MulticlassClassificationEvaluator

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

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: pyspark.ml.param.Param) → None

Clears a param from the param map if it has been explicitly set.

copy(extra: Optional[ParamMap] = None) → JP

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: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → float

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: Union[str, pyspark.ml.param.Param]) → str

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: Optional[ParamMap] = None) → ParamMap

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() → float

Gets the value of beta or its default value.

New in version 3.0.0.

getEps() → float

Gets the value of eps or its default value.

New in version 3.0.0.

getLabelCol() → str

Gets the value of labelCol or its default value.

getMetricLabel() → float

Gets the value of metricLabel or its default value.

New in version 3.0.0.

getMetricName() → MulticlassClassificationEvaluatorMetricType

Gets the value of metricName or its default value.

New in version 1.5.0.

getOrDefault(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]

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: str) → pyspark.ml.param.Param

Gets a param by its name.

getPredictionCol() → str

Gets the value of predictionCol or its default value.

getProbabilityCol() → str

Gets the value of probabilityCol or its default value.

getWeightCol() → str

Gets the value of weightCol or its default value.

hasDefault(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param has a default value.

hasParam(paramName: str) → bool

Tests whether this instance contains a param with a given (string) name.

isDefined(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param is explicitly set by user or has a default value.

isLargerBetter() → bool

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: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL

Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read() → pyspark.ml.util.JavaMLReader[RL]

Returns an MLReader instance for this class.

save(path: str) → None

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

set(param: pyspark.ml.param.Param, value: Any) → None

Sets a parameter in the embedded param map.

setBeta(value: float) → pyspark.ml.evaluation.MulticlassClassificationEvaluator

Sets the value of beta.

New in version 3.0.0.

setEps(value: float) → pyspark.ml.evaluation.MulticlassClassificationEvaluator

Sets the value of eps.

New in version 3.0.0.

setLabelCol(value: str) → pyspark.ml.evaluation.MulticlassClassificationEvaluator

Sets the value of labelCol.

setMetricLabel(value: float) → pyspark.ml.evaluation.MulticlassClassificationEvaluator

Sets the value of metricLabel.

New in version 3.0.0.

setMetricName(value: MulticlassClassificationEvaluatorMetricType) → MulticlassClassificationEvaluator

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)

Sets params for multiclass classification evaluator.

New in version 1.5.0.

setPredictionCol(value: str) → pyspark.ml.evaluation.MulticlassClassificationEvaluator

Sets the value of predictionCol.

setProbabilityCol(value: str) → pyspark.ml.evaluation.MulticlassClassificationEvaluator

Sets the value of probabilityCol.

New in version 3.0.0.

setWeightCol(value: str) → pyspark.ml.evaluation.MulticlassClassificationEvaluator

Sets the value of weightCol.

New in version 3.0.0.

write() → pyspark.ml.util.JavaMLWriter

Returns an MLWriter instance for this ML instance.

Attributes Documentation

beta: pyspark.ml.param.Param[float] = Param(parent='undefined', name='beta', doc='The beta value used in weightedFMeasure|fMeasureByLabel. Must be > 0. The default value is 1.')

eps: pyspark.ml.param.Param[float] = 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: pyspark.ml.param.Param[float] = 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: pyspark.ml.param.Param[MulticlassClassificationEvaluatorMetricType] = 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.')

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