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
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
|
/*
* 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.classification
import java.util.{List => JList}
import java.util.UUID
import scala.collection.JavaConverters._
import scala.language.existentials
import org.apache.hadoop.fs.Path
import org.json4s.{DefaultFormats, JObject, _}
import org.json4s.JsonDSL._
import org.json4s.jackson.JsonMethods._
import org.apache.spark.SparkContext
import org.apache.spark.annotation.Since
import org.apache.spark.ml._
import org.apache.spark.ml.attribute._
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.ml.param.{Param, ParamMap, ParamPair, Params}
import org.apache.spark.ml.util._
import org.apache.spark.sql.{DataFrame, Dataset, Row}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
import org.apache.spark.storage.StorageLevel
private[ml] trait ClassifierTypeTrait {
// scalastyle:off structural.type
type ClassifierType = Classifier[F, E, M] forSome {
type F
type M <: ClassificationModel[F, M]
type E <: Classifier[F, E, M]
}
// scalastyle:on structural.type
}
/**
* Params for [[OneVsRest]].
*/
private[ml] trait OneVsRestParams extends PredictorParams with ClassifierTypeTrait {
/**
* param for the base binary classifier that we reduce multiclass classification into.
* The base classifier input and output columns are ignored in favor of
* the ones specified in [[OneVsRest]].
* @group param
*/
val classifier: Param[ClassifierType] = new Param(this, "classifier", "base binary classifier")
/** @group getParam */
def getClassifier: ClassifierType = $(classifier)
}
private[ml] object OneVsRestParams extends ClassifierTypeTrait {
def validateParams(instance: OneVsRestParams): Unit = {
def checkElement(elem: Params, name: String): Unit = elem match {
case stage: MLWritable => // good
case other =>
throw new UnsupportedOperationException("OneVsRest write will fail " +
s" because it contains $name which does not implement MLWritable." +
s" Non-Writable $name: ${other.uid} of type ${other.getClass}")
}
instance match {
case ovrModel: OneVsRestModel => ovrModel.models.foreach(checkElement(_, "model"))
case _ => // no need to check OneVsRest here
}
checkElement(instance.getClassifier, "classifier")
}
def saveImpl(
path: String,
instance: OneVsRestParams,
sc: SparkContext,
extraMetadata: Option[JObject] = None): Unit = {
val params = instance.extractParamMap().toSeq
val jsonParams = render(params
.filter { case ParamPair(p, v) => p.name != "classifier" }
.map { case ParamPair(p, v) => p.name -> parse(p.jsonEncode(v)) }
.toList)
DefaultParamsWriter.saveMetadata(instance, path, sc, extraMetadata, Some(jsonParams))
val classifierPath = new Path(path, "classifier").toString
instance.getClassifier.asInstanceOf[MLWritable].save(classifierPath)
}
def loadImpl(
path: String,
sc: SparkContext,
expectedClassName: String): (DefaultParamsReader.Metadata, ClassifierType) = {
val metadata = DefaultParamsReader.loadMetadata(path, sc, expectedClassName)
val classifierPath = new Path(path, "classifier").toString
val estimator = DefaultParamsReader.loadParamsInstance[ClassifierType](classifierPath, sc)
(metadata, estimator)
}
}
/**
* Model produced by [[OneVsRest]].
* This stores the models resulting from training k binary classifiers: one for each class.
* Each example is scored against all k models, and the model with the highest score
* is picked to label the example.
*
* @param labelMetadata Metadata of label column if it exists, or Nominal attribute
* representing the number of classes in training dataset otherwise.
* @param models The binary classification models for the reduction.
* The i-th model is produced by testing the i-th class (taking label 1) vs the rest
* (taking label 0).
*/
@Since("1.4.0")
final class OneVsRestModel private[ml] (
@Since("1.4.0") override val uid: String,
private[ml] val labelMetadata: Metadata,
@Since("1.4.0") val models: Array[_ <: ClassificationModel[_, _]])
extends Model[OneVsRestModel] with OneVsRestParams with MLWritable {
/** A Python-friendly auxiliary constructor. */
private[ml] def this(uid: String, models: JList[_ <: ClassificationModel[_, _]]) = {
this(uid, Metadata.empty, models.asScala.toArray)
}
@Since("1.4.0")
override def transformSchema(schema: StructType): StructType = {
validateAndTransformSchema(schema, fitting = false, getClassifier.featuresDataType)
}
@Since("2.0.0")
override def transform(dataset: Dataset[_]): DataFrame = {
// Check schema
transformSchema(dataset.schema, logging = true)
// determine the input columns: these need to be passed through
val origCols = dataset.schema.map(f => col(f.name))
// add an accumulator column to store predictions of all the models
val accColName = "mbc$acc" + UUID.randomUUID().toString
val initUDF = udf { () => Map[Int, Double]() }
val newDataset = dataset.withColumn(accColName, initUDF())
// persist if underlying dataset is not persistent.
val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
if (handlePersistence) {
newDataset.persist(StorageLevel.MEMORY_AND_DISK)
}
// update the accumulator column with the result of prediction of models
val aggregatedDataset = models.zipWithIndex.foldLeft[DataFrame](newDataset) {
case (df, (model, index)) =>
val rawPredictionCol = model.getRawPredictionCol
val columns = origCols ++ List(col(rawPredictionCol), col(accColName))
// add temporary column to store intermediate scores and update
val tmpColName = "mbc$tmp" + UUID.randomUUID().toString
val updateUDF = udf { (predictions: Map[Int, Double], prediction: Vector) =>
predictions + ((index, prediction(1)))
}
val transformedDataset = model.transform(df).select(columns: _*)
val updatedDataset = transformedDataset
.withColumn(tmpColName, updateUDF(col(accColName), col(rawPredictionCol)))
val newColumns = origCols ++ List(col(tmpColName))
// switch out the intermediate column with the accumulator column
updatedDataset.select(newColumns: _*).withColumnRenamed(tmpColName, accColName)
}
if (handlePersistence) {
newDataset.unpersist()
}
// output the index of the classifier with highest confidence as prediction
val labelUDF = udf { (predictions: Map[Int, Double]) =>
predictions.maxBy(_._2)._1.toDouble
}
// output label and label metadata as prediction
aggregatedDataset
.withColumn($(predictionCol), labelUDF(col(accColName)), labelMetadata)
.drop(accColName)
}
@Since("1.4.1")
override def copy(extra: ParamMap): OneVsRestModel = {
val copied = new OneVsRestModel(
uid, labelMetadata, models.map(_.copy(extra).asInstanceOf[ClassificationModel[_, _]]))
copyValues(copied, extra).setParent(parent)
}
@Since("2.0.0")
override def write: MLWriter = new OneVsRestModel.OneVsRestModelWriter(this)
}
@Since("2.0.0")
object OneVsRestModel extends MLReadable[OneVsRestModel] {
@Since("2.0.0")
override def read: MLReader[OneVsRestModel] = new OneVsRestModelReader
@Since("2.0.0")
override def load(path: String): OneVsRestModel = super.load(path)
/** [[MLWriter]] instance for [[OneVsRestModel]] */
private[OneVsRestModel] class OneVsRestModelWriter(instance: OneVsRestModel) extends MLWriter {
OneVsRestParams.validateParams(instance)
override protected def saveImpl(path: String): Unit = {
val extraJson = ("labelMetadata" -> instance.labelMetadata.json) ~
("numClasses" -> instance.models.length)
OneVsRestParams.saveImpl(path, instance, sc, Some(extraJson))
instance.models.zipWithIndex.foreach { case (model: MLWritable, idx) =>
val modelPath = new Path(path, s"model_$idx").toString
model.save(modelPath)
}
}
}
private class OneVsRestModelReader extends MLReader[OneVsRestModel] {
/** Checked against metadata when loading model */
private val className = classOf[OneVsRestModel].getName
override def load(path: String): OneVsRestModel = {
implicit val format = DefaultFormats
val (metadata, classifier) = OneVsRestParams.loadImpl(path, sc, className)
val labelMetadata = Metadata.fromJson((metadata.metadata \ "labelMetadata").extract[String])
val numClasses = (metadata.metadata \ "numClasses").extract[Int]
val models = Range(0, numClasses).toArray.map { idx =>
val modelPath = new Path(path, s"model_$idx").toString
DefaultParamsReader.loadParamsInstance[ClassificationModel[_, _]](modelPath, sc)
}
val ovrModel = new OneVsRestModel(metadata.uid, labelMetadata, models)
DefaultParamsReader.getAndSetParams(ovrModel, metadata)
ovrModel.set("classifier", classifier)
ovrModel
}
}
}
/**
* Reduction of Multiclass Classification to Binary Classification.
* Performs reduction using one against all strategy.
* For a multiclass classification with k classes, train k models (one per class).
* Each example is scored against all k models and the model with highest score
* is picked to label the example.
*/
@Since("1.4.0")
final class OneVsRest @Since("1.4.0") (
@Since("1.4.0") override val uid: String)
extends Estimator[OneVsRestModel] with OneVsRestParams with MLWritable {
@Since("1.4.0")
def this() = this(Identifiable.randomUID("oneVsRest"))
/** @group setParam */
@Since("1.4.0")
def setClassifier(value: Classifier[_, _, _]): this.type = {
set(classifier, value.asInstanceOf[ClassifierType])
}
/** @group setParam */
@Since("1.5.0")
def setLabelCol(value: String): this.type = set(labelCol, value)
/** @group setParam */
@Since("1.5.0")
def setFeaturesCol(value: String): this.type = set(featuresCol, value)
/** @group setParam */
@Since("1.5.0")
def setPredictionCol(value: String): this.type = set(predictionCol, value)
@Since("1.4.0")
override def transformSchema(schema: StructType): StructType = {
validateAndTransformSchema(schema, fitting = true, getClassifier.featuresDataType)
}
@Since("2.0.0")
override def fit(dataset: Dataset[_]): OneVsRestModel = {
transformSchema(dataset.schema)
// determine number of classes either from metadata if provided, or via computation.
val labelSchema = dataset.schema($(labelCol))
val computeNumClasses: () => Int = () => {
val Row(maxLabelIndex: Double) = dataset.agg(max(col($(labelCol)).cast(DoubleType))).head()
// classes are assumed to be numbered from 0,...,maxLabelIndex
maxLabelIndex.toInt + 1
}
val numClasses = MetadataUtils.getNumClasses(labelSchema).fold(computeNumClasses())(identity)
val multiclassLabeled = dataset.select($(labelCol), $(featuresCol))
// persist if underlying dataset is not persistent.
val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
if (handlePersistence) {
multiclassLabeled.persist(StorageLevel.MEMORY_AND_DISK)
}
// create k columns, one for each binary classifier.
val models = Range(0, numClasses).par.map { index =>
// generate new label metadata for the binary problem.
val newLabelMeta = BinaryAttribute.defaultAttr.withName("label").toMetadata()
val labelColName = "mc2b$" + index
val trainingDataset = multiclassLabeled.withColumn(
labelColName, when(col($(labelCol)) === index.toDouble, 1.0).otherwise(0.0), newLabelMeta)
val classifier = getClassifier
val paramMap = new ParamMap()
paramMap.put(classifier.labelCol -> labelColName)
paramMap.put(classifier.featuresCol -> getFeaturesCol)
paramMap.put(classifier.predictionCol -> getPredictionCol)
classifier.fit(trainingDataset, paramMap)
}.toArray[ClassificationModel[_, _]]
if (handlePersistence) {
multiclassLabeled.unpersist()
}
// extract label metadata from label column if present, or create a nominal attribute
// to output the number of labels
val labelAttribute = Attribute.fromStructField(labelSchema) match {
case _: NumericAttribute | UnresolvedAttribute =>
NominalAttribute.defaultAttr.withName("label").withNumValues(numClasses)
case attr: Attribute => attr
}
val model = new OneVsRestModel(uid, labelAttribute.toMetadata(), models).setParent(this)
copyValues(model)
}
@Since("1.4.1")
override def copy(extra: ParamMap): OneVsRest = {
val copied = defaultCopy(extra).asInstanceOf[OneVsRest]
if (isDefined(classifier)) {
copied.setClassifier($(classifier).copy(extra))
}
copied
}
@Since("2.0.0")
override def write: MLWriter = new OneVsRest.OneVsRestWriter(this)
}
@Since("2.0.0")
object OneVsRest extends MLReadable[OneVsRest] {
@Since("2.0.0")
override def read: MLReader[OneVsRest] = new OneVsRestReader
@Since("2.0.0")
override def load(path: String): OneVsRest = super.load(path)
/** [[MLWriter]] instance for [[OneVsRest]] */
private[OneVsRest] class OneVsRestWriter(instance: OneVsRest) extends MLWriter {
OneVsRestParams.validateParams(instance)
override protected def saveImpl(path: String): Unit = {
OneVsRestParams.saveImpl(path, instance, sc)
}
}
private class OneVsRestReader extends MLReader[OneVsRest] {
/** Checked against metadata when loading model */
private val className = classOf[OneVsRest].getName
override def load(path: String): OneVsRest = {
val (metadata, classifier) = OneVsRestParams.loadImpl(path, sc, className)
val ovr = new OneVsRest(metadata.uid)
DefaultParamsReader.getAndSetParams(ovr, metadata)
ovr.setClassifier(classifier)
}
}
}
|