BinaryClassificationEvaluator¶

class pyspark.ml.evaluation.BinaryClassificationEvaluator(*, rawPredictionCol: str = 'rawPrediction', labelCol: str = 'label', metricName: BinaryClassificationEvaluatorMetricType = 'areaUnderROC', weightCol: Optional[str] = None, numBins: int = 1000)¶

Evaluator for binary classification, which expects input columns rawPrediction, label and an optional weight column. The rawPrediction column can be of type double (binary 0/1 prediction, or probability of label 1) or of type vector (length-2 vector of raw predictions, scores, or label probabilities).

Examples

>>> from pyspark.ml.linalg import Vectors
>>> scoreAndLabels = map(lambda x: (Vectors.dense([1.0 - x[0], x[0]]), x[1]),
...    [(0.1, 0.0), (0.1, 1.0), (0.4, 0.0), (0.6, 0.0), (0.6, 1.0), (0.6, 1.0), (0.8, 1.0)])
>>> dataset = spark.createDataFrame(scoreAndLabels, ["raw", "label"])
...
>>> evaluator = BinaryClassificationEvaluator()
>>> evaluator.setRawPredictionCol("raw")
BinaryClassificationEvaluator...
>>> evaluator.evaluate(dataset)
0.70...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "areaUnderPR"})
0.83...
>>> bce_path = temp_path + "/bce"
>>> evaluator.save(bce_path)
>>> evaluator2 = BinaryClassificationEvaluator.load(bce_path)
>>> str(evaluator2.getRawPredictionCol())
'raw'
>>> scoreAndLabelsAndWeight = map(lambda x: (Vectors.dense([1.0 - x[0], x[0]]), x[1], x[2]),
...    [(0.1, 0.0, 1.0), (0.1, 1.0, 0.9), (0.4, 0.0, 0.7), (0.6, 0.0, 0.9),
...     (0.6, 1.0, 1.0), (0.6, 1.0, 0.3), (0.8, 1.0, 1.0)])
>>> dataset = spark.createDataFrame(scoreAndLabelsAndWeight, ["raw", "label", "weight"])
...
>>> evaluator = BinaryClassificationEvaluator(rawPredictionCol="raw", weightCol="weight")
>>> evaluator.evaluate(dataset)
0.70...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "areaUnderPR"})
0.82...
>>> evaluator.getNumBins()
1000

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.
`getNumBins`()	Gets the value of numBins 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.
`getRawPredictionCol`()	Gets the value of rawPredictionCol 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.
`setLabelCol`(value)	Sets the value of `labelCol`.
`setMetricName`(value)	Sets the value of `metricName`.
`setNumBins`(value)	Sets the value of `numBins`.
`setParams`(self, \*[, rawPredictionCol, …])	Sets params for binary classification evaluator.
`setRawPredictionCol`(value)	Sets the value of `rawPredictionCol`.
`setWeightCol`(value)	Sets the value of `weightCol`.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`labelCol`
`metricName`
`numBins`
`params`	Returns all params ordered by name.
`rawPredictionCol`
`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

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

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

getNumBins() → int¶: Gets the value of numBins 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.

getRawPredictionCol() → str¶: Gets the value of rawPredictionCol 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.

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

setMetricName(value: BinaryClassificationEvaluatorMetricType) → BinaryClassificationEvaluator¶: Sets the value of metricName.

setNumBins(value: int) → pyspark.ml.evaluation.BinaryClassificationEvaluator ¶: Sets the value of numBins.

setParams(self, \*, rawPredictionCol="rawPrediction", labelCol="label", metricName="areaUnderROC", weightCol=None, numBins=1000)¶: Sets params for binary classification evaluator.

setRawPredictionCol(value: str) → pyspark.ml.evaluation.BinaryClassificationEvaluator ¶: Sets the value of rawPredictionCol.

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

write() → pyspark.ml.util.JavaMLWriter¶: Returns an MLWriter instance for this ML instance.

Attributes Documentation

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

metricName: pyspark.ml.param.Param[BinaryClassificationEvaluatorMetricType] = Param(parent='undefined', name='metricName', doc='metric name in evaluation (areaUnderROC|areaUnderPR)')¶

numBins: pyspark.ml.param.Param[int] = Param(parent='undefined', name='numBins', doc='Number of bins to down-sample the curves (ROC curve, PR curve) in area computation. If 0, no down-sampling will occur. Must be >= 0.')¶

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

rawPredictionCol = Param(parent='undefined', name='rawPredictionCol', doc='raw prediction (a.k.a. confidence) column name.')¶

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.')¶

Evaluator

RegressionEvaluator