.4, -9.9, -27.0),
Vectors.sparse(19, Array.empty, Array.empty))
test("Polynomial expansion with default parameter") {
val df = spark.createDataFrame(data.zip(twoDegreeExpansion)).toDF("features", "expected")
val polynomialExpansion = new PolynomialExpansion()
.setInputCol("features")
.setOutputCol("polyFeatures")
polynomialExpansion.transform(df).select("polyFeatures", "expected").collect().foreach {
case Row(expanded: DenseVector, expected: DenseVector) =>
assert(expanded ~== expected absTol 1e-1)
case Row(expanded: SparseVector, expected: SparseVector) =>
assert(expanded ~== expected absTol 1e-1)
case _ =>
throw new TestFailedException("Unmatched data types after polynomial expansion", 0)
}
}
test("Polynomial expansion with setter") {
val df = spark.createDataFrame(data.zip(threeDegreeExpansion)).toDF("features", "expected")
val polynomialExpansion = new PolynomialExpansion()
.setInputCol("features")
.setOutputCol("polyFeatures")
.setDegree(3)
polynomialExpansion.transform(df).select("polyFeatures", "expected").collect().foreach {
case Row(expanded: DenseVector, expected: DenseVector) =>
assert(expanded ~== expected absTol 1e-1)
case Row(expanded: SparseVector, expected: SparseVector) =>
assert(expanded ~== expected absTol 1e-1)
case _ =>
throw new TestFailedException("Unmatched data types after polynomial expansion", 0)
}
}
test("Polynomial expansion with degree 1 is identity on vectors") {
val df = spark.createDataFrame(data.zip(data)).toDF("features", "expected")
val polynomialExpansion = new PolynomialExpansion()
.setInputCol("features")
.setOutputCol("polyFeatures")
.setDegree(1)
polynomialExpansion.transform(df).select("polyFeatures", "expected").collect().foreach {
case Row(expanded: Vector, expected: Vector) =>
assert(expanded ~== expected absTol 1e-1)
case _ =>
throw new TestFailedException("Unmatched data types after polynomial expansion", 0)
}
}
test("read/write") {
val t = new PolynomialExpansion()
.setInputCol("myInputCol")
.setOutputCol("myOutputCol")
.setDegree(3)
testDefaultReadWrite(t)
}
test("SPARK-17027. Integer overflow in PolynomialExpansion.getPolySize") {
val data: Array[(Vector, Int, Int)] = Array(
(Vectors.dense(1.0, 2.0, 3.0, 4.0, 5.0), 3002, 4367),
(Vectors.sparse(5, Seq((0, 1.0), (4, 5.0))), 3002, 4367),
(Vectors.dense(1.0, 2.0, 3.0, 4.0, 5.0, 6.0), 8007, 12375)
)
val df = spark.createDataFrame(data)
.toDF("features", "expectedPoly10size", "expectedPoly11size")
val t = new PolynomialExpansion()
.setInputCol("features")
.setOutputCol("polyFeatures")
for (i <- Seq(10, 11)) {
val transformed = t.setDegree(i)
.transform(df)
.select(s"expectedPoly${i}size", "polyFeatures")
.rdd.map { case Row(expected: Int, v: Vector) => expected == v.size }
assert(transformed.collect.forall(identity))
}
}
}
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