#
# 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.
#
import sys
if sys.version > '3':
basestring = str
from pyspark import SparkContext
from pyspark import since
from pyspark.ml import Estimator, Model, Transformer
from pyspark.ml.param import Param, Params
from pyspark.ml.util import keyword_only, JavaMLWriter, JavaMLReader
from pyspark.ml.wrapper import JavaWrapper
from pyspark.mllib.common import inherit_doc
def _stages_java2py(java_stages):
"""
Transforms the parameter Python stages from a list of Java stages.
:param java_stages: An array of Java stages.
:return: An array of Python stages.
"""
return [JavaWrapper._transfer_stage_from_java(stage) for stage in java_stages]
def _stages_py2java(py_stages, cls):
"""
Transforms the parameter of Python stages to a Java array of Java stages.
:param py_stages: An array of Python stages.
:return: A Java array of Java Stages.
"""
for stage in py_stages:
assert(isinstance(stage, JavaWrapper),
"Python side implementation is not supported in the meta-PipelineStage currently.")
gateway = SparkContext._gateway
java_stages = gateway.new_array(cls, len(py_stages))
for idx, stage in enumerate(py_stages):
java_stages[idx] = stage._transfer_stage_to_java()
return java_stages
@inherit_doc
class PipelineMLWriter(JavaMLWriter, JavaWrapper):
"""
Private Pipeline utility class that can save ML instances through their Scala implementation.
"""
def __init__(self, instance):
cls = SparkContext._jvm.org.apache.spark.ml.PipelineStage
self._java_obj = self._new_java_obj("org.apache.spark.ml.Pipeline", instance.uid)
self._java_obj.setStages(_stages_py2java(instance.getStages(), cls))
self._jwrite = self._java_obj.write()
@inherit_doc
class PipelineMLReader(JavaMLReader):
"""
Private utility class that can load Pipeline instances through their Scala implementation.
"""
def load(self, path):
"""Load the Pipeline instance from the input path."""
if not isinstance(path, basestring):
raise TypeError("path should be a basestring, got type %s" % type(path))
java_obj = self._jread.load(path)
instance = self._clazz()
instance._resetUid(java_obj.uid())
instance.setStages(_stages_java2py(java_obj.getStages()))
return instance
@inherit_doc
class Pipeline(Estimator):
"""
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 an :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
"""
self._paramMap[self.stages] = value
return self
@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 JavaMLWriter instance for this ML instance."""
return PipelineMLWriter(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 JavaMLReader instance for this class."""
return PipelineMLReader(cls)
@classmethod
@since("2.0.0")
def load(cls, path):
"""Reads an ML instance from the input path, a shortcut of `read().load(path)`."""
return cls.read().load(path)
@inherit_doc
class PipelineModelMLWriter(JavaMLWriter, JavaWrapper):
"""
Private PipelineModel utility class that can save ML instances through their Scala
implementation.
"""
def __init__(self, instance):
cls = SparkContext._jvm.org.apache.spark.ml.Transformer
self._java_obj = self._new_java_obj("org.apache.spark.ml.PipelineModel",
instance.uid,
_stages_py2java(instance.stages, cls))
self._jwrite = self._java_obj.write()
@inherit_doc
class PipelineModelMLReader(JavaMLReader):
"""
Private utility class that can load PipelineModel instances through their Scala implementation.
"""
def load(self, path):
"""Load the PipelineModel instance from the input path."""
if not isinstance(path, basestring):
raise TypeError("path should be a basestring, got type %s" % type(path))
java_obj = self._jread.load(path)
instance = self._clazz(_stages_java2py(java_obj.stages()))
instance._resetUid(java_obj.uid())
return instance
@inherit_doc
class PipelineModel(Model):
"""
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 JavaMLWriter instance for this ML instance."""
return PipelineModelMLWriter(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 JavaMLReader instance for this class."""
return PipelineModelMLReader(cls)
@classmethod
@since("2.0.0")
def load(cls, path):
"""Reads an ML instance from the input path, a shortcut of `read().load(path)`."""
return cls.read().load(path)
