MulticlassClassificationEvaluator¶
-
class
pyspark.ml.evaluation.
MulticlassClassificationEvaluator
(predictionCol='prediction', labelCol='label', metricName='f1', weightCol=None, metricLabel=0.0, beta=1.0, probabilityCol='probability', eps=1e-15)[source]¶ Evaluator for Multiclass Classification, which expects input columns: prediction, label, weight (optional) and probabilityCol (only for logLoss).
>>> 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...
New in version 1.5.0.
Methods
Attributes
Methods Documentation
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clear
(param)¶ Clears a param from the param map if it has been explicitly set.
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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
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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.
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explainParam
(param)¶ Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
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explainParams
()¶ Returns the documentation of all params with their optionally default values and user-supplied values.
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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
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getLabelCol
()¶ Gets the value of labelCol or its default value.
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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.
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getParam
(paramName)¶ Gets a param by its name.
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getPredictionCol
()¶ Gets the value of predictionCol or its default value.
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getProbabilityCol
()¶ Gets the value of probabilityCol or its default value.
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getWeightCol
()¶ Gets the value of weightCol or its default value.
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hasDefault
(param)¶ Checks whether a param has a default value.
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hasParam
(paramName)¶ Tests whether this instance contains a param with a given (string) name.
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isDefined
(param)¶ Checks whether a param is explicitly set by user or has a default value.
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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.
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isSet
(param)¶ Checks whether a param is explicitly set by user.
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classmethod
load
(path)¶ Reads an ML instance from the input path, a shortcut of read().load(path).
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classmethod
read
()¶ Returns an MLReader instance for this class.
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save
(path)¶ Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
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set
(param, value)¶ Sets a parameter in the embedded param map.
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setMetricLabel
(value)[source]¶ Sets the value of
metricLabel
.New in version 3.0.0.
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setMetricName
(value)[source]¶ Sets the value of
metricName
.New in version 1.5.0.
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setParams
(self, predictionCol='prediction', labelCol='label', metricName='f1', weightCol=None, metricLabel=0.0, beta=1.0, probabilityCol='probability', eps=1e-15)[source]¶ Sets params for multiclass classification evaluator.
New in version 1.5.0.
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setPredictionCol
(value)[source]¶ Sets the value of
predictionCol
.
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setProbabilityCol
(value)[source]¶ Sets the value of
probabilityCol
.New in version 3.0.0.
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write
()¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
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beta
= Param(parent='undefined', name='beta', doc='The beta value used in weightedFMeasure|fMeasureByLabel. Must be > 0. The default value is 1.')¶
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eps
= 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.')¶
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labelCol
= Param(parent='undefined', name='labelCol', doc='label column name.')¶
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metricLabel
= 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.')¶
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metricName
= Param(parent='undefined', name='metricName', doc='metric name in evaluation (f1|accuracy|weightedPrecision|weightedRecall|weightedTruePositiveRate| weightedFalsePositiveRate|weightedFMeasure|truePositiveRateByLabel| falsePositiveRateByLabel|precisionByLabel|recallByLabel|fMeasureByLabel| logLoss|hammingLoss)')¶
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params
¶ Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
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predictionCol
= Param(parent='undefined', name='predictionCol', doc='prediction column name.')¶
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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.')¶
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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.')¶
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