RankingEvaluator¶
-
class
pyspark.ml.evaluation.
RankingEvaluator
(*, predictionCol: str = 'prediction', labelCol: str = 'label', metricName: RankingEvaluatorMetricType = 'meanAveragePrecision', k: int = 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: pyspark.ml.param.Param) → None¶ Clears a param from the param map if it has been explicitly set.
-
copy
(extra: Optional[ParamMap] = None) → JP¶ 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: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → float¶ 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: Union[str, pyspark.ml.param.Param]) → str¶ Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
-
explainParams
() → str¶ Returns the documentation of all params with their optionally default values and user-supplied values.
-
extractParamMap
(extra: Optional[ParamMap] = None) → ParamMap¶ 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
() → str¶ Gets the value of labelCol or its default value.
-
getMetricName
() → RankingEvaluatorMetricType[source]¶ Gets the value of metricName or its default value.
New in version 3.0.0.
-
getOrDefault
(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]¶ 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: str) → pyspark.ml.param.Param¶ Gets a param by its name.
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getPredictionCol
() → str¶ Gets the value of predictionCol or its default value.
-
hasDefault
(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶ Checks whether a param has a default value.
-
hasParam
(paramName: str) → bool¶ Tests whether this instance contains a param with a given (string) name.
-
isDefined
(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶ Checks whether a param is explicitly set by user or has a default value.
-
isLargerBetter
() → bool¶ 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: Union[str, pyspark.ml.param.Param[Any]]) → bool¶ Checks whether a param is explicitly set by user.
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classmethod
load
(path: str) → RL¶ Reads an ML instance from the input path, a shortcut of read().load(path).
-
classmethod
read
() → pyspark.ml.util.JavaMLReader[RL]¶ Returns an MLReader instance for this class.
-
save
(path: str) → None¶ Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
-
set
(param: pyspark.ml.param.Param, value: Any) → None¶ Sets a parameter in the embedded param map.
-
setK
(value: int) → pyspark.ml.evaluation.RankingEvaluator[source]¶ Sets the value of
k
.New in version 3.0.0.
-
setLabelCol
(value: str) → pyspark.ml.evaluation.RankingEvaluator[source]¶ Sets the value of
labelCol
.New in version 3.0.0.
-
setMetricName
(value: RankingEvaluatorMetricType) → RankingEvaluator[source]¶ Sets the value of
metricName
.New in version 3.0.0.
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setParams
(self, \*, predictionCol="prediction", labelCol="label", metricName="meanAveragePrecision", k=10)[source]¶ Sets params for ranking evaluator.
New in version 3.0.0.
-
setPredictionCol
(value: str) → pyspark.ml.evaluation.RankingEvaluator[source]¶ Sets the value of
predictionCol
.New in version 3.0.0.
-
write
() → pyspark.ml.util.JavaMLWriter¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
-
k
: pyspark.ml.param.Param[int] = Param(parent='undefined', name='k', doc='The ranking position value used in meanAveragePrecisionAtK|precisionAtK|ndcgAtK|recallAtK. Must be > 0. The default value is 10.')¶
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labelCol
= Param(parent='undefined', name='labelCol', doc='label column name.')¶
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metricName
: pyspark.ml.param.Param[RankingEvaluatorMetricType] = Param(parent='undefined', name='metricName', doc='metric name in evaluation (meanAveragePrecision|meanAveragePrecisionAtK|precisionAtK|ndcgAtK|recallAtK)')¶
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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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