Packages

class KMeans extends Estimator[KMeansModel] with KMeansParams with DefaultParamsWritable

K-means clustering with support for k-means|| initialization proposed by Bahmani et al.

Annotations
@Since( "1.5.0" )
Source
KMeans.scala
See also

Bahmani et al., Scalable k-means++.

Linear Supertypes
DefaultParamsWritable, MLWritable, KMeansParams, HasMaxBlockSizeInMB, HasSolver, HasWeightCol, HasDistanceMeasure, HasTol, HasPredictionCol, HasSeed, HasFeaturesCol, HasMaxIter, Estimator[KMeansModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. KMeans
  2. DefaultParamsWritable
  3. MLWritable
  4. KMeansParams
  5. HasMaxBlockSizeInMB
  6. HasSolver
  7. HasWeightCol
  8. HasDistanceMeasure
  9. HasTol
  10. HasPredictionCol
  11. HasSeed
  12. HasFeaturesCol
  13. HasMaxIter
  14. Estimator
  15. PipelineStage
  16. Logging
  17. Params
  18. Serializable
  19. Serializable
  20. Identifiable
  21. AnyRef
  22. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new KMeans()
    Annotations
    @Since( "1.5.0" )
  2. new KMeans(uid: String)
    Annotations
    @Since( "1.5.0" )

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. final def clear(param: Param[_]): KMeans.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  7. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native() @IntrinsicCandidate()
  8. def copy(extra: ParamMap): KMeans

    Creates a copy of this instance with the same UID and some extra 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. See defaultCopy().

    Definition Classes
    KMeans → Estimator → PipelineStage → Params
    Annotations
    @Since( "1.5.0" )
  9. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

    Copies param values from this instance to another instance for params shared by them.

    Copies param values from this instance to another instance for params shared by them.

    This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

    to

    the target instance, which should work with the same set of default Params as this source instance

    extra

    extra params to be copied to the target's paramMap

    returns

    the target instance with param values copied

    Attributes
    protected
    Definition Classes
    Params
  10. final def defaultCopy[T <: Params](extra: ParamMap): T

    Default implementation of copy with extra params.

    Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

    Attributes
    protected
    Definition Classes
    Params
  11. final val distanceMeasure: Param[String]

    Param for The distance measure.

    Param for The distance measure. Supported options: 'euclidean' and 'cosine'.

    Definition Classes
    HasDistanceMeasure
  12. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  14. def explainParam(param: Param[_]): String

    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes
    Params
  15. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  16. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  17. final def extractParamMap(extra: ParamMap): ParamMap

    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 less than user-supplied values less than 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 less than user-supplied values less than extra.

    Definition Classes
    Params
  18. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  19. def fit(dataset: Dataset[_]): KMeansModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    KMeans → Estimator
    Annotations
    @Since( "2.0.0" )
  20. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[KMeansModel]

    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  21. def fit(dataset: Dataset[_], paramMap: ParamMap): KMeansModel

    Fits a single model to the input data with provided parameter map.

    Fits a single model to the input data with provided parameter map.

    dataset

    input dataset

    paramMap

    Parameter map. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  22. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): KMeansModel

    Fits a single model to the input data with optional parameters.

    Fits a single model to the input data with optional parameters.

    dataset

    input dataset

    firstParamPair

    the first param pair, overrides embedded params

    otherParamPairs

    other param pairs. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  23. final def get[T](param: Param[T]): Option[T]

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  24. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @IntrinsicCandidate()
  25. final def getDefault[T](param: Param[T]): Option[T]

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  26. final def getDistanceMeasure: String

    Definition Classes
    HasDistanceMeasure
  27. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  28. def getInitMode: String

    Definition Classes
    KMeansParams
    Annotations
    @Since( "1.5.0" )
  29. def getInitSteps: Int

    Definition Classes
    KMeansParams
    Annotations
    @Since( "1.5.0" )
  30. def getK: Int

    Definition Classes
    KMeansParams
    Annotations
    @Since( "1.5.0" )
  31. final def getMaxBlockSizeInMB: Double

    Definition Classes
    HasMaxBlockSizeInMB
  32. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  33. final def getOrDefault[T](param: Param[T]): T

    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes
    Params
  34. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  35. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  36. final def getSeed: Long

    Definition Classes
    HasSeed
  37. final def getSolver: String

    Definition Classes
    HasSolver
  38. final def getTol: Double

    Definition Classes
    HasTol
  39. final def getWeightCol: String

    Definition Classes
    HasWeightCol
  40. final def hasDefault[T](param: Param[T]): Boolean

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  41. def hasParam(paramName: String): Boolean

    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes
    Params
  42. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @IntrinsicCandidate()
  43. final val initMode: Param[String]

    Param for the initialization algorithm.

    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||.

    Definition Classes
    KMeansParams
    Annotations
    @Since( "1.5.0" )
  44. final val initSteps: IntParam

    Param for the number of steps for the k-means|| initialization mode.

    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.

    Definition Classes
    KMeansParams
    Annotations
    @Since( "1.5.0" )
  45. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  46. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  47. final def isDefined(param: Param[_]): Boolean

    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes
    Params
  48. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  49. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  50. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  51. final val k: IntParam

    The number of clusters to create (k).

    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.

    Definition Classes
    KMeansParams
    Annotations
    @Since( "1.5.0" )
  52. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  53. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  54. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  55. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  56. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  57. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  58. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  59. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  60. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. final val maxBlockSizeInMB: DoubleParam

    Param for Maximum memory in MB for stacking input data into blocks.

    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..

    Definition Classes
    HasMaxBlockSizeInMB
  65. final val maxIter: IntParam

    Param for maximum number of iterations (>= 0).

    Param for maximum number of iterations (>= 0).

    Definition Classes
    HasMaxIter
  66. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  67. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @IntrinsicCandidate()
  68. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @IntrinsicCandidate()
  69. lazy val params: Array[Param[_]]

    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes
    Params
    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

  70. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  71. def save(path: String): Unit

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  72. final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes
    HasSeed
  73. final def set(paramPair: ParamPair[_]): KMeans.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  74. final def set(param: String, value: Any): KMeans.this.type

    Sets a parameter (by name) in the embedded param map.

    Sets a parameter (by name) in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  75. final def set[T](param: Param[T], value: T): KMeans.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  76. final def setDefault(paramPairs: ParamPair[_]*): KMeans.this.type

    Sets default values for a list of params.

    Sets default values for a list of params.

    Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

    paramPairs

    a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

    Attributes
    protected
    Definition Classes
    Params
  77. final def setDefault[T](param: Param[T], value: T): KMeans.this.type

    Sets a default value for a param.

    Sets a default value for a param.

    param

    param to set the default value. Make sure that this param is initialized before this method gets called.

    value

    the default value

    Attributes
    protected[ml]
    Definition Classes
    Params
  78. def setDistanceMeasure(value: String): KMeans.this.type

    Annotations
    @Since( "2.4.0" )
  79. def setFeaturesCol(value: String): KMeans.this.type

    Annotations
    @Since( "1.5.0" )
  80. def setInitMode(value: String): KMeans.this.type

    Annotations
    @Since( "1.5.0" )
  81. def setInitSteps(value: Int): KMeans.this.type

    Annotations
    @Since( "1.5.0" )
  82. def setK(value: Int): KMeans.this.type

    Annotations
    @Since( "1.5.0" )
  83. def setMaxBlockSizeInMB(value: Double): KMeans.this.type

    Sets the value of param maxBlockSizeInMB.

    Sets the value of param maxBlockSizeInMB. Default is 0.0, then 1.0 MB will be chosen.

    Annotations
    @Since( "3.4.0" )
  84. def setMaxIter(value: Int): KMeans.this.type

    Annotations
    @Since( "1.5.0" )
  85. def setPredictionCol(value: String): KMeans.this.type

    Annotations
    @Since( "1.5.0" )
  86. def setSeed(value: Long): KMeans.this.type

    Annotations
    @Since( "1.5.0" )
  87. def setSolver(value: String): KMeans.this.type

    Sets the value of param solver.

    Sets the value of param solver. Default is "auto".

    Annotations
    @Since( "3.4.0" )
  88. def setTol(value: Double): KMeans.this.type

    Annotations
    @Since( "1.5.0" )
  89. def setWeightCol(value: String): KMeans.this.type

    Sets the value of param weightCol.

    Sets the value of param weightCol. 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.

    Annotations
    @Since( "3.0.0" )
  90. final val solver: Param[String]

    Param for the name of optimization method used in KMeans.

    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".
    Definition Classes
    KMeansParams → HasSolver
    Annotations
    @Since( "3.4.0" )
  91. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  92. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  93. final val tol: DoubleParam

    Param for the convergence tolerance for iterative algorithms (>= 0).

    Param for the convergence tolerance for iterative algorithms (>= 0).

    Definition Classes
    HasTol
  94. def transformSchema(schema: StructType): StructType

    Check transform validity and derive the output schema from the input schema.

    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 by Param.validate().

    Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

    Definition Classes
    KMeans → PipelineStage
    Annotations
    @Since( "1.5.0" )
  95. def transformSchema(schema: StructType, logging: Boolean): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema and parameters, optionally with logging.

    This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  96. val uid: String

    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    KMeans → Identifiable
    Annotations
    @Since( "1.5.0" )
  97. def validateAndTransformSchema(schema: StructType): StructType

    Validates and transforms the input schema.

    Validates and transforms the input schema.

    schema

    input schema

    returns

    output schema

    Attributes
    protected
    Definition Classes
    KMeansParams
  98. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  99. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  100. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  101. final val weightCol: Param[String]

    Param for weight column name.

    Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

    Definition Classes
    HasWeightCol
  102. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritable → MLWritable

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from KMeansParams

Inherited from HasMaxBlockSizeInMB

Inherited from HasSolver

Inherited from HasWeightCol

Inherited from HasDistanceMeasure

Inherited from HasTol

Inherited from HasPredictionCol

Inherited from HasSeed

Inherited from HasFeaturesCol

Inherited from HasMaxIter

Inherited from Estimator[KMeansModel]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

Members

Parameter setters

Parameter getters

(expert-only) Parameters

A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

(expert-only) Parameter setters

(expert-only) Parameter getters