public class FPGrowthModel extends Model<FPGrowthModel> implements FPGrowthParams, MLWritable
param: freqItemsets frequent itemsets in the format of DataFrame("items"[Array], "freq"[Long])
Modifier and Type | Method and Description |
---|---|
Dataset<Row> |
associationRules()
Get association rules fitted using the minConfidence.
|
FPGrowthModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
Dataset<Row> |
freqItemsets() |
Param<String> |
itemsCol()
Items column name.
|
static FPGrowthModel |
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<FPGrowthModel> |
read() |
FPGrowthModel |
setItemsCol(String value) |
FPGrowthModel |
setMinConfidence(double value) |
FPGrowthModel |
setPredictionCol(String value) |
String |
toString() |
Dataset<Row> |
transform(Dataset<?> dataset)
The transform method first generates the association rules according to the frequent itemsets.
|
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.
|
MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
transform, transform, transform
params
getItemsCol, getMinConfidence, getMinSupport, getNumPartitions, validateAndTransformSchema
getPredictionCol
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
save
$init$, 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 MLReader<FPGrowthModel> read()
public static FPGrowthModel load(String path)
public Param<String> itemsCol()
FPGrowthParams
itemsCol
in interface FPGrowthParams
public DoubleParam minSupport()
FPGrowthParams
minSupport
in interface FPGrowthParams
public IntParam numPartitions()
FPGrowthParams
numPartitions
in interface FPGrowthParams
public DoubleParam minConfidence()
FPGrowthParams
minConfidence
in interface FPGrowthParams
public final Param<String> predictionCol()
HasPredictionCol
predictionCol
in interface HasPredictionCol
public String uid()
Identifiable
uid
in interface Identifiable
public FPGrowthModel setMinConfidence(double value)
public FPGrowthModel setItemsCol(String value)
public FPGrowthModel setPredictionCol(String value)
public Dataset<Row> associationRules()
public Dataset<Row> transform(Dataset<?> dataset)
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 FPGrowthModel copy(ParamMap extra)
Params
defaultCopy()
.copy
in interface Params
copy
in class Model<FPGrowthModel>
extra
- (undocumented)public MLWriter write()
MLWritable
MLWriter
instance for this ML instance.write
in interface MLWritable
public String toString()
toString
in interface Identifiable
toString
in class Object