Evaluator

class pyspark.ml.evaluation.Evaluator

Base class for evaluators that compute metrics from predictions.

New in version 1.4.0.

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

Attributes

params

Returns all params ordered by name.

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 1.4.0.

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

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.

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). 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.

set(param, value)

Sets a parameter in the embedded param map.

Attributes Documentation

params

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

TrainValidationSplitModel BinaryClassificationEvaluator