MulticlassClassificationEvaluator#

class pyspark.ml.evaluation.MulticlassClassificationEvaluator(*, predictionCol='prediction', labelCol='label', metricName='f1', weightCol=None, metricLabel=0.0, beta=1.0, probabilityCol='probability', eps=1e-15)[source]#

Evaluator for Multiclass Classification, which expects input columns: prediction, label, weight (optional) and probabilityCol (only for logLoss).

New in version 1.5.0.

Examples

>>> scoreAndLabels = [(0.0, 0.0), (0.0, 1.0), (0.0, 0.0),
...     (1.0, 0.0), (1.0, 1.0), (1.0, 1.0), (1.0, 1.0), (2.0, 2.0), (2.0, 0.0)]
>>> dataset = spark.createDataFrame(scoreAndLabels, ["prediction", "label"])
>>> evaluator = MulticlassClassificationEvaluator()
>>> evaluator.setPredictionCol("prediction")
MulticlassClassificationEvaluator...
>>> evaluator.evaluate(dataset)
0.66...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "accuracy"})
0.66...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "truePositiveRateByLabel",
...     evaluator.metricLabel: 1.0})
0.75...
>>> evaluator.setMetricName("hammingLoss")
MulticlassClassificationEvaluator...
>>> evaluator.evaluate(dataset)
0.33...
>>> mce_path = temp_path + "/mce"
>>> evaluator.save(mce_path)
>>> evaluator2 = MulticlassClassificationEvaluator.load(mce_path)
>>> str(evaluator2.getPredictionCol())
'prediction'
>>> scoreAndLabelsAndWeight = [(0.0, 0.0, 1.0), (0.0, 1.0, 1.0), (0.0, 0.0, 1.0),
...     (1.0, 0.0, 1.0), (1.0, 1.0, 1.0), (1.0, 1.0, 1.0), (1.0, 1.0, 1.0),
...     (2.0, 2.0, 1.0), (2.0, 0.0, 1.0)]
>>> dataset = spark.createDataFrame(scoreAndLabelsAndWeight, ["prediction", "label", "weight"])
>>> evaluator = MulticlassClassificationEvaluator(predictionCol="prediction",
...     weightCol="weight")
>>> evaluator.evaluate(dataset)
0.66...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "accuracy"})
0.66...
>>> predictionAndLabelsWithProbabilities = [
...      (1.0, 1.0, 1.0, [0.1, 0.8, 0.1]), (0.0, 2.0, 1.0, [0.9, 0.05, 0.05]),
...      (0.0, 0.0, 1.0, [0.8, 0.2, 0.0]), (1.0, 1.0, 1.0, [0.3, 0.65, 0.05])]
>>> dataset = spark.createDataFrame(predictionAndLabelsWithProbabilities, ["prediction",
...     "label", "weight", "probability"])
>>> evaluator = MulticlassClassificationEvaluator(predictionCol="prediction",
...     probabilityCol="probability")
>>> evaluator.setMetricName("logLoss")
MulticlassClassificationEvaluator...
>>> evaluator.evaluate(dataset)
0.9682...

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.
`getBeta`()	Gets the value of beta or its default value.
`getEps`()	Gets the value of eps or its default value.
`getLabelCol`()	Gets the value of labelCol or its default value.
`getMetricLabel`()	Gets the value of metricLabel 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.
`getProbabilityCol`()	Gets the value of probabilityCol or its default value.
`getWeightCol`()	Gets the value of weightCol 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)	Reads an ML instance from the input path, a shortcut of read().load(path).
`read`()	Returns an MLReader instance for this class.
`save`(path)	Save this ML instance to the given path, a shortcut of 'write().save(path)'.
`set`(param, value)	Sets a parameter in the embedded param map.
`setBeta`(value)	Sets the value of `beta`.
`setEps`(value)	Sets the value of `eps`.
`setLabelCol`(value)	Sets the value of `labelCol`.
`setMetricLabel`(value)	Sets the value of `metricLabel`.
`setMetricName`(value)	Sets the value of `metricName`.
`setParams`(self, \*[, predictionCol, ...])	Sets params for multiclass classification evaluator.
`setPredictionCol`(value)	Sets the value of `predictionCol`.
`setProbabilityCol`(value)	Sets the value of `probabilityCol`.
`setWeightCol`(value)	Sets the value of `weightCol`.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`beta`
`eps`
`labelCol`
`metricLabel`
`metricName`
`params`	Returns all params ordered by name.
`predictionCol`
`probabilityCol`
`weightCol`

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. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters

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

Returns

JavaParams: 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

getBeta()[source]#: Gets the value of beta or its default value.

New in version 3.0.0.

getEps()[source]#: Gets the value of eps or its default value.

New in version 3.0.0.

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

getMetricLabel()[source]#: Gets the value of metricLabel or its default value.

New in version 3.0.0.

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

New in version 1.5.0.

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.

getProbabilityCol()#: Gets the value of probabilityCol or its default value.

getWeightCol()#: Gets the value of weightCol 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). 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)#: Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read()#: Returns an MLReader instance for this class.

save(path)#: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

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

setBeta(value)[source]#: Sets the value of beta.

New in version 3.0.0.

setEps(value)[source]#: Sets the value of eps.

New in version 3.0.0.

setLabelCol(value)[source]#: Sets the value of labelCol.

setMetricLabel(value)[source]#: Sets the value of metricLabel.

New in version 3.0.0.

setMetricName(value)[source]#: Sets the value of metricName.

New in version 1.5.0.

setParams(self, \*, predictionCol="prediction", labelCol="label", metricName="f1", weightCol=None, metricLabel=0.0, beta=1.0, probabilityCol="probability", eps=1e-15)[source]#: Sets params for multiclass classification evaluator.

New in version 1.5.0.

setPredictionCol(value)[source]#: Sets the value of predictionCol.

setProbabilityCol(value)[source]#: Sets the value of probabilityCol.

New in version 3.0.0.

setWeightCol(value)[source]#: Sets the value of weightCol.

New in version 3.0.0.

write()#: Returns an MLWriter instance for this ML instance.

Attributes Documentation

beta = Param(parent='undefined', name='beta', doc='The beta value used in weightedFMeasure|fMeasureByLabel. Must be > 0. The default value is 1.')#

eps = Param(parent='undefined', name='eps', doc='log-loss is undefined for p=0 or p=1, so probabilities are clipped to max(eps, min(1 - eps, p)). Must be in range (0, 0.5). The default value is 1e-15.')#

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

metricLabel = Param(parent='undefined', name='metricLabel', doc='The class whose metric will be computed in truePositiveRateByLabel|falsePositiveRateByLabel|precisionByLabel|recallByLabel|fMeasureByLabel. Must be >= 0. The default value is 0.')#

metricName = Param(parent='undefined', name='metricName', doc='metric name in evaluation (f1|accuracy|weightedPrecision|weightedRecall|weightedTruePositiveRate| weightedFalsePositiveRate|weightedFMeasure|truePositiveRateByLabel| falsePositiveRateByLabel|precisionByLabel|recallByLabel|fMeasureByLabel| logLoss|hammingLoss)')#

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.')#

probabilityCol = Param(parent='undefined', name='probabilityCol', doc='Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.')#

weightCol = Param(parent='undefined', name='weightCol', doc='weight column name. If this is not set or empty, we treat all instance weights as 1.0.')#

uid#: A unique id for the object.