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 |
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MinMaxScaler() |
MinMaxScaler(String uid) |
Modifier and Type | Method and Description |
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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.
|
params
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
getMax, getMin, validateAndTransformSchema
getInputCol
getOutputCol
clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn
toString
write
save
$init$, 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_, uninitialize
public static MinMaxScaler load(String path)
public static MLReader<T> read()
public DoubleParam min()
MinMaxScalerParams
min
in interface MinMaxScalerParams
public DoubleParam max()
MinMaxScalerParams
max
in interface MinMaxScalerParams
public final Param<String> outputCol()
HasOutputCol
outputCol
in interface HasOutputCol
public final Param<String> inputCol()
HasInputCol
inputCol
in interface HasInputCol
public String uid()
Identifiable
uid
in interface Identifiable
public MinMaxScaler setInputCol(String value)
public MinMaxScaler setOutputCol(String value)
public MinMaxScaler setMin(double value)
public MinMaxScaler setMax(double value)
public MinMaxScalerModel fit(Dataset<?> dataset)
Estimator
fit
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 PipelineStage
schema
- (undocumented)public MinMaxScaler copy(ParamMap extra)
Params
defaultCopy()
.copy
in interface Params
copy
in class Estimator<MinMaxScalerModel>
extra
- (undocumented)