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
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
|
/*
* 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
import scala.collection.mutable.ListBuffer
import org.apache.spark.Logging
import org.apache.spark.annotation.AlphaComponent
import org.apache.spark.ml.param.{Params, Param, ParamMap}
import org.apache.spark.sql.{SchemaRDD, StructType}
/**
* :: AlphaComponent ::
* A stage in a pipeline, either an [[Estimator]] or a [[Transformer]].
*/
@AlphaComponent
abstract class PipelineStage extends Serializable with Logging {
/**
* Derives the output schema from the input schema and parameters.
*/
private[ml] def transformSchema(schema: StructType, paramMap: ParamMap): StructType
/**
* Derives the output schema from the input schema and parameters, optionally with logging.
*/
protected def transformSchema(
schema: StructType,
paramMap: ParamMap,
logging: Boolean): StructType = {
if (logging) {
logDebug(s"Input schema: ${schema.json}")
}
val outputSchema = transformSchema(schema, paramMap)
if (logging) {
logDebug(s"Expected output schema: ${outputSchema.json}")
}
outputSchema
}
}
/**
* :: AlphaComponent ::
* A simple pipeline, which acts as an estimator. A Pipeline consists of a sequence of stages, each
* of which is either an [[Estimator]] or a [[Transformer]]. When [[Pipeline.fit]] is called, the
* stages are executed in order. If a stage is an [[Estimator]], its [[Estimator.fit]] method will
* be called on the input dataset to fit a model. Then the model, which is a transformer, will be
* used to transform the dataset as the input to the next stage. If a stage is a [[Transformer]],
* its [[Transformer.transform]] method will be called to produce the dataset for the next stage.
* The fitted model from a [[Pipeline]] is an [[PipelineModel]], which consists of fitted models and
* transformers, corresponding to the pipeline stages. If there are no stages, the pipeline acts as
* an identity transformer.
*/
@AlphaComponent
class Pipeline extends Estimator[PipelineModel] {
/** param for pipeline stages */
val stages: Param[Array[PipelineStage]] = new Param(this, "stages", "stages of the pipeline")
def setStages(value: Array[PipelineStage]): this.type = { set(stages, value); this }
def getStages: Array[PipelineStage] = get(stages)
/**
* Fits the pipeline to the input dataset with additional parameters. If a stage is an
* [[Estimator]], its [[Estimator.fit]] method will be called on the input dataset to fit a model.
* Then the model, which is a transformer, will be used to transform the dataset as the input to
* the next stage. If a stage is a [[Transformer]], its [[Transformer.transform]] method will be
* called to produce the dataset for the next stage. The fitted model from a [[Pipeline]] is an
* [[PipelineModel]], which consists of fitted models and transformers, corresponding to the
* pipeline stages. If there are no stages, the output model acts as an identity transformer.
*
* @param dataset input dataset
* @param paramMap parameter map
* @return fitted pipeline
*/
override def fit(dataset: SchemaRDD, paramMap: ParamMap): PipelineModel = {
transformSchema(dataset.schema, paramMap, logging = true)
val map = this.paramMap ++ paramMap
val theStages = map(stages)
// Search for the last estimator.
var indexOfLastEstimator = -1
theStages.view.zipWithIndex.foreach { case (stage, index) =>
stage match {
case _: Estimator[_] =>
indexOfLastEstimator = index
case _ =>
}
}
var curDataset = dataset
val transformers = ListBuffer.empty[Transformer]
theStages.view.zipWithIndex.foreach { case (stage, index) =>
if (index <= indexOfLastEstimator) {
val transformer = stage match {
case estimator: Estimator[_] =>
estimator.fit(curDataset, paramMap)
case t: Transformer =>
t
case _ =>
throw new IllegalArgumentException(
s"Do not support stage $stage of type ${stage.getClass}")
}
curDataset = transformer.transform(curDataset, paramMap)
transformers += transformer
} else {
transformers += stage.asInstanceOf[Transformer]
}
}
new PipelineModel(this, map, transformers.toArray)
}
private[ml] override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
val map = this.paramMap ++ paramMap
val theStages = map(stages)
require(theStages.toSet.size == theStages.size,
"Cannot have duplicate components in a pipeline.")
theStages.foldLeft(schema)((cur, stage) => stage.transformSchema(cur, paramMap))
}
}
/**
* :: AlphaComponent ::
* Represents a compiled pipeline.
*/
@AlphaComponent
class PipelineModel private[ml] (
override val parent: Pipeline,
override val fittingParamMap: ParamMap,
private[ml] val stages: Array[Transformer])
extends Model[PipelineModel] with Logging {
/**
* Gets the model produced by the input estimator. Throws an NoSuchElementException is the input
* estimator does not exist in the pipeline.
*/
def getModel[M <: Model[M]](stage: Estimator[M]): M = {
val matched = stages.filter {
case m: Model[_] => m.parent.eq(stage)
case _ => false
}
if (matched.isEmpty) {
throw new NoSuchElementException(s"Cannot find stage $stage from the pipeline.")
} else if (matched.size > 1) {
throw new IllegalStateException(s"Cannot have duplicate estimators in the sample pipeline.")
} else {
matched.head.asInstanceOf[M]
}
}
override def transform(dataset: SchemaRDD, paramMap: ParamMap): SchemaRDD = {
// Precedence of ParamMaps: paramMap > this.paramMap > fittingParamMap
val map = (fittingParamMap ++ this.paramMap) ++ paramMap
transformSchema(dataset.schema, map, logging = true)
stages.foldLeft(dataset)((cur, transformer) => transformer.transform(cur, map))
}
private[ml] override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
// Precedence of ParamMaps: paramMap > this.paramMap > fittingParamMap
val map = (fittingParamMap ++ this.paramMap) ++ paramMap
stages.foldLeft(schema)((cur, transformer) => transformer.transformSchema(cur, map))
}
}
|
