Serializable
, org.apache.spark.internal.Logging
, Params
, HasInputCols
, HasOutputCol
, DefaultParamsWritable
, Identifiable
, MLWritable
For example, given the input feature values Double(2)
and Vector(3, 4)
, the output would be Vector(6, 8)
if all input features were numeric. If the first feature was instead nominal with four categories, the output would then be Vector(0, 0, 0, 0, 3, 4, 0, 0)
.
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.
Param for input column names.
Param for output column name.
Transforms the input dataset.
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 Interaction()
Param for output column name.
outputCol
in interface HasOutputCol
Param for input column names.
inputCols
in interface HasInputCols
An immutable unique ID for the object and its derivatives.
uid
in interface Identifiable
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)
Transforms the input dataset.
transform
in class Transformer
dataset
- (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 Transformer
extra
- (undocumented)
toString
in interface Identifiable
toString
in class Object
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