baa3e633e1
## What changes were proposed in this pull request? Now we have PySpark picklers for new and old vector/matrix, individually. However, they are all implemented under `PythonMLlibAPI`. To separate spark.mllib from spark.ml, we should implement the picklers of new vector/matrix under `spark.ml.python` instead. ## How was this patch tested? Existing tests. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #13219 from viirya/pyspark-pickler-ml.
119 lines
3.7 KiB
Python
119 lines
3.7 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from abc import ABCMeta, abstractmethod
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from pyspark import since
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from pyspark.ml.param import Params
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from pyspark.ml.common import inherit_doc
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@inherit_doc
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class Estimator(Params):
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"""
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Abstract class for estimators that fit models to data.
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.. versionadded:: 1.3.0
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"""
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__metaclass__ = ABCMeta
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@abstractmethod
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def _fit(self, dataset):
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"""
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Fits a model to the input dataset. This is called by the default implementation of fit.
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:param dataset: input dataset, which is an instance of :py:class:`pyspark.sql.DataFrame`
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:returns: fitted model
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"""
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raise NotImplementedError()
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@since("1.3.0")
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def fit(self, dataset, params=None):
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"""
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Fits a model to the input dataset with optional parameters.
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:param dataset: input dataset, which is an instance of :py:class:`pyspark.sql.DataFrame`
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:param params: an optional param map that overrides embedded params. If a list/tuple of
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param maps is given, this calls fit on each param map and returns a list of
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models.
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:returns: fitted model(s)
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"""
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if params is None:
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params = dict()
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if isinstance(params, (list, tuple)):
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return [self.fit(dataset, paramMap) for paramMap in params]
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elif isinstance(params, dict):
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if params:
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return self.copy(params)._fit(dataset)
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else:
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return self._fit(dataset)
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else:
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raise ValueError("Params must be either a param map or a list/tuple of param maps, "
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"but got %s." % type(params))
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@inherit_doc
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class Transformer(Params):
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"""
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Abstract class for transformers that transform one dataset into another.
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.. versionadded:: 1.3.0
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"""
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__metaclass__ = ABCMeta
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@abstractmethod
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def _transform(self, dataset):
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"""
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Transforms the input dataset.
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:param dataset: input dataset, which is an instance of :py:class:`pyspark.sql.DataFrame`
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:returns: transformed dataset
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"""
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raise NotImplementedError()
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@since("1.3.0")
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def transform(self, dataset, params=None):
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"""
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Transforms the input dataset with optional parameters.
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:param dataset: input dataset, which is an instance of :py:class:`pyspark.sql.DataFrame`
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:param params: an optional param map that overrides embedded params.
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:returns: transformed dataset
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"""
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if params is None:
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params = dict()
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if isinstance(params, dict):
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if params:
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return self.copy(params)._transform(dataset)
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else:
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return self._transform(dataset)
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else:
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raise ValueError("Params must be a param map but got %s." % type(params))
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@inherit_doc
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class Model(Transformer):
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"""
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Abstract class for models that are fitted by estimators.
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.. versionadded:: 1.4.0
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"""
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__metaclass__ = ABCMeta
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