Source code for pyspark.ml.pipeline

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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
[docs]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")
[docs] 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
[docs]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 with optional parameters. :param dataset: input dataset, which is an instance of :py:class:`pyspark.sql.DataFrame` :returns: transformed dataset """ raise NotImplementedError() @since("1.3.0")
[docs] 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
[docs]class Model(Transformer): """ Abstract class for models that are fitted by estimators. .. versionadded:: 1.4.0 """ __metaclass__ = ABCMeta
@inherit_doc
[docs]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 """ @keyword_only def __init__(self, stages=None): """ __init__(self, stages=None) """ if stages is None: stages = [] super(Pipeline, self).__init__() #: Param for pipeline stages. self.stages = Param(self, "stages", "pipeline stages") kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @since("1.3.0")
[docs] 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")
[docs] def getStages(self): """ Get pipeline stages. """ if self.stages in self._paramMap: return self._paramMap[self.stages]
@keyword_only @since("1.3.0")
[docs] 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")
[docs] 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
[docs]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")
[docs] 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)