public final class VarianceThresholdSelector extends Estimator<VarianceThresholdSelectorModel> implements VarianceThresholdSelectorParams, DefaultParamsWritable
Constructor and Description |
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VarianceThresholdSelector() |
VarianceThresholdSelector(String uid) |
Modifier and Type | Method and Description |
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VarianceThresholdSelector |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
Param<String> |
featuresCol()
Param for features column name.
|
VarianceThresholdSelectorModel |
fit(Dataset<?> dataset)
Fits a model to the input data.
|
static VarianceThresholdSelector |
load(String path) |
Param<String> |
outputCol()
Param for output column name.
|
static MLReader<T> |
read() |
VarianceThresholdSelector |
setFeaturesCol(String value) |
VarianceThresholdSelector |
setOutputCol(String value) |
VarianceThresholdSelector |
setVarianceThreshold(double value) |
StructType |
transformSchema(StructType schema)
Check transform validity and derive the output schema from the input schema.
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String |
uid()
An immutable unique ID for the object and its derivatives.
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DoubleParam |
varianceThreshold()
Param for variance threshold.
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params
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
getVarianceThreshold
getFeaturesCol
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 VarianceThresholdSelector(String uid)
public VarianceThresholdSelector()
public static VarianceThresholdSelector load(String path)
public static MLReader<T> read()
public final DoubleParam varianceThreshold()
VarianceThresholdSelectorParams
varianceThreshold
in interface VarianceThresholdSelectorParams
public final Param<String> outputCol()
HasOutputCol
outputCol
in interface HasOutputCol
public final Param<String> featuresCol()
HasFeaturesCol
featuresCol
in interface HasFeaturesCol
public String uid()
Identifiable
uid
in interface Identifiable
public VarianceThresholdSelector setVarianceThreshold(double value)
public VarianceThresholdSelector setFeaturesCol(String value)
public VarianceThresholdSelector setOutputCol(String value)
public VarianceThresholdSelectorModel fit(Dataset<?> dataset)
Estimator
fit
in class Estimator<VarianceThresholdSelectorModel>
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 VarianceThresholdSelector copy(ParamMap extra)
Params
defaultCopy()
.copy
in interface Params
copy
in class Estimator<VarianceThresholdSelectorModel>
extra
- (undocumented)