Serializable
, org.apache.spark.internal.Logging
, MaxAbsScalerParams
, Params
, HasInputCol
, HasOutputCol
, DefaultParamsWritable
, Identifiable
, MLWritable
Rescale each feature individually to range [-1, 1] by dividing through the largest maximum absolute value in each feature. It does not shift/center the data, and thus does not destroy any sparsity.
org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter
Constructors
Creates a copy of this instance with the same UID and some extra params.
Fits a model to the input data.
Param for input column name.
Param for output column name.
Check transform validity and derive the output schema from the input schema.
An immutable unique ID for the object and its derivatives.
Methods inherited from interface org.apache.spark.internal.LogginginitializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext
Methods inherited from interface org.apache.spark.ml.util.MLWritablesave
Methods inherited from interface org.apache.spark.ml.param.Paramsclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn
public MaxAbsScaler()
Param for output column name.
outputCol
in interface HasOutputCol
Param for input column name.
inputCol
in interface HasInputCol
An immutable unique ID for the object and its derivatives.
uid
in interface Identifiable
Fits a model to the input data.
fit
in class Estimator<MaxAbsScalerModel>
dataset
- (undocumented)
Check 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 PipelineStage
schema
- (undocumented)
Params
Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy()
.
copy
in interface Params
copy
in class Estimator<MaxAbsScalerModel>
extra
- (undocumented)
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