# prepare environment
source(snakemake@params$setup_code_fname)
devtools::load_all("../..")
# run model
fit <- pareg::pareg(
study$df %>% select(gene, pvalue),
df_terms,
network_param = 10000, term_network = term_similarities_sub
)
df <- fit %>%
as.data.frame() %>%
mutate(method = "pareg_network_10000", enrichment = abs(enrichment))
df %>%
arrange(desc(abs(enrichment))) %>%
head()
# misc
fname_model <- file.path(dirname(snakemake@output$fname), "model")
fit$obj$model$save(fname_model)
fit$obj$model <- fname_model
saveRDS(fit, file.path(dirname(snakemake@output$fname), "fit.rds"))
df_loss <- do.call(rbind, fit$obj$loss_hist) %>%
as_tibble() %>%
unnest(everything()) %>%
mutate(iteration = seq_along(fit$obj$loss_hist))
df_loss %>%
write_csv(file.path(dirname(snakemake@output$fname), "loss.csv"))
df_loss %>%
pivot_longer(-iteration) %>%
ggplot(aes(x = iteration, y = value, color = name)) +
geom_line() +
# geom_point() +
theme_minimal()
ggsave(
file.path(dirname(snakemake@output$fname), "loss.pdf"),
width = 8,
height = 6
)
data.frame(
pseudo_r_squared = fit$obj$pseudo_r_squared,
mse = fit$obj$mse
) %>%
write_csv(file.path(dirname(snakemake@output$fname), "extra_stats.csv"))
# save result
df %>%
write_csv(snakemake@output$fname)
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