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).

New in version 1.4.0.

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.

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

getLabelCol() → str

Gets the value of labelCol or its default value.

getMetricName() → str

Gets the value of metricName or its default value.

New in version 1.4.0.

getNumBins() → int

Gets the value of numBins or its default value.

New in version 3.0.0.

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.

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.

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.

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

Sets the value of labelCol.

setMetricName(value: BinaryClassificationEvaluatorMetricType) → BinaryClassificationEvaluator

Sets the value of metricName.

New in version 1.4.0.

setNumBins(value: int) → pyspark.ml.evaluation.BinaryClassificationEvaluator

Sets the value of numBins.

New in version 3.0.0.

setParams(self, \*, rawPredictionCol="rawPrediction", labelCol="label", metricName="areaUnderROC", weightCol=None, numBins=1000)

Sets params for binary classification evaluator.

New in version 1.4.0.

setRawPredictionCol(value: str) → pyspark.ml.evaluation.BinaryClassificationEvaluator

Sets the value of rawPredictionCol.

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

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

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