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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.annotation.AlphaComponent
import org.apache.spark.ml.Transformer
import org.apache.spark.ml.attribute.BinaryAttribute
import org.apache.spark.ml.param._
import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
import org.apache.spark.ml.util.SchemaUtils
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.{DoubleType, StructType}
/**
* :: AlphaComponent ::
* Binarize a column of continuous features given a threshold.
*/
@AlphaComponent
final class Binarizer extends Transformer with HasInputCol with HasOutputCol {
/**
* Param for threshold used to binarize continuous features.
* The features greater than the threshold, will be binarized to 1.0.
* The features equal to or less than the threshold, will be binarized to 0.0.
* @group param
*/
val threshold: DoubleParam =
new DoubleParam(this, "threshold", "threshold used to binarize continuous features")
/** @group getParam */
def getThreshold: Double = $(threshold)
/** @group setParam */
def setThreshold(value: Double): this.type = set(threshold, value)
setDefault(threshold -> 0.0)
/** @group setParam */
def setInputCol(value: String): this.type = set(inputCol, value)
/** @group setParam */
def setOutputCol(value: String): this.type = set(outputCol, value)
override def transform(dataset: DataFrame): DataFrame = {
transformSchema(dataset.schema, logging = true)
val td = $(threshold)
val binarizer = udf { in: Double => if (in > td) 1.0 else 0.0 }
val outputColName = $(outputCol)
val metadata = BinaryAttribute.defaultAttr.withName(outputColName).toMetadata()
dataset.select(col("*"),
binarizer(col($(inputCol))).as(outputColName, metadata))
}
override def transformSchema(schema: StructType): StructType = {
SchemaUtils.checkColumnType(schema, $(inputCol), DoubleType)
val inputFields = schema.fields
val outputColName = $(outputCol)
require(inputFields.forall(_.name != outputColName),
s"Output column $outputColName already exists.")
val attr = BinaryAttribute.defaultAttr.withName(outputColName)
val outputFields = inputFields :+ attr.toStructField()
StructType(outputFields)
}
}
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