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).

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.

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.

getEps() → float¶: Gets the value of eps or its default value.

getLabelCol() → str¶: Gets the value of labelCol or its default value.

getMetricLabel() → float¶: Gets the value of metricLabel or its default value.

getMetricName() → MulticlassClassificationEvaluatorMetricType¶: Gets the value of metricName or its default value.

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.

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.

setEps(value: float) → pyspark.ml.evaluation.MulticlassClassificationEvaluator ¶: Sets the value of eps.

setLabelCol(value: str) → pyspark.ml.evaluation.MulticlassClassificationEvaluator ¶: Sets the value of labelCol.

setMetricLabel(value: float) → pyspark.ml.evaluation.MulticlassClassificationEvaluator ¶: Sets the value of metricLabel.

setMetricName(value: MulticlassClassificationEvaluatorMetricType) → MulticlassClassificationEvaluator¶: Sets the value of metricName.

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.

setPredictionCol(value: str) → pyspark.ml.evaluation.MulticlassClassificationEvaluator ¶: Sets the value of predictionCol.

setProbabilityCol(value: str) → pyspark.ml.evaluation.MulticlassClassificationEvaluator ¶: Sets the value of probabilityCol.

setWeightCol(value: str) → pyspark.ml.evaluation.MulticlassClassificationEvaluator ¶: Sets the value of weightCol.

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.')¶

RegressionEvaluator

MultilabelClassificationEvaluator