CollabDataLoaders_from_df: CollabDataLoaders_from_df

In fastai: Interface to 'fastai'

CollabDataLoaders_from_df

Description

Create a 'DataLoaders' suitable for collaborative filtering from 'ratings'.

Usage

CollabDataLoaders_from_df(
  ratings,
  valid_pct = 0.2,
  user_name = NULL,
  item_name = NULL,
  rating_name = NULL,
  seed = NULL,
  path = ".",
  bs = 64,
  val_bs = NULL,
  shuffle_train = TRUE,
  device = NULL
)

Arguments

ratings	ratings
valid_pct	The random percentage of the dataset to set aside for validation (with an optional seed)
user_name	The name of the column containing the user (defaults to the first column)
item_name	The name of the column containing the item (defaults to the second column)
rating_name	The name of the column containing the rating (defaults to the third column)
seed	random seed
path	The folder where to work
bs	The batch size
val_bs	The batch size for the validation DataLoader (defaults to bs)
shuffle_train	If we shuffle the training DataLoader or not
device	the device, e.g. cpu, cuda, and etc.

Value

None

Examples

## Not run: 

URLs_MOVIE_LENS_ML_100k()
c(user,item,title)  %<-% list('userId','movieId','title')
ratings = fread('ml-100k/u.data', col.names = c(user,item,'rating','timestamp'))
movies = fread('ml-100k/u.item', col.names = c(item, 'title', 'date', 'N', 'url',
                                               paste('g',1:19,sep = '')))
rating_movie = ratings[movies[, .SD, .SDcols=c(item,title)], on = item]
dls = CollabDataLoaders_from_df(rating_movie, seed = 42, valid_pct = 0.1, bs = 64,
item_name=title, path='ml-100k')


## End(Not run)

fastai documentation built on June 22, 2024, 11:15 a.m.