| spark.fpGrowth {SparkR} | R Documentation |
A parallel FP-growth algorithm to mine frequent itemsets.
spark.fpGrowth fits a FP-growth model on a SparkDataFrame. Users can
spark.freqItemsets to get frequent itemsets, spark.associationRules to get
association rules, predict to make predictions on new data based on generated association
rules, and write.ml/read.ml to save/load fitted models.
For more details, see
FP-growth.
spark.fpGrowth(data, ...) spark.freqItemsets(object) spark.associationRules(object) ## S4 method for signature 'SparkDataFrame' spark.fpGrowth( data, minSupport = 0.3, minConfidence = 0.8, itemsCol = "items", numPartitions = NULL ) ## S4 method for signature 'FPGrowthModel' spark.freqItemsets(object) ## S4 method for signature 'FPGrowthModel' spark.associationRules(object) ## S4 method for signature 'FPGrowthModel' predict(object, newData) ## S4 method for signature 'FPGrowthModel,character' write.ml(object, path, overwrite = FALSE)
data |
A SparkDataFrame for training. |
... |
additional argument(s) passed to the method. |
object |
a fitted FPGrowth model. |
minSupport |
Minimal support level. |
minConfidence |
Minimal confidence level. |
itemsCol |
Features column name. |
numPartitions |
Number of partitions used for fitting. |
newData |
a SparkDataFrame for testing. |
path |
the directory where the model is saved. |
overwrite |
logical value indicating whether to overwrite if the output path already exists. Default is FALSE which means throw exception if the output path exists. |
spark.fpGrowth returns a fitted FPGrowth model.
A SparkDataFrame with frequent itemsets.
The SparkDataFrame contains two columns:
items (an array of the same type as the input column)
and freq (frequency of the itemset).
A SparkDataFrame with association rules.
The SparkDataFrame contains four columns:
antecedent (an array of the same type as the input column),
consequent (an array of the same type as the input column),
condfidence (confidence for the rule)
and lift (lift for the rule)
predict returns a SparkDataFrame containing predicted values.
spark.fpGrowth since 2.2.0
spark.freqItemsets(FPGrowthModel) since 2.2.0
spark.associationRules(FPGrowthModel) since 2.2.0
predict(FPGrowthModel) since 2.2.0
write.ml(FPGrowthModel, character) since 2.2.0
## Not run:
##D raw_data <- read.df(
##D "data/mllib/sample_fpgrowth.txt",
##D source = "csv",
##D schema = structType(structField("raw_items", "string")))
##D
##D data <- selectExpr(raw_data, "split(raw_items, ' ') as items")
##D model <- spark.fpGrowth(data)
##D
##D # Show frequent itemsets
##D frequent_itemsets <- spark.freqItemsets(model)
##D showDF(frequent_itemsets)
##D
##D # Show association rules
##D association_rules <- spark.associationRules(model)
##D showDF(association_rules)
##D
##D # Predict on new data
##D new_itemsets <- data.frame(items = c("t", "t,s"))
##D new_data <- selectExpr(createDataFrame(new_itemsets), "split(items, ',') as items")
##D predict(model, new_data)
##D
##D # Save and load model
##D path <- "/path/to/model"
##D write.ml(model, path)
##D read.ml(path)
##D
##D # Optional arguments
##D baskets_data <- selectExpr(createDataFrame(itemsets), "split(items, ',') as baskets")
##D another_model <- spark.fpGrowth(data, minSupport = 0.1, minConfidence = 0.5,
##D itemsCol = "baskets", numPartitions = 10)
## End(Not run)