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Showing content from http://spark.apache.org/docs/latest/api/java/org/apache/spark/ml/feature/Bucketizer.html below:

Bucketizer (Spark 4.0.0 JavaDoc)

All Implemented Interfaces:

Serializable, org.apache.spark.internal.Logging, Params, HasHandleInvalid, HasInputCol, HasInputCols, HasOutputCol, HasOutputCols, DefaultParamsWritable, Identifiable, MLWritable

maps a column of continuous features to a column of feature buckets.

Since 2.3.0, Bucketizer can map multiple columns at once by setting the inputCols parameter. Note that when both the inputCol and inputCols parameters are set, an Exception will be thrown. The splits parameter is only used for single column usage, and splitsArray is for multiple columns.

See Also:

• Serialized Form

• Nested Class Summary
  Nested classes/interfaces inherited from interface org.apache.spark.internal.Loggingorg.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter

• Constructor Summary

  Constructors

• Method Summary

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

  double[]

  double[][]

  Param for how to handle invalid entries containing NaN values.

  Param for input column name.

  Param for input column names.

  Param for output column name.

  Param for output column names.

  Parameter for mapping continuous features into buckets.

  Parameter for specifying multiple splits parameters.

  Transforms the input dataset.

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

  An immutable unique ID for the object and its derivatives.

  Methods inherited from interface org.apache.spark.internal.LogginginitializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext
  Methods inherited from interface org.apache.spark.ml.util.MLWritablesave Methods inherited from interface org.apache.spark.ml.param.Paramsclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

• Constructor Details
  • Bucketizer
  • Bucketizer

    public Bucketizer()

• Method Details
  • load
  • read
  • outputCols

    Param for output column names.

    Specified by:

    outputCols in interface HasOutputCols

    Returns:

    (undocumented)

  • inputCols

    Param for input column names.

    Specified by:

    inputCols in interface HasInputCols

    Returns:

    (undocumented)

  • outputCol

    Param for output column name.

    Specified by:

    outputCol in interface HasOutputCol

    Returns:

    (undocumented)

  • inputCol

    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)

  • splits

    Parameter for mapping continuous features into buckets. With n+1 splits, there are n buckets. A bucket defined by splits x,y holds values in the range [x,y) except the last bucket, which also includes y. Splits should be of length greater than or equal to 3 and strictly increasing. Values at -inf, inf must be explicitly provided to cover all Double values; otherwise, values outside the splits specified will be treated as errors.

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

    Returns:

    (undocumented)

  • getSplits

    public double[] getSplits()

  • setSplits
  • setInputCol
  • setOutputCol
  • handleInvalid

    Param for how to handle invalid entries containing NaN values. Values outside the splits will always be treated as errors. 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 column 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"

    Specified by:

    handleInvalid in interface HasHandleInvalid

    Returns:

    (undocumented)

  • setHandleInvalid
  • splitsArray

    Returns:

    (undocumented)

  • getSplitsArray

    public double[][] getSplitsArray()

  • setSplitsArray public Bucketizer setSplitsArray(double[][] value)
  • 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<Bucketizer>

    Parameters:

    extra - (undocumented)

    Returns:

    (undocumented)

  • toString

    Specified by:

    toString in interface Identifiable

    Overrides:

    toString in class Object

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