public class RobustScalerModel extends Model<RobustScalerModel> implements RobustScalerParams, MLWritable
RobustScaler.
param: range quantile range for each original column during fitting param: median median value for each original column during fitting
| Modifier and Type | Method and Description |
|---|---|
RobustScalerModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
Param<String> |
inputCol()
Param for input column name.
|
static RobustScalerModel |
load(String path) |
DoubleParam |
lower()
Lower quantile to calculate quantile range, shared by all features
Default: 0.25
|
Vector |
median() |
Param<String> |
outputCol()
Param for output column name.
|
Vector |
range() |
static MLReader<RobustScalerModel> |
read() |
RobustScalerModel |
setInputCol(String value) |
RobustScalerModel |
setOutputCol(String value) |
Dataset<Row> |
transform(Dataset<?> dataset)
Transforms the input dataset.
|
StructType |
transformSchema(StructType schema)
:: DeveloperApi ::
|
String |
uid()
An immutable unique ID for the object and its derivatives.
|
DoubleParam |
upper()
Upper quantile to calculate quantile range, shared by all features
Default: 0.75
|
BooleanParam |
withCentering()
Whether to center the data with median before scaling.
|
BooleanParam |
withScaling()
Whether to scale the data to quantile range.
|
MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
transform, transform, transformparamsequals, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitgetLower, getUpper, getWithCentering, getWithScaling, validateAndTransformSchemagetInputColgetOutputColclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwntoStringsaveinitializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarningpublic static MLReader<RobustScalerModel> read()
public static RobustScalerModel load(String path)
public DoubleParam lower()
RobustScalerParamslower in interface RobustScalerParamspublic DoubleParam upper()
RobustScalerParamsupper in interface RobustScalerParamspublic BooleanParam withCentering()
RobustScalerParamswithCentering in interface RobustScalerParamspublic BooleanParam withScaling()
RobustScalerParamswithScaling in interface RobustScalerParamspublic final Param<String> outputCol()
HasOutputColoutputCol in interface HasOutputColpublic final Param<String> inputCol()
HasInputColinputCol in interface HasInputColpublic String uid()
Identifiableuid in interface Identifiablepublic Vector range()
public Vector median()
public RobustScalerModel setInputCol(String value)
public RobustScalerModel setOutputCol(String value)
public Dataset<Row> transform(Dataset<?> dataset)
Transformertransform in class Transformerdataset - (undocumented)public StructType transformSchema(StructType schema)
PipelineStageCheck 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.
transformSchema in class PipelineStageschema - (undocumented)public RobustScalerModel copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Model<RobustScalerModel>extra - (undocumented)public MLWriter write()
MLWritableMLWriter instance for this ML instance.write in interface MLWritable