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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.mllib.util.MLlibTestSparkContext
import org.apache.spark.sql.{DataFrame, Row}
class BinarizerSuite extends SparkFunSuite with MLlibTestSparkContext {
@transient var data: Array[Double] = _
override def beforeAll(): Unit = {
super.beforeAll()
data = Array(0.1, -0.5, 0.2, -0.3, 0.8, 0.7, -0.1, -0.4)
}
test("Binarize continuous features with default parameter") {
val defaultBinarized: Array[Double] = data.map(x => if (x > 0.0) 1.0 else 0.0)
val dataFrame: DataFrame = sqlContext.createDataFrame(
data.zip(defaultBinarized)).toDF("feature", "expected")
val binarizer: Binarizer = new Binarizer()
.setInputCol("feature")
.setOutputCol("binarized_feature")
binarizer.transform(dataFrame).select("binarized_feature", "expected").collect().foreach {
case Row(x: Double, y: Double) =>
assert(x === y, "The feature value is not correct after binarization.")
}
}
test("Binarize continuous features with setter") {
val threshold: Double = 0.2
val thresholdBinarized: Array[Double] = data.map(x => if (x > threshold) 1.0 else 0.0)
val dataFrame: DataFrame = sqlContext.createDataFrame(
data.zip(thresholdBinarized)).toDF("feature", "expected")
val binarizer: Binarizer = new Binarizer()
.setInputCol("feature")
.setOutputCol("binarized_feature")
.setThreshold(threshold)
binarizer.transform(dataFrame).select("binarized_feature", "expected").collect().foreach {
case Row(x: Double, y: Double) =>
assert(x === y, "The feature value is not correct after binarization.")
}
}
}
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