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.
*/
// scalastyle:off println
package org.apache.spark.examples.ml
// $example on$
import org.apache.spark.ml.{Pipeline, PipelineModel}
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.sql.Row
// $example off$
import org.apache.spark.sql.SparkSession
object PipelineExample {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder
.appName("PipelineExample")
.getOrCreate()
// $example on$
// Prepare training documents from a list of (id, text, label) tuples.
val training = spark.createDataFrame(Seq(
(0L, "a b c d e spark", 1.0),
(1L, "b d", 0.0),
(2L, "spark f g h", 1.0),
(3L, "hadoop mapreduce", 0.0)
)).toDF("id", "text", "label")
// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
val tokenizer = new Tokenizer()
.setInputCol("text")
.setOutputCol("words")
val hashingTF = new HashingTF()
.setNumFeatures(1000)
.setInputCol(tokenizer.getOutputCol)
.setOutputCol("features")
val lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(0.01)
val pipeline = new Pipeline()
.setStages(Array(tokenizer, hashingTF, lr))
// Fit the pipeline to training documents.
val model = pipeline.fit(training)
// Now we can optionally save the fitted pipeline to disk
model.write.overwrite().save("/tmp/spark-logistic-regression-model")
// We can also save this unfit pipeline to disk
pipeline.write.overwrite().save("/tmp/unfit-lr-model")
// And load it back in during production
val sameModel = PipelineModel.load("/tmp/spark-logistic-regression-model")
// Prepare test documents, which are unlabeled (id, text) tuples.
val test = spark.createDataFrame(Seq(
(4L, "spark i j k"),
(5L, "l m n"),
(6L, "mapreduce spark"),
(7L, "apache hadoop")
)).toDF("id", "text")
// Make predictions on test documents.
model.transform(test)
.select("id", "text", "probability", "prediction")
.collect()
.foreach { case Row(id: Long, text: String, prob: Vector, prediction: Double) =>
println(s"($id, $text) --> prob=$prob, prediction=$prediction")
}
// $example off$
spark.stop()
}
}
// scalastyle:on println
|
