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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
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# http://www.apache.org/licenses/LICENSE-2.0
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import sys
if sys.version > '3':
basestring = str
from pyspark import since, keyword_only, SparkContext
from pyspark.ml import Estimator, Model, Transformer
from pyspark.ml.param import Param, Params
from pyspark.ml.util import JavaMLWriter, JavaMLReader, MLReadable, MLWritable
from pyspark.ml.wrapper import JavaParams
from pyspark.ml.common import inherit_doc
@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 there are no stages, the pipeline acts as an
identity transformer.
.. versionadded:: 1.3.0
"""
stages = Param(Params._dummy(), "stages", "pipeline stages")
@keyword_only
def __init__(self, stages=None):
"""
__init__(self, stages=None)
"""
if stages is None:
stages = []
super(Pipeline, self).__init__()
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
@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)
@since("1.3.0")
def getStages(self):
"""
Get pipeline stages.
"""
if self.stages in self._paramMap:
return self._paramMap[self.stages]
@keyword_only
@since("1.3.0")
def setParams(self, stages=None):
"""
setParams(self, stages=None)
Sets params for Pipeline.
"""
if stages is None:
stages = []
kwargs = self.setParams._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)
@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)
@since("2.0.0")
def write(self):
"""Returns an MLWriter instance for this ML instance."""
return JavaMLWriter(self)
@since("2.0.0")
def save(self, path):
"""Save this ML instance to the given path, a shortcut of `write().save(path)`."""
self.write().save(path)
@classmethod
@since("2.0.0")
def read(cls):
"""Returns an MLReader instance for this class."""
return JavaMLReader(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
@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
@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)
@since("2.0.0")
def write(self):
"""Returns an MLWriter instance for this ML instance."""
return JavaMLWriter(self)
@since("2.0.0")
def save(self, path):
"""Save this ML instance to the given path, a shortcut of `write().save(path)`."""
self.write().save(path)
@classmethod
@since("2.0.0")
def read(cls):
"""Returns an MLReader instance for this class."""
return JavaMLReader(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
