Package org.apache.spark.ml.feature
Class VectorAssembler
Object
org.apache.spark.ml.PipelineStage
org.apache.spark.ml.Transformer
org.apache.spark.ml.feature.VectorAssembler
- All Implemented Interfaces:
Serializable,org.apache.spark.internal.Logging,Params,HasHandleInvalid,HasInputCols,HasOutputCol,DefaultParamsWritable,Identifiable,MLWritable
public class VectorAssembler
extends Transformer
implements HasInputCols, HasOutputCol, HasHandleInvalid, DefaultParamsWritable
A feature transformer that merges multiple columns into a vector column.
This requires one pass over the entire dataset. In case we need to infer column lengths from the data we require an additional call to the 'first' Dataset method, see 'handleInvalid' parameter.
- See Also:
-
Nested Class Summary
Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging
org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionCreates a copy of this instance with the same UID and some extra params.Param for how to handle invalid data (NULL values).final StringArrayParamParam for input column names.static VectorAssemblerParam for output column name.static MLReader<T>read()setHandleInvalid(String value) setInputCols(String[] value) setOutputCol(String value) toString()Transforms the input dataset.transformSchema(StructType schema) Check transform validity and derive the output schema from the input schema.uid()An immutable unique ID for the object and its derivatives.Methods inherited from class org.apache.spark.ml.Transformer
transform, transform, transformMethods inherited from class org.apache.spark.ml.PipelineStage
paramsMethods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, waitMethods inherited from interface org.apache.spark.ml.util.DefaultParamsWritable
writeMethods inherited from interface org.apache.spark.ml.param.shared.HasHandleInvalid
getHandleInvalidMethods inherited from interface org.apache.spark.ml.param.shared.HasInputCols
getInputColsMethods inherited from interface org.apache.spark.ml.param.shared.HasOutputCol
getOutputColMethods inherited from interface org.apache.spark.internal.Logging
initializeForcefully, 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, withLogContextMethods inherited from interface org.apache.spark.ml.util.MLWritable
saveMethods inherited from interface org.apache.spark.ml.param.Params
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
-
Constructor Details
-
VectorAssembler
-
VectorAssembler
public VectorAssembler()
-
-
Method Details
-
load
-
read
-
outputCol
Description copied from interface:HasOutputColParam for output column name.- Specified by:
outputColin interfaceHasOutputCol- Returns:
- (undocumented)
-
inputCols
Description copied from interface:HasInputColsParam for input column names.- Specified by:
inputColsin interfaceHasInputCols- Returns:
- (undocumented)
-
uid
Description copied from interface:IdentifiableAn immutable unique ID for the object and its derivatives.- Specified by:
uidin interfaceIdentifiable- Returns:
- (undocumented)
-
setInputCols
-
setOutputCol
-
setHandleInvalid
-
handleInvalid
Param for how to handle invalid data (NULL values). Options are 'skip' (filter out rows with invalid data), 'error' (throw an error), or 'keep' (return relevant number of NaN in the output). Column lengths are taken from the size of ML Attribute Group, which can be set usingVectorSizeHintin a pipeline beforeVectorAssembler. Column lengths can also be inferred from first rows of the data since it is safe to do so but only in case of 'error' or 'skip'. Default: "error"- Specified by:
handleInvalidin interfaceHasHandleInvalid- Returns:
- (undocumented)
-
transform
Description copied from class:TransformerTransforms the input dataset.- Specified by:
transformin classTransformer- Parameters:
dataset- (undocumented)- Returns:
- (undocumented)
-
transformSchema
Description copied from class:PipelineStageCheck transform validity and derive the output schema from the input schema.We check validity for interactions between parameters during
transformSchemaand raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled byParam.validate().Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
- Specified by:
transformSchemain classPipelineStage- Parameters:
schema- (undocumented)- Returns:
- (undocumented)
-
copy
Description copied from interface:ParamsCreates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. SeedefaultCopy().- Specified by:
copyin interfaceParams- Specified by:
copyin classTransformer- Parameters:
extra- (undocumented)- Returns:
- (undocumented)
-
toString
- Specified by:
toStringin interfaceIdentifiable- Overrides:
toStringin classObject
-