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#
# 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.
#
from abc import ABCMeta, abstractmethod
from pyspark import since
from pyspark.ml.param import Param, Params
from pyspark.ml.util import keyword_only
from pyspark.mllib.common import inherit_doc
@inherit_doc
class Estimator(Params):
"""
Abstract class for estimators that fit models to data.
.. versionadded:: 1.3.0
"""
__metaclass__ = ABCMeta
@abstractmethod
def _fit(self, dataset):
"""
Fits a model to the input dataset. This is called by the
default implementation of fit.
:param dataset: input dataset, which is an instance of
:py:class:`pyspark.sql.DataFrame`
:returns: fitted model
"""
raise NotImplementedError()
@since("1.3.0")
def fit(self, dataset, params=None):
"""
Fits a model to the input dataset with optional parameters.
:param dataset: input dataset, which is an instance of
:py:class:`pyspark.sql.DataFrame`
:param params: an optional param map that overrides embedded
params. If a list/tuple of param maps is given,
this calls fit on each param map and returns a
list of models.
:returns: fitted model(s)
"""
if params is None:
params = dict()
if isinstance(params, (list, tuple)):
return [self.fit(dataset, paramMap) for paramMap in params]
elif isinstance(params, dict):
if params:
return self.copy(params)._fit(dataset)
else:
return self._fit(dataset)
else:
raise ValueError("Params must be either a param map or a list/tuple of param maps, "
"but got %s." % type(params))
@inherit_doc
class Transformer(Params):
"""
Abstract class for transformers that transform one dataset into
another.
.. versionadded:: 1.3.0
"""
__metaclass__ = ABCMeta
@abstractmethod
def _transform(self, dataset):
"""
Transforms the input dataset.
:param dataset: input dataset, which is an instance of
:py:class:`pyspark.sql.DataFrame`
:returns: transformed dataset
"""
raise NotImplementedError()
@since("1.3.0")
def transform(self, dataset, params=None):
"""
Transforms the input dataset with optional parameters.
:param dataset: input dataset, which is an instance of
:py:class:`pyspark.sql.DataFrame`
:param params: an optional param map that overrides embedded
params.
:returns: transformed dataset
"""
if params is None:
params = dict()
if isinstance(params, dict):
if params:
return self.copy(params,)._transform(dataset)
else:
return self._transform(dataset)
else:
raise ValueError("Params must be either a param map but got %s." % type(params))
@inherit_doc
class Model(Transformer):
"""
Abstract class for models that are fitted by estimators.
.. versionadded:: 1.4.0
"""
__metaclass__ = ABCMeta
@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)
@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)
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