# 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")