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, Logging, FPGrowthParams, Params, HasPredictionCol, Identifiable, MLWritable

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

:: Experimental :: Model fitted by FPGrowth.

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

See Also:

Serialized Form

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()
static FPGrowthModel	load(String path)
static MLReader<FPGrowthModel>	read()
FPGrowthModel	setItemsCol(String value)
FPGrowthModel	setMinConfidence(double value)
FPGrowthModel	setPredictionCol(String value)
Dataset<Row>	transform(Dataset<?> dataset) The transform method first generates the association rules according to the frequent itemsets.
StructType	transformSchema(StructType schema) :: DeveloperApi ::
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 Object

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

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

getItemsCol, getMinConfidence, getMinSupport, getNumPartitions, itemsCol, minConfidence, minSupport, numPartitions, validateAndTransformSchema

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

getPredictionCol, predictionCol

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, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

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

toString

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

save

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

initializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Method Detail

read

public static MLReader<FPGrowthModel> read()

load

public static FPGrowthModel load(String path)

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 four fields, "antecedent", "consequent", "confidence" and "lift", where "antecedent" and "consequent" are Array[T], whereas "confidence" and "lift" 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

:: DeveloperApi ::

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)