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.
-
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.
-
setMetricName
(value)[source]¶ Sets the value of
metricName
.New in version 1.4.0.
-
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
.
-
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 typeParam
.
-
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.')¶
-