public class MinMaxScalerModel extends Model<MinMaxScalerModel> implements MinMaxScalerParams, MLWritable
MinMaxScaler.
param: originalMin min value for each original column during fitting param: originalMax max value for each original column during fitting
| Modifier and Type | Method and Description |
|---|---|
MinMaxScalerModel |
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 MinMaxScalerModel |
load(String path) |
DoubleParam |
max()
upper bound after transformation, shared by all features
Default: 1.0
|
DoubleParam |
min()
lower bound after transformation, shared by all features
Default: 0.0
|
Vector |
originalMax() |
Vector |
originalMin() |
Param<String> |
outputCol()
Param for output column name.
|
static MLReader<MinMaxScalerModel> |
read() |
MinMaxScalerModel |
setInputCol(String value) |
MinMaxScalerModel |
setMax(double value) |
MinMaxScalerModel |
setMin(double value) |
MinMaxScalerModel |
setOutputCol(String value) |
String |
toString() |
Dataset<Row> |
transform(Dataset<?> dataset)
Transforms the input dataset.
|
StructType |
transformSchema(StructType schema)
Check transform validity and derive the output schema from the input schema.
|
String |
uid()
An immutable unique ID for the object and its derivatives.
|
MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
transform, transform, transformparamsgetMax, getMin, validateAndTransformSchemagetInputColgetOutputColclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwnsave$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitializepublic static MLReader<MinMaxScalerModel> read()
public static MinMaxScalerModel load(String path)
public DoubleParam min()
MinMaxScalerParamsmin in interface MinMaxScalerParamspublic DoubleParam max()
MinMaxScalerParamsmax in interface MinMaxScalerParamspublic final Param<String> outputCol()
HasOutputColoutputCol in interface HasOutputColpublic final Param<String> inputCol()
HasInputColinputCol in interface HasInputColpublic String uid()
Identifiableuid in interface Identifiablepublic Vector originalMin()
public Vector originalMax()
public MinMaxScalerModel setInputCol(String value)
public MinMaxScalerModel setOutputCol(String value)
public MinMaxScalerModel setMin(double value)
public MinMaxScalerModel setMax(double value)
public Dataset<Row> transform(Dataset<?> dataset)
Transformertransform in class Transformerdataset - (undocumented)public StructType transformSchema(StructType schema)
PipelineStage
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 MinMaxScalerModel copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Model<MinMaxScalerModel>extra - (undocumented)public MLWriter write()
MLWritableMLWriter instance for this ML instance.write in interface MLWritablepublic String toString()
toString in interface IdentifiabletoString in class Object