/
BisectingKMeans.scala
314 lines (262 loc) · 10.9 KB
/
BisectingKMeans.scala
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
/*
* 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.clustering
import org.apache.hadoop.fs.Path
import org.apache.spark.SparkException
import org.apache.spark.annotation.{Experimental, Since}
import org.apache.spark.ml.{Estimator, Model}
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.ml.param._
import org.apache.spark.ml.param.shared._
import org.apache.spark.ml.util._
import org.apache.spark.mllib.clustering.{BisectingKMeans => MLlibBisectingKMeans,
BisectingKMeansModel => MLlibBisectingKMeansModel}
import org.apache.spark.mllib.linalg.VectorImplicits._
import org.apache.spark.sql.{DataFrame, Dataset, Row}
import org.apache.spark.sql.functions.udf
import org.apache.spark.sql.types.{IntegerType, StructType}
/**
* Common params for BisectingKMeans and BisectingKMeansModel
*/
private[clustering] trait BisectingKMeansParams extends Params with HasMaxIter
with HasFeaturesCol with HasSeed with HasPredictionCol with HasDistanceMeasure {
/**
* The desired number of leaf clusters. Must be > 1. Default: 4.
* The actual number could be smaller if there are no divisible leaf clusters.
* @group param
*/
@Since("2.0.0")
final val k = new IntParam(this, "k", "The desired number of leaf clusters. " +
"Must be > 1.", ParamValidators.gt(1))
/** @group getParam */
@Since("2.0.0")
def getK: Int = $(k)
/**
* The minimum number of points (if greater than or equal to 1.0) or the minimum proportion
* of points (if less than 1.0) of a divisible cluster (default: 1.0).
* @group expertParam
*/
@Since("2.0.0")
final val minDivisibleClusterSize = new DoubleParam(this, "minDivisibleClusterSize",
"The minimum number of points (if >= 1.0) or the minimum proportion " +
"of points (if < 1.0) of a divisible cluster.", ParamValidators.gt(0.0))
/** @group expertGetParam */
@Since("2.0.0")
def getMinDivisibleClusterSize: Double = $(minDivisibleClusterSize)
/**
* Validates and transforms the input schema.
* @param schema input schema
* @return output schema
*/
protected def validateAndTransformSchema(schema: StructType): StructType = {
SchemaUtils.validateVectorCompatibleColumn(schema, getFeaturesCol)
SchemaUtils.appendColumn(schema, $(predictionCol), IntegerType)
}
}
/**
* Model fitted by BisectingKMeans.
*
* @param parentModel a model trained by [[org.apache.spark.mllib.clustering.BisectingKMeans]].
*/
@Since("2.0.0")
class BisectingKMeansModel private[ml] (
@Since("2.0.0") override val uid: String,
private val parentModel: MLlibBisectingKMeansModel
) extends Model[BisectingKMeansModel] with BisectingKMeansParams with MLWritable {
@Since("2.0.0")
override def copy(extra: ParamMap): BisectingKMeansModel = {
val copied = copyValues(new BisectingKMeansModel(uid, parentModel), extra)
copied.setSummary(trainingSummary).setParent(this.parent)
}
/** @group setParam */
@Since("2.1.0")
def setFeaturesCol(value: String): this.type = set(featuresCol, value)
/** @group setParam */
@Since("2.1.0")
def setPredictionCol(value: String): this.type = set(predictionCol, value)
/** @group expertSetParam */
@Since("2.4.0")
def setDistanceMeasure(value: String): this.type = set(distanceMeasure, value)
@Since("2.0.0")
override def transform(dataset: Dataset[_]): DataFrame = {
transformSchema(dataset.schema, logging = true)
val predictUDF = udf((vector: Vector) => predict(vector))
dataset.withColumn($(predictionCol),
predictUDF(DatasetUtils.columnToVector(dataset, getFeaturesCol)))
}
@Since("2.0.0")
override def transformSchema(schema: StructType): StructType = {
validateAndTransformSchema(schema)
}
private[clustering] def predict(features: Vector): Int = parentModel.predict(features)
@Since("2.0.0")
def clusterCenters: Array[Vector] = parentModel.clusterCenters.map(_.asML)
/**
* Computes the sum of squared distances between the input points and their corresponding cluster
* centers.
*/
@Since("2.0.0")
def computeCost(dataset: Dataset[_]): Double = {
SchemaUtils.validateVectorCompatibleColumn(dataset.schema, getFeaturesCol)
val data = DatasetUtils.columnToOldVector(dataset, getFeaturesCol)
parentModel.computeCost(data)
}
@Since("2.0.0")
override def write: MLWriter = new BisectingKMeansModel.BisectingKMeansModelWriter(this)
private var trainingSummary: Option[BisectingKMeansSummary] = None
private[clustering] def setSummary(summary: Option[BisectingKMeansSummary]): this.type = {
this.trainingSummary = summary
this
}
/**
* Return true if there exists summary of model.
*/
@Since("2.1.0")
def hasSummary: Boolean = trainingSummary.nonEmpty
/**
* Gets summary of model on training set. An exception is
* thrown if `trainingSummary == None`.
*/
@Since("2.1.0")
def summary: BisectingKMeansSummary = trainingSummary.getOrElse {
throw new SparkException(
s"No training summary available for the ${this.getClass.getSimpleName}")
}
}
object BisectingKMeansModel extends MLReadable[BisectingKMeansModel] {
@Since("2.0.0")
override def read: MLReader[BisectingKMeansModel] = new BisectingKMeansModelReader
@Since("2.0.0")
override def load(path: String): BisectingKMeansModel = super.load(path)
/** [[MLWriter]] instance for [[BisectingKMeansModel]] */
private[BisectingKMeansModel]
class BisectingKMeansModelWriter(instance: BisectingKMeansModel) extends MLWriter {
override protected def saveImpl(path: String): Unit = {
// Save metadata and Params
DefaultParamsWriter.saveMetadata(instance, path, sc)
val dataPath = new Path(path, "data").toString
instance.parentModel.save(sc, dataPath)
}
}
private class BisectingKMeansModelReader extends MLReader[BisectingKMeansModel] {
/** Checked against metadata when loading model */
private val className = classOf[BisectingKMeansModel].getName
override def load(path: String): BisectingKMeansModel = {
val metadata = DefaultParamsReader.loadMetadata(path, sc, className)
val dataPath = new Path(path, "data").toString
val mllibModel = MLlibBisectingKMeansModel.load(sc, dataPath)
val model = new BisectingKMeansModel(metadata.uid, mllibModel)
metadata.getAndSetParams(model)
model
}
}
}
/**
* A bisecting k-means algorithm based on the paper "A comparison of document clustering techniques"
* by Steinbach, Karypis, and Kumar, with modification to fit Spark.
* The algorithm starts from a single cluster that contains all points.
* Iteratively it finds divisible clusters on the bottom level and bisects each of them using
* k-means, until there are `k` leaf clusters in total or no leaf clusters are divisible.
* The bisecting steps of clusters on the same level are grouped together to increase parallelism.
* If bisecting all divisible clusters on the bottom level would result more than `k` leaf clusters,
* larger clusters get higher priority.
*
* @see <a href="http://glaros.dtc.umn.edu/gkhome/fetch/papers/docclusterKDDTMW00.pdf">
* Steinbach, Karypis, and Kumar, A comparison of document clustering techniques,
* KDD Workshop on Text Mining, 2000.</a>
*/
@Since("2.0.0")
class BisectingKMeans @Since("2.0.0") (
@Since("2.0.0") override val uid: String)
extends Estimator[BisectingKMeansModel] with BisectingKMeansParams with DefaultParamsWritable {
setDefault(
k -> 4,
maxIter -> 20,
minDivisibleClusterSize -> 1.0)
@Since("2.0.0")
override def copy(extra: ParamMap): BisectingKMeans = defaultCopy(extra)
@Since("2.0.0")
def this() = this(Identifiable.randomUID("bisecting-kmeans"))
/** @group setParam */
@Since("2.0.0")
def setFeaturesCol(value: String): this.type = set(featuresCol, value)
/** @group setParam */
@Since("2.0.0")
def setPredictionCol(value: String): this.type = set(predictionCol, value)
/** @group setParam */
@Since("2.0.0")
def setK(value: Int): this.type = set(k, value)
/** @group setParam */
@Since("2.0.0")
def setMaxIter(value: Int): this.type = set(maxIter, value)
/** @group setParam */
@Since("2.0.0")
def setSeed(value: Long): this.type = set(seed, value)
/** @group expertSetParam */
@Since("2.0.0")
def setMinDivisibleClusterSize(value: Double): this.type = set(minDivisibleClusterSize, value)
/** @group expertSetParam */
@Since("2.4.0")
def setDistanceMeasure(value: String): this.type = set(distanceMeasure, value)
@Since("2.0.0")
override def fit(dataset: Dataset[_]): BisectingKMeansModel = {
transformSchema(dataset.schema, logging = true)
val rdd = DatasetUtils.columnToOldVector(dataset, getFeaturesCol)
val instr = Instrumentation.create(this, dataset)
instr.logParams(featuresCol, predictionCol, k, maxIter, seed,
minDivisibleClusterSize, distanceMeasure)
val bkm = new MLlibBisectingKMeans()
.setK($(k))
.setMaxIterations($(maxIter))
.setMinDivisibleClusterSize($(minDivisibleClusterSize))
.setSeed($(seed))
.setDistanceMeasure($(distanceMeasure))
val parentModel = bkm.run(rdd)
val model = copyValues(new BisectingKMeansModel(uid, parentModel).setParent(this))
val summary = new BisectingKMeansSummary(
model.transform(dataset), $(predictionCol), $(featuresCol), $(k))
model.setSummary(Some(summary))
instr.logNamedValue("clusterSizes", summary.clusterSizes)
instr.logSuccess(model)
model
}
@Since("2.0.0")
override def transformSchema(schema: StructType): StructType = {
validateAndTransformSchema(schema)
}
}
@Since("2.0.0")
object BisectingKMeans extends DefaultParamsReadable[BisectingKMeans] {
@Since("2.0.0")
override def load(path: String): BisectingKMeans = super.load(path)
}
/**
* :: Experimental ::
* Summary of BisectingKMeans.
*
* @param predictions `DataFrame` produced by `BisectingKMeansModel.transform()`.
* @param predictionCol Name for column of predicted clusters in `predictions`.
* @param featuresCol Name for column of features in `predictions`.
* @param k Number of clusters.
*/
@Since("2.1.0")
@Experimental
class BisectingKMeansSummary private[clustering] (
predictions: DataFrame,
predictionCol: String,
featuresCol: String,
k: Int) extends ClusteringSummary(predictions, predictionCol, featuresCol, k)