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# 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
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import sys
import os
if sys.version > '3':
basestring = str
from pyspark import since, keyword_only, SparkContext
from pyspark.ml.base import Estimator, Model, Transformer
from pyspark.ml.param import Param, Params
from pyspark.ml.util import *
from pyspark.ml.wrapper import JavaParams, JavaWrapper
from pyspark.ml.common import inherit_doc, _java2py, _py2java
[docs]@inherit_doc
class Pipeline(Estimator, MLReadable, MLWritable):
"""
A simple pipeline, which acts as an estimator. A Pipeline consists
of a sequence of stages, each of which is either an
:py:class:`Estimator` or a :py:class:`Transformer`. When
:py:meth:`Pipeline.fit` is called, the stages are executed in
order. If a stage is an :py:class:`Estimator`, its
:py:meth:`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 :py:class:`Transformer`, its
:py:meth:`Transformer.transform` method will be called to produce
the dataset for the next stage. The fitted model from a
:py:class:`Pipeline` is a :py:class:`PipelineModel`, which
consists of fitted models and transformers, corresponding to the
pipeline stages. If stages is an empty list, the pipeline acts as an
identity transformer.
.. versionadded:: 1.3.0
"""
stages = Param(Params._dummy(), "stages", "a list of pipeline stages")
@keyword_only
def __init__(self, stages=None):
"""
__init__(self, stages=None)
"""
super(Pipeline, self).__init__()
kwargs = self._input_kwargs
self.setParams(**kwargs)
[docs] @since("1.3.0")
def setStages(self, value):
"""
Set pipeline stages.
:param value: a list of transformers or estimators
:return: the pipeline instance
"""
return self._set(stages=value)
[docs] @since("1.3.0")
def getStages(self):
"""
Get pipeline stages.
"""
return self.getOrDefault(self.stages)
[docs] @keyword_only
@since("1.3.0")
def setParams(self, stages=None):
"""
setParams(self, stages=None)
Sets params for Pipeline.
"""
kwargs = self._input_kwargs
return self._set(**kwargs)
def _fit(self, dataset):
stages = self.getStages()
for stage in stages:
if not (isinstance(stage, Estimator) or isinstance(stage, Transformer)):
raise TypeError(
"Cannot recognize a pipeline stage of type %s." % type(stage))
indexOfLastEstimator = -1
for i, stage in enumerate(stages):
if isinstance(stage, Estimator):
indexOfLastEstimator = i
transformers = []
for i, stage in enumerate(stages):
if i <= indexOfLastEstimator:
if isinstance(stage, Transformer):
transformers.append(stage)
dataset = stage.transform(dataset)
else: # must be an Estimator
model = stage.fit(dataset)
transformers.append(model)
if i < indexOfLastEstimator:
dataset = model.transform(dataset)
else:
transformers.append(stage)
return PipelineModel(transformers)
[docs] @since("1.4.0")
def copy(self, extra=None):
"""
Creates a copy of this instance.
:param extra: extra parameters
:returns: new instance
"""
if extra is None:
extra = dict()
that = Params.copy(self, extra)
stages = [stage.copy(extra) for stage in that.getStages()]
return that.setStages(stages)
[docs] @since("2.0.0")
def write(self):
"""Returns an MLWriter instance for this ML instance."""
allStagesAreJava = PipelineSharedReadWrite.checkStagesForJava(self.getStages())
if allStagesAreJava:
return JavaMLWriter(self)
return PipelineWriter(self)
[docs] @classmethod
@since("2.0.0")
def read(cls):
"""Returns an MLReader instance for this class."""
return PipelineReader(cls)
@classmethod
def _from_java(cls, java_stage):
"""
Given a Java Pipeline, create and return a Python wrapper of it.
Used for ML persistence.
"""
# Create a new instance of this stage.
py_stage = cls()
# Load information from java_stage to the instance.
py_stages = [JavaParams._from_java(s) for s in java_stage.getStages()]
py_stage.setStages(py_stages)
py_stage._resetUid(java_stage.uid())
return py_stage
def _to_java(self):
"""
Transfer this instance to a Java Pipeline. Used for ML persistence.
:return: Java object equivalent to this instance.
"""
gateway = SparkContext._gateway
cls = SparkContext._jvm.org.apache.spark.ml.PipelineStage
java_stages = gateway.new_array(cls, len(self.getStages()))
for idx, stage in enumerate(self.getStages()):
java_stages[idx] = stage._to_java()
_java_obj = JavaParams._new_java_obj("org.apache.spark.ml.Pipeline", self.uid)
_java_obj.setStages(java_stages)
return _java_obj
def _make_java_param_pair(self, param, value):
"""
Makes a Java param pair.
"""
sc = SparkContext._active_spark_context
param = self._resolveParam(param)
java_param = sc._jvm.org.apache.spark.ml.param.Param(param.parent, param.name, param.doc)
if isinstance(value, Params) and hasattr(value, "_to_java"):
# Convert JavaEstimator/JavaTransformer object or Estimator/Transformer object which
# implements `_to_java` method (such as OneVsRest, Pipeline object) to java object.
# used in the case of an estimator having another estimator as a parameter
# the reason why this is not in _py2java in common.py is that importing
# Estimator and Model in common.py results in a circular import with inherit_doc
java_value = value._to_java()
else:
java_value = _py2java(sc, value)
return java_param.w(java_value)
def _transfer_param_map_to_java(self, pyParamMap):
"""
Transforms a Python ParamMap into a Java ParamMap.
"""
paramMap = JavaWrapper._new_java_obj("org.apache.spark.ml.param.ParamMap")
for param in self.params:
if param in pyParamMap:
pair = self._make_java_param_pair(param, pyParamMap[param])
paramMap.put([pair])
return paramMap
def _transfer_param_map_from_java(self, javaParamMap):
"""
Transforms a Java ParamMap into a Python ParamMap.
"""
sc = SparkContext._active_spark_context
paramMap = dict()
for pair in javaParamMap.toList():
param = pair.param()
if self.hasParam(str(param.name())):
java_obj = pair.value()
if sc._jvm.Class.forName("org.apache.spark.ml.PipelineStage").isInstance(java_obj):
# Note: JavaParams._from_java support both JavaEstimator/JavaTransformer class
# and Estimator/Transformer class which implements `_from_java` static method
# (such as OneVsRest, Pipeline class).
py_obj = JavaParams._from_java(java_obj)
else:
py_obj = _java2py(sc, java_obj)
paramMap[self.getParam(param.name())] = py_obj
return paramMap
@inherit_doc
class PipelineWriter(MLWriter):
"""
(Private) Specialization of :py:class:`MLWriter` for :py:class:`Pipeline` types
"""
def __init__(self, instance):
super(PipelineWriter, self).__init__()
self.instance = instance
def saveImpl(self, path):
stages = self.instance.getStages()
PipelineSharedReadWrite.validateStages(stages)
PipelineSharedReadWrite.saveImpl(self.instance, stages, self.sc, path)
@inherit_doc
class PipelineReader(MLReader):
"""
(Private) Specialization of :py:class:`MLReader` for :py:class:`Pipeline` types
"""
def __init__(self, cls):
super(PipelineReader, self).__init__()
self.cls = cls
def load(self, path):
metadata = DefaultParamsReader.loadMetadata(path, self.sc)
if 'language' not in metadata['paramMap'] or metadata['paramMap']['language'] != 'Python':
return JavaMLReader(self.cls).load(path)
else:
uid, stages = PipelineSharedReadWrite.load(metadata, self.sc, path)
return Pipeline(stages=stages)._resetUid(uid)
@inherit_doc
class PipelineModelWriter(MLWriter):
"""
(Private) Specialization of :py:class:`MLWriter` for :py:class:`PipelineModel` types
"""
def __init__(self, instance):
super(PipelineModelWriter, self).__init__()
self.instance = instance
def saveImpl(self, path):
stages = self.instance.stages
PipelineSharedReadWrite.validateStages(stages)
PipelineSharedReadWrite.saveImpl(self.instance, stages, self.sc, path)
@inherit_doc
class PipelineModelReader(MLReader):
"""
(Private) Specialization of :py:class:`MLReader` for :py:class:`PipelineModel` types
"""
def __init__(self, cls):
super(PipelineModelReader, self).__init__()
self.cls = cls
def load(self, path):
metadata = DefaultParamsReader.loadMetadata(path, self.sc)
if 'language' not in metadata['paramMap'] or metadata['paramMap']['language'] != 'Python':
return JavaMLReader(self.cls).load(path)
else:
uid, stages = PipelineSharedReadWrite.load(metadata, self.sc, path)
return PipelineModel(stages=stages)._resetUid(uid)
[docs]@inherit_doc
class PipelineModel(Model, MLReadable, MLWritable):
"""
Represents a compiled pipeline with transformers and fitted models.
.. versionadded:: 1.3.0
"""
def __init__(self, stages):
super(PipelineModel, self).__init__()
self.stages = stages
def _transform(self, dataset):
for t in self.stages:
dataset = t.transform(dataset)
return dataset
[docs] @since("1.4.0")
def copy(self, extra=None):
"""
Creates a copy of this instance.
:param extra: extra parameters
:returns: new instance
"""
if extra is None:
extra = dict()
stages = [stage.copy(extra) for stage in self.stages]
return PipelineModel(stages)
[docs] @since("2.0.0")
def write(self):
"""Returns an MLWriter instance for this ML instance."""
allStagesAreJava = PipelineSharedReadWrite.checkStagesForJava(self.stages)
if allStagesAreJava:
return JavaMLWriter(self)
return PipelineModelWriter(self)
[docs] @classmethod
@since("2.0.0")
def read(cls):
"""Returns an MLReader instance for this class."""
return PipelineModelReader(cls)
@classmethod
def _from_java(cls, java_stage):
"""
Given a Java PipelineModel, create and return a Python wrapper of it.
Used for ML persistence.
"""
# Load information from java_stage to the instance.
py_stages = [JavaParams._from_java(s) for s in java_stage.stages()]
# Create a new instance of this stage.
py_stage = cls(py_stages)
py_stage._resetUid(java_stage.uid())
return py_stage
def _to_java(self):
"""
Transfer this instance to a Java PipelineModel. Used for ML persistence.
:return: Java object equivalent to this instance.
"""
gateway = SparkContext._gateway
cls = SparkContext._jvm.org.apache.spark.ml.Transformer
java_stages = gateway.new_array(cls, len(self.stages))
for idx, stage in enumerate(self.stages):
java_stages[idx] = stage._to_java()
_java_obj =\
JavaParams._new_java_obj("org.apache.spark.ml.PipelineModel", self.uid, java_stages)
return _java_obj
@inherit_doc
class PipelineSharedReadWrite():
"""
Functions for :py:class:`MLReader` and :py:class:`MLWriter` shared between
:py:class:`Pipeline` and :py:class:`PipelineModel`
.. versionadded:: 2.3.0
"""
@staticmethod
def checkStagesForJava(stages):
return all(isinstance(stage, JavaMLWritable) for stage in stages)
@staticmethod
def validateStages(stages):
"""
Check that all stages are Writable
"""
for stage in stages:
if not isinstance(stage, MLWritable):
raise ValueError("Pipeline write will fail on this pipeline " +
"because stage %s of type %s is not MLWritable",
stage.uid, type(stage))
@staticmethod
def saveImpl(instance, stages, sc, path):
"""
Save metadata and stages for a :py:class:`Pipeline` or :py:class:`PipelineModel`
- save metadata to path/metadata
- save stages to stages/IDX_UID
"""
stageUids = [stage.uid for stage in stages]
jsonParams = {'stageUids': stageUids, 'language': 'Python'}
DefaultParamsWriter.saveMetadata(instance, path, sc, paramMap=jsonParams)
stagesDir = os.path.join(path, "stages")
for index, stage in enumerate(stages):
stage.write().save(PipelineSharedReadWrite
.getStagePath(stage.uid, index, len(stages), stagesDir))
@staticmethod
def load(metadata, sc, path):
"""
Load metadata and stages for a :py:class:`Pipeline` or :py:class:`PipelineModel`
:return: (UID, list of stages)
"""
stagesDir = os.path.join(path, "stages")
stageUids = metadata['paramMap']['stageUids']
stages = []
for index, stageUid in enumerate(stageUids):
stagePath = \
PipelineSharedReadWrite.getStagePath(stageUid, index, len(stageUids), stagesDir)
stage = DefaultParamsReader.loadParamsInstance(stagePath, sc)
stages.append(stage)
return (metadata['uid'], stages)
@staticmethod
def getStagePath(stageUid, stageIdx, numStages, stagesDir):
"""
Get path for saving the given stage.
"""
stageIdxDigits = len(str(numStages))
stageDir = str(stageIdx).zfill(stageIdxDigits) + "_" + stageUid
stagePath = os.path.join(stagesDir, stageDir)
return stagePath
