Packages

final class QuantileDiscretizer extends Estimator[Bucketizer] with QuantileDiscretizerBase with DefaultParamsWritable with HasInputCols with HasOutputCols

QuantileDiscretizer takes a column with continuous features and outputs a column with binned categorical features. The number of bins can be set using the numBuckets parameter. It is possible that the number of buckets used will be smaller than this value, for example, if there are too few distinct values of the input to create enough distinct quantiles. Since 2.3.0, QuantileDiscretizer can map multiple columns at once by setting the inputCols parameter. If both of the inputCol and inputCols parameters are set, an Exception will be thrown. To specify the number of buckets for each column, the numBucketsArray parameter can be set, or if the number of buckets should be the same across columns, numBuckets can be set as a convenience.

NaN handling: null and NaN values will be ignored from the column during QuantileDiscretizer fitting. This will produce a Bucketizer model for making predictions. During the transformation, Bucketizer will raise an error when it finds NaN values in the dataset, but the user can also choose to either keep or remove NaN values within the dataset by setting handleInvalid. If the user chooses to keep NaN values, they will be handled specially and placed into their own bucket, for example, if 4 buckets are used, then non-NaN data will be put into buckets[0-3], but NaNs will be counted in a special bucket[4].

Algorithm: The bin ranges are chosen using an approximate algorithm (see the documentation for org.apache.spark.sql.DataFrameStatFunctions.approxQuantile for a detailed description). The precision of the approximation can be controlled with the relativeError parameter. The lower and upper bin bounds will be -Infinity and +Infinity, covering all real values.

Annotations
@Since( "1.6.0" )
Linear Supertypes
HasOutputCols, HasInputCols, DefaultParamsWritable, MLWritable, QuantileDiscretizerBase, HasOutputCol, HasInputCol, HasHandleInvalid, Estimator[Bucketizer], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. QuantileDiscretizer
  2. HasOutputCols
  3. HasInputCols
  4. DefaultParamsWritable
  5. MLWritable
  6. QuantileDiscretizerBase
  7. HasOutputCol
  8. HasInputCol
  9. HasHandleInvalid
  10. Estimator
  11. PipelineStage
  12. Logging
  13. Params
  14. Serializable
  15. Serializable
  16. Identifiable
  17. AnyRef
  18. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new QuantileDiscretizer()
    Annotations
    @Since( "1.6.0" )
  2. new QuantileDiscretizer(uid: String)
    Annotations
    @Since( "1.6.0" )

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[_]): QuantileDiscretizer.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[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  8. def copy(extra: ParamMap): QuantileDiscretizer

    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
    QuantileDiscretizer → Estimator → PipelineStage → Params
    Annotations
    @Since( "1.6.0" )
  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 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[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  18. def fit(dataset: Dataset[_]): Bucketizer

    Fits a model to the input data.

    Fits a model to the input data.

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

    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): Bucketizer

    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[_]*): Bucketizer

    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. final def getInputCols: Array[String]

    Definition Classes
    HasInputCols
  28. def getNumBuckets: Int

    Definition Classes
    QuantileDiscretizerBase
  29. def getNumBucketsArray: Array[Int]

    Definition Classes
    QuantileDiscretizerBase
  30. 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
  31. final def getOutputCol: String

    Definition Classes
    HasOutputCol
  32. final def getOutputCols: Array[String]

    Definition Classes
    HasOutputCols
  33. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  34. def getRelativeError: Double

    Definition Classes
    QuantileDiscretizerBase
  35. val handleInvalid: Param[String]

    Param for how to handle invalid entries.

    Param for how to handle invalid entries. Options are 'skip' (filter out rows with invalid values), 'error' (throw an error), or 'keep' (keep invalid values in a special additional bucket). Note that in the multiple columns case, the invalid handling is applied to all columns. That said for 'error' it will throw an error if any invalids are found in any column, for 'skip' it will skip rows with any invalids in any columns, etc. Default: "error"

    Definition Classes
    QuantileDiscretizerBase → HasHandleInvalid
    Annotations
    @Since( "2.1.0" )
  36. 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
  37. 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
  38. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  39. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  40. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  41. final val inputCol: Param[String]

    Param for input column name.

    Param for input column name.

    Definition Classes
    HasInputCol
  42. final val inputCols: StringArrayParam

    Param for input column names.

    Param for input column names.

    Definition Classes
    HasInputCols
  43. 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
  44. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  45. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  46. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  47. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  48. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  49. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  50. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  51. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  52. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  53. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  54. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  55. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  56. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  57. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  58. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  59. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  60. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  61. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  62. val numBuckets: IntParam

    Number of buckets (quantiles, or categories) into which data points are grouped.

    Number of buckets (quantiles, or categories) into which data points are grouped. Must be greater than or equal to 2.

    See also handleInvalid, which can optionally create an additional bucket for NaN values.

    default: 2

    Definition Classes
    QuantileDiscretizerBase
  63. val numBucketsArray: IntArrayParam

    Array of number of buckets (quantiles, or categories) into which data points are grouped.

    Array of number of buckets (quantiles, or categories) into which data points are grouped. Each value must be greater than or equal to 2

    See also handleInvalid, which can optionally create an additional bucket for NaN values.

    Definition Classes
    QuantileDiscretizerBase
  64. final val outputCol: Param[String]

    Param for output column name.

    Param for output column name.

    Definition Classes
    HasOutputCol
  65. final val outputCols: StringArrayParam

    Param for output column names.

    Param for output column names.

    Definition Classes
    HasOutputCols
  66. 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.

  67. val relativeError: DoubleParam

    Relative error (see documentation for org.apache.spark.sql.DataFrameStatFunctions.approxQuantile for description) Must be in the range [0, 1].

    Relative error (see documentation for org.apache.spark.sql.DataFrameStatFunctions.approxQuantile for description) Must be in the range [0, 1]. Note that in multiple columns case, relative error is applied to all columns. default: 0.001

    Definition Classes
    QuantileDiscretizerBase
  68. 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( ... )
  69. final def set(paramPair: ParamPair[_]): QuantileDiscretizer.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  70. final def set(param: String, value: Any): QuantileDiscretizer.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
  71. final def set[T](param: Param[T], value: T): QuantileDiscretizer.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  72. final def setDefault(paramPairs: ParamPair[_]*): QuantileDiscretizer.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
  73. final def setDefault[T](param: Param[T], value: T): QuantileDiscretizer.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
  74. def setHandleInvalid(value: String): QuantileDiscretizer.this.type

    Annotations
    @Since( "2.1.0" )
  75. def setInputCol(value: String): QuantileDiscretizer.this.type

    Annotations
    @Since( "1.6.0" )
  76. def setInputCols(value: Array[String]): QuantileDiscretizer.this.type

    Annotations
    @Since( "2.3.0" )
  77. def setNumBuckets(value: Int): QuantileDiscretizer.this.type

    Annotations
    @Since( "1.6.0" )
  78. def setNumBucketsArray(value: Array[Int]): QuantileDiscretizer.this.type

    Annotations
    @Since( "2.3.0" )
  79. def setOutputCol(value: String): QuantileDiscretizer.this.type

    Annotations
    @Since( "1.6.0" )
  80. def setOutputCols(value: Array[String]): QuantileDiscretizer.this.type

    Annotations
    @Since( "2.3.0" )
  81. def setRelativeError(value: Double): QuantileDiscretizer.this.type

    Annotations
    @Since( "2.0.0" )
  82. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  83. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  84. def transformSchema(schema: StructType): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    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
    QuantileDiscretizer → PipelineStage
    Annotations
    @Since( "1.6.0" )
  85. 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()
  86. 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
    QuantileDiscretizer → Identifiable
    Annotations
    @Since( "1.6.0" )
  87. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  88. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  89. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  90. 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 HasOutputCols

Inherited from HasInputCols

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from QuantileDiscretizerBase

Inherited from HasOutputCol

Inherited from HasInputCol

Inherited from HasHandleInvalid

Inherited from Estimator[Bucketizer]

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