tests/testthat/test-recom.R

In audachang/recommenderlab.test: Lab for Developing and Testing Recommender Algorithms

library("testthat")
library("recommenderlab")


data("MovieLense")

### test all real rating recommenders
methods <- unique(sapply(recommenderRegistry$get_entries(
  dataType="realRatingMatrix"), "[[", "method"))

MovieLense100 <- MovieLense[rowCounts(MovieLense) > 100,]
MovieLense100 <- MovieLense[, colCounts(MovieLense100) > 100,]
train <- MovieLense100[1:20]
test1 <- MovieLense100[101]
test3 <- MovieLense100[101:103]

for(m in methods) {
  context(paste("Algorithm:", m))
  cat("Algorithm:", m, "\n")
  rec <- Recommender(train, method = m)
  rec

  ### default is top-N list
  pre <- predict(rec, test1, n = 10)
  pre
  l <- as(pre, "list")
  expect_identical(length(l), 1L)
  expect_identical(length(l[[1]]), 10L)

  ### default is ton-N list
  pre <- predict(rec, test3, n = 10)
  pre
  l <- as(pre, "list")
  expect_identical(length(l), 3L)
  expect_equal(as.integer(sapply(l, length)), c(10L, 10L, 10L))

  ### contains NAs for known ratings
  pre <- predict(rec, test1, n = 10, type = "ratings")
  pre
  expect_gt(sum(is.na(as(pre, "matrix"))), 0L)

  ### full rating matrix
  ### RERECOMMEND cannot do it
  if(m != "RERECOMMEND") {
    pre <- predict(rec, test1, n = 10, type = "ratingMatrix")
    pre
    ### there can be NAs
    #expect_equal(sum(is.na(as(pre, "matrix"))), 0L)
  }

  pre <- predict(rec, test3, n = 10, type = "ratings")
  pre
  expect_gt(sum(is.na(as(pre, "matrix"))), 0L)

  ### RERECOMMEND cannot do it
  if(m != "RERECOMMEND") {
    pre <- predict(rec, test3, n = 10, type = "ratingMatrix")
    pre
  }
  ### there can be NAs
  #expect_equal(sum(is.na(as(pre, "matrix"))), 0L)
}

### test all binary recommenders
methods <- unique(sapply(recommenderRegistry$get_entries(
  dataType="binaryRatingMatrix"), "[[", "method"))

MovieLense100_bin <- binarize(MovieLense100, minRating = 3)
train <- MovieLense100_bin[1:50]
test1 <- MovieLense100_bin[101]
test3 <- MovieLense100_bin[101:103]

for(m in methods) {
  context(paste("Algorithm:", m))
  cat("Algorithm:", m, "\n")
  rec <- Recommender(train, method = m)
  rec

  ### default is top-N list
  pre <- predict(rec, test1, n = 10)
  pre
  l <- as(pre, "list")
  expect_identical(length(l), 1L)
  expect_identical(length(l[[1]]), 10L)

  pre <- predict(rec, test3, n = 10)
  pre
  l <- as(pre, "list")
  expect_identical(length(l), 3L)
  expect_equal(as.integer(sapply(l, length)), c(10L, 10L, 10L))

  ### AR and RERECOMMEND cannot do it
  #if(m != "AR" && m != "RERECOMMEND") {
  #  pre <- predict(rec, test1, n = 10, type = "ratings")
  #  pre <- predict(rec, test1, n = 10, type = "ratingMatrix")
  #}
}