Source code for pyspark.ml.base
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# http://www.apache.org/licenses/LICENSE-2.0
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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
@inherit_doc
[docs]class Model(Transformer):
"""
Abstract class for models that are fitted by estimators.
.. versionadded:: 1.4.0
"""
__metaclass__ = ABCMeta