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
|
/*
* 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.feature
import org.apache.spark.SparkFunSuite
import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.sql.Row
class MinMaxScalerSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
test("MinMaxScaler fit basic case") {
val data = Array(
Vectors.dense(1, 0, Long.MinValue),
Vectors.dense(2, 0, 0),
Vectors.sparse(3, Array(0, 2), Array(3, Long.MaxValue)),
Vectors.sparse(3, Array(0), Array(1.5)))
val expected: Array[Vector] = Array(
Vectors.dense(-5, 0, -5),
Vectors.dense(0, 0, 0),
Vectors.sparse(3, Array(0, 2), Array(5, 5)),
Vectors.sparse(3, Array(0), Array(-2.5)))
val df = sqlContext.createDataFrame(data.zip(expected)).toDF("features", "expected")
val scaler = new MinMaxScaler()
.setInputCol("features")
.setOutputCol("scaled")
.setMin(-5)
.setMax(5)
val model = scaler.fit(df)
model.transform(df).select("expected", "scaled").collect()
.foreach { case Row(vector1: Vector, vector2: Vector) =>
assert(vector1.equals(vector2), "Transformed vector is different with expected.")
}
// copied model must have the same parent.
MLTestingUtils.checkCopy(model)
}
test("MinMaxScaler arguments max must be larger than min") {
withClue("arguments max must be larger than min") {
val dummyDF = sqlContext.createDataFrame(Seq(
(1, Vectors.dense(1.0, 2.0)))).toDF("id", "feature")
intercept[IllegalArgumentException] {
val scaler = new MinMaxScaler().setMin(10).setMax(0).setInputCol("feature")
scaler.transformSchema(dummyDF.schema)
}
intercept[IllegalArgumentException] {
val scaler = new MinMaxScaler().setMin(0).setMax(0).setInputCol("feature")
scaler.transformSchema(dummyDF.schema)
}
}
}
test("MinMaxScaler read/write") {
val t = new MinMaxScaler()
.setInputCol("myInputCol")
.setOutputCol("myOutputCol")
.setMax(1.0)
.setMin(-1.0)
testDefaultReadWrite(t)
}
test("MinMaxScalerModel read/write") {
val instance = new MinMaxScalerModel(
"myMinMaxScalerModel", Vectors.dense(-1.0, 0.0), Vectors.dense(1.0, 10.0))
.setInputCol("myInputCol")
.setOutputCol("myOutputCol")
.setMin(-1.0)
.setMax(1.0)
val newInstance = testDefaultReadWrite(instance)
assert(newInstance.originalMin === instance.originalMin)
assert(newInstance.originalMax === instance.originalMax)
}
}
|
