Source code for pyspark.ml.base

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from abc import ABCMeta, abstractmethod

from pyspark import since
from pyspark.ml.param import Params
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. :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 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