BinaryClassificationEvaluator

class pyspark.ml.evaluation.BinaryClassificationEvaluator(rawPredictionCol='rawPrediction', labelCol='label', metricName='areaUnderROC', weightCol=None, numBins=1000)[source]

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).

>>> 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

New in version 1.4.0.

Methods

Attributes

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

extra – Extra parameters to copy to the new instance

Returns

Copy of this instance

evaluate(dataset, params=None)

Evaluates the output with optional parameters.

Parameters
  • dataset – a dataset that contains labels/observations and predictions

  • params – an optional param map that overrides embedded params

Returns

metric

New in version 1.4.0.

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

extra – extra param values

Returns

merged param map

getLabelCol()

Gets the value of labelCol or its default value.

getMetricName()[source]

Gets the value of metricName or its default value.

New in version 1.4.0.

getNumBins()[source]

Gets the value of numBins or its default value.

New in version 3.0.0.

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.

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). 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.

setLabelCol(value)[source]

Sets the value of labelCol.

setMetricName(value)[source]

Sets the value of metricName.

New in version 1.4.0.

setNumBins(value)[source]

Sets the value of numBins.

New in version 3.0.0.

setParams(self, rawPredictionCol='rawPrediction', labelCol='label', metricName='areaUnderROC', weightCol=None, numBins=1000)[source]

Sets params for binary classification evaluator.

New in version 1.4.0.

setRawPredictionCol(value)[source]

Sets the value of rawPredictionCol.

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

labelCol = Param(parent='undefined', name='labelCol', doc='label column name.')
metricName = Param(parent='undefined', name='metricName', doc='metric name in evaluation (areaUnderROC|areaUnderPR)')
numBins = 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.')