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, transform
params
getInputCol
getOutputCol
getNumFeatures
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
write
$init$, 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 static HashingTF load(String path)
public final IntParam numFeatures()
HasNumFeatures
numFeatures
in interface HasNumFeatures
public final Param<String> outputCol()
HasOutputCol
outputCol
in interface HasOutputCol
public final Param<String> inputCol()
HasInputCol
inputCol
in interface HasInputCol
public String uid()
Identifiable
uid
in interface Identifiable
public 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)
Transformer
transform
in class Transformer
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 int indexOf(Object term)
term
- (undocumented)public HashingTF copy(ParamMap extra)
Params
defaultCopy()
.copy
in interface Params
copy
in class Transformer
extra
- (undocumented)public String toString()
toString
in interface Identifiable
toString
in class Object
public void save(String path)
MLWritable
write.save(path)
.save
in interface MLWritable
path
- (undocumented)