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
|
/*
* 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.regression
import org.scalatest.FunSuite
import org.apache.spark.ml.impl.TreeTests
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.tree.{EnsembleTestHelper, RandomForest => OldRandomForest}
import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.DataFrame
/**
* Test suite for [[RandomForestRegressor]].
*/
class RandomForestRegressorSuite extends FunSuite with MLlibTestSparkContext {
import RandomForestRegressorSuite.compareAPIs
private var orderedLabeledPoints50_1000: RDD[LabeledPoint] = _
override def beforeAll() {
super.beforeAll()
orderedLabeledPoints50_1000 =
sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 50, 1000))
}
/////////////////////////////////////////////////////////////////////////////
// Tests calling train()
/////////////////////////////////////////////////////////////////////////////
def regressionTestWithContinuousFeatures(rf: RandomForestRegressor) {
val categoricalFeaturesInfo = Map.empty[Int, Int]
val newRF = rf
.setImpurity("variance")
.setMaxDepth(2)
.setMaxBins(10)
.setNumTrees(1)
.setFeatureSubsetStrategy("auto")
.setSeed(123)
compareAPIs(orderedLabeledPoints50_1000, newRF, categoricalFeaturesInfo)
}
test("Regression with continuous features:" +
" comparing DecisionTree vs. RandomForest(numTrees = 1)") {
val rf = new RandomForestRegressor()
regressionTestWithContinuousFeatures(rf)
}
test("Regression with continuous features and node Id cache :" +
" comparing DecisionTree vs. RandomForest(numTrees = 1)") {
val rf = new RandomForestRegressor()
.setCacheNodeIds(true)
regressionTestWithContinuousFeatures(rf)
}
/////////////////////////////////////////////////////////////////////////////
// Tests of model save/load
/////////////////////////////////////////////////////////////////////////////
// TODO: Reinstate test once save/load are implemented SPARK-6725
/*
test("model save/load") {
val tempDir = Utils.createTempDir()
val path = tempDir.toURI.toString
val trees = Range(0, 3).map(_ => OldDecisionTreeSuite.createModel(OldAlgo.Regression)).toArray
val oldModel = new OldRandomForestModel(OldAlgo.Regression, trees)
val newModel = RandomForestRegressionModel.fromOld(oldModel)
// Save model, load it back, and compare.
try {
newModel.save(sc, path)
val sameNewModel = RandomForestRegressionModel.load(sc, path)
TreeTests.checkEqual(newModel, sameNewModel)
} finally {
Utils.deleteRecursively(tempDir)
}
}
*/
}
private object RandomForestRegressorSuite extends FunSuite {
/**
* Train 2 models on the given dataset, one using the old API and one using the new API.
* Convert the old model to the new format, compare them, and fail if they are not exactly equal.
*/
def compareAPIs(
data: RDD[LabeledPoint],
rf: RandomForestRegressor,
categoricalFeatures: Map[Int, Int]): Unit = {
val oldStrategy =
rf.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, rf.getOldImpurity)
val oldModel = OldRandomForest.trainRegressor(
data, oldStrategy, rf.getNumTrees, rf.getFeatureSubsetStrategy, rf.getSeed.toInt)
val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0)
val newModel = rf.fit(newData)
// Use parent, fittingParamMap from newTree since these are not checked anyways.
val oldModelAsNew = RandomForestRegressionModel.fromOld(oldModel, newModel.parent,
newModel.fittingParamMap, categoricalFeatures)
TreeTests.checkEqual(oldModelAsNew, newModel)
}
}
|
