Package org.apache.spark.ml.feature

Class RobustScaler

Object
org.apache.spark.ml.PipelineStage
org.apache.spark.ml.Estimator<RobustScalerModel>
org.apache.spark.ml.feature.RobustScaler
All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging, RobustScalerParams, Params, HasInputCol, HasOutputCol, HasRelativeError, DefaultParamsWritable, Identifiable, MLWritable
public class RobustScaler extends Estimator<RobustScalerModel> implements RobustScalerParams, DefaultParamsWritable
Scale features using statistics that are robust to outliers. RobustScaler removes the median and scales the data according to the quantile range. The quantile range is by default IQR (Interquartile Range, quantile range between the 1st quartile = 25th quantile and the 3rd quartile = 75th quantile) but can be configured. Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Median and quantile range are then stored to be used on later data using the transform method. Standardization of a dataset is a common requirement for many machine learning estimators. Typically this is done by removing the mean and scaling to unit variance. However, outliers can often influence the sample mean / variance in a negative way. In such cases, the median and the quantile range often give better results. Note that NaN values are ignored in the computation of medians and ranges.
See Also:

  • Constructor Details

    • RobustScaler

      public RobustScaler(String uid)

    • RobustScaler

      public RobustScaler()

  • Method Details

    • load

      public static RobustScaler load(String path)

    • read

      public static MLReader<T> read()

    • lower

      public DoubleParam lower()
      Description copied from interface: RobustScalerParams
      Lower quantile to calculate quantile range, shared by all features Default: 0.25
      Specified by:
      lower in interface RobustScalerParams
      Returns:
      (undocumented)

    • upper

      public DoubleParam upper()
      Description copied from interface: RobustScalerParams
      Upper quantile to calculate quantile range, shared by all features Default: 0.75
      Specified by:
      upper in interface RobustScalerParams
      Returns:
      (undocumented)

    • withCentering

      public BooleanParam withCentering()
      Description copied from interface: RobustScalerParams
      Whether to center the data with median before scaling. It will build a dense output, so take care when applying to sparse input. Default: false
      Specified by:
      withCentering in interface RobustScalerParams
      Returns:
      (undocumented)

    • withScaling

      public BooleanParam withScaling()
      Description copied from interface: RobustScalerParams
      Whether to scale the data to quantile range. Default: true
      Specified by:
      withScaling in interface RobustScalerParams
      Returns:
      (undocumented)

    • relativeError

      public final DoubleParam relativeError()
      Description copied from interface: HasRelativeError
      Param for the relative target precision for the approximate quantile algorithm. Must be in the range [0, 1].
      Specified by:
      relativeError in interface HasRelativeError
      Returns:
      (undocumented)

    • outputCol

      public final Param<String> outputCol()
      Description copied from interface: HasOutputCol
      Param for output column name.
      Specified by:
      outputCol in interface HasOutputCol
      Returns:
      (undocumented)

    • inputCol

      public final Param<String> inputCol()
      Description copied from interface: HasInputCol
      Param for input column name.
      Specified by:
      inputCol in interface HasInputCol
      Returns:
      (undocumented)

    • uid

      public String uid()
      Description copied from interface: Identifiable
      An immutable unique ID for the object and its derivatives.
      Specified by:
      uid in interface Identifiable
      Returns:
      (undocumented)

    • setInputCol

      public RobustScaler setInputCol(String value)

    • setOutputCol

      public RobustScaler setOutputCol(String value)

    • setLower

      public RobustScaler setLower(double value)

    • setUpper

      public RobustScaler setUpper(double value)

    • setWithCentering

      public RobustScaler setWithCentering(boolean value)

    • setWithScaling

      public RobustScaler setWithScaling(boolean value)

    • setRelativeError

      public RobustScaler setRelativeError(double value)

    • fit

      public RobustScalerModel fit(Dataset<?> dataset)
      Description copied from class: Estimator
      Fits a model to the input data.
      Specified by:
      fit in class Estimator<RobustScalerModel>
      Parameters:
      dataset - (undocumented)
      Returns:
      (undocumented)

    • transformSchema

      public StructType transformSchema(StructType schema)
      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 by Param.validate().

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

      Specified by:
      transformSchema in class PipelineStage
      Parameters:
      schema - (undocumented)
      Returns:
      (undocumented)

    • copy

      public RobustScaler copy(ParamMap extra)
      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. See defaultCopy().
      Specified by:
      copy in interface Params
      Specified by:
      copy in class Estimator<RobustScalerModel>
      Parameters:
      extra - (undocumented)
      Returns:
      (undocumented)