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
import copy
import threading
from pyspark import since
from pyspark.ml.param.shared import *
from pyspark.ml.common import inherit_doc
from pyspark.sql.functions import udf
from pyspark.sql.types import StructField, StructType
class _FitMultipleIterator(object):
"""
Used by default implementation of Estimator.fitMultiple to produce models in a thread safe
iterator. This class handles the simple case of fitMultiple where each param map should be
fit independently.
:param fitSingleModel: Function: (int => Model) which fits an estimator to a dataset.
`fitSingleModel` may be called up to `numModels` times, with a unique index each time.
Each call to `fitSingleModel` with an index should return the Model associated with
that index.
:param numModel: Number of models this iterator should produce.
See Estimator.fitMultiple for more info.
"""
def __init__(self, fitSingleModel, numModels):
"""
"""
self.fitSingleModel = fitSingleModel
self.numModel = numModels
self.counter = 0
self.lock = threading.Lock()
def __iter__(self):
return self
def __next__(self):
with self.lock:
index = self.counter
if index >= self.numModel:
raise StopIteration("No models remaining.")
self.counter += 1
return index, self.fitSingleModel(index)
def next(self):
"""For python2 compatibility."""
return self.__next__()
[docs]@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()
[docs] @since("2.3.0")
def fitMultiple(self, dataset, paramMaps):
"""
Fits a model to the input dataset for each param map in `paramMaps`.
:param dataset: input dataset, which is an instance of :py:class:`pyspark.sql.DataFrame`.
:param paramMaps: A Sequence of param maps.
:return: A thread safe iterable which contains one model for each param map. Each
call to `next(modelIterator)` will return `(index, model)` where model was fit
using `paramMaps[index]`. `index` values may not be sequential.
.. note:: DeveloperApi
.. note:: Experimental
"""
estimator = self.copy()
def fitSingleModel(index):
return estimator.fit(dataset, paramMaps[index])
return _FitMultipleIterator(fitSingleModel, len(paramMaps))
[docs] @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)):
models = [None] * len(params)
for index, model in self.fitMultiple(dataset, params):
models[index] = model
return models
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))
[docs]@inherit_doc
class Model(Transformer):
"""
Abstract class for models that are fitted by estimators.
.. versionadded:: 1.4.0
"""
__metaclass__ = ABCMeta
