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[SPARK-5891][ML] Add Binarizer ML Transformer #5699
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1682f8c
Add Binarizer ML Transformer.
viirya 7564c63
Merge remote-tracking branch 'upstream/master' into add_binarizer
viirya cc4f03c
Implement threshold param and use merged params map.
viirya bc397f2
Merge remote-tracking branch 'upstream/master' into add_binarizer
viirya 1a0b9a4
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85 changes: 85 additions & 0 deletions
85
mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala
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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. | ||
*/ | ||
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package org.apache.spark.ml.feature | ||
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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} | ||
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/** | ||
* :: AlphaComponent :: | ||
* Binarize a column of continuous features given a threshold. | ||
*/ | ||
@AlphaComponent | ||
final class Binarizer extends Transformer with HasInputCol with HasOutputCol { | ||
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/** | ||
* 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") | ||
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/** @group getParam */ | ||
def getThreshold: Double = getOrDefault(threshold) | ||
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/** @group setParam */ | ||
def setThreshold(value: Double): this.type = set(threshold, value) | ||
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setDefault(threshold -> 0.0) | ||
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/** @group setParam */ | ||
def setInputCol(value: String): this.type = set(inputCol, value) | ||
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/** @group setParam */ | ||
def setOutputCol(value: String): this.type = set(outputCol, value) | ||
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override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { | ||
transformSchema(dataset.schema, paramMap, logging = true) | ||
val map = extractParamMap(paramMap) | ||
val td = map(threshold) | ||
val binarizer = udf { in: Double => if (in > td) 1.0 else 0.0 } | ||
val outputColName = map(outputCol) | ||
val metadata = BinaryAttribute.defaultAttr.withName(outputColName).toMetadata() | ||
dataset.select(col("*"), | ||
binarizer(col(map(inputCol))).as(outputColName, metadata)) | ||
} | ||
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override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { | ||
val map = extractParamMap(paramMap) | ||
SchemaUtils.checkColumnType(schema, map(inputCol), DoubleType) | ||
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val inputFields = schema.fields | ||
val outputColName = map(outputCol) | ||
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require(inputFields.forall(_.name != outputColName), | ||
s"Output column $outputColName already exists.") | ||
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val attr = BinaryAttribute.defaultAttr.withName(outputColName) | ||
val outputFields = inputFields :+ attr.toStructField() | ||
StructType(outputFields) | ||
} | ||
} |
69 changes: 69 additions & 0 deletions
69
mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala
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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. | ||
*/ | ||
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package org.apache.spark.ml.feature | ||
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import org.scalatest.FunSuite | ||
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import org.apache.spark.mllib.util.MLlibTestSparkContext | ||
import org.apache.spark.mllib.util.TestingUtils._ | ||
import org.apache.spark.sql.{DataFrame, Row, SQLContext} | ||
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class BinarizerSuite extends FunSuite with MLlibTestSparkContext { | ||
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@transient var data: Array[Double] = _ | ||
@transient var sqlContext: SQLContext = _ | ||
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override def beforeAll(): Unit = { | ||
super.beforeAll() | ||
sqlContext = new SQLContext(sc) | ||
data = Array(0.1, -0.5, 0.2, -0.3, 0.8, 0.7, -0.1, -0.4) | ||
} | ||
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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") | ||
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val binarizer: Binarizer = new Binarizer() | ||
.setInputCol("feature") | ||
.setOutputCol("binarized_feature") | ||
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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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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") | ||
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val binarizer: Binarizer = new Binarizer() | ||
.setInputCol("feature") | ||
.setOutputCol("binarized_feature") | ||
.setThreshold(threshold) | ||
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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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minor:
data
could be aval