All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging, ImputerParams, Params, HasInputCol, HasInputCols, HasOutputCol, HasOutputCols, HasRelativeError, Identifiable, MLWritable

Model fitted by Imputer.

param: surrogateDF a DataFrame containing inputCols and their corresponding surrogates, which are used to replace the missing values in the input DataFrame.

See Also:

  • Nested Class Summary

    Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

    org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter

  • Method Summary

    Creates a copy of this instance with the same UID and some extra params.

    inputCol()

    Param for input column name.

    inputCols()

    Param for input column names.

    The placeholder for the missing values.

    outputCol()

    Param for output column name.

    Param for output column names.

    read()

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

    strategy()

    toString()

    Transforms the input dataset.

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

    uid()

    An immutable unique ID for the object and its derivatives.

    write()

    Returns an MLWriter instance for this ML instance.

    Methods inherited from interface org.apache.spark.internal.Logging

    initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logBasedOnLevel, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, MDC, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext

    Methods inherited from interface org.apache.spark.ml.util.MLWritable

    save

  • Method Details

    • read

    • load

    • strategy

      The imputation strategy. Currently only "mean" and "median" are supported. If "mean", then replace missing values using the mean value of the feature. If "median", then replace missing values using the approximate median value of the feature. If "mode", then replace missing using the most frequent value of the feature. Default: mean

      Specified by:
      strategy in interface ImputerParams
      Returns:
      (undocumented)

    • missingValue

      The placeholder for the missing values. All occurrences of missingValue will be imputed. Note that null values are always treated as missing. Default: Double.NaN

      Specified by:
      missingValue in interface ImputerParams
      Returns:
      (undocumented)

    • relativeError

      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)

    • outputCols

      Param for output column names.

      Specified by:
      outputCols in interface HasOutputCols
      Returns:
      (undocumented)

    • outputCol

      Param for output column name.

      Specified by:
      outputCol in interface HasOutputCol
      Returns:
      (undocumented)

    • inputCols

      Param for input column names.

      Specified by:
      inputCols in interface HasInputCols
      Returns:
      (undocumented)

    • inputCol

      Description copied from interface: HasInputCol

      Param for input column name.

      Specified by:
      inputCol in interface HasInputCol
      Returns:
      (undocumented)

    • uid

      An immutable unique ID for the object and its derivatives.

      Specified by:
      uid in interface Identifiable
      Returns:
      (undocumented)

    • surrogateDF

    • setInputCol

    • setOutputCol

    • setInputCols

    • setOutputCols

    • transform

      Transforms the input dataset.

      Specified by:
      transform in class Transformer
      Parameters:
      dataset - (undocumented)
      Returns:
      (undocumented)

    • transformSchema

      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

      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 Model<ImputerModel>
      Parameters:
      extra - (undocumented)
      Returns:
      (undocumented)

    • write

      Description copied from interface: MLWritable

      Returns an MLWriter instance for this ML instance.

      Specified by:
      write in interface MLWritable
      Returns:
      (undocumented)

    • toString

      Specified by:
      toString in interface Identifiable
      Overrides:
      toString in class Object