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
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
|
/*
* 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.hadoop.fs.Path
import org.apache.spark.annotation.{Experimental, Since}
import org.apache.spark.ml._
import org.apache.spark.ml.linalg.{Vector, VectorUDT}
import org.apache.spark.ml.param._
import org.apache.spark.ml.param.shared._
import org.apache.spark.ml.util._
import org.apache.spark.mllib.feature
import org.apache.spark.mllib.linalg.{Vector => OldVector, Vectors => OldVectors}
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.rdd.RDD
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.StructType
/**
* Params for [[IDF]] and [[IDFModel]].
*/
private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol {
/**
* The minimum number of documents in which a term should appear.
* Default: 0
* @group param
*/
final val minDocFreq = new IntParam(
this, "minDocFreq", "minimum number of documents in which a term should appear for filtering")
setDefault(minDocFreq -> 0)
/** @group getParam */
def getMinDocFreq: Int = $(minDocFreq)
/**
* Validate and transform the input schema.
*/
protected def validateAndTransformSchema(schema: StructType): StructType = {
SchemaUtils.checkColumnType(schema, $(inputCol), new VectorUDT)
SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT)
}
}
/**
* :: Experimental ::
* Compute the Inverse Document Frequency (IDF) given a collection of documents.
*/
@Experimental
@Since("1.4.0")
final class IDF @Since("1.4.0") (@Since("1.4.0") override val uid: String)
extends Estimator[IDFModel] with IDFBase with DefaultParamsWritable {
@Since("1.4.0")
def this() = this(Identifiable.randomUID("idf"))
/** @group setParam */
@Since("1.4.0")
def setInputCol(value: String): this.type = set(inputCol, value)
/** @group setParam */
@Since("1.4.0")
def setOutputCol(value: String): this.type = set(outputCol, value)
/** @group setParam */
@Since("1.4.0")
def setMinDocFreq(value: Int): this.type = set(minDocFreq, value)
@Since("2.0.0")
override def fit(dataset: Dataset[_]): IDFModel = {
transformSchema(dataset.schema, logging = true)
val input: RDD[OldVector] = dataset.select($(inputCol)).rdd.map {
case Row(v: Vector) => OldVectors.fromML(v)
}
val idf = new feature.IDF($(minDocFreq)).fit(input)
copyValues(new IDFModel(uid, idf).setParent(this))
}
@Since("1.4.0")
override def transformSchema(schema: StructType): StructType = {
validateAndTransformSchema(schema)
}
@Since("1.4.1")
override def copy(extra: ParamMap): IDF = defaultCopy(extra)
}
@Since("1.6.0")
object IDF extends DefaultParamsReadable[IDF] {
@Since("1.6.0")
override def load(path: String): IDF = super.load(path)
}
/**
* :: Experimental ::
* Model fitted by [[IDF]].
*/
@Experimental
@Since("1.4.0")
class IDFModel private[ml] (
@Since("1.4.0") override val uid: String,
idfModel: feature.IDFModel)
extends Model[IDFModel] with IDFBase with MLWritable {
import IDFModel._
/** @group setParam */
@Since("1.4.0")
def setInputCol(value: String): this.type = set(inputCol, value)
/** @group setParam */
@Since("1.4.0")
def setOutputCol(value: String): this.type = set(outputCol, value)
@Since("2.0.0")
override def transform(dataset: Dataset[_]): DataFrame = {
transformSchema(dataset.schema, logging = true)
// TODO: Make the idfModel.transform natively in ml framework to avoid extra conversion.
val idf = udf { vec: Vector => idfModel.transform(OldVectors.fromML(vec)).asML }
dataset.withColumn($(outputCol), idf(col($(inputCol))))
}
@Since("1.4.0")
override def transformSchema(schema: StructType): StructType = {
validateAndTransformSchema(schema)
}
@Since("1.4.1")
override def copy(extra: ParamMap): IDFModel = {
val copied = new IDFModel(uid, idfModel)
copyValues(copied, extra).setParent(parent)
}
/** Returns the IDF vector. */
@Since("2.0.0")
def idf: Vector = idfModel.idf.asML
@Since("1.6.0")
override def write: MLWriter = new IDFModelWriter(this)
}
@Since("1.6.0")
object IDFModel extends MLReadable[IDFModel] {
private[IDFModel] class IDFModelWriter(instance: IDFModel) extends MLWriter {
private case class Data(idf: Vector)
override protected def saveImpl(path: String): Unit = {
DefaultParamsWriter.saveMetadata(instance, path, sc)
val data = Data(instance.idf)
val dataPath = new Path(path, "data").toString
sparkSession.createDataFrame(Seq(data)).repartition(1).write.parquet(dataPath)
}
}
private class IDFModelReader extends MLReader[IDFModel] {
private val className = classOf[IDFModel].getName
override def load(path: String): IDFModel = {
val metadata = DefaultParamsReader.loadMetadata(path, sc, className)
val dataPath = new Path(path, "data").toString
val data = sparkSession.read.parquet(dataPath)
val Row(idf: Vector) = MLUtils.convertVectorColumnsToML(data, "idf")
.select("idf")
.head()
val model = new IDFModel(metadata.uid, new feature.IDFModel(OldVectors.fromML(idf)))
DefaultParamsReader.getAndSetParams(model, metadata)
model
}
}
@Since("1.6.0")
override def read: MLReader[IDFModel] = new IDFModelReader
@Since("1.6.0")
override def load(path: String): IDFModel = super.load(path)
}
|