add curaction scripts
Browse files- src/00_setup_curation.sh +24 -0
- src/01_gather_data.R +17 -0
- src/02.1_assemble_datasets.R +250 -0
- src/02.2_check_assembled_datasets.R +64 -0
- src/03.1_uplaod_data.py +156 -0
- src/03.2_check_uploaded_data.py +40 -0
- src/summarize_map.R +346 -0
src/00_setup_curation.sh
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# from a base directory
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mkdir data
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mkdir intermeidate
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mkdir product
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cd product
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git clone https://huggingface.co/RosettaCommons/MIP
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cd ..
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# Run each numbered script in product/MIP/src/ in order (starting with this one)
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#
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# Tips:
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# 1) Make sure to set the working directory to the base directory (outside of the HF repo)
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# 2) While most of the scripts should work, I recommend running them interactively
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# 3) Some stages require more memory than others, all can be done with < 400GB of memory
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# but perhaps more more could reduce memory requirements
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# 4)
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src/01_gather_data.R
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# Download data from: https://zenodo.org/records/6611431
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# Sequence-structure-function relationships in the microbial protein universe
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# 45.4 GB
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system("cd data && curl -o microbiome_immunity_project_dataset.zip https://zenodo.org/records/6611431/files/microbiome_immunity_project_dataset.zip?download=1")
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md5sum_expected <- "b3e021609ffa052d2ab2333dc998964b data/microbiome_immunity_project_dataset.zip"
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md5sum <- system(
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"md5cksum data/microbiome_immunity_project_dataset.zip",
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intern = TRUE)
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if (md5sum != md5sum_expected) {
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cat("Expected and obtained md5sum values don't match\n")
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}
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system("cd data && unzip microbiome_immunity_project_dataset.zip")
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src/02.1_assemble_datasets.R
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#' Assemble a Rosetta models dataset
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#'
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#' @param data_path character directory .pdb.gz files are located
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#' @param output_path character output .parquet path
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#'
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#' Write output_path .parquet file with columns
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#' <id> <pdb> [<scores>]
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#' where [<scores>] are key-value entries following
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#' the TER line in each .pdb.gz file
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assemble_rosetta_models <- function(
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data_path,
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output_path) {
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cat(
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"data path: ", data_path, "\n",
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"output path: ", output_path, "\n",
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sep = "")
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file_index <- 1
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models <- list.files(
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path = data_path,
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full.names = TRUE,
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pattern = "*.pdb.gz",
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recursive = TRUE) |>
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purrr::map_dfr(.f = function(path) {
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file_handle <- path |>
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file(open = "rb") |>
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gzcon()
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if( file_index %% 1000 == 0) {
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cat("Reading '", path, "' ", file_index, "\n", sep = "")
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}
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file_index <<- file_index + 1
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lines <- file_handle |> readLines()
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file_handle |> close()
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ter_line_index <- which(
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lines |> stringr::str_detect("^TER"),
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arr.ind = TRUE)
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lines[(ter_line_index + 1) : (length(lines) - 1)] |>
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paste0(collapse = "\n") |>
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readr::read_delim(
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delim = " ",
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col_names = c("key", "value"),
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show_col_types = FALSE) |>
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51 |
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dplyr::mutate(
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id = path |>
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basename() |>
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stringr::str_replace_all(".pdb.gz", ""),
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.before = 1) |>
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dplyr::mutate(
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pdb = lines[1:ter_line_index] |> paste0(collapse = "\n"))
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})
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models <- arrow::arrow_table(models)
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models$pdb <- models$pdb$cast(arrow::string())
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models |> arrow::write_parquet(output_path)
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}
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# call assemble_rosetta_models for the high_quality and low_quality datasets
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dataset_tag <- "rosetta_high_quality_models"
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assemble_rosetta_models(
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data_path = paste0(
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"data/microbiome_immunity_project_dataset/dataset/",
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dataset_tag),
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output_path = paste0("intermediate/", dataset_tag, ".parquet"))
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dataset_tag <- "rosetta_low_quality_models"
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assemble_rosetta_models(
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data_path = paste0(
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"data/microbiome_immunity_project_dataset/dataset/",
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dataset_tag),
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output_path = paste0("intermediate/", dataset_tag, ".parquet"))
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#' Assemble a DMP-Fold models dataset
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#'
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#' @param data_path character directory .pdb.gz files are located
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#' @param output_path character output .parquet path
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#'
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#' Write output_path .parquet file with columns
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#' <id> <pdb>
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#'
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#' Note that dmpfold doesn't write out score lines like Rosetta
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assemble_dmpfold_models <- function(
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data_path,
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output_path) {
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cat(
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"data path: ", data_path, "\n",
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"output path: ", output_path, "\n",
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sep = "")
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file_index <- 1
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models <- list.files(
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path = data_path,
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full.names = TRUE,
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pattern = "*.pdb.gz",
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recursive = TRUE) |>
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purrr::map_dfr(.f = function(path) {
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file_handle <- path |>
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file(open = "rb") |>
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gzcon()
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if (file_index %% 1000 == 0) {
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cat("Reading '", path, "' ", file_index, "\n", sep = "")
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}
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file_index <<- file_index + 1
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lines <- file_handle |> readLines()
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file_handle |> close()
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ter_line_index <- which(
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lines |> stringr::str_detect("^TER"),
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arr.ind = TRUE)
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data.frame(
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id = path |> basename() |> stringr::str_replace_all(".pdb.gz", ""),
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pdb = lines[1:ter_line_index] |> paste0(collapse = "\n"))
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})
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models |>
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arrow::write_parquet(output_path)
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}
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# call assemble_rosetta_models for the high_quality and low_quality datasets
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dataset_tag <- "dmpfold_high_quality_models"
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assemble_dmpfold_models(
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data_path = paste0(
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"data/microbiome_immunity_project_dataset/dataset/",
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dataset_tag),
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output_path = paste0("intermediate/", dataset_tag, ".parquet"))
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dataset_tag <- "dmpfold_low_quality_models"
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assemble_dmpfold_models(
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data_path = paste0(
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"data/microbiome_immunity_project_dataset/dataset/",
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dataset_tag),
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output_path = paste0("intermediate/", dataset_tag, ".parquet"))
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####################################
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## ##
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## Assemble Function Predictions ##
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## ##
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####################################
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#' Assemble DeepFRI Function Prediction dataset
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#'
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#' @param data_path character directory where *_pred_scores.json.gz files are located
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#' @param output_path character output .parquet path
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#'
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#' Write output_path .parquet file with columns
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#' <id> <term_id> <term_name> <Y_hat>
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#'
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#' <id>: Structure identifier like `MIP_00004873`
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#' <term_ontology>: term onlogy, one of [BP, CC, EC, or MF]
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#' <term_id>: GO or EC term identifiers like `GO:0009225`
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#' <term_name> is the description of the term
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assemble_DeepFRI_function_predictions <- function(
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data_path,
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output_path) {
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cat(
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"data path: ", data_path, "\n",
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"output path: ", output_path, "\n",
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sep = "")
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173 |
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file_index <- 1
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scores <- c("BP", "CC", "EC", "MF") |>
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purrr::map_dfr(.f = function(ontology) {
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cat("Reading predictions cores for ontology ", ontology, "\n", sep = "")
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list.files(
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path = data_path,
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full.names = TRUE,
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pattern = paste0("*_", ontology, "_pred_scores.json.gz"),
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recursive = TRUE) |>
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purrr::map_dfr(.f = function(path) {
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cat("Reading '", path, "' ", file_index, "\n", sep = "")
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186 |
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file_index <<- file_index + 1
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187 |
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188 |
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data <- jsonlite::fromJSON(txt = path)
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scores <- as.data.frame(data$Y_hat)
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names(scores) <- data$goterms
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scores <- scores |>
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dplyr::mutate(
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id = data$pdb_chains,
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.before = 1) |>
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tidyr::pivot_longer(
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cols = -"id",
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names_to = "term_id",
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values_to = "Y_hat") |>
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dplyr::left_join(
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data.frame(
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term_ontology = ontology,
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term_id = data$goterms,
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term_name = data$gonames),
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by = "term_id") |>
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dplyr::select(
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id,
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term_ontology,
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term_id,
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term_name,
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Y_hat)
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})
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})
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scores |>
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arrow::write_parquet(output_path)
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}
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# call assemble_rosetta_models for all the datasets
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dataset_tag <- "rosetta_high_quality_function_predictions"
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assemble_DeepFRI_function_predictions(
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data_path = paste0(
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"data/microbiome_immunity_project_dataset/dataset/",
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dataset_tag),
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output_path = paste0("intermediate/", dataset_tag, ".parquet"))
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227 |
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228 |
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dataset_tag <- "rosetta_low_quality_function_predictions"
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229 |
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assemble_DeepFRI_function_predictions(
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230 |
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data_path = paste0(
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231 |
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"data/microbiome_immunity_project_dataset/dataset/",
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dataset_tag),
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233 |
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output_path = paste0("intermediate/", dataset_tag, ".parquet"))
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234 |
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235 |
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236 |
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dataset_tag <- "dmpfold_high_quality_function_predictions"
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237 |
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assemble_DeepFRI_function_predictions(
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238 |
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data_path = paste0(
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239 |
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"data/mxoicrobiome_immunity_project_dataset/dataset/",
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240 |
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dataset_tag),
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241 |
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output_path = paste0("intermediate/", dataset_tag, ".parquet"))
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242 |
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243 |
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dataset_tag <- "dmpfold_low_quality_function_predictions"
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244 |
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assemble_DeepFRI_function_predictions(
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245 |
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data_path = paste0(
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246 |
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"data/microbiome_immunity_project_dataset/dataset/",
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247 |
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dataset_tag),
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248 |
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output_path = paste0("intermediate/", dataset_tag, ".parquet"))
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249 |
+
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250 |
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src/02.2_check_assembled_datasets.R
ADDED
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1 |
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# consistency between models and function predictions
|
4 |
+
source("product/MPI/src/summarize_map.R")
|
5 |
+
|
6 |
+
|
7 |
+
check_function_prediction_pivot <- function(dataset_tag, verbose = FALSE) {
|
8 |
+
|
9 |
+
if (verbose) {
|
10 |
+
"Checking function prediction pivot for dataset ", dataset_tag, "\n", sep = "")
|
11 |
+
}
|
12 |
+
dataset_long <- arrow::read_parquet(
|
13 |
+
paste0("intermediate/", dataset_tag, "_function_predictions.parquet"))
|
14 |
+
|
15 |
+
dataset_wide <- dataset_long |>
|
16 |
+
dplyr::select(-term_name) |>
|
17 |
+
tidyr::pivot_wider(
|
18 |
+
id_cols = id,
|
19 |
+
names_from = term_id,
|
20 |
+
values_from = Y_hat)
|
21 |
+
|
22 |
+
sum(is.na(dataset_wide))
|
23 |
+
}
|
24 |
+
|
25 |
+
check_function_prediction_pivot("rosetta_high_quality")
|
26 |
+
check_function_prediction_pivot("rosetta_low_quality")
|
27 |
+
check_function_prediction_pivot("dmpfold_high_quality")
|
28 |
+
check_function_prediction_pivot("dmpfold_low_quality")
|
29 |
+
|
30 |
+
|
31 |
+
|
32 |
+
check_id_consistency <- function(dataset_tag, verbose = FALSE) {
|
33 |
+
if (verbose) {
|
34 |
+
cat("Loading model ids...\n")
|
35 |
+
}
|
36 |
+
ids_model <- arrow::read_parquet(
|
37 |
+
paste0("intermediate/", dataset_tag, "_models.parquet"),
|
38 |
+
col_select = "id")
|
39 |
+
|
40 |
+
if (verbose) {
|
41 |
+
cat("Loading function prediction ids...\n")
|
42 |
+
}
|
43 |
+
ids_anno <- arrow::read_parquet(
|
44 |
+
paste0("intermediate/", dataset_tag, "_function_predictions.parquet"),
|
45 |
+
col_select = "id") |>
|
46 |
+
dplyr::distinct(id)
|
47 |
+
|
48 |
+
problems <- dplyr::full_join(
|
49 |
+
ids_model |>
|
50 |
+
dplyr::mutate(model_id = id),
|
51 |
+
ids_anno |>
|
52 |
+
dplyr::mutate(anno_id = id),
|
53 |
+
by = "id") |>
|
54 |
+
summarize_map(
|
55 |
+
x_cols = model_id,
|
56 |
+
y_cols = anno_id,
|
57 |
+
verbose = verbose)
|
58 |
+
problems
|
59 |
+
}
|
60 |
+
|
61 |
+
check_id_consistency("rosetta_high_quality")
|
62 |
+
check_id_consistency("rosetta_low_quality")
|
63 |
+
check_id_consistency("dmpfold_high_quality")
|
64 |
+
check_id_consistency("dmpfold_low_quality")
|
src/03.1_uplaod_data.py
ADDED
@@ -0,0 +1,156 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
|
2 |
+
|
3 |
+
|
4 |
+
# install huggingface_hub from the command line:
|
5 |
+
#
|
6 |
+
# pip install huggingface_hub
|
7 |
+
# pip install datasets
|
8 |
+
#
|
9 |
+
# Log into huggingface hub
|
10 |
+
#
|
11 |
+
# huggingface-cli login
|
12 |
+
#
|
13 |
+
# This will ask you for an access token
|
14 |
+
|
15 |
+
|
16 |
+
import datasets
|
17 |
+
|
18 |
+
##### rosetta_high_quality_models #######
|
19 |
+
dataset = datasets.load_dataset(
|
20 |
+
"parquet",
|
21 |
+
name = "rosetta_high_quality_models",
|
22 |
+
data_dir = "./intermediate",
|
23 |
+
data_files = {"train" : "rosetta_high_quality_models.parquet"},
|
24 |
+
cache_dir = "/scratch/maom_root/maom0/maom",
|
25 |
+
split = "train",
|
26 |
+
keep_in_memory = True)
|
27 |
+
|
28 |
+
dataset.push_to_hub(
|
29 |
+
repo_id = "RosettaCommons/MIP",
|
30 |
+
config_name = "rosetta_high_quality_models",
|
31 |
+
data_dir = "rosetta_high_quality_models/data")
|
32 |
+
|
33 |
+
|
34 |
+
|
35 |
+
##### rosetta_low_quality_models #######
|
36 |
+
dataset = datasets.load_dataset(
|
37 |
+
"parquet",
|
38 |
+
name = "rosetta_low_quality_models",
|
39 |
+
data_dir = "./intermediate",
|
40 |
+
data_files = {"train" : "rosetta_low_quality_models.parquet"},
|
41 |
+
cache_dir = "/scratch/maom_root/maom0/maom",
|
42 |
+
split = "train",
|
43 |
+
keep_in_memory = True)
|
44 |
+
|
45 |
+
dataset.push_to_hub(
|
46 |
+
repo_id = "RosettaCommons/MIP",
|
47 |
+
config_name = "rosetta_low_quality_models",
|
48 |
+
data_dir = "rosetta_low_quality_models/data")
|
49 |
+
|
50 |
+
|
51 |
+
##### dmpfold_high_quality_models #######
|
52 |
+
dataset = datasets.load_dataset(
|
53 |
+
"parquet",
|
54 |
+
name = "dmpfold_high_quality_models",
|
55 |
+
data_dir = "./intermediate",
|
56 |
+
data_files = {"train" : "dmpfold_high_quality_models.parquet"},
|
57 |
+
cache_dir = "/scratch/maom_root/maom0/maom",
|
58 |
+
split = "train",
|
59 |
+
keep_in_memory = True)
|
60 |
+
|
61 |
+
dataset.push_to_hub(
|
62 |
+
repo_id = "RosettaCommons/MIP",
|
63 |
+
config_name = "dmpfold_high_quality_models",
|
64 |
+
data_dir = "dmpfold_high_quality_models/data")
|
65 |
+
|
66 |
+
|
67 |
+
|
68 |
+
##### dmpfold_low_quality_models #######
|
69 |
+
dataset = datasets.load_dataset(
|
70 |
+
"parquet",
|
71 |
+
name = "dmpfold_low_quality_models",
|
72 |
+
data_dir = "./intermediate",
|
73 |
+
data_files = {"train" : "dmpfold_low_quality_models.parquet"},
|
74 |
+
cache_dir = "/scratch/maom_root/maom0/maom",
|
75 |
+
split = "train",
|
76 |
+
keep_in_memory = True)
|
77 |
+
|
78 |
+
dataset.push_to_hub(
|
79 |
+
repo_id = "RosettaCommons/MIP",
|
80 |
+
config_name = "dmpfold_low_quality_models",
|
81 |
+
data_dir = "dmpfold_low_quality_models/data")
|
82 |
+
|
83 |
+
##########################
|
84 |
+
## Function Predictions ##
|
85 |
+
##########################
|
86 |
+
|
87 |
+
#### rosetta_high_quality_function_predictions
|
88 |
+
dataset = datasets.load_dataset(
|
89 |
+
"parquet",
|
90 |
+
name = "rosetta_high_quality_function_predictions",
|
91 |
+
data_dir = "./intermediate",
|
92 |
+
data_files = {"train" : "rosetta_high_quality_function_predictions.parquet"},
|
93 |
+
cache_dir = "/scratch/maom_root/maom0/maom",
|
94 |
+
split = "train",
|
95 |
+
keep_in_memory = True)
|
96 |
+
|
97 |
+
dataset.push_to_hub(
|
98 |
+
repo_id = "RosettaCommons/MIP",
|
99 |
+
config_name = "rosetta_high_quality_function_predictions",
|
100 |
+
data_dir = "rosetta_high_quality_function_predictions/data")
|
101 |
+
|
102 |
+
#### rosetta_low_quality_function_predictions
|
103 |
+
dataset = datasets.load_dataset(
|
104 |
+
"parquet",
|
105 |
+
name = "rosetta_low_quality_function_predictions",
|
106 |
+
data_dir = "./intermediate",
|
107 |
+
data_files = {"train" : "rosetta_low_quality_function_predictions.parquet"},
|
108 |
+
cache_dir = "/scratch/maom_root/maom0/maom",
|
109 |
+
split = "train",
|
110 |
+
keep_in_memory = True)
|
111 |
+
|
112 |
+
dataset.push_to_hub(
|
113 |
+
repo_id = "RosettaCommons/MIP",
|
114 |
+
config_name = "rosetta_low_quality_function_predictions",
|
115 |
+
data_dir = "rosetta_low_quality_function_predictions/data")
|
116 |
+
|
117 |
+
|
118 |
+
|
119 |
+
|
120 |
+
#### dmpfold_high_quality_function_predictions
|
121 |
+
dataset = datasets.load_dataset(
|
122 |
+
"parquet",
|
123 |
+
name = "dmpfold_high_quality_function_predictions",
|
124 |
+
data_dir = "./intermediate",
|
125 |
+
data_files = {"train" : "dmpfold_high_quality_function_predictions.parquet"},
|
126 |
+
cache_dir = "/scratch/maom_root/maom0/maom",
|
127 |
+
split = "train",
|
128 |
+
keep_in_memory = True)
|
129 |
+
|
130 |
+
dataset.push_to_hub(
|
131 |
+
repo_id = "RosettaCommons/MIP",
|
132 |
+
config_name = "dmpfold_high_quality_function_predictions",
|
133 |
+
data_dir = "dmpfold_high_quality_function_predictions/data")
|
134 |
+
|
135 |
+
#### dmpfold_low_quality_function_predictions
|
136 |
+
dataset = datasets.load_dataset(
|
137 |
+
"parquet",
|
138 |
+
name = "dmpfold_low_quality_function_predictions",
|
139 |
+
data_dir = "./intermediate",
|
140 |
+
data_files = {"train" : "dmpfold_low_quality_function_predictions.parquet"},
|
141 |
+
cache_dir = "/scratch/maom_root/maom0/maom",
|
142 |
+
split = "train",
|
143 |
+
keep_in_memory = True)
|
144 |
+
|
145 |
+
dataset.push_to_hub(
|
146 |
+
repo_id = "RosettaCommons/MIP",
|
147 |
+
config_name = "dmpfold_low_quality_function_predictions",
|
148 |
+
data_dir = "dmpfold_low_quality_function_predictions/data")
|
149 |
+
|
150 |
+
|
151 |
+
|
152 |
+
|
153 |
+
|
154 |
+
|
155 |
+
|
156 |
+
|
src/03.2_check_uploaded_data.py
ADDED
@@ -0,0 +1,40 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
|
2 |
+
|
3 |
+
import datasets
|
4 |
+
import pyarrow
|
5 |
+
|
6 |
+
def test_local_hf_match(dataset_tag):
|
7 |
+
print(f"For dataset : '{dataset_tag}' testing if local and remote ids match ...")
|
8 |
+
ids_hf = datasets.load_dataset(
|
9 |
+
path = "RosettaCommons/MIP",
|
10 |
+
name = dataset_tag,
|
11 |
+
data_dir = dataset_tag,
|
12 |
+
cache_dir = "/scratch/maom_root/maom0/maom",
|
13 |
+
keep_in_memory = True).data['train'].select(['id']).to_pandas()
|
14 |
+
ids_local = pyarrow.parquet.read_table(
|
15 |
+
source = f"intermediate/{dataset_tag}.parquet",
|
16 |
+
columns = ["id"]).to_pandas()
|
17 |
+
assert ids_local.equals(ids_hf)
|
18 |
+
|
19 |
+
|
20 |
+
test_local_hf_match("rosetta_high_quality_models")
|
21 |
+
test_local_hf_match("rosetta_low_quality_models")
|
22 |
+
test_local_hf_match("dmpfold_high_quality_models")
|
23 |
+
test_local_hf_match("dmpfold_low_quality_models")
|
24 |
+
|
25 |
+
test_local_hf_match("rosetta_high_quality_function_predictions")
|
26 |
+
test_local_hf_match("rosetta_low_quality_function_predictions")
|
27 |
+
test_local_hf_match("dmpfold_high_quality_function_predictions")
|
28 |
+
test_local_hf_match("dmpfold_low_quality_function_predictions")
|
29 |
+
|
30 |
+
|
31 |
+
|
32 |
+
import pandas
|
33 |
+
dataset_long = pyarrow.parquet.read_table(
|
34 |
+
"intermediate/dmpfold_low_quality_function_predictions.parquet").to_pandas()
|
35 |
+
|
36 |
+
dataset_wide = pandas.pivot(
|
37 |
+
dataset_long[["id", "term_id", "Y_hat"]],
|
38 |
+
columns = "term_id",
|
39 |
+
index = "id",
|
40 |
+
values = "Y_hat")
|
src/summarize_map.R
ADDED
@@ -0,0 +1,346 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
|
|
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|
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|
|
|
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|
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|
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|
|
|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
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|
1 |
+
|
2 |
+
#' Diagnostics for messy joins
|
3 |
+
#'
|
4 |
+
#' given a data frame with two ways to group rows,
|
5 |
+
#' summarize and give examples of situations where the mapping is not 1-1
|
6 |
+
#'
|
7 |
+
#' @param x_cols tidyselect specification of a set of columns defining objects
|
8 |
+
#' @param y_cols tidyselect specification of a set of columns defining objects
|
9 |
+
#'
|
10 |
+
#' data <- data.frame(
|
11 |
+
#' x=c(1,2,NA,3,4,5,6,6,6,7,7),
|
12 |
+
#' y=c("a",NA,"c","d","d","d","e","f","g","h","h"))
|
13 |
+
#'
|
14 |
+
#' data |> summarize_map(
|
15 |
+
#' x_cols = x),
|
16 |
+
#' y_cols = y))
|
17 |
+
#' X<-[x]:
|
18 |
+
#' |X|: 7 # number of groups
|
19 |
+
#' |is.na.X|: 1 # number of groups with NA in atleaset 1 col
|
20 |
+
#' range(|x|:X): 1, 3 # size range of groups
|
21 |
+
#' Y<-[y]:
|
22 |
+
#' |Y|: 7
|
23 |
+
#' |is.na.Y|: 1
|
24 |
+
#' range(|y|:Y): 1, 3
|
25 |
+
#' [X U Y]: # grouping by the union of xcols and ycols
|
26 |
+
#' |X U Y|: 8
|
27 |
+
#' |is.na.XUY|: 2
|
28 |
+
#' range(|z|:X U Y): 1, 2
|
29 |
+
#' [X @ Y]:
|
30 |
+
#' |X ~ Y|: 5
|
31 |
+
#' |X:X < Y|, |Y:Y < X|: 1, 1
|
32 |
+
#' |X:X > Y|, |Y:Y < X|: 3, 3
|
33 |
+
#' $is.na.X
|
34 |
+
#' x y
|
35 |
+
#' 1 NA c
|
36 |
+
#'
|
37 |
+
#' $is.na.Y
|
38 |
+
#' x y
|
39 |
+
#' 1 2 <NA>
|
40 |
+
#'
|
41 |
+
#' $dup.XUY
|
42 |
+
#' x y
|
43 |
+
#' 1 7 h
|
44 |
+
#' 2 7 h
|
45 |
+
#'
|
46 |
+
#' $dup.X
|
47 |
+
#' x y
|
48 |
+
#' 1 6 e
|
49 |
+
#' 2 6 f
|
50 |
+
#' 3 6 g
|
51 |
+
#'
|
52 |
+
#' $dup.Y
|
53 |
+
#' y x
|
54 |
+
#' 1 d 3
|
55 |
+
#' 2 d 4
|
56 |
+
#' 3 d 5
|
57 |
+
#' @export
|
58 |
+
summarize_map <- function(
|
59 |
+
data,
|
60 |
+
x_cols,
|
61 |
+
y_cols,
|
62 |
+
n_examples = 4,
|
63 |
+
verbose = FALSE) {
|
64 |
+
|
65 |
+
# convert column selections named vectors of column indices into data
|
66 |
+
x_cols <- tidyselect::eval_select(rlang::enquo(x_cols), data)
|
67 |
+
y_cols <- tidyselect::eval_select(rlang::enquo(y_cols), data)
|
68 |
+
xUy_cols <- union(x_cols, y_cols)
|
69 |
+
names(xUy_cols) <- names(data[xUy_cols])
|
70 |
+
|
71 |
+
if(verbose) {
|
72 |
+
cat("The following is a report of the relationship between two different ways of identifying instances\n")
|
73 |
+
}
|
74 |
+
|
75 |
+
# example rows
|
76 |
+
problems <- list()
|
77 |
+
|
78 |
+
count_xUy <- data |>
|
79 |
+
dplyr::count(dplyr::across(tidyselect::all_of(xUy_cols))) |>
|
80 |
+
dplyr::ungroup()
|
81 |
+
count_x <- count_xUy |>
|
82 |
+
dplyr::count(dplyr::across(tidyselect::all_of(names(x_cols))), name = "size") |>
|
83 |
+
dplyr::ungroup()
|
84 |
+
count_y <- count_xUy |>
|
85 |
+
dplyr::count(dplyr::across(tidyselect::all_of(names(y_cols))), name = "size") |>
|
86 |
+
dplyr::ungroup()
|
87 |
+
|
88 |
+
if (verbose) {
|
89 |
+
cat("\nProperties of X identifiers:\n")
|
90 |
+
}
|
91 |
+
cat("X<-[", paste(names(x_cols), collapse = ", "), "]:\n", sep = "")
|
92 |
+
cat(" |X|: ", count_x |> stats::na.omit(method = "r") |> nrow(), sep = "")
|
93 |
+
|
94 |
+
na_count <- data |>
|
95 |
+
dplyr::select(tidyselect::all_of(x_cols)) |>
|
96 |
+
stats::complete.cases() |>
|
97 |
+
magrittr::not() |>
|
98 |
+
sum()
|
99 |
+
cat(
|
100 |
+
ifelse(
|
101 |
+
na_count == 0,
|
102 |
+
"",
|
103 |
+
paste0(" (", na_count, " NA)")),
|
104 |
+
"\n", sep = "")
|
105 |
+
|
106 |
+
size_dist <- count_x |>
|
107 |
+
stats::na.omit(method = "r") |>
|
108 |
+
dplyr::count(size) |>
|
109 |
+
dplyr::ungroup()
|
110 |
+
if (nrow(size_dist) < 12) {
|
111 |
+
cat(" count*size: ",
|
112 |
+
paste(size_dist$n, size_dist$size, sep = "*", collapse = ", "),
|
113 |
+
"\n", sep = "")
|
114 |
+
} else {
|
115 |
+
top <- 1:6
|
116 |
+
bottom <- (nrow(size_dist) - 6+1):nrow(size_dist)
|
117 |
+
cat(" count*size: ",
|
118 |
+
paste(
|
119 |
+
size_dist$n[top],
|
120 |
+
size_dist$size[top], sep = "*", collapse = ", "),
|
121 |
+
", ... ",
|
122 |
+
paste(
|
123 |
+
size_dist$n[bottom],
|
124 |
+
size_dist$size[bottom], sep = "*", collapse = ", "),
|
125 |
+
"\n", sep="")
|
126 |
+
}
|
127 |
+
|
128 |
+
if (verbose) {
|
129 |
+
cat("\nProperties of the Y identifiers:\n")
|
130 |
+
}
|
131 |
+
cat("Y<-[", paste(names(y_cols), collapse = ", "), "]:\n", sep = "")
|
132 |
+
cat(" |Y|: ", count_y |> stats::na.omit(method = "r") |> nrow(), sep = "")
|
133 |
+
na_count <- data |>
|
134 |
+
dplyr:::select(tidyselect::all_of(y_cols)) |>
|
135 |
+
stats::complete.cases() |>
|
136 |
+
magrittr::not() |>
|
137 |
+
sum()
|
138 |
+
cat(ifelse(na_count == 0, "", paste0(" (", na_count, " NA)")), "\n", sep = "")
|
139 |
+
|
140 |
+
size_dist <- count_y |>
|
141 |
+
stats::na.omit(method = "r") |>
|
142 |
+
dplyr::count(size) |>
|
143 |
+
dplyr::ungroup()
|
144 |
+
if (nrow(size_dist) < 12) {
|
145 |
+
cat(" count*size: ",
|
146 |
+
paste(size_dist$n, size_dist$size, sep = "*", collapse = ", "),
|
147 |
+
"\n", sep = "")
|
148 |
+
} else {
|
149 |
+
top <- 1:6
|
150 |
+
bottom <- (nrow(size_dist) - 6+1):nrow(size_dist)
|
151 |
+
cat(" count*size: ",
|
152 |
+
paste(
|
153 |
+
size_dist$n[top],
|
154 |
+
size_dist$size[top], sep = "*", collapse = ", "),
|
155 |
+
", ... ",
|
156 |
+
paste(
|
157 |
+
size_dist$n[bottom],
|
158 |
+
size_dist$size[bottom], sep = "*", collapse = ", "),
|
159 |
+
"\n", sep="")
|
160 |
+
}
|
161 |
+
|
162 |
+
if (verbose) {
|
163 |
+
cat("\nProperties of the intersection of union of the X and Y identifiers:\n")
|
164 |
+
}
|
165 |
+
cat("[X U Y]:\n")
|
166 |
+
cat(" |X U Y|: ", count_xUy |> stats::na.omit(method = "r") |> nrow(), sep = "")
|
167 |
+
na_count <- data |>
|
168 |
+
dplyr:::select(!!!xUy_cols) |>
|
169 |
+
stats::complete.cases() |>
|
170 |
+
magrittr::not() |>
|
171 |
+
sum()
|
172 |
+
cat(ifelse(na_count == 0, "", paste0(" (", na_count, " NA)")), "\n", sep = "")
|
173 |
+
|
174 |
+
size_dist <- count_xUy |>
|
175 |
+
stats::na.omit(method = "r") |>
|
176 |
+
dplyr::rename(size = n) |>
|
177 |
+
dplyr::count(size) |>
|
178 |
+
dplyr::ungroup()
|
179 |
+
if (nrow(size_dist) < 12) {
|
180 |
+
cat(" count*size: ",
|
181 |
+
paste(size_dist$n, size_dist$size, sep = "*", collapse = ", "),
|
182 |
+
"\n", sep="")
|
183 |
+
} else {
|
184 |
+
top <- 1:6
|
185 |
+
bottom <- (nrow(size_dist) - 6+1):nrow(size_dist)
|
186 |
+
cat(" count*size: ",
|
187 |
+
paste(
|
188 |
+
size_dist$n[top],
|
189 |
+
size_dist$size[top], sep = "*", collapse = ", "),
|
190 |
+
", ... ",
|
191 |
+
paste(
|
192 |
+
size_dist$n[bottom],
|
193 |
+
size_dist$size[bottom], sep = "*", collapse = ", "),
|
194 |
+
"\n", sep = "")
|
195 |
+
}
|
196 |
+
|
197 |
+
|
198 |
+
count_xUy <- count_xUy |> stats::na.omit(method = "r")
|
199 |
+
|
200 |
+
if (verbose) {
|
201 |
+
cat("Properties of the intersection of the X and Y identifiers:\n")
|
202 |
+
}
|
203 |
+
cat("[X @ Y]:\n")
|
204 |
+
if (verbose) {
|
205 |
+
cat(" Number of X and Y identifiers that are 1 to 1:\n")
|
206 |
+
}
|
207 |
+
cat(" |X ~ Y|: ",
|
208 |
+
count_xUy |>
|
209 |
+
dplyr::semi_join(
|
210 |
+
count_x |> dplyr::filter(size == 1),
|
211 |
+
by = names(x_cols)) |>
|
212 |
+
dplyr::semi_join(
|
213 |
+
count_y |> dplyr::filter(size == 1),
|
214 |
+
by = names(y_cols)) |>
|
215 |
+
nrow(),
|
216 |
+
"\n", sep = "")
|
217 |
+
|
218 |
+
if (verbose) {
|
219 |
+
cat(" Number of X and Y identifiers where an X identifier maps to multiple Y identifiers:\n")
|
220 |
+
}
|
221 |
+
cat(
|
222 |
+
" |X:X < Y|, |Y:Y < X|: ",
|
223 |
+
count_xUy |>
|
224 |
+
dplyr::semi_join(
|
225 |
+
count_x |> dplyr::filter(size > 1),
|
226 |
+
by = names(x_cols)) |>
|
227 |
+
nrow(),
|
228 |
+
", ",
|
229 |
+
count_xUy |>
|
230 |
+
dplyr::count(
|
231 |
+
dplyr::across(tidyselect::all_of(names(x_cols))),
|
232 |
+
name = "size") |>
|
233 |
+
dplyr::filter(size > 1) |>
|
234 |
+
nrow(),
|
235 |
+
"\n", sep = "")
|
236 |
+
|
237 |
+
if (verbose) {
|
238 |
+
cat(
|
239 |
+
" Number of X and Y identifiers where a Y identifier maps to ",
|
240 |
+
"multiple X identifiers:\n")
|
241 |
+
}
|
242 |
+
cat(
|
243 |
+
" |X:X > Y|, |Y:Y > X|: ",
|
244 |
+
count_xUy |>
|
245 |
+
dplyr::semi_join(
|
246 |
+
count_y |>
|
247 |
+
dplyr::filter(size > 1),
|
248 |
+
by = names(y_cols)) |>
|
249 |
+
nrow(),
|
250 |
+
", ",
|
251 |
+
count_xUy |>
|
252 |
+
dplyr::count(
|
253 |
+
dplyr::across(tidyselect::all_of(names(y_cols))),
|
254 |
+
name = "size") |>
|
255 |
+
dplyr::filter(size > 1) |>
|
256 |
+
nrow(),
|
257 |
+
"\n", sep = "")
|
258 |
+
|
259 |
+
#is.na.X
|
260 |
+
ex_rows <- data |>
|
261 |
+
dplyr:::select(tidyselect::all_of(x_cols)) |>
|
262 |
+
stats::complete.cases() |>
|
263 |
+
magrittr::not() |>
|
264 |
+
which()
|
265 |
+
if (length(ex_rows)) {
|
266 |
+
if (!is.null(n_examples) && (n_examples < length(ex_rows))) {
|
267 |
+
ex_rows <- ex_rows |> sample(n_examples, replace = FALSE)
|
268 |
+
}
|
269 |
+
problems$is.na.X <- data |>
|
270 |
+
dplyr::slice(ex_rows) |>
|
271 |
+
dplyr::arrange(dplyr::across(tidyselect::all_of(names(x_cols))))
|
272 |
+
}
|
273 |
+
|
274 |
+
#is.na.Y
|
275 |
+
ex_rows <- data |>
|
276 |
+
dplyr:::select(tidyselect::all_of(y_cols)) |>
|
277 |
+
stats::complete.cases() |>
|
278 |
+
magrittr::not() |>
|
279 |
+
which()
|
280 |
+
if (length(ex_rows)) {
|
281 |
+
if (!is.null(n_examples) && (n_examples < length(ex_rows))) {
|
282 |
+
ex_rows <- ex_rows |> sample(n_examples, replace = FALSE)
|
283 |
+
}
|
284 |
+
problems$is.na.Y <- data |>
|
285 |
+
dplyr::slice(ex_rows) |>
|
286 |
+
dplyr::arrange(dplyr::across(tidyselect::all_of(names(y_cols))))
|
287 |
+
}
|
288 |
+
|
289 |
+
#dup.X
|
290 |
+
dup.X <- count_xUy |>
|
291 |
+
dplyr::filter(n == 1) |>
|
292 |
+
dplyr::count(
|
293 |
+
dplyr::across(tidyselect::all_of(names(x_cols))),
|
294 |
+
name = "size") |>
|
295 |
+
dplyr::filter(size > 1) |>
|
296 |
+
dplyr::ungroup() |>
|
297 |
+
dplyr:::select(-size)
|
298 |
+
if (nrow(dup.X) > 1) {
|
299 |
+
if (!is.null(n_examples) && (n_examples < nrow(dup.X))) {
|
300 |
+
dup.X <- dup.X |> dplyr::sample_n(n_examples, replace = FALSE)
|
301 |
+
}
|
302 |
+
problems$dup.X <- dup.X |>
|
303 |
+
dplyr::left_join(data, by = names(x_cols)) |>
|
304 |
+
dplyr::arrange(dplyr::across(tidyselect::all_of(names(x_cols))))
|
305 |
+
}
|
306 |
+
|
307 |
+
#dup.Y
|
308 |
+
dup.Y <- count_xUy |>
|
309 |
+
dplyr::filter(n == 1) |>
|
310 |
+
dplyr::count(
|
311 |
+
dplyr::across(tidyselect::all_of(names(y_cols))),
|
312 |
+
name = "size") |>
|
313 |
+
dplyr::filter(size > 1) |>
|
314 |
+
dplyr::ungroup() |>
|
315 |
+
dplyr:::select(-size)
|
316 |
+
if (nrow(dup.Y) > 1) {
|
317 |
+
if (!is.null(n_examples) && (n_examples < nrow(dup.Y))) {
|
318 |
+
dup.Y <- dup.Y |> dplyr::sample_n(n_examples, replace = FALSE)
|
319 |
+
}
|
320 |
+
problems$dup.Y <- dup.Y |>
|
321 |
+
dplyr::left_join(data, by = names(ycols)) |>
|
322 |
+
dplyr::arrange(dplyr::across(tidyselect::all_of(names(y_cols))))
|
323 |
+
}
|
324 |
+
|
325 |
+
#dup.XUY
|
326 |
+
dup.XUY <- count_xUy |>
|
327 |
+
dplyr::filter(n > 1) |>
|
328 |
+
dplyr:::select(-n)
|
329 |
+
if (nrow(dup.XUY) > 1) {
|
330 |
+
if (!is.null(n_examples) && (n_examples < nrow(dup.XUY))) {
|
331 |
+
dup.XUY <- dup.XUY |> dplyr::sample_n(n_examples, replace = FALSE)
|
332 |
+
}
|
333 |
+
problems$dup.XUY <- dup.XUY |>
|
334 |
+
dplyr::left_join(data, by = names(xUy_cols)) |>
|
335 |
+
dplyr::arrange(dplyr::across(tidyselect::all_of(names(xUy_cols))))
|
336 |
+
}
|
337 |
+
if (verbose) {
|
338 |
+
cat("Returned instances where:\n")
|
339 |
+
cat("\tis.na.X: The X identifier is NA\n")
|
340 |
+
cat("\tis.na.Y: The Y identifier is NA\n")
|
341 |
+
cat("\tdup.X: The X identifier is not unique\n")
|
342 |
+
cat("\tdup.Y: The Y identifier is not unique\n")
|
343 |
+
cat("\tdup.XUY: The X and Y identifiers together are not unique\n")
|
344 |
+
}
|
345 |
+
problems
|
346 |
+
}
|