ClusteringEvaluator

class pyspark.ml.evaluation.ClusteringEvaluator(predictionCol='prediction', featuresCol='features', metricName='silhouette', distanceMeasure='squaredEuclidean')[source]

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.

>>> 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...
>>> ce_path = temp_path + "/ce"
>>> evaluator.save(ce_path)
>>> evaluator2 = ClusteringEvaluator.load(ce_path)
>>> str(evaluator2.getPredictionCol())
'prediction'

New in version 2.3.0.

Methods

Attributes

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

extra – Extra parameters to copy to the new instance

Returns

Copy of this instance

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.

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

extra – extra param values

Returns

merged param map

getDistanceMeasure()[source]

Gets the value of distanceMeasure

New in version 2.4.0.

getFeaturesCol()

Gets the value of featuresCol or its default value.

getMetricName()[source]

Gets the value of metricName or its default value.

New in version 2.3.0.

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.

setDistanceMeasure(value)[source]

Sets the value of distanceMeasure.

New in version 2.4.0.

setFeaturesCol(value)[source]

Sets the value of featuresCol.

setMetricName(value)[source]

Sets the value of metricName.

New in version 2.3.0.

setParams(self, predictionCol='prediction', featuresCol='features', metricName='silhouette', distanceMeasure='squaredEuclidean')[source]

Sets params for clustering evaluator.

New in version 2.3.0.

setPredictionCol(value)[source]

Sets the value of predictionCol.

write()

Returns an MLWriter instance for this ML instance.

Attributes Documentation

distanceMeasure = 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 = 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.')