CrossValidator¶
-
class
pyspark.ml.tuning.
CrossValidator
(estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3, seed=None, parallelism=1, collectSubModels=False)[source]¶ K-fold cross validation performs model selection by splitting the dataset into a set of non-overlapping randomly partitioned folds which are used as separate training and test datasets e.g., with k=3 folds, K-fold cross validation will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing. Each fold is used as the test set exactly once.
>>> from pyspark.ml.classification import LogisticRegression >>> from pyspark.ml.evaluation import BinaryClassificationEvaluator >>> from pyspark.ml.linalg import Vectors >>> from pyspark.ml.tuning import CrossValidatorModel >>> import tempfile >>> dataset = spark.createDataFrame( ... [(Vectors.dense([0.0]), 0.0), ... (Vectors.dense([0.4]), 1.0), ... (Vectors.dense([0.5]), 0.0), ... (Vectors.dense([0.6]), 1.0), ... (Vectors.dense([1.0]), 1.0)] * 10, ... ["features", "label"]) >>> lr = LogisticRegression() >>> grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build() >>> evaluator = BinaryClassificationEvaluator() >>> cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator, ... parallelism=2) >>> cvModel = cv.fit(dataset) >>> cvModel.getNumFolds() 3 >>> cvModel.avgMetrics[0] 0.5 >>> path = tempfile.mkdtemp() >>> model_path = path + "/model" >>> cvModel.write().save(model_path) >>> cvModelRead = CrossValidatorModel.read().load(model_path) >>> cvModelRead.avgMetrics [0.5, ... >>> evaluator.evaluate(cvModel.transform(dataset)) 0.8333...
New in version 1.4.0.
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 creates a deep copy of the embedded paramMap, and copies the embedded and extra parameters over.
- Parameters
extra – Extra parameters to copy to the new instance
- Returns
Copy of this instance
New in version 1.4.0.
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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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fit
(dataset, params=None)¶ Fits a model to 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. 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)
New in version 1.3.0.
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fitMultiple
(dataset, paramMaps)¶ Fits a model to the input dataset for each param map in paramMaps.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
.paramMaps – A Sequence of param maps.
- Returns
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.
New in version 2.3.0.
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getCollectSubModels
()¶ Gets the value of collectSubModels or its default value.
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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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getNumFolds
()¶ Gets the value of numFolds or its default value.
New in version 1.4.0.
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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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getParallelism
()¶ Gets the value of parallelism or its default value.
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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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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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setCollectSubModels
(value)[source]¶ Sets the value of
collectSubModels
.
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setEstimatorParamMaps
(value)[source]¶ Sets the value of
estimatorParamMaps
.New in version 2.0.0.
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setParallelism
(value)[source]¶ Sets the value of
parallelism
.
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setParams
(estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3, seed=None, parallelism=1, collectSubModels=False)[source]¶ setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3, seed=None, parallelism=1, collectSubModels=False): Sets params for cross validator.
New in version 1.4.0.
Attributes Documentation
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collectSubModels
= Param(parent='undefined', name='collectSubModels', doc='Param for whether to collect a list of sub-models trained during tuning. If set to false, then only the single best sub-model will be available after fitting. If set to true, then all sub-models will be available. Warning: For large models, collecting all sub-models can cause OOMs on the Spark driver.')¶
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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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numFolds
= Param(parent='undefined', name='numFolds', doc='number of folds for cross validation')¶
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parallelism
= Param(parent='undefined', name='parallelism', doc='the number of threads to use when running parallel algorithms (>= 1).')¶
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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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