RankingEvaluator#
- class pyspark.ml.evaluation.RankingEvaluator(*, predictionCol='prediction', labelCol='label', metricName='meanAveragePrecision', k=10)[source]#
Evaluator for Ranking, which expects two input columns: prediction and label.
New in version 3.0.0.
Notes
Experimental
Examples
>>> scoreAndLabels = [([1.0, 6.0, 2.0, 7.0, 8.0, 3.0, 9.0, 10.0, 4.0, 5.0], ... [1.0, 2.0, 3.0, 4.0, 5.0]), ... ([4.0, 1.0, 5.0, 6.0, 2.0, 7.0, 3.0, 8.0, 9.0, 10.0], [1.0, 2.0, 3.0]), ... ([1.0, 2.0, 3.0, 4.0, 5.0], [])] >>> dataset = spark.createDataFrame(scoreAndLabels, ["prediction", "label"]) ... >>> evaluator = RankingEvaluator() >>> evaluator.setPredictionCol("prediction") RankingEvaluator... >>> evaluator.evaluate(dataset) 0.35... >>> evaluator.evaluate(dataset, {evaluator.metricName: "precisionAtK", evaluator.k: 2}) 0.33... >>> ranke_path = temp_path + "/ranke" >>> evaluator.save(ranke_path) >>> evaluator2 = RankingEvaluator.load(ranke_path) >>> str(evaluator2.getPredictionCol()) 'prediction'
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.
getK
()Gets the value of k or its default value.
Gets the value of labelCol 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.
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.
setK
(value)Sets the value of
k
.setLabelCol
(value)Sets the value of
labelCol
.setMetricName
(value)Sets the value of
metricName
.setParams
(self, \*[, predictionCol, labelCol, k])Sets params for ranking evaluator.
setPredictionCol
(value)Sets the value of
predictionCol
.write
()Returns an MLWriter instance for this ML instance.
Attributes
Returns all params ordered by name.
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
- extradict, optional
Extra parameters to copy to the new instance
- Returns
JavaParams
Copy of this instance
- 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
- 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
- extradict, optional
extra param values
- Returns
- dict
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.
- getPredictionCol()#
Gets the value of predictionCol 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 3.0.0.
- setParams(self, \*, predictionCol="prediction", labelCol="label", metricName="meanAveragePrecision", k=10)[source]#
Sets params for ranking evaluator.
New in version 3.0.0.
- setPredictionCol(value)[source]#
Sets the value of
predictionCol
.New in version 3.0.0.
- write()#
Returns an MLWriter instance for this ML instance.
Attributes Documentation
- k = Param(parent='undefined', name='k', doc='The ranking position value used in meanAveragePrecisionAtK|precisionAtK|ndcgAtK|recallAtK. Must be > 0. The default value is 10.')#
- labelCol = Param(parent='undefined', name='labelCol', doc='label column name.')#
- metricName = Param(parent='undefined', name='metricName', doc='metric name in evaluation (meanAveragePrecision|meanAveragePrecisionAtK|precisionAtK|ndcgAtK|recallAtK)')#
- params#
Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
- predictionCol = Param(parent='undefined', name='predictionCol', doc='prediction column name.')#
- uid#
A unique id for the object.