impute_recosystem: impute_recosystem

In autostats: Auto Stats

impute_recosystem

Description

Imputes missing values of a numeric matrix using stochastic gradient descent. recosystem

Usage

impute_recosystem(
  .data,
  lrate = c(0.05, 0.1),
  costp_l1 = c(0, 0.05),
  costq_l1 = c(0, 0.05),
  costp_l2 = c(0, 0.05),
  costq_l2 = c(0, 0.05),
  nthread = 8,
  loss = "l2",
  niter = 15,
  verbose = FALSE,
  nfold = 4,
  seed = 1
)

Arguments

.data	long format data frame
lrate	learning rate
costp_l1	l1 cost p
costq_l1	l1 cost q
costp_l2	l2 cost p
costq_l2	l2 cost q
nthread	nthreads
loss	loss function. also can use "l1"
niter	training iterations for tune
verbose	show training loss?
nfold	folds for tune validation
seed	seed for randomness

Details

input is a long data frame with 3 columns: ID col, Item col (the column names from pivoting longer), and the ratings (values from pivoting longer)

pre-processing generally requires pivoting a wide user x item matrix to long format. The missing values from the matrix must be retained as NA values in the rating column. The values will be predicted and filled in by the algorithm. Output is a long data frame with the same number of rows as input, but no missing values.

This function automatically tunes the recosystem learner before applying. Parameter values can be supplied for tuning. To avoid tuning, use single values for the parameters.

Value

long format data frame

autostats documentation built on Nov. 10, 2022, 6:13 p.m.