org.apache.spark.ml.feature

Interface QuantileDiscretizerBase

All Superinterfaces:

HasHandleInvalid, HasInputCol, HasOutputCol, Identifiable, Params, java.io.Serializable

All Known Implementing Classes:

QuantileDiscretizer

public interface QuantileDiscretizerBase
extends Params, HasHandleInvalid, HasInputCol, HasOutputCol

Params for QuantileDiscretizer.

Method Summary

Modifier and Type	Method and Description
int	getNumBuckets()
int[]	getNumBucketsArray()
double	getRelativeError()
Param<String>	handleInvalid() Param for how to handle invalid entries.
IntParam	numBuckets() Number of buckets (quantiles, or categories) into which data points are grouped.
IntArrayParam	numBucketsArray() Array of number of buckets (quantiles, or categories) into which data points are grouped.
DoubleParam	relativeError() Relative error (see documentation for org.apache.spark.sql.DataFrameStatFunctions.approxQuantile for description) Must be in the range [0, 1].

Methods inherited from interface org.apache.spark.ml.param.shared.HasHandleInvalid

getHandleInvalid

Methods inherited from interface org.apache.spark.ml.param.shared.HasInputCol

getInputCol, inputCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasOutputCol

getOutputCol, outputCol

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copy, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

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

toString, uid

Method Detail

getNumBuckets

int getNumBuckets()

getNumBucketsArray

int[] getNumBucketsArray()

getRelativeError

double getRelativeError()

handleInvalid

Param<String> handleInvalid()

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"

Specified by:

handleInvalid in interface HasHandleInvalid

Returns:

(undocumented)

numBuckets

IntParam numBuckets()

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

Returns:

(undocumented)

numBucketsArray

IntArrayParam numBucketsArray()

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.

Returns:

(undocumented)

relativeError

DoubleParam relativeError()

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

Returns:

(undocumented)