TrainValidationSplitModel¶
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class
pyspark.ml.tuning.
TrainValidationSplitModel
(bestModel, validationMetrics=[], subModels=None)[source]¶ Model from train validation split.
New in version 2.0.0.
Methods
Attributes
Methods Documentation
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clear
(param)¶ Clears a param from the param map if it has been explicitly set.
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copy
(extra=None)[source]¶ Creates a copy of this instance with a randomly generated uid and some extra params. This copies the underlying bestModel, creates a deep copy of the embedded paramMap, and copies the embedded and extra parameters over. And, this creates a shallow copy of the validationMetrics. It does not copy the extra Params into the subModels.
- Parameters
extra – Extra parameters to copy to the new instance
- Returns
Copy of this instance
New in version 2.0.0.
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explainParam
(param)¶ Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
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explainParams
()¶ Returns the documentation of all params with their optionally default values and user-supplied values.
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extractParamMap
(extra=None)¶ Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.
- Parameters
extra – extra param values
- Returns
merged param map
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getEstimator
()¶ Gets the value of estimator or its default value.
New in version 2.0.0.
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getEstimatorParamMaps
()¶ Gets the value of estimatorParamMaps or its default value.
New in version 2.0.0.
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getEvaluator
()¶ Gets the value of evaluator or its default value.
New in version 2.0.0.
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getOrDefault
(param)¶ Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
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getParam
(paramName)¶ Gets a param by its name.
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getSeed
()¶ Gets the value of seed or its default value.
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getTrainRatio
()¶ Gets the value of trainRatio or its default value.
New in version 2.0.0.
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hasDefault
(param)¶ Checks whether a param has a default value.
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hasParam
(paramName)¶ Tests whether this instance contains a param with a given (string) name.
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isDefined
(param)¶ Checks whether a param is explicitly set by user or has a default value.
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isSet
(param)¶ Checks whether a param is explicitly set by user.
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classmethod
load
(path)¶ Reads an ML instance from the input path, a shortcut of read().load(path).
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save
(path)¶ Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
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set
(param, value)¶ Sets a parameter in the embedded param map.
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transform
(dataset, params=None)¶ Transforms the input dataset with optional parameters.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
params – an optional param map that overrides embedded params.
- Returns
transformed dataset
New in version 1.3.0.
Attributes Documentation
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estimator
= Param(parent='undefined', name='estimator', doc='estimator to be cross-validated')¶
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estimatorParamMaps
= Param(parent='undefined', name='estimatorParamMaps', doc='estimator param maps')¶
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evaluator
= Param(parent='undefined', name='evaluator', doc='evaluator used to select hyper-parameters that maximize the validator metric')¶
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params
¶ Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
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seed
= Param(parent='undefined', name='seed', doc='random seed.')¶
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trainRatio
= Param(parent='undefined', name='trainRatio', doc='Param for ratio between train and validation data. Must be between 0 and 1.')¶
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