KMeansModel

class pyspark.ml.clustering.KMeansModel(java_model=None)[source]

Model fitted by KMeans.

New in version 1.5.0.

Methods

Attributes

Methods Documentation

clear(param)

Clears a param from the param map if it has been explicitly set.

clusterCenters()[source]

Get the cluster centers, represented as a list of NumPy arrays.

New in version 1.5.0.

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

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()

Gets the value of distanceMeasure or its default value.

getFeaturesCol()

Gets the value of featuresCol or its default value.

getInitMode()

Gets the value of initMode

New in version 1.5.0.

getInitSteps()

Gets the value of initSteps

New in version 1.5.0.

getK()

Gets the value of k

New in version 1.5.0.

getMaxIter()

Gets the value of maxIter 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.

getSeed()

Gets the value of seed or its default value.

getTol()

Gets the value of tol 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.

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).

predict(value)[source]

Predict label for the given features.

New in version 3.0.0.

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.

setFeaturesCol(value)[source]

Sets the value of featuresCol.

New in version 3.0.0.

setPredictionCol(value)[source]

Sets the value of predictionCol.

New in version 3.0.0.

transform(dataset, params=None)

Transforms the input dataset with optional parameters.

Parameters
  • dataset – input dataset, which is an instance of pyspark.sql.DataFrame

  • params – an optional param map that overrides embedded params.

Returns

transformed dataset

New in version 1.3.0.

write()

Returns an GeneralMLWriter instance for this ML instance.

Attributes Documentation

distanceMeasure = Param(parent='undefined', name='distanceMeasure', doc="the distance measure. Supported options: 'euclidean' and 'cosine'.")
featuresCol = Param(parent='undefined', name='featuresCol', doc='features column name.')
hasSummary

Indicates whether a training summary exists for this model instance.

New in version 2.1.0.

initMode = Param(parent='undefined', name='initMode', doc='The initialization algorithm. This can be either "random" to choose random points as initial cluster centers, or "k-means||" to use a parallel variant of k-means++')
initSteps = Param(parent='undefined', name='initSteps', doc='The number of steps for k-means|| initialization mode. Must be > 0.')
k = Param(parent='undefined', name='k', doc='The number of clusters to create. Must be > 1.')
maxIter = Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')
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.')
seed = Param(parent='undefined', name='seed', doc='random seed.')
summary

Gets summary (e.g. cluster assignments, cluster sizes) of the model trained on the training set. An exception is thrown if no summary exists.

New in version 2.1.0.

tol = Param(parent='undefined', name='tol', doc='the convergence tolerance for iterative algorithms (>= 0).')
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.')