3ce1dff7ba
### What changes were proposed in this pull request? jira link: https://issues.apache.org/jira/browse/SPARK-30930 Remove ML/MLLIB DeveloperApi annotations. ### Why are the changes needed? The Developer APIs in ML/MLLIB have been there for a long time. They are stable now and are very unlikely to be changed or removed, so I unmark these Developer APIs in this PR. ### Does this PR introduce any user-facing change? Yes. DeveloperApi annotations are removed from docs. ### How was this patch tested? existing tests Closes #27859 from huaxingao/spark-30930. Authored-by: Huaxin Gao <huaxing@us.ibm.com> Signed-off-by: Sean Owen <srowen@gmail.com>
326 lines
9.9 KiB
Python
326 lines
9.9 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.param.shared import *
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from pyspark.ml.common import inherit_doc
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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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:param fitSingleModel: Function: (int => Model) 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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:param numModel: Number of models this iterator should produce.
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See 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):
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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("2.3.0")
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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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:param dataset: input dataset, which is an instance of :py:class:`pyspark.sql.DataFrame`.
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:param paramMaps: A Sequence of param maps.
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:return: 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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@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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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 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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@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):
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"""
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Estimator for prediction tasks (regression and classification).
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"""
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__metaclass__ = ABCMeta
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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):
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"""
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Model for prediction tasks (regression and classification).
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"""
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__metaclass__ = ABCMeta
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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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