context("regression")
source("utils.R")
test_succeeds('iris load', {
df = iris
df$Species = as.numeric(as.factor(df$Species))
})
test_succeeds('dls create', {
procs = list(FillMissing(),Categorify(),Normalize())
dls = TabularDataTable(df, procs, NULL, names(iris)[1:4],
y_names="Species", splits = list(c(1:120),c(121:150))) %>%
dataloaders(bs=10)
})
test_succeeds('tabular ops create model', {
model = dls %>% tabular_learner(layers=c(200,100), metrics=list(rmse(),mse()))
})
test_succeeds('tabular ops dims==batch', {
dls %>% one_batch(TRUE) -> list_1
# no embeddings
expect_equal(dim(list_1[[1]]), c(10,0))
expect_equal(dim(list_1[[2]]), c(10,4))
expect_equal(dim(list_1[[3]]), c(10,1))
})
test_succeeds('tabular ops train model', {
model %>% fit(1,1e-2)
})
test_succeeds('tabular ops predict', {
res = model %>% predict(df[4,])
expect_equal(names(res),colnames(iris)[5])
expect_length(res,1)
})
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