Package org.apache.spark.ml.clustering
Class KMeans
- All Implemented Interfaces:
Serializable
,org.apache.spark.internal.Logging
,KMeansParams
,Params
,HasDistanceMeasure
,HasFeaturesCol
,HasMaxBlockSizeInMB
,HasMaxIter
,HasPredictionCol
,HasSeed
,HasSolver
,HasTol
,HasWeightCol
,DefaultParamsWritable
,Identifiable
,MLWritable
K-means clustering with support for k-means|| initialization proposed by Bahmani et al.
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Nested Class Summary
Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging
org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter
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Constructor Summary
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Method Summary
Modifier and TypeMethodDescriptionCreates a copy of this instance with the same UID and some extra params.Param for The distance measure.Param for features column name.Fits a model to the input data.initMode()
Param for the initialization algorithm.final IntParam
Param for the number of steps for the k-means|| initialization mode.final IntParam
k()
The number of clusters to create (k).static KMeans
final DoubleParam
Param for Maximum memory in MB for stacking input data into blocks.final IntParam
maxIter()
Param for maximum number of iterations (>= 0).Param for prediction column name.static MLReader<T>
read()
final LongParam
seed()
Param for random seed.setDistanceMeasure
(String value) setFeaturesCol
(String value) setInitMode
(String value) setInitSteps
(int value) setK
(int value) setMaxBlockSizeInMB
(double value) Sets the value of parammaxBlockSizeInMB()
.setMaxIter
(int value) setPredictionCol
(String value) setSeed
(long value) Sets the value of paramsolver()
.setTol
(double value) setWeightCol
(String value) Sets the value of paramweightCol()
.solver()
Param for the name of optimization method used in KMeans.final DoubleParam
tol()
Param for the convergence tolerance for iterative algorithms (>= 0).transformSchema
(StructType schema) Check transform validity and derive the output schema from the input schema.uid()
An immutable unique ID for the object and its derivatives.Param for weight column name.Methods inherited from class org.apache.spark.ml.PipelineStage
params
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
Methods inherited from interface org.apache.spark.ml.util.DefaultParamsWritable
write
Methods inherited from interface org.apache.spark.ml.param.shared.HasDistanceMeasure
getDistanceMeasure
Methods inherited from interface org.apache.spark.ml.param.shared.HasFeaturesCol
getFeaturesCol
Methods inherited from interface org.apache.spark.ml.param.shared.HasMaxBlockSizeInMB
getMaxBlockSizeInMB
Methods inherited from interface org.apache.spark.ml.param.shared.HasMaxIter
getMaxIter
Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionCol
getPredictionCol
Methods inherited from interface org.apache.spark.ml.param.shared.HasWeightCol
getWeightCol
Methods inherited from interface org.apache.spark.ml.util.Identifiable
toString
Methods inherited from interface org.apache.spark.ml.clustering.KMeansParams
getInitMode, getInitSteps, getK, validateAndTransformSchema
Methods inherited from interface org.apache.spark.internal.Logging
initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext
Methods inherited from interface org.apache.spark.ml.util.MLWritable
save
Methods inherited from interface org.apache.spark.ml.param.Params
clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn
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Constructor Details
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KMeans
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KMeans
public KMeans()
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Method Details
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load
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read
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k
Description copied from interface:KMeansParams
The number of clusters to create (k). Must be > 1. Note that it is possible for fewer than k clusters to be returned, for example, if there are fewer than k distinct points to cluster. Default: 2.- Specified by:
k
in interfaceKMeansParams
- Returns:
- (undocumented)
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initMode
Description copied from interface:KMeansParams
Param for 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++ (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.- Specified by:
initMode
in interfaceKMeansParams
- Returns:
- (undocumented)
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initSteps
Description copied from interface:KMeansParams
Param for the number of steps for the k-means|| initialization mode. This is an advanced setting -- the default of 2 is almost always enough. Must be > 0. Default: 2.- Specified by:
initSteps
in interfaceKMeansParams
- Returns:
- (undocumented)
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solver
Description copied from interface:KMeansParams
Param for the name of optimization method used in KMeans. Supported options: - "auto": Automatically select the solver based on the input schema and sparsity: If input instances are arrays or input vectors are dense, set to "block". Else, set to "row". - "row": input instances are processed row by row, and triangle-inequality is applied to accelerate the training. - "block": input instances are stacked to blocks, and GEMM is applied to compute the distances. Default is "auto".- Specified by:
solver
in interfaceHasSolver
- Specified by:
solver
in interfaceKMeansParams
- Returns:
- (undocumented)
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maxBlockSizeInMB
Description copied from interface:HasMaxBlockSizeInMB
Param for 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..- Specified by:
maxBlockSizeInMB
in interfaceHasMaxBlockSizeInMB
- Returns:
- (undocumented)
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weightCol
Description copied from interface:HasWeightCol
Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.- Specified by:
weightCol
in interfaceHasWeightCol
- Returns:
- (undocumented)
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distanceMeasure
Description copied from interface:HasDistanceMeasure
Param for The distance measure. Supported options: 'euclidean' and 'cosine'.- Specified by:
distanceMeasure
in interfaceHasDistanceMeasure
- Returns:
- (undocumented)
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tol
Description copied from interface:HasTol
Param for the convergence tolerance for iterative algorithms (>= 0). -
predictionCol
Description copied from interface:HasPredictionCol
Param for prediction column name.- Specified by:
predictionCol
in interfaceHasPredictionCol
- Returns:
- (undocumented)
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seed
Description copied from interface:HasSeed
Param for random seed. -
featuresCol
Description copied from interface:HasFeaturesCol
Param for features column name.- Specified by:
featuresCol
in interfaceHasFeaturesCol
- Returns:
- (undocumented)
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maxIter
Description copied from interface:HasMaxIter
Param for maximum number of iterations (>= 0).- Specified by:
maxIter
in interfaceHasMaxIter
- Returns:
- (undocumented)
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uid
Description copied from interface:Identifiable
An immutable unique ID for the object and its derivatives.- Specified by:
uid
in interfaceIdentifiable
- Returns:
- (undocumented)
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copy
Description copied from interface:Params
Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. SeedefaultCopy()
.- Specified by:
copy
in interfaceParams
- Specified by:
copy
in classEstimator<KMeansModel>
- Parameters:
extra
- (undocumented)- Returns:
- (undocumented)
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setFeaturesCol
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setPredictionCol
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setK
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setInitMode
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setDistanceMeasure
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setInitSteps
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setMaxIter
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setTol
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setSeed
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setWeightCol
Sets the value of paramweightCol()
. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one.- Parameters:
value
- (undocumented)- Returns:
- (undocumented)
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setSolver
Sets the value of paramsolver()
. Default is "auto".- Parameters:
value
- (undocumented)- Returns:
- (undocumented)
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setMaxBlockSizeInMB
Sets the value of parammaxBlockSizeInMB()
. Default is 0.0, then 1.0 MB will be chosen.- Parameters:
value
- (undocumented)- Returns:
- (undocumented)
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fit
Description copied from class:Estimator
Fits a model to the input data.- Specified by:
fit
in classEstimator<KMeansModel>
- Parameters:
dataset
- (undocumented)- Returns:
- (undocumented)
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transformSchema
Description copied from class:PipelineStage
Check transform validity and derive the output schema from the input schema.We check validity for interactions between parameters during
transformSchema
and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled byParam.validate()
.Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
- Specified by:
transformSchema
in classPipelineStage
- Parameters:
schema
- (undocumented)- Returns:
- (undocumented)
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