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
|
/*
* 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 org.apache.spark.SparkFunSuite
import org.apache.spark.ml.util.DefaultReadWriteTest
import org.apache.spark.ml.util.MLTestingUtils
import org.apache.spark.mllib.classification.LogisticRegressionSuite._
import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
import org.apache.spark.mllib.evaluation.MulticlassMetrics
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.mllib.util.TestingUtils._
import org.apache.spark.sql.{DataFrame, Dataset, Row}
class MultilayerPerceptronClassifierSuite
extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
@transient var dataset: Dataset[_] = _
override def beforeAll(): Unit = {
super.beforeAll()
dataset = sqlContext.createDataFrame(Seq(
(Vectors.dense(0.0, 0.0), 0.0),
(Vectors.dense(0.0, 1.0), 1.0),
(Vectors.dense(1.0, 0.0), 1.0),
(Vectors.dense(1.0, 1.0), 0.0))
).toDF("features", "label")
}
test("Input Validation") {
val mlpc = new MultilayerPerceptronClassifier()
intercept[IllegalArgumentException] {
mlpc.setLayers(Array[Int]())
}
intercept[IllegalArgumentException] {
mlpc.setLayers(Array[Int](1))
}
intercept[IllegalArgumentException] {
mlpc.setLayers(Array[Int](0, 1))
}
intercept[IllegalArgumentException] {
mlpc.setLayers(Array[Int](1, 0))
}
mlpc.setLayers(Array[Int](1, 1))
}
test("XOR function learning as binary classification problem with two outputs.") {
val layers = Array[Int](2, 5, 2)
val trainer = new MultilayerPerceptronClassifier()
.setLayers(layers)
.setBlockSize(1)
.setSeed(123L)
.setMaxIter(100)
val model = trainer.fit(dataset)
val result = model.transform(dataset)
val predictionAndLabels = result.select("prediction", "label").collect()
predictionAndLabels.foreach { case Row(p: Double, l: Double) =>
assert(p == l)
}
}
test("Test setWeights by training restart") {
val dataFrame = sqlContext.createDataFrame(Seq(
(Vectors.dense(0.0, 0.0), 0.0),
(Vectors.dense(0.0, 1.0), 1.0),
(Vectors.dense(1.0, 0.0), 1.0),
(Vectors.dense(1.0, 1.0), 0.0))
).toDF("features", "label")
val layers = Array[Int](2, 5, 2)
val trainer = new MultilayerPerceptronClassifier()
.setLayers(layers)
.setBlockSize(1)
.setSeed(12L)
.setMaxIter(1)
.setTol(1e-6)
val initialWeights = trainer.fit(dataFrame).weights
trainer.setWeights(initialWeights.copy)
val weights1 = trainer.fit(dataFrame).weights
trainer.setWeights(initialWeights.copy)
val weights2 = trainer.fit(dataFrame).weights
assert(weights1 ~== weights2 absTol 10e-5,
"Training should produce the same weights given equal initial weights and number of steps")
}
test("3 class classification with 2 hidden layers") {
val nPoints = 1000
// The following coefficients are taken from OneVsRestSuite.scala
// they represent 3-class iris dataset
val coefficients = Array(
-0.57997, 0.912083, -0.371077, -0.819866, 2.688191,
-0.16624, -0.84355, -0.048509, -0.301789, 4.170682)
val xMean = Array(5.843, 3.057, 3.758, 1.199)
val xVariance = Array(0.6856, 0.1899, 3.116, 0.581)
// the input seed is somewhat magic, to make this test pass
val rdd = sc.parallelize(generateMultinomialLogisticInput(
coefficients, xMean, xVariance, true, nPoints, 1), 2)
val dataFrame = sqlContext.createDataFrame(rdd).toDF("label", "features")
val numClasses = 3
val numIterations = 100
val layers = Array[Int](4, 5, 4, numClasses)
val trainer = new MultilayerPerceptronClassifier()
.setLayers(layers)
.setBlockSize(1)
.setSeed(11L) // currently this seed is ignored
.setMaxIter(numIterations)
val model = trainer.fit(dataFrame)
val numFeatures = dataFrame.select("features").first().getAs[Vector](0).size
assert(model.numFeatures === numFeatures)
val mlpPredictionAndLabels = model.transform(dataFrame).select("prediction", "label").rdd.map {
case Row(p: Double, l: Double) => (p, l)
}
// train multinomial logistic regression
val lr = new LogisticRegressionWithLBFGS()
.setIntercept(true)
.setNumClasses(numClasses)
lr.optimizer.setRegParam(0.0)
.setNumIterations(numIterations)
val lrModel = lr.run(rdd)
val lrPredictionAndLabels = lrModel.predict(rdd.map(_.features)).zip(rdd.map(_.label))
// MLP's predictions should not differ a lot from LR's.
val lrMetrics = new MulticlassMetrics(lrPredictionAndLabels)
val mlpMetrics = new MulticlassMetrics(mlpPredictionAndLabels)
assert(mlpMetrics.confusionMatrix ~== lrMetrics.confusionMatrix absTol 100)
}
test("read/write: MultilayerPerceptronClassifier") {
val mlp = new MultilayerPerceptronClassifier()
.setLayers(Array(2, 3, 2))
.setMaxIter(5)
.setBlockSize(2)
.setSeed(42)
.setTol(0.1)
.setFeaturesCol("myFeatures")
.setLabelCol("myLabel")
.setPredictionCol("myPrediction")
testDefaultReadWrite(mlp, testParams = true)
}
test("read/write: MultilayerPerceptronClassificationModel") {
val mlp = new MultilayerPerceptronClassifier().setLayers(Array(2, 3, 2)).setMaxIter(5)
val mlpModel = mlp.fit(dataset)
val newMlpModel = testDefaultReadWrite(mlpModel, testParams = true)
assert(newMlpModel.layers === mlpModel.layers)
assert(newMlpModel.weights === mlpModel.weights)
}
test("should support all NumericType labels and not support other types") {
val layers = Array(3, 2)
val mpc = new MultilayerPerceptronClassifier().setLayers(layers).setMaxIter(1)
MLTestingUtils.checkNumericTypes[
MultilayerPerceptronClassificationModel, MultilayerPerceptronClassifier](
mpc, isClassification = true, sqlContext) { (expected, actual) =>
assert(expected.layers === actual.layers)
assert(expected.weights === actual.weights)
}
}
}
|
