1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
|
/*
* 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 scala.collection.mutable.ArrayBuilder
import org.apache.spark.annotation.{Experimental, Since}
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._
import org.apache.spark.mllib.linalg._
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
/**
* :: Experimental ::
* Binarize a column of continuous features given a threshold.
*/
@Experimental
final class Binarizer(override val uid: String)
extends Transformer with HasInputCol with HasOutputCol with DefaultParamsWritable {
def this() = this(Identifiable.randomUID("binarizer"))
/**
* 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.
* Default: 0.0
* @group param
*/
val threshold: DoubleParam =
new DoubleParam(this, "threshold", "threshold used to binarize continuous features")
/** @group getParam */
def getThreshold: Double = $(threshold)
/** @group setParam */
def setThreshold(value: Double): this.type = set(threshold, value)
setDefault(threshold -> 0.0)
/** @group setParam */
def setInputCol(value: String): this.type = set(inputCol, value)
/** @group setParam */
def setOutputCol(value: String): this.type = set(outputCol, value)
@Since("2.0.0")
override def transform(dataset: Dataset[_]): DataFrame = {
val outputSchema = transformSchema(dataset.schema, logging = true)
val schema = dataset.schema
val inputType = schema($(inputCol)).dataType
val td = $(threshold)
val binarizerDouble = udf { in: Double => if (in > td) 1.0 else 0.0 }
val binarizerVector = udf { (data: Vector) =>
val indices = ArrayBuilder.make[Int]
val values = ArrayBuilder.make[Double]
data.foreachActive { (index, value) =>
if (value > td) {
indices += index
values += 1.0
}
}
Vectors.sparse(data.size, indices.result(), values.result()).compressed
}
val metadata = outputSchema($(outputCol)).metadata
inputType match {
case DoubleType =>
dataset.select(col("*"), binarizerDouble(col($(inputCol))).as($(outputCol), metadata))
case _: VectorUDT =>
dataset.select(col("*"), binarizerVector(col($(inputCol))).as($(outputCol), metadata))
}
}
override def transformSchema(schema: StructType): StructType = {
val inputType = schema($(inputCol)).dataType
val outputColName = $(outputCol)
val outCol: StructField = inputType match {
case DoubleType =>
BinaryAttribute.defaultAttr.withName(outputColName).toStructField()
case _: VectorUDT =>
new StructField(outputColName, new VectorUDT, true)
case other =>
throw new IllegalArgumentException(s"Data type $other is not supported.")
}
if (schema.fieldNames.contains(outputColName)) {
throw new IllegalArgumentException(s"Output column $outputColName already exists.")
}
StructType(schema.fields :+ outCol)
}
override def copy(extra: ParamMap): Binarizer = defaultCopy(extra)
}
@Since("1.6.0")
object Binarizer extends DefaultParamsReadable[Binarizer] {
@Since("1.6.0")
override def load(path: String): Binarizer = super.load(path)
}
|
