Packages

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

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. Note that in multiple columns case, relative error is applied to all columns.

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")
Source
QuantileDiscretizer.scala
Linear Supertypes
DefaultParamsWritable, MLWritable, QuantileDiscretizerBase, HasRelativeError, HasOutputCols, HasInputCols, HasOutputCol, HasInputCol, HasHandleInvalid, Estimator[Bucketizer], PipelineStage, Logging, Params, Serializable, Identifiable, AnyRef, Any
Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. QuantileDiscretizer
  2. DefaultParamsWritable
  3. MLWritable
  4. QuantileDiscretizerBase
  5. HasRelativeError
  6. HasOutputCols
  7. HasInputCols
  8. HasOutputCol
  9. HasInputCol
  10. HasHandleInvalid
  11. Estimator
  12. PipelineStage
  13. Logging
  14. Params
  15. Serializable
  16. Identifiable
  17. AnyRef
  18. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. Protected

Parameters

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

  1. 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")
  2. final val inputCol: Param[String]

    Param for input column name.

    Param for input column name.

    Definition Classes
    HasInputCol
  3. final val inputCols: StringArrayParam

    Param for input column names.

    Param for input column names.

    Definition Classes
    HasInputCols
  4. 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
  5. 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
  6. final val outputCol: Param[String]

    Param for output column name.

    Param for output column name.

    Definition Classes
    HasOutputCol
  7. final val outputCols: StringArrayParam

    Param for output column names.

    Param for output column names.

    Definition Classes
    HasOutputCols

Members

  1. implicit class LogStringContext extends AnyRef
    Definition Classes
    Logging
  1. 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
  2. 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")
  3. 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
  4. def explainParams(): String

    Explains all params of this instance.

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

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

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  6. 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
  7. 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")
  8. def fit(dataset: Dataset[_], paramMaps: Seq[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")
  9. 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")
  10. 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()
  11. 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
  12. 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
  13. 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
  14. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  15. 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
  16. 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
  17. 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
  18. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  19. 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.

  20. 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("If the input path already exists but overwrite is not enabled.")
  21. 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
  22. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  23. 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
    QuantileDiscretizer → PipelineStage
    Annotations
    @Since("1.6.0")
  24. 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")
  25. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritable → MLWritable

Parameter setters

  1. def setHandleInvalid(value: String): QuantileDiscretizer.this.type

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

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

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

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

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

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

    Annotations
    @Since("2.3.0")

Parameter getters

  1. final def getHandleInvalid: String

    Definition Classes
    HasHandleInvalid
  2. final def getInputCol: String

    Definition Classes
    HasInputCol
  3. final def getInputCols: Array[String]

    Definition Classes
    HasInputCols
  4. def getNumBuckets: Int

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

    Definition Classes
    QuantileDiscretizerBase
  6. final def getOutputCol: String

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

    Definition Classes
    HasOutputCols

(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.

  1. final val relativeError: DoubleParam

    Param for the relative target precision for the approximate quantile algorithm.

    Param for the relative target precision for the approximate quantile algorithm. Must be in the range [0, 1].

    Definition Classes
    HasRelativeError

(expert-only) Parameter setters

  1. def setRelativeError(value: Double): QuantileDiscretizer.this.type

    Annotations
    @Since("2.0.0")

(expert-only) Parameter getters

  1. final def getRelativeError: Double

    Definition Classes
    HasRelativeError