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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
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package org.apache.spark.ml.feature

import org.apache.spark.SparkFunSuite
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.param.ParamsSuite
import org.apache.spark.ml.util.DefaultReadWriteTest
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.sql.{DataFrame, Row}

class BinarizerSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {

  import testImplicits._

  @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("params") {
    ParamsSuite.checkParams(new Binarizer)
  }

  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 = data.zip(defaultBinarized).toSeq.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 = data.zip(thresholdBinarized).toSeq.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.")
    }
  }

  test("Binarize vector of continuous features with default parameter") {
    val defaultBinarized: Array[Double] = data.map(x => if (x > 0.0) 1.0 else 0.0)
    val dataFrame: DataFrame = Seq(
      (Vectors.dense(data), Vectors.dense(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: Vector, y: Vector) =>
        assert(x == y, "The feature value is not correct after binarization.")
    }
  }

  test("Binarize vector of continuous features with setter") {
    val threshold: Double = 0.2
    val defaultBinarized: Array[Double] = data.map(x => if (x > threshold) 1.0 else 0.0)
    val dataFrame: DataFrame = Seq(
      (Vectors.dense(data), Vectors.dense(defaultBinarized))
    ).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: Vector, y: Vector) =>
        assert(x == y, "The feature value is not correct after binarization.")
    }
  }


  test("read/write") {
    val t = new Binarizer()
      .setInputCol("myInputCol")
      .setOutputCol("myOutputCol")
      .setThreshold(0.1)
    testDefaultReadWrite(t)
  }
}
