public class FPGrowth extends Estimator<FPGrowthModel> implements FPGrowthParams, DefaultParamsWritable
| Modifier and Type | Method and Description |
|---|---|
FPGrowth |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
FPGrowthModel |
fit(Dataset<?> dataset)
Fits a model to the input data.
|
Param<String> |
itemsCol()
Items column name.
|
static FPGrowth |
load(String path) |
DoubleParam |
minConfidence()
Minimal confidence for generating Association Rule.
|
DoubleParam |
minSupport()
Minimal support level of the frequent pattern.
|
IntParam |
numPartitions()
Number of partitions (at least 1) used by parallel FP-growth.
|
Param<String> |
predictionCol()
Param for prediction column name.
|
static MLReader<T> |
read() |
FPGrowth |
setItemsCol(String value) |
FPGrowth |
setMinConfidence(double value) |
FPGrowth |
setMinSupport(double value) |
FPGrowth |
setNumPartitions(int value) |
FPGrowth |
setPredictionCol(String value) |
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.
|
paramsequals, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitgetItemsCol, getMinConfidence, getMinSupport, getNumPartitions, validateAndTransformSchemagetPredictionColclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwntoStringwritesave$init$, 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 FPGrowth load(String path)
public static MLReader<T> read()
public Param<String> itemsCol()
FPGrowthParamsitemsCol in interface FPGrowthParamspublic DoubleParam minSupport()
FPGrowthParamsminSupport in interface FPGrowthParamspublic IntParam numPartitions()
FPGrowthParamsnumPartitions in interface FPGrowthParamspublic DoubleParam minConfidence()
FPGrowthParamsminConfidence in interface FPGrowthParamspublic final Param<String> predictionCol()
HasPredictionColpredictionCol in interface HasPredictionColpublic String uid()
Identifiableuid in interface Identifiablepublic FPGrowth setMinSupport(double value)
public FPGrowth setNumPartitions(int value)
public FPGrowth setMinConfidence(double value)
public FPGrowth setItemsCol(String value)
public FPGrowth setPredictionCol(String value)
public FPGrowthModel fit(Dataset<?> dataset)
Estimatorfit in class Estimator<FPGrowthModel>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 PipelineStageschema - (undocumented)public FPGrowth copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Estimator<FPGrowthModel>extra - (undocumented)