public class HashingTF extends Transformer implements HasInputCol, HasOutputCol, HasNumFeatures, DefaultParamsWritable
| Modifier and Type | Method and Description |
|---|---|
BooleanParam |
binary()
Binary toggle to control term frequency counts.
|
HashingTF |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
boolean |
getBinary() |
int |
hashFuncVersion() |
int |
indexOf(Object term)
Returns the index of the input term.
|
Param<String> |
inputCol()
Param for input column name.
|
static HashingTF |
load(String path) |
IntParam |
numFeatures()
Param for Number of features.
|
Param<String> |
outputCol()
Param for output column name.
|
static MLReader<HashingTF> |
read() |
void |
save(String path)
Saves this ML instance to the input path, a shortcut of
write.save(path). |
HashingTF |
setBinary(boolean value) |
HashingTF |
setInputCol(String value) |
HashingTF |
setNumFeatures(int value) |
HashingTF |
setOutputCol(String value) |
String |
toString() |
Dataset<Row> |
transform(Dataset<?> dataset)
Transforms the input dataset.
|
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.
|
transform, transform, transformparamsgetInputColgetOutputColgetNumFeaturesclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwnwrite$init$, 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_, uninitializepublic static HashingTF load(String path)
public final IntParam numFeatures()
HasNumFeaturesnumFeatures in interface HasNumFeaturespublic final Param<String> outputCol()
HasOutputColoutputCol in interface HasOutputColpublic final Param<String> inputCol()
HasInputColinputCol in interface HasInputColpublic String uid()
Identifiableuid in interface Identifiablepublic int hashFuncVersion()
public HashingTF setInputCol(String value)
public HashingTF setOutputCol(String value)
public BooleanParam binary()
public HashingTF setNumFeatures(int value)
public boolean getBinary()
public HashingTF setBinary(boolean value)
public Dataset<Row> transform(Dataset<?> dataset)
Transformertransform in class Transformerdataset - (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 PipelineStageschema - (undocumented)public int indexOf(Object term)
term - (undocumented)public HashingTF copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Transformerextra - (undocumented)public String toString()
toString in interface IdentifiabletoString in class Objectpublic void save(String path)
MLWritablewrite.save(path).save in interface MLWritablepath - (undocumented)