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
|
/*
* 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.fpm
import org.apache.spark.SparkFunSuite
import org.apache.spark.ml.util.DefaultReadWriteTest
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
class FPGrowthSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
@transient var dataset: Dataset[_] = _
override def beforeAll(): Unit = {
super.beforeAll()
dataset = FPGrowthSuite.getFPGrowthData(spark)
}
test("FPGrowth fit and transform with different data types") {
Array(IntegerType, StringType, ShortType, LongType, ByteType).foreach { dt =>
val data = dataset.withColumn("features", col("features").cast(ArrayType(dt)))
val model = new FPGrowth().setMinSupport(0.5).fit(data)
val generatedRules = model.setMinConfidence(0.5).associationRules
val expectedRules = spark.createDataFrame(Seq(
(Array("2"), Array("1"), 1.0),
(Array("1"), Array("2"), 0.75)
)).toDF("antecedent", "consequent", "confidence")
.withColumn("antecedent", col("antecedent").cast(ArrayType(dt)))
.withColumn("consequent", col("consequent").cast(ArrayType(dt)))
assert(expectedRules.sort("antecedent").rdd.collect().sameElements(
generatedRules.sort("antecedent").rdd.collect()))
val transformed = model.transform(data)
val expectedTransformed = spark.createDataFrame(Seq(
(0, Array("1", "2"), Array.emptyIntArray),
(0, Array("1", "2"), Array.emptyIntArray),
(0, Array("1", "2"), Array.emptyIntArray),
(0, Array("1", "3"), Array(2))
)).toDF("id", "features", "prediction")
.withColumn("features", col("features").cast(ArrayType(dt)))
.withColumn("prediction", col("prediction").cast(ArrayType(dt)))
assert(expectedTransformed.collect().toSet.equals(
transformed.collect().toSet))
}
}
test("FPGrowth getFreqItems") {
val model = new FPGrowth().setMinSupport(0.7).fit(dataset)
val expectedFreq = spark.createDataFrame(Seq(
(Array("1"), 4L),
(Array("2"), 3L),
(Array("1", "2"), 3L),
(Array("2", "1"), 3L) // duplicate as the items sequence is not guaranteed
)).toDF("items", "expectedFreq")
val freqItems = model.freqItemsets
val checkDF = freqItems.join(expectedFreq, "items")
assert(checkDF.count() == 3 && checkDF.filter(col("freq") === col("expectedFreq")).count() == 3)
}
test("FPGrowth getFreqItems with Null") {
val df = spark.createDataFrame(Seq(
(1, Array("1", "2", "3", "5")),
(2, Array("1", "2", "3", "4")),
(3, null.asInstanceOf[Array[String]])
)).toDF("id", "features")
val model = new FPGrowth().setMinSupport(0.7).fit(dataset)
val prediction = model.transform(df)
assert(prediction.select("prediction").where("id=3").first().getSeq[String](0).isEmpty)
}
test("FPGrowth parameter check") {
val fpGrowth = new FPGrowth().setMinSupport(0.4567)
val model = fpGrowth.fit(dataset)
.setMinConfidence(0.5678)
assert(fpGrowth.getMinSupport === 0.4567)
assert(model.getMinConfidence === 0.5678)
}
test("read/write") {
def checkModelData(model: FPGrowthModel, model2: FPGrowthModel): Unit = {
assert(model.freqItemsets.sort("items").collect() ===
model2.freqItemsets.sort("items").collect())
}
val fPGrowth = new FPGrowth()
testEstimatorAndModelReadWrite(fPGrowth, dataset, FPGrowthSuite.allParamSettings,
FPGrowthSuite.allParamSettings, checkModelData)
}
test("FPGrowth prediction should not contain duplicates") {
// This should generate rule 1 -> 3, 2 -> 3
val dataset = spark.createDataFrame(Seq(
Array("1", "3"),
Array("2", "3")
).map(Tuple1(_))).toDF("features")
val model = new FPGrowth().fit(dataset)
val prediction = model.transform(
spark.createDataFrame(Seq(Tuple1(Array("1", "2")))).toDF("features")
).first().getAs[Seq[String]]("prediction")
assert(prediction === Seq("3"))
}
}
object FPGrowthSuite {
def getFPGrowthData(spark: SparkSession): DataFrame = {
spark.createDataFrame(Seq(
(0, Array("1", "2")),
(0, Array("1", "2")),
(0, Array("1", "2")),
(0, Array("1", "3"))
)).toDF("id", "features")
}
/**
* Mapping from all Params to valid settings which differ from the defaults.
* This is useful for tests which need to exercise all Params, such as save/load.
* This excludes input columns to simplify some tests.
*/
val allParamSettings: Map[String, Any] = Map(
"minSupport" -> 0.321,
"minConfidence" -> 0.456,
"numPartitions" -> 5,
"predictionCol" -> "myPrediction"
)
}
|
