MulticlassClassificationEvaluator#

class pyspark.ml.connect.evaluation.MulticlassClassificationEvaluator(metricName='accuracy', labelCol='label', predictionCol='prediction')[source]#

Evaluator for multiclass classification, which expects input columns prediction and label. Supported metrics are ‘accuracy’.

New in version 3.5.0.

Examples

>>> from pyspark.ml.connect.evaluation import MulticlassClassificationEvaluator
>>> eva = MulticlassClassificationEvaluator(metricName='accuracy')
>>> dataset = spark.createDataFrame(
...     [(1, 1), (0, 0), (2, 2), (1, 0), (2, 1)],
...     schema=['label', 'prediction']
... )
>>> eva.evaluate(dataset)
0.6000000238418579
>>> eva.isLargerBetter()
True

Methods

`clear`(param)	Clears a param from the param map if it has been explicitly set.
`copy`([extra])	Creates a copy of this instance with the same uid and some extra params.
`evaluate`(dataset[, params])	Evaluates the output with optional parameters.
`explainParam`(param)	Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
`explainParams`()	Returns the documentation of all params with their optionally default values and user-supplied values.
`extractParamMap`([extra])	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.
`getLabelCol`()	Gets the value of labelCol or its default value.
`getMetricName`()	Gets the value of metricName or its default value.
`getOrDefault`(param)	Gets the value of a param in the user-supplied param map or its default value.
`getParam`(paramName)	Gets a param by its name.
`getPredictionCol`()	Gets the value of predictionCol or its default value.
`hasDefault`(param)	Checks whether a param has a default value.
`hasParam`(paramName)	Tests whether this instance contains a param with a given (string) name.
`isDefined`(param)	Checks whether a param is explicitly set by user or has a default value.
`isLargerBetter`()	Indicates whether the metric returned by `evaluate()` should be maximized (True, default) or minimized (False).
`isSet`(param)	Checks whether a param is explicitly set by user.
`load`(path)	Load Estimator / Transformer / Model / Evaluator from provided cloud storage path.
`loadFromLocal`(path)	Load Estimator / Transformer / Model / Evaluator from provided local path.
`save`(path, *[, overwrite])	Save Estimator / Transformer / Model / Evaluator to provided cloud storage path.
`saveToLocal`(path, *[, overwrite])	Save Estimator / Transformer / Model / Evaluator to provided local path.
`set`(param, value)	Sets a parameter in the embedded param map.

Attributes

`labelCol`
`metricName`
`params`	Returns all params ordered by name.
`predictionCol`

Methods Documentation

clear(param)#: Clears a param from the param map if it has been explicitly set.

copy(extra=None)#

Creates a copy of this instance with the same uid and some extra params. The default implementation creates a shallow copy using copy.copy(), and then copies the embedded and extra parameters over and returns the copy. Subclasses should override this method if the default approach is not sufficient.

Parameters

extradict, optional: Extra parameters to copy to the new instance

Returns

Params: Copy of this instance

evaluate(dataset, params=None)#

Evaluates the output with optional parameters.

New in version 3.5.0.

Parameters

datasetpyspark.sql.DataFrame: a dataset that contains labels/observations and predictions
paramsdict, optional: an optional param map that overrides embedded params

Returns

float: metric

explainParam(param)#: Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams()#: Returns the documentation of all params with their optionally default values and user-supplied values.

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

extradict, optional: extra param values

Returns

dict: merged param map

getLabelCol()#: Gets the value of labelCol or its default value.

getMetricName()#: Gets the value of metricName or its default value.

New in version 3.5.0.

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.

getParam(paramName)#: Gets a param by its name.

getPredictionCol()#: Gets the value of predictionCol or its default value.

hasDefault(param)#: Checks whether a param has a default value.

hasParam(paramName)#: Tests whether this instance contains a param with a given (string) name.

isDefined(param)#: Checks whether a param is explicitly set by user or has a default value.

isLargerBetter()[source]#: Indicates whether the metric returned by evaluate() should be maximized (True, default) or minimized (False). A given evaluator may support multiple metrics which may be maximized or minimized.

New in version 1.5.0.

isSet(param)#: Checks whether a param is explicitly set by user.

classmethod load(path)#: Load Estimator / Transformer / Model / Evaluator from provided cloud storage path.

New in version 3.5.0.

classmethod loadFromLocal(path)#: Load Estimator / Transformer / Model / Evaluator from provided local path.

New in version 3.5.0.

save(path, *, overwrite=False)#: Save Estimator / Transformer / Model / Evaluator to provided cloud storage path.

New in version 3.5.0.

saveToLocal(path, *, overwrite=False)#: Save Estimator / Transformer / Model / Evaluator to provided local path.

New in version 3.5.0.

set(param, value)#: Sets a parameter in the embedded param map.

Attributes Documentation

labelCol = Param(parent='undefined', name='labelCol', doc='label column name.')#

metricName = Param(parent='undefined', name='metricName', doc="metric name for the regression evaluator, valid values are 'mse' and 'r2'")#

params#: Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

predictionCol = Param(parent='undefined', name='predictionCol', doc='prediction column name.')#

uid#: A unique id for the object.