MulticlassClassificationEvaluator¶
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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).
New in version 1.5.0.
Examples
>>> 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...
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.
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.
getBeta()Gets the value of beta or its default value.
getEps()Gets the value of eps or its default value.
Gets the value of labelCol or its default value.
Gets the value of metricLabel or its default value.
Gets the value of metricName 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.
Gets the value of predictionCol or its default value.
Gets the value of probabilityCol or its default value.
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.
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.
setBeta(value)Sets the value of
beta.setEps(value)Sets the value of
eps.setLabelCol(value)Sets the value of
labelCol.setMetricLabel(value)Sets the value of
metricLabel.setMetricName(value)Sets the value of
metricName.setParams(self, \*[, predictionCol, …])Sets params for multiclass classification evaluator.
setPredictionCol(value)Sets the value of
predictionCol.setProbabilityCol(value)Sets the value of
probabilityCol.setWeightCol(value)Sets the value of
weightCol.write()Returns an MLWriter instance for this ML instance.
Attributes
Returns all params ordered by name.
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
- extradict, optional
Extra parameters to copy to the new instance
- Returns
JavaParamsCopy of this instance
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evaluate(dataset, params=None)¶ Evaluates the output with optional parameters.
New in version 1.4.0.
- Parameters
- dataset
pyspark.sql.DataFrame a dataset that contains labels/observations and predictions
- paramsdict, optional
an optional param map that overrides embedded params
- dataset
- Returns
- float
metric
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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
- extradict, optional
extra param values
- Returns
- dict
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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