public class MinMaxScaler extends Estimator<MinMaxScalerModel> implements MinMaxScalerParams, DefaultParamsWritable
$$ Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) + min $$
For the case \(E_{max} == E_{min}\), \(Rescaled(e_i) = 0.5 * (max + min)\).
| Constructor and Description |
|---|
MinMaxScaler() |
MinMaxScaler(String uid) |
| Modifier and Type | Method and Description |
|---|---|
MinMaxScaler |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
MinMaxScalerModel |
fit(Dataset<?> dataset)
Fits a model to the input data.
|
Param<String> |
inputCol()
Param for input column name.
|
static MinMaxScaler |
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
|
Param<String> |
outputCol()
Param for output column name.
|
static MLReader<T> |
read() |
MinMaxScaler |
setInputCol(String value) |
MinMaxScaler |
setMax(double value) |
MinMaxScaler |
setMin(double value) |
MinMaxScaler |
setOutputCol(String value) |
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.
|
paramsequals, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitgetMax, getMin, validateAndTransformSchemagetInputColgetOutputColclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwntoStringwritesave$init$, 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 MinMaxScaler load(String path)
public static MLReader<T> read()
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 MinMaxScaler setInputCol(String value)
public MinMaxScaler setOutputCol(String value)
public MinMaxScaler setMin(double value)
public MinMaxScaler setMax(double value)
public MinMaxScalerModel fit(Dataset<?> dataset)
Estimatorfit in class Estimator<MinMaxScalerModel>dataset - (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 MinMaxScaler copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Estimator<MinMaxScalerModel>extra - (undocumented)