ClusteringEvaluator¶

class pyspark.ml.evaluation.ClusteringEvaluator(*, predictionCol: str = 'prediction', featuresCol: str = 'features', metricName: ClusteringEvaluatorMetricType = 'silhouette', distanceMeasure: str = 'squaredEuclidean', weightCol: Optional[str] = None)¶

Evaluator for Clustering results, which expects two input columns: prediction and features. The metric computes the Silhouette measure using the squared Euclidean distance.

The Silhouette is a measure for the validation of the consistency within clusters. It ranges between 1 and -1, where a value close to 1 means that the points in a cluster are close to the other points in the same cluster and far from the points of the other clusters.

Examples

>>> from pyspark.ml.linalg import Vectors
>>> featureAndPredictions = map(lambda x: (Vectors.dense(x[0]), x[1]),
...     [([0.0, 0.5], 0.0), ([0.5, 0.0], 0.0), ([10.0, 11.0], 1.0),
...     ([10.5, 11.5], 1.0), ([1.0, 1.0], 0.0), ([8.0, 6.0], 1.0)])
>>> dataset = spark.createDataFrame(featureAndPredictions, ["features", "prediction"])
...
>>> evaluator = ClusteringEvaluator()
>>> evaluator.setPredictionCol("prediction")
ClusteringEvaluator...
>>> evaluator.evaluate(dataset)
0.9079...
>>> featureAndPredictionsWithWeight = map(lambda x: (Vectors.dense(x[0]), x[1], x[2]),
...     [([0.0, 0.5], 0.0, 2.5), ([0.5, 0.0], 0.0, 2.5), ([10.0, 11.0], 1.0, 2.5),
...     ([10.5, 11.5], 1.0, 2.5), ([1.0, 1.0], 0.0, 2.5), ([8.0, 6.0], 1.0, 2.5)])
>>> dataset = spark.createDataFrame(
...     featureAndPredictionsWithWeight, ["features", "prediction", "weight"])
>>> evaluator = ClusteringEvaluator()
>>> evaluator.setPredictionCol("prediction")
ClusteringEvaluator...
>>> evaluator.setWeightCol("weight")
ClusteringEvaluator...
>>> evaluator.evaluate(dataset)
0.9079...
>>> ce_path = temp_path + "/ce"
>>> evaluator.save(ce_path)
>>> evaluator2 = ClusteringEvaluator.load(ce_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.
`explainParams`()	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.
`getDistanceMeasure`()	Gets the value of distanceMeasure
`getFeaturesCol`()	Gets the value of featuresCol or its default value.
`getMetricName`()	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.
`getPredictionCol`()	Gets the value of predictionCol 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).
`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.
`setDistanceMeasure`(value)	Sets the value of `distanceMeasure`.
`setFeaturesCol`(value)	Sets the value of `featuresCol`.
`setMetricName`(value)	Sets the value of `metricName`.
`setParams`(self, \*[, predictionCol, …])	Sets params for clustering evaluator.
`setPredictionCol`(value)	Sets the value of `predictionCol`.
`setWeightCol`(value)	Sets the value of `weightCol`.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`distanceMeasure`
`featuresCol`
`metricName`
`params`	Returns all params ordered by name.
`predictionCol`
`weightCol`

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.

Parameters

datasetpyspark.sql.DataFrame: a dataset that contains labels/observations and predictions
paramsdict, optional: an optional param map that overrides embedded params

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

getDistanceMeasure() → ClusteringEvaluatorDistanceMeasureType¶: Gets the value of distanceMeasure

getFeaturesCol() → str¶: Gets the value of featuresCol or its default value.

getMetricName() → ClusteringEvaluatorMetricType¶: Gets the value of metricName or its default value.

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.

getPredictionCol() → str¶: Gets the value of predictionCol or its default value.

getWeightCol() → str¶: Gets the value of weightCol 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.

isSet(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param is explicitly set by user.

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.

setDistanceMeasure(value: ClusteringEvaluatorDistanceMeasureType) → ClusteringEvaluator¶: Sets the value of distanceMeasure.

setFeaturesCol(value: str) → pyspark.ml.evaluation.ClusteringEvaluator ¶: Sets the value of featuresCol.

setMetricName(value: ClusteringEvaluatorMetricType) → ClusteringEvaluator¶: Sets the value of metricName.

setParams(self, \*, predictionCol="prediction", featuresCol="features", metricName="silhouette", distanceMeasure="squaredEuclidean", weightCol=None)¶: Sets params for clustering evaluator.

setPredictionCol(value: str) → pyspark.ml.evaluation.ClusteringEvaluator ¶: Sets the value of predictionCol.

setWeightCol(value: str) → pyspark.ml.evaluation.ClusteringEvaluator ¶: Sets the value of weightCol.

write() → pyspark.ml.util.JavaMLWriter¶: Returns an MLWriter instance for this ML instance.

Attributes Documentation

distanceMeasure: pyspark.ml.param.Param[ClusteringEvaluatorDistanceMeasureType] = Param(parent='undefined', name='distanceMeasure', doc="The distance measure. Supported options: 'squaredEuclidean' and 'cosine'.")¶

featuresCol = Param(parent='undefined', name='featuresCol', doc='features column name.')¶

metricName: pyspark.ml.param.Param[ClusteringEvaluatorMetricType] = Param(parent='undefined', name='metricName', doc='metric name in evaluation (silhouette)')¶

params¶: Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

predictionCol = Param(parent='undefined', name='predictionCol', doc='prediction 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.')¶

MultilabelClassificationEvaluator

RankingEvaluator