Packages

class VectorIndexer extends Estimator[VectorIndexerModel] with VectorIndexerParams with DefaultParamsWritable

Class for indexing categorical feature columns in a dataset of Vector.

This has 2 usage modes:

  • Automatically identify categorical features (default behavior)
    • This helps process a dataset of unknown vectors into a dataset with some continuous features and some categorical features. The choice between continuous and categorical is based upon a maxCategories parameter.
    • Set maxCategories to the maximum number of categorical any categorical feature should have.
    • E.g.: Feature 0 has unique values {-1.0, 0.0}, and feature 1 values {1.0, 3.0, 5.0}. If maxCategories = 2, then feature 0 will be declared categorical and use indices {0, 1}, and feature 1 will be declared continuous.
  • Index all features, if all features are categorical
    • If maxCategories is set to be very large, then this will build an index of unique values for all features.
    • Warning: This can cause problems if features are continuous since this will collect ALL unique values to the driver.
    • E.g.: Feature 0 has unique values {-1.0, 0.0}, and feature 1 values {1.0, 3.0, 5.0}. If maxCategories is greater than or equal to 3, then both features will be declared categorical.

This returns a model which can transform categorical features to use 0-based indices.

Index stability:

  • This is not guaranteed to choose the same category index across multiple runs.
  • If a categorical feature includes value 0, then this is guaranteed to map value 0 to index 0. This maintains vector sparsity.
  • More stability may be added in the future.

TODO: Future extensions: The following functionality is planned for the future:

  • Preserve metadata in transform; if a feature's metadata is already present, do not recompute.
  • Specify certain features to not index, either via a parameter or via existing metadata.
  • Add warning if a categorical feature has only 1 category.
Annotations
@Since( "1.4.0" )
Source
VectorIndexer.scala
Linear Supertypes
DefaultParamsWritable, MLWritable, VectorIndexerParams, HasHandleInvalid, HasOutputCol, HasInputCol, Estimator[VectorIndexerModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. VectorIndexer
  2. DefaultParamsWritable
  3. MLWritable
  4. VectorIndexerParams
  5. HasHandleInvalid
  6. HasOutputCol
  7. HasInputCol
  8. Estimator
  9. PipelineStage
  10. Logging
  11. Params
  12. Serializable
  13. Serializable
  14. Identifiable
  15. AnyRef
  16. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new VectorIndexer()
    Annotations
    @Since( "1.4.0" )
  2. new VectorIndexer(uid: String)
    Annotations
    @Since( "1.4.0" )

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[_]): VectorIndexer.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()
  8. def copy(extra: ParamMap): VectorIndexer

    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
    VectorIndexer → Estimator → PipelineStage → Params
    Annotations
    @Since( "1.4.1" )
  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 def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  13. 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
  14. def explainParams(): String

    Explains all params of this instance.

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

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

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  16. 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
  17. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  18. def fit(dataset: Dataset[_]): VectorIndexerModel

    Fits a model to the input data.

    Fits a model to the input data.

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

    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" )
  20. def fit(dataset: Dataset[_], paramMap: ParamMap): VectorIndexerModel

    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" )
  21. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): VectorIndexerModel

    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()
  22. 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
  23. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  24. 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
  25. final def getHandleInvalid: String

    Definition Classes
    HasHandleInvalid
  26. final def getInputCol: String

    Definition Classes
    HasInputCol
  27. def getMaxCategories: Int

    Definition Classes
    VectorIndexerParams
  28. 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
  29. final def getOutputCol: String

    Definition Classes
    HasOutputCol
  30. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  31. val handleInvalid: Param[String]

    Param for how to handle invalid data (unseen labels or NULL values).

    Param for how to handle invalid data (unseen labels or NULL values). Note: this param only applies to categorical features, not continuous ones. Options are: 'skip': filter out rows with invalid data. 'error': throw an error. 'keep': put invalid data in a special additional bucket, at index of the number of categories of the feature. Default value: "error"

    Definition Classes
    VectorIndexerParams → HasHandleInvalid
    Annotations
    @Since( "2.3.0" )
  32. 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
  33. 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
  34. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  35. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  36. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  37. final val inputCol: Param[String]

    Param for input column name.

    Param for input column name.

    Definition Classes
    HasInputCol
  38. 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
  39. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  40. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  41. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  42. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  43. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  44. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  45. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  46. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  47. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  48. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  49. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  50. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  51. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  52. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  53. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  54. val maxCategories: IntParam

    Threshold for the number of values a categorical feature can take.

    Threshold for the number of values a categorical feature can take. If a feature is found to have > maxCategories values, then it is declared continuous. Must be greater than or equal to 2.

    (default = 20)

    Definition Classes
    VectorIndexerParams
  55. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  56. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  57. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  58. final val outputCol: Param[String]

    Param for output column name.

    Param for output column name.

    Definition Classes
    HasOutputCol
  59. 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.

  60. 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( ... )
  61. final def set(paramPair: ParamPair[_]): VectorIndexer.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  62. final def set(param: String, value: Any): VectorIndexer.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
  63. final def set[T](param: Param[T], value: T): VectorIndexer.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  64. final def setDefault(paramPairs: ParamPair[_]*): VectorIndexer.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
  65. final def setDefault[T](param: Param[T], value: T): VectorIndexer.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
    Definition Classes
    Params
  66. def setHandleInvalid(value: String): VectorIndexer.this.type

    Annotations
    @Since( "2.3.0" )
  67. def setInputCol(value: String): VectorIndexer.this.type

    Annotations
    @Since( "1.4.0" )
  68. def setMaxCategories(value: Int): VectorIndexer.this.type

    Annotations
    @Since( "1.4.0" )
  69. def setOutputCol(value: String): VectorIndexer.this.type

    Annotations
    @Since( "1.4.0" )
  70. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  71. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  72. 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
    VectorIndexer → PipelineStage
    Annotations
    @Since( "1.4.0" )
  73. 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()
  74. 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
    VectorIndexer → Identifiable
    Annotations
    @Since( "1.4.0" )
  75. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  76. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  77. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  78. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from VectorIndexerParams

Inherited from HasHandleInvalid

Inherited from HasOutputCol

Inherited from HasInputCol

Inherited from Estimator[VectorIndexerModel]

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