org.apache.spark.ml.fpm

Class FPGrowthModel

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

org.apache.spark.ml.Model<FPGrowthModel>

org.apache.spark.ml.fpm.FPGrowthModel

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging, FPGrowthParams, Params, HasPredictionCol, Identifiable, MLWritable

public class FPGrowthModel
extends Model<FPGrowthModel>
implements FPGrowthParams, MLWritable

Model fitted by FPGrowth.

param: freqItemsets frequent itemsets in the format of DataFrame("items"[Array], "freq"[Long])

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

org.apache.spark.internal.Logging.SparkShellLoggingFilter

Method Summary

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.

Methods inherited from class org.apache.spark.ml.Model

hasParent, parent, setParent

Methods inherited from class org.apache.spark.ml.Transformer

transform, transform, transform

Methods inherited from class org.apache.spark.ml.PipelineStage

params

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.fpm.FPGrowthParams

getItemsCol, getMinConfidence, getMinSupport, getNumPartitions, validateAndTransformSchema

Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionCol

getPredictionCol

Methods 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

Methods inherited from interface org.apache.spark.ml.util.MLWritable

save

Methods inherited from interface org.apache.spark.internal.Logging

$init$, 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

Method Detail

read

public static MLReader<FPGrowthModel> read()

load

public static FPGrowthModel load(String path)

itemsCol

public Param<String> itemsCol()

Description copied from interface: FPGrowthParams

Items column name. Default: "items"

Specified by:

itemsCol in interface FPGrowthParams

Returns:

(undocumented)

minSupport

public DoubleParam minSupport()

Description copied from interface: FPGrowthParams

Minimal support level of the frequent pattern. [0.0, 1.0]. Any pattern that appears more than (minSupport * size-of-the-dataset) times will be output in the frequent itemsets. Default: 0.3

Specified by:

minSupport in interface FPGrowthParams

Returns:

(undocumented)

numPartitions

public IntParam numPartitions()

Description copied from interface: FPGrowthParams

Number of partitions (at least 1) used by parallel FP-growth. By default the param is not set, and partition number of the input dataset is used.

Specified by:

numPartitions in interface FPGrowthParams

Returns:

(undocumented)

minConfidence

public DoubleParam minConfidence()

Description copied from interface: FPGrowthParams

Minimal confidence for generating Association Rule. minConfidence will not affect the mining for frequent itemsets, but will affect the association rules generation. Default: 0.8

Specified by:

minConfidence in interface FPGrowthParams

Returns:

(undocumented)

predictionCol

public final Param<String> predictionCol()

Description copied from interface: HasPredictionCol

Param for prediction column name.

Specified by:

predictionCol in interface HasPredictionCol

Returns:

(undocumented)

uid

public String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

freqItemsets

public Dataset<Row> freqItemsets()

setMinConfidence

public FPGrowthModel setMinConfidence(double value)

setItemsCol

public FPGrowthModel setItemsCol(String value)

setPredictionCol

public FPGrowthModel setPredictionCol(String value)

associationRules

public Dataset<Row> associationRules()

Get association rules fitted using the minConfidence. Returns a dataframe with five fields, "antecedent", "consequent", "confidence", "lift" and "support", where "antecedent" and "consequent" are Array[T], whereas "confidence", "lift" and "support" are Double.

Returns:

(undocumented)

transform

public Dataset<Row> transform(Dataset<?> dataset)

The transform method first generates the association rules according to the frequent itemsets. Then for each transaction in itemsCol, the transform method will compare its items against the antecedents of each association rule. If the record contains all the antecedents of a specific association rule, the rule will be considered as applicable and its consequents will be added to the prediction result. The transform method will summarize the consequents from all the applicable rules as prediction. The prediction column has the same data type as the input column(Array[T]) and will not contain existing items in the input column. The null values in the itemsCol columns are treated as empty sets. WARNING: internally it collects association rules to the driver and uses broadcast for efficiency. This may bring pressure to driver memory for large set of association rules.

Specified by:

transform in class Transformer

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

Check transform validity and derive the output schema from the input schema.

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.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)

copy

public FPGrowthModel copy(ParamMap extra)

Description copied from interface: Params

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

Specified by:

copy in interface Params

Specified by:

copy in class Model<FPGrowthModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

write

public MLWriter write()

Description copied from interface: MLWritable

Returns an MLWriter instance for this ML instance.

Specified by:

write in interface MLWritable

Returns:

(undocumented)

toString

public String toString()

Specified by:

toString in interface Identifiable

Overrides:

toString in class Object