44b7931936
### What changes were proposed in this pull request? This PR corrects some exception type when the function input params are failed to validate due to TypeError. In order to convenient to review, there are 3 commits in this PR: - Standardize input validation error type on sql - Standardize input validation error type on ml - Standardize input validation error type on pandas ### Why are the changes needed? As suggestion from Python exception doc [1]: "Raised when an operation or function is applied to an object of inappropriate type.", but there are many Value error are raised in some pyspark code, this patch fix them. [1] https://docs.python.org/3/library/exceptions.html#TypeError Note that: this patch only addresses the exsiting some wrong raise type for input validation, the input validation decorator/framework which mentioned in [SPARK-35176](https://issues.apache.org/jira/browse/SPARK-35176), would be submited in a speparated patch. ### Does this PR introduce _any_ user-facing change? Yes, code can raise the right TypeError instead of ValueError. ### How was this patch tested? Existing test case and UT Closes #32368 from Yikun/SPARK-35176. Authored-by: Yikun Jiang <yikunkero@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
368 lines
11 KiB
Python
368 lines
11 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, abstractproperty
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import copy
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import threading
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from pyspark import since
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from pyspark.ml.common import inherit_doc
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from pyspark.ml.param.shared import HasInputCol, HasOutputCol, HasLabelCol, HasFeaturesCol, \
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HasPredictionCol, Params
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from pyspark.sql.functions import udf
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from pyspark.sql.types import StructField, StructType
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class _FitMultipleIterator(object):
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"""
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Used by default implementation of Estimator.fitMultiple to produce models in a thread safe
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iterator. This class handles the simple case of fitMultiple where each param map should be
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fit independently.
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Parameters
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----------
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fitSingleModel : function
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Callable[[int], Transformer] which fits an estimator to a dataset.
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`fitSingleModel` may be called up to `numModels` times, with a unique index each time.
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Each call to `fitSingleModel` with an index should return the Model associated with
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that index.
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numModel : int
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Number of models this iterator should produce.
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Notes
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-----
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See :py:meth:`Estimator.fitMultiple` for more info.
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"""
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def __init__(self, fitSingleModel, numModels):
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"""
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"""
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self.fitSingleModel = fitSingleModel
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self.numModel = numModels
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self.counter = 0
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self.lock = threading.Lock()
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def __iter__(self):
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return self
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def __next__(self):
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with self.lock:
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index = self.counter
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if index >= self.numModel:
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raise StopIteration("No models remaining.")
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self.counter += 1
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return index, self.fitSingleModel(index)
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def next(self):
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"""For python2 compatibility."""
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return self.__next__()
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@inherit_doc
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class Estimator(Params, metaclass=ABCMeta):
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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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pass
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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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Parameters
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----------
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dataset : :py:class:`pyspark.sql.DataFrame`
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input dataset
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Returns
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-------
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:class:`Transformer`
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fitted model
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"""
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raise NotImplementedError()
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def fitMultiple(self, dataset, paramMaps):
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"""
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Fits a model to the input dataset for each param map in `paramMaps`.
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.. versionadded:: 2.3.0
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Parameters
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----------
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dataset : :py:class:`pyspark.sql.DataFrame`
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input dataset.
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paramMaps : :py:class:`collections.abc.Sequence`
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A Sequence of param maps.
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Returns
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-------
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:py:class:`_FitMultipleIterator`
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A thread safe iterable which contains one model for each param map. Each
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call to `next(modelIterator)` will return `(index, model)` where model was fit
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using `paramMaps[index]`. `index` values may not be sequential.
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"""
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estimator = self.copy()
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def fitSingleModel(index):
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return estimator.fit(dataset, paramMaps[index])
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return _FitMultipleIterator(fitSingleModel, len(paramMaps))
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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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.. versionadded:: 1.3.0
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Parameters
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----------
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dataset : :py:class:`pyspark.sql.DataFrame`
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input dataset.
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params : dict or list or tuple, optional
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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
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-------
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:py:class:`Transformer` or a list of :py:class:`Transformer`
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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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models = [None] * len(params)
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for index, model in self.fitMultiple(dataset, params):
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models[index] = model
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return models
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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 TypeError("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, metaclass=ABCMeta):
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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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pass
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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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Parameters
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----------
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dataset : :py:class:`pyspark.sql.DataFrame`
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input dataset.
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Returns
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-------
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:py:class:`pyspark.sql.DataFrame`
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transformed dataset
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"""
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raise NotImplementedError()
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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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.. versionadded:: 1.3.0
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Parameters
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----------
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dataset : :py:class:`pyspark.sql.DataFrame`
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input dataset
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params : dict, optional
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an optional param map that overrides embedded params.
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Returns
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-------
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:py:class:`pyspark.sql.DataFrame`
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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 TypeError("Params must be a param map but got %s." % type(params))
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@inherit_doc
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class Model(Transformer, metaclass=ABCMeta):
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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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pass
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@inherit_doc
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class UnaryTransformer(HasInputCol, HasOutputCol, Transformer):
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"""
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Abstract class for transformers that take one input column, apply transformation,
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and output the result as a new column.
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.. versionadded:: 2.3.0
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"""
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def setInputCol(self, value):
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"""
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Sets the value of :py:attr:`inputCol`.
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"""
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return self._set(inputCol=value)
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def setOutputCol(self, value):
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"""
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Sets the value of :py:attr:`outputCol`.
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"""
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return self._set(outputCol=value)
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@abstractmethod
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def createTransformFunc(self):
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"""
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Creates the transform function using the given param map. The input param map already takes
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account of the embedded param map. So the param values should be determined
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solely by the input param map.
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"""
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raise NotImplementedError()
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@abstractmethod
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def outputDataType(self):
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"""
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Returns the data type of the output column.
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"""
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raise NotImplementedError()
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@abstractmethod
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def validateInputType(self, inputType):
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"""
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Validates the input type. Throw an exception if it is invalid.
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"""
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raise NotImplementedError()
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def transformSchema(self, schema):
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inputType = schema[self.getInputCol()].dataType
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self.validateInputType(inputType)
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if self.getOutputCol() in schema.names:
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raise ValueError("Output column %s already exists." % self.getOutputCol())
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outputFields = copy.copy(schema.fields)
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outputFields.append(StructField(self.getOutputCol(),
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self.outputDataType(),
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nullable=False))
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return StructType(outputFields)
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def _transform(self, dataset):
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self.transformSchema(dataset.schema)
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transformUDF = udf(self.createTransformFunc(), self.outputDataType())
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transformedDataset = dataset.withColumn(self.getOutputCol(),
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transformUDF(dataset[self.getInputCol()]))
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return transformedDataset
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@inherit_doc
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class _PredictorParams(HasLabelCol, HasFeaturesCol, HasPredictionCol):
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"""
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Params for :py:class:`Predictor` and :py:class:`PredictorModel`.
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.. versionadded:: 3.0.0
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"""
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pass
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@inherit_doc
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class Predictor(Estimator, _PredictorParams, metaclass=ABCMeta):
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"""
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Estimator for prediction tasks (regression and classification).
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"""
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@since("3.0.0")
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def setLabelCol(self, value):
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"""
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Sets the value of :py:attr:`labelCol`.
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"""
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return self._set(labelCol=value)
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@since("3.0.0")
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def setFeaturesCol(self, value):
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"""
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Sets the value of :py:attr:`featuresCol`.
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"""
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return self._set(featuresCol=value)
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@since("3.0.0")
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def setPredictionCol(self, value):
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"""
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Sets the value of :py:attr:`predictionCol`.
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"""
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return self._set(predictionCol=value)
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@inherit_doc
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class PredictionModel(Model, _PredictorParams, metaclass=ABCMeta):
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"""
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Model for prediction tasks (regression and classification).
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"""
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@since("3.0.0")
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def setFeaturesCol(self, value):
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"""
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Sets the value of :py:attr:`featuresCol`.
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"""
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return self._set(featuresCol=value)
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@since("3.0.0")
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def setPredictionCol(self, value):
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"""
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Sets the value of :py:attr:`predictionCol`.
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"""
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return self._set(predictionCol=value)
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@abstractproperty
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@since("2.1.0")
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def numFeatures(self):
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"""
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Returns the number of features the model was trained on. If unknown, returns -1
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"""
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raise NotImplementedError()
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@abstractmethod
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@since("3.0.0")
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def predict(self, value):
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"""
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Predict label for the given features.
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"""
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raise NotImplementedError()
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