KMeans#
- class pyspark.ml.clustering.KMeans(*, featuresCol='features', predictionCol='prediction', k=2, initMode='k-means||', initSteps=2, tol=0.0001, maxIter=20, seed=None, distanceMeasure='euclidean', weightCol=None, solver='auto', maxBlockSizeInMB=0.0)[source]#
- K-means clustering with a k-means++ like initialization mode (the k-means|| algorithm by Bahmani et al). - New in version 1.5.0. - Examples - >>> from pyspark.ml.linalg import Vectors >>> data = [(Vectors.dense([0.0, 0.0]), 2.0), (Vectors.dense([1.0, 1.0]), 2.0), ... (Vectors.dense([9.0, 8.0]), 2.0), (Vectors.dense([8.0, 9.0]), 2.0)] >>> df = spark.createDataFrame(data, ["features", "weighCol"]) >>> kmeans = KMeans(k=2) >>> kmeans.setSeed(1) KMeans... >>> kmeans.setWeightCol("weighCol") KMeans... >>> kmeans.setMaxIter(10) KMeans... >>> kmeans.getMaxIter() 10 >>> kmeans.clear(kmeans.maxIter) >>> kmeans.getSolver() 'auto' >>> model = kmeans.fit(df) >>> model.getMaxBlockSizeInMB() 0.0 >>> model.getDistanceMeasure() 'euclidean' >>> model.setPredictionCol("newPrediction") KMeansModel... >>> model.predict(df.head().features) 0 >>> centers = model.clusterCenters() >>> len(centers) 2 >>> transformed = model.transform(df).select("features", "newPrediction") >>> rows = transformed.collect() >>> rows[0].newPrediction == rows[1].newPrediction True >>> rows[2].newPrediction == rows[3].newPrediction True >>> model.hasSummary True >>> summary = model.summary >>> summary.k 2 >>> summary.clusterSizes [2, 2] >>> summary.trainingCost 4.0 >>> kmeans_path = temp_path + "/kmeans" >>> kmeans.save(kmeans_path) >>> kmeans2 = KMeans.load(kmeans_path) >>> kmeans2.getK() 2 >>> model_path = temp_path + "/kmeans_model" >>> model.save(model_path) >>> model2 = KMeansModel.load(model_path) >>> model2.hasSummary False >>> model.clusterCenters()[0] == model2.clusterCenters()[0] array([ True, True], dtype=bool) >>> model.clusterCenters()[1] == model2.clusterCenters()[1] array([ True, True], dtype=bool) >>> model.transform(df).take(1) == model2.transform(df).take(1) True - 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. - 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. - fit(dataset[, params])- Fits a model to the input dataset with optional parameters. - fitMultiple(dataset, paramMaps)- Fits a model to the input dataset for each param map in paramMaps. - Gets the value of distanceMeasure or its default value. - Gets the value of featuresCol or its default value. - Gets the value of initMode - Gets the value of initSteps - getK()- Gets the value of k - 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 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.- setInitMode(value)- Sets the value of - initMode.- setInitSteps(value)- Sets the value of - initSteps.- setK(value)- Sets the value of - k.- setMaxBlockSizeInMB(value)- Sets the value of - maxBlockSizeInMB.- setMaxIter(value)- Sets the value of - maxIter.- setParams(self, \*[, featuresCol, ...])- Sets params for KMeans. - setPredictionCol(value)- Sets the value of - predictionCol.- setSeed(value)- Sets the value of - seed.- setSolver(value)- Sets the value of - solver.- setTol(value)- Sets the value of - tol.- setWeightCol(value)- Sets the value of - weightCol.- write()- Returns an MLWriter instance for this ML instance. - Attributes - Returns all params ordered by name. - 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
- extradict, optional
- Extra parameters to copy to the new instance 
 
- Returns
- JavaParams
- 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
- extradict, optional
- extra param values 
 
- Returns
- dict
- merged param map 
 
 
 - fit(dataset, params=None)#
- Fits a model to the input dataset with optional parameters. - New in version 1.3.0. - Parameters
- datasetpyspark.sql.DataFrame
- input dataset. 
- paramsdict or list or tuple, optional
- an optional param map that overrides embedded params. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models. 
 
- dataset
- Returns
- Transformeror a list of- Transformer
- fitted model(s) 
 
 
 - fitMultiple(dataset, paramMaps)#
- Fits a model to the input dataset for each param map in paramMaps. - New in version 2.3.0. - Parameters
- datasetpyspark.sql.DataFrame
- input dataset. 
- paramMapscollections.abc.Sequence
- A Sequence of param maps. 
 
- dataset
- Returns
- _FitMultipleIterator
- A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential. 
 
 
 - 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. 
 - 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 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. 
 - 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 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). 
 - 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.- New in version 1.5.0. 
 - setMaxBlockSizeInMB(value)[source]#
- Sets the value of - maxBlockSizeInMB.- New in version 3.4.0. 
 - setParams(self, \*, featuresCol="features", predictionCol="prediction", k=2, initMode="k-means||", initSteps=2, tol=1e-4, maxIter=20, seed=None, distanceMeasure="euclidean", weightCol=None, solver="auto", maxBlockSizeInMB=0.0)[source]#
- Sets params for KMeans. - New in version 1.5.0. 
 - setPredictionCol(value)[source]#
- Sets the value of - predictionCol.- New in version 1.5.0. 
 - write()#
- Returns an MLWriter 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.')#
 - 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.')#
 - maxBlockSizeInMB = Param(parent='undefined', name='maxBlockSizeInMB', doc='maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0.')#
 - 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.')#
 - solver = Param(parent='undefined', name='solver', doc='The solver algorithm for optimization. Supported options: auto, row, block.')#
 - 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.')#
 - uid#
- A unique id for the object.