trainMOA.MOA_recommender | R Documentation |
Train a MOA recommender (e.g. a BRISMFPredictor) on a datastream
## S3 method for class 'MOA_recommender' trainMOA(model, formula, data, subset, na.action = na.exclude, transFUN = identity, chunksize = 1000, trace = FALSE, options = list(maxruntime = +Inf), ...)
model |
an object of class |
formula |
a symbolic description of the model to be fit. This should be of the form rating ~ userid + itemid, in that sequence.
These should be columns in the |
data |
an object of class |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
na.action |
a function which indicates what should happen when the data contain |
transFUN |
a function which is used after obtaining |
chunksize |
the number of rows to obtain from the |
trace |
logical, indicating to show information on how many datastream chunks are already processed
as a |
options |
a names list of further options. Currently not used. |
... |
other arguments, currently not used yet |
An object of class MOA_trainedmodel which is a list with elements
model: the updated supplied model
object of class MOA_recommender
call: the matched call
na.action: the value of na.action
terms: the terms
in the model
transFUN: the transFUN argument
MOA_recommender
, datastream_file
, datastream_dataframe
,
datastream_matrix
, datastream_ffdf
, datastream
,
predict.MOA_trainedmodel
require(recommenderlab) data(MovieLense) x <- getData.frame(MovieLense) x$itemid <- as.integer(as.factor(x$item)) x$userid <- as.integer(as.factor(x$user)) x$rating <- as.numeric(x$rating) x <- head(x, 5000) movielensestream <- datastream_dataframe(data=x) movielensestream$get_points(3) ctrl <- MOAoptions(model = "BRISMFPredictor", features = 10) brism <- BRISMFPredictor(control=ctrl) mymodel <- trainMOA(model = brism, rating ~ userid + itemid, data = movielensestream, chunksize = 1000, trace=TRUE) summary(mymodel$model)
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