FPGrowthModel (Spark 2.4.0 JavaDoc)

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, Params, Identifiable, MLWritable
```
public class FPGrowthModel
extends Model<FPGrowthModel>
implements 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

All Methods Static Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method and Description
`Dataset<Row>`	`associationRules()` Get association rules fitted using the minConfidence.
`static Params`	`clear(Param<?> param)`
`FPGrowthModel`	`copy(ParamMap extra)` Creates a copy of this instance with the same UID and some extra params.
`static String`	`explainParam(Param<?> param)`
`static String`	`explainParams()`
`static ParamMap`	`extractParamMap()`
`static ParamMap`	`extractParamMap(ParamMap extra)`
`Dataset<Row>`	`freqItemsets()`
`static <T> scala.Option<T>`	`get(Param<T> param)`
`static <T> scala.Option<T>`	`getDefault(Param<T> param)`
`static String`	`getItemsCol()`
`String`	`getItemsCol()`
`static double`	`getMinConfidence()`
`double`	`getMinConfidence()`
`static double`	`getMinSupport()`
`double`	`getMinSupport()`
`static int`	`getNumPartitions()`
`int`	`getNumPartitions()`
`static <T> T`	`getOrDefault(Param<T> param)`
`static Param<Object>`	`getParam(String paramName)`
`static String`	`getPredictionCol()`
`static <T> boolean`	`hasDefault(Param<T> param)`
`static boolean`	`hasParam(String paramName)`
`static boolean`	`hasParent()`
`static boolean`	`isDefined(Param<?> param)`
`static boolean`	`isSet(Param<?> param)`
`static Param<String>`	`itemsCol()`
`Param<String>`	`itemsCol()` Items column name.
`static FPGrowthModel`	`load(String path)`
`static DoubleParam`	`minConfidence()`
`DoubleParam`	`minConfidence()` Minimal confidence for generating Association Rule.
`static DoubleParam`	`minSupport()`
`DoubleParam`	`minSupport()` Minimal support level of the frequent pattern.
`static IntParam`	`numPartitions()`
`IntParam`	`numPartitions()` Number of partitions (at least 1) used by parallel FP-growth.
`static Param<?>[]`	`params()`
`static void`	`parent_$eq(Estimator<M> x$1)`
`static Estimator<M>`	`parent()`
`static Param<String>`	`predictionCol()`
`static MLReader<FPGrowthModel>`	`read()`
`static void`	`save(String path)`
`static <T> Params`	`set(Param<T> param, T value)`
`FPGrowthModel`	`setItemsCol(String value)`
`FPGrowthModel`	`setMinConfidence(double value)`
`static M`	`setParent(Estimator<M> parent)`
`FPGrowthModel`	`setPredictionCol(String value)`
`static String`	`toString()`
`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.
`StructType`	`validateAndTransformSchema(StructType schema)` Validates and transforms the input schema.
`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.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)
```
  - toString
```
public static String toString()
```
  - params
```
public static Param<?>[] params()
```
  - explainParam
```
public static String explainParam(Param<?> param)
```
  - explainParams
```
public static String explainParams()
```
  - isSet
```
public static final boolean isSet(Param<?> param)
```
  - isDefined
```
public static final boolean isDefined(Param<?> param)
```
  - hasParam
```
public static boolean hasParam(String paramName)
```
  - getParam
```
public static Param<Object> getParam(String paramName)
```
  - set
```
public static final <T> Params set(Param<T> param,
                                   T value)
```
  - get
```
public static final <T> scala.Option<T> get(Param<T> param)
```
  - clear
```
public static final Params clear(Param<?> param)
```
  - getOrDefault
```
public static final <T> T getOrDefault(Param<T> param)
```
  - getDefault
```
public static final <T> scala.Option<T> getDefault(Param<T> param)
```
  - hasDefault
```
public static final <T> boolean hasDefault(Param<T> param)
```
  - extractParamMap
```
public static final ParamMap extractParamMap(ParamMap extra)
```
  - extractParamMap
```
public static final ParamMap extractParamMap()
```
  - parent
```
public static Estimator<M> parent()
```
  - parent_$eq
```
public static void parent_$eq(Estimator<M> x$1)
```
  - setParent
```
public static M setParent(Estimator<M> parent)
```
  - hasParent
```
public static boolean hasParent()
```
  - predictionCol
```
public static final Param<String> predictionCol()
```
  - getPredictionCol
```
public static final String getPredictionCol()
```
  - itemsCol
```
public static Param<String> itemsCol()
```
  - getItemsCol
```
public static String getItemsCol()
```
  - minSupport
```
public static DoubleParam minSupport()
```
  - getMinSupport
```
public static double getMinSupport()
```
  - numPartitions
```
public static IntParam numPartitions()
```
  - getNumPartitions
```
public static int getNumPartitions()
```
  - minConfidence
```
public static DoubleParam minConfidence()
```
  - getMinConfidence
```
public static double getMinConfidence()
```
  - save
```
public static void save(String path)
                 throws java.io.IOException
```
    Throws:
    
    java.io.IOException
  - 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)
  - getItemsCol
```
public String getItemsCol()
```
  - getMinConfidence
```
public double getMinConfidence()
```
  - getMinSupport
```
public double getMinSupport()
```
  - getNumPartitions
```
public int getNumPartitions()
```
  - itemsCol
```
public Param<String> itemsCol()
```
    Items column name. Default: "items"
    
    Returns:
    
    (undocumented)
  - minConfidence
```
public DoubleParam minConfidence()
```
    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
    
    Returns:
    
    (undocumented)
  - minSupport
```
public DoubleParam minSupport()
```
    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
    
    Returns:
    
    (undocumented)
  - numPartitions
```
public IntParam numPartitions()
```
    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.
    
    Returns:
    
    (undocumented)
  - validateAndTransformSchema
```
public StructType validateAndTransformSchema(StructType schema)
```
    Validates and transforms the input schema.
    
    Parameters:
    
    schema - input schema
    
    Returns:
    
    output schema

Class FPGrowthModel

Method Summary

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

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

Methods inherited from class Object

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

Methods inherited from interface org.apache.spark.ml.param.Params

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

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

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

Method Detail

read

load

toString

params

explainParam

explainParams

isSet

isDefined

hasParam

getParam

set

get

clear

getOrDefault

getDefault

hasDefault

extractParamMap

extractParamMap

parent

parent_$eq

setParent

hasParent

predictionCol

getPredictionCol

itemsCol

getItemsCol

minSupport

getMinSupport

numPartitions

getNumPartitions

minConfidence

getMinConfidence

save

uid

freqItemsets

setMinConfidence

setItemsCol

setPredictionCol

associationRules

transform

transformSchema

copy

write

getItemsCol

getMinConfidence

getMinSupport

getNumPartitions

itemsCol

minConfidence

minSupport

numPartitions

validateAndTransformSchema