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# consistency between models and function predictions
source("product/MPI/src/summarize_map.R")
check_function_prediction_pivot <- function(dataset_tag, verbose = FALSE) {
if (verbose) {
"Checking function prediction pivot for dataset ", dataset_tag, "\n", sep = "")
}
dataset_long <- arrow::read_parquet(
paste0("intermediate/", dataset_tag, "_function_predictions.parquet"))
dataset_wide <- dataset_long |>
dplyr::select(-term_name) |>
tidyr::pivot_wider(
id_cols = id,
names_from = term_id,
values_from = Y_hat)
sum(is.na(dataset_wide))
}
check_function_prediction_pivot("rosetta_high_quality")
check_function_prediction_pivot("rosetta_low_quality")
check_function_prediction_pivot("dmpfold_high_quality")
check_function_prediction_pivot("dmpfold_low_quality")
check_id_consistency <- function(dataset_tag, verbose = FALSE) {
if (verbose) {
cat("Loading model ids...\n")
}
ids_model <- arrow::read_parquet(
paste0("intermediate/", dataset_tag, "_models.parquet"),
col_select = "id")
if (verbose) {
cat("Loading function prediction ids...\n")
}
ids_anno <- arrow::read_parquet(
paste0("intermediate/", dataset_tag, "_function_predictions.parquet"),
col_select = "id") |>
dplyr::distinct(id)
problems <- dplyr::full_join(
ids_model |>
dplyr::mutate(model_id = id),
ids_anno |>
dplyr::mutate(anno_id = id),
by = "id") |>
summarize_map(
x_cols = model_id,
y_cols = anno_id,
verbose = verbose)
problems
}
check_id_consistency("rosetta_high_quality")
check_id_consistency("rosetta_low_quality")
check_id_consistency("dmpfold_high_quality")
check_id_consistency("dmpfold_low_quality")
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