KMeansModel#
- classpyspark.ml.clustering.KMeansModel(java_model=None)[source]#
Model fitted by KMeans.
New in version 1.5.0.
Methods
clear(param)Clears a param from the param map if it has been explicitly set.
Get the cluster centers, represented as a list of NumPy arrays.
copy([extra])Creates a copy of this instance with the same uid and some extra params.
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.
Gets the value of distanceMeasure or its default value.
Gets the value of featuresCol or its default value.
Gets the value ofinitMode
Gets the value ofinitSteps
getK()Gets the value ofk
Gets the value of maxBlockSizeInMB or its default value.
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.
getParam(paramName)Gets a param by its name.
Gets the value of predictionCol or its default value.
getSeed()Gets the value of seed or its default value.
Gets the value of solver or its default value.
getTol()Gets the value of tol or its default value.
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.
load(path)Reads an ML instance from the input path, a shortcut ofread().load(path).
predict(value)Predict label for the given features.
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)Sets the value of
featuresCol.setPredictionCol(value)Sets the value of
predictionCol.transform(dataset[, params])Transforms the input dataset with optional parameters.
write()Returns an GeneralMLWriter instance for this ML instance.
Attributes
Indicates whether a training summary exists for this model instance.
Returns all params ordered by name.
Gets summary (cluster assignments, cluster sizes) of the model trained on the training set.
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 someextra params. This implementation first calls Params.copy andthen make a copy of the companion Java pipeline component withextra params. So both the Python wrapper and the Java pipelinecomponent get copied.
- Parameters
- extradict, optional
Extra parameters to copy to the new instance
- Returns
JavaParamsCopy of this instance
- explainParam(param)#
Explains a single param and returns its name, doc, and optionaldefault value and user-supplied value in a string.
- explainParams()#
Returns the documentation of all params with their optionallydefault values and user-supplied values.
- extractParamMap(extra=None)#
Extracts the embedded default param values and user-suppliedvalues, and then merges them with extra values from input intoa flat param map, where the latter value is used if there existconflicts, i.e., with ordering: default param values <user-supplied values < extra.
- Parameters
- extradict, optional
extra param values
- Returns
- dict
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 ofinitMode
New in version 1.5.0.
- getInitSteps()#
Gets the value ofinitSteps
New in version 1.5.0.
- getK()#
Gets the value ofk
New in version 1.5.0.
- getMaxBlockSizeInMB()#
Gets the value of maxBlockSizeInMB or its default value.
- 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 itsdefault 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.
- getSolver()#
Gets the value of solver 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 hasa default value.
- isSet(param)#
Checks whether a param is explicitly set by user.
- classmethodload(path)#
Reads an ML instance from the input path, a shortcut ofread().load(path).
- classmethodread()#
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.
New in version 1.3.0.
- Parameters
- dataset
pyspark.sql.DataFrame input dataset
- paramsdict, optional
an optional param map that overrides embedded params.
- dataset
- Returns
pyspark.sql.DataFrametransformed dataset
- write()#
Returns an GeneralMLWriter instance for this ML instance.
Attributes Documentation
- distanceMeasure=Param(parent='undefined',name='distanceMeasure',doc="thedistancemeasure.Supportedoptions:'euclidean'and'cosine'.")#
- featuresCol=Param(parent='undefined',name='featuresCol',doc='featurescolumnname.')#
- hasSummary#
Indicates whether a training summary exists for this modelinstance.
New in version 2.1.0.
- initMode=Param(parent='undefined',name='initMode',doc='Theinitializationalgorithm.Thiscanbeeither"random"tochooserandompointsasinitialclustercenters,or"k-means||"touseaparallelvariantofk-means++')#
- initSteps=Param(parent='undefined',name='initSteps',doc='Thenumberofstepsfork-means||initializationmode.Mustbe>0.')#
- k=Param(parent='undefined',name='k',doc='Thenumberofclusterstocreate.Mustbe>1.')#
- maxBlockSizeInMB=Param(parent='undefined',name='maxBlockSizeInMB',doc='maximummemoryinMBforstackinginputdataintoblocks.Dataisstackedwithinpartitions.Ifmorethanremainingdatasizeinapartitionthenitisadjustedtothedatasize.Default0.0representschoosingoptimalvalue,dependsonspecificalgorithm.Mustbe>=0.')#
- maxIter=Param(parent='undefined',name='maxIter',doc='maxnumberofiterations(>=0).')#
- params#
Returns all params ordered by name. The default implementationuses
dir()to get all attributes of typeParam.
- predictionCol=Param(parent='undefined',name='predictionCol',doc='predictioncolumnname.')#
- seed=Param(parent='undefined',name='seed',doc='randomseed.')#
- solver=Param(parent='undefined',name='solver',doc='Thesolveralgorithmforoptimization.Supportedoptions:auto,row,block.')#
- summary#
Gets summary (cluster assignments, cluster sizes) of the model trained on thetraining set. An exception is thrown if no summary exists.
New in version 2.1.0.
- tol=Param(parent='undefined',name='tol',doc='theconvergencetoleranceforiterativealgorithms(>=0).')#
- weightCol=Param(parent='undefined',name='weightCol',doc='weightcolumnname.Ifthisisnotsetorempty,wetreatallinstanceweightsas1.0.')#
- uid#
A unique id for the object.