blob: 3c588c61620c508ba556aafdf0608e89b2fdc145 (
plain) (
blame)
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
|
/*
* 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 spark.mllib.regression
import scala.util.Random
import org.scalatest.BeforeAndAfterAll
import org.scalatest.FunSuite
import spark.SparkContext
import spark.SparkContext._
class RidgeRegressionSuite extends FunSuite with BeforeAndAfterAll {
val sc = new SparkContext("local", "test")
override def afterAll() {
sc.stop()
System.clearProperty("spark.driver.port")
}
// Test if we can correctly learn Y = 3 + X1 + X2 when
// X1 and X2 are collinear.
test("multi-collinear variables") {
val rnd = new Random(43)
val x1 = Array.fill[Double](20)(rnd.nextGaussian())
// Pick a mean close to mean of x1
val rnd1 = new Random(42) //new NormalDistribution(0.1, 0.01)
val x2 = Array.fill[Double](20)(0.1 + rnd1.nextGaussian() * 0.01)
val xMat = (0 until 20).map(i => Array(x1(i), x2(i))).toArray
val y = xMat.map(i => 3 + i(0) + i(1))
val testData = (0 until 20).map(i => (y(i), xMat(i))).toArray
val testRDD = sc.parallelize(testData, 2)
testRDD.cache()
val ridgeReg = new RidgeRegression().setLowLambda(0)
.setHighLambda(10)
val model = ridgeReg.train(testRDD)
assert(model.intercept >= 2.9 && model.intercept <= 3.1)
assert(model.weights.length === 2)
assert(model.weights.get(0) >= 0.9 && model.weights.get(0) <= 1.1)
assert(model.weights.get(1) >= 0.9 && model.weights.get(1) <= 1.1)
}
}
|
