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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.SparkFunSuite
import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.sql.Row
class MaxAbsScalerSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
test("MaxAbsScaler fit basic case") {
val data = Array(
Vectors.dense(1, 0, 100),
Vectors.dense(2, 0, 0),
Vectors.sparse(3, Array(0, 2), Array(-2, -100)),
Vectors.sparse(3, Array(0), Array(-1.5)))
val expected: Array[Vector] = Array(
Vectors.dense(0.5, 0, 1),
Vectors.dense(1, 0, 0),
Vectors.sparse(3, Array(0, 2), Array(-1, -1)),
Vectors.sparse(3, Array(0), Array(-0.75)))
val df = sqlContext.createDataFrame(data.zip(expected)).toDF("features", "expected")
val scaler = new MaxAbsScaler()
.setInputCol("features")
.setOutputCol("scaled")
val model = scaler.fit(df)
model.transform(df).select("expected", "scaled").collect()
.foreach { case Row(vector1: Vector, vector2: Vector) =>
assert(vector1.equals(vector2), s"MaxAbsScaler ut error: $vector2 should be $vector1")
}
// copied model must have the same parent.
MLTestingUtils.checkCopy(model)
}
test("MaxAbsScaler read/write") {
val t = new MaxAbsScaler()
.setInputCol("myInputCol")
.setOutputCol("myOutputCol")
testDefaultReadWrite(t)
}
test("MaxAbsScalerModel read/write") {
val instance = new MaxAbsScalerModel(
"myMaxAbsScalerModel", Vectors.dense(1.0, 10.0))
.setInputCol("myInputCol")
.setOutputCol("myOutputCol")
val newInstance = testDefaultReadWrite(instance)
assert(newInstance.maxAbs === instance.maxAbs)
}
}
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