From 4ab3087b803b356572d967e9cdee549e1ef7975d Mon Sep 17 00:00:00 2001 From: Jeroen Date: Sun, 10 Mar 2024 13:30:55 +0100 Subject: [PATCH] Added support for R --- .github/workflows/checks.yaml | 5 ++ models/model.joblib => model.joblib | Bin model.rds | Bin 0 -> 122918 bytes packages.R | 3 + Dockerfile => python.Dockerfile | 2 +- r.Dockerfile | 11 +++ run.R | 113 ++++++++++++++++++++++++++++ settings.json | 1 + submission.R | 54 +++++++++++++ submission.py | 4 +- 10 files changed, 190 insertions(+), 3 deletions(-) rename models/model.joblib => model.joblib (100%) create mode 100644 model.rds create mode 100644 packages.R rename Dockerfile => python.Dockerfile (92%) create mode 100644 r.Dockerfile create mode 100644 run.R create mode 100644 settings.json create mode 100644 submission.R diff --git a/.github/workflows/checks.yaml b/.github/workflows/checks.yaml index 48b13d0c..92bc9152 100644 --- a/.github/workflows/checks.yaml +++ b/.github/workflows/checks.yaml @@ -23,6 +23,10 @@ jobs: with: fetch-depth: 0 + - name: Determine Dockerfile + id: dockerfile + run: cat settings.json | jq -r .dockerfile | awk '{printf "filename=%s", $0}' >> "$GITHUB_OUTPUT" + - name: Set up Docker Buildx uses: docker/setup-buildx-action@v2 @@ -30,6 +34,7 @@ jobs: uses: docker/build-push-action@v4 with: context: . + file: ${{steps.dockerfile.outputs.filename}} tags: eyra-rank:latest load: true diff --git a/models/model.joblib b/model.joblib similarity index 100% rename from models/model.joblib rename to model.joblib diff --git a/model.rds b/model.rds new file mode 100644 index 0000000000000000000000000000000000000000..aa96beb4bade77fa13b2c2c0b56edd7a245df186 GIT binary patch literal 122918 zcma&NWmFX07q}}W(jrQCmmo;jkb-n~qjY!9NOvhAIVjy-(jX-ViryrEzD7+Nm0dAYE0WyuSu=n=yBf*W1=lkw(q)v?tzo-_orUxbbda76NsY?6shM>c+CT<+o^ ztcLf?8Mz3cA#Ps?eTKtIAh)WV^Xr!5od&X~nwR9GJjDJ@Y;S{u4i@S-`Skn^Jt0TV z$4~uoV@U?WuxC86gk<475sv>3DA(LgsEkXA8wGlQl~t18GTlRZM~6bd2i{|+gDIS- 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+install.packages(c("dplyr","data.table","tidyr"), repos="https://cran.r-project.org") diff --git a/Dockerfile b/python.Dockerfile similarity index 92% rename from Dockerfile rename to python.Dockerfile index beed213e..ef32f759 100644 --- a/Dockerfile +++ b/python.Dockerfile @@ -7,7 +7,7 @@ RUN mkdir /app COPY data /data COPY *.py / -COPY models /models +COPY *.joblib / ENTRYPOINT ["conda", "run", "-n", "eyra-rank", "python", "/run.py"] CMD ["predict", "/data/fake_data.csv"] \ No newline at end of file diff --git a/r.Dockerfile b/r.Dockerfile new file mode 100644 index 00000000..7323e5a3 --- /dev/null +++ b/r.Dockerfile @@ -0,0 +1,11 @@ +FROM r-base:4.3.3 + + +COPY data /data +COPY *.R / +COPY *.rds / + +RUN Rscript packages.R + +ENTRYPOINT ["Rscript", "run.R"] +CMD ["predict", "/data/fake_data.csv"] \ No newline at end of file diff --git a/run.R b/run.R new file mode 100644 index 00000000..45513821 --- /dev/null +++ b/run.R @@ -0,0 +1,113 @@ +#!/usr/bin/env Rscript + +library(dplyr) +library(tidyr) + + +source("submission.R") + +print_usage <- function() { + cat("Usage:\n") + cat(" Rscript script.R predict INPUT_FILE [--output OUTPUT_FILE]\n") + cat(" Rscript script.R score --prediction PREDICTION_FILE --ground_truth GROUND_TRUTH_FILE [--output OUTPUT_FILE]\n") +} + +parse_arguments <- function() { + args <- list() + command_args <- commandArgs(trailingOnly = TRUE) + if (length(command_args) > 0) { + args$command <- command_args[1] + + if (is.null(args$command)) { + stop("Error: No command provided.") + } + + if (args$command == "predict") { + args$input <- commandArgs(trailingOnly = TRUE)[2] + args$output <- get_argument("--output") + } else if (args$command == "score") { + args$prediction <- get_argument("--prediction") + args$ground_truth <- get_argument("--ground_truth") + args$output <- get_argument("--output") + } + } else { + stop("Error: No command provided. Run the script with predict or score.") + } + + return(args) +} + +get_argument <- function(arg_name) { + if (arg_name %in% commandArgs(trailingOnly = TRUE)) { + arg_index <- which(commandArgs(trailingOnly = TRUE) == arg_name) + if (arg_index < length(commandArgs(trailingOnly = TRUE))) { + return(commandArgs(trailingOnly = TRUE)[arg_index + 1]) + } + } + return(NULL) +} + +parse_and_run_predict <- function(args) { + if (is.null(args$input)) { + stop("Error: Please provide --input argument for prediction.") + } + + cat("Processing input data for prediction from:", args$input, "\n") + if (!is.null(args$output)) { + cat("Output will be saved to:", args$output, "\n") + } + run_predict(args$input, args$output) +} + +run_score <- function(args) { + if (is.null(args$prediction) || is.null(args$ground_truth)) { + stop("Error: Please provide --prediction and --ground_truth arguments for scoring.") + } + + cat("Scoring predictions from:", args$prediction, "\n") + cat("Ground truth data from:", args$ground_truth, "\n") + if (!is.null(args$output)) { + cat("Evaluation score will be saved to:", args$output, "\n") + } + # Call your submission function for scoring here +} + +run_predict <- function(input_path, output=NULL) { + if (is.null(output)) { + output <- stdout() + } + + + # Read data from input file + df <- read.csv(input_path, encoding="latin1") + + # Clean the data + df <- clean_df(df) # Assuming clean_df is a function in the submission package + + # Make predictions + predictions <- predict_outcomes(df) # Assuming predict_outcomes is a function in the submission package + + # Check if predictions have the required format + stopifnot(ncol(predictions) == 2, + all(c("nomem_encr", "prediction") %in% colnames(predictions))) + + # Write predictions to output file + write.csv(predictions, output, row.names = FALSE) +} + + +# Main function +main <- function() { + args <- parse_arguments() + + if (args$command == "predict") { + parse_and_run_predict(args) + } else if (args$command == "score") { + run_score(args) + } else { + stop("Error: Invalid command. Use 'predict' or 'score'.") + } +} + +# Call main function +main() diff --git a/settings.json b/settings.json new file mode 100644 index 00000000..d5e8670d --- /dev/null +++ b/settings.json @@ -0,0 +1 @@ +{"dockerfile": "python.Dockerfile"} \ No newline at end of file diff --git a/submission.R b/submission.R new file mode 100644 index 00000000..a77db063 --- /dev/null +++ b/submission.R @@ -0,0 +1,54 @@ +# edit the preprocessing function using the code you used for preprocesing the train data +clean_df <- function(df){ + # Process the input data to feed the model + + ## Selecting variables + keepcols = c('nomem_encr', 'birthyear_bg', 'gender_bg', 'burgstat_2020','oplmet_2020', 'cf20m454') + + df <- df %>% select(all_of(keepcols)) + + # imputing missing values with mode (for factors) or median (for interval variables) + my_mode <- function(x) { + x <-x[!is.na(x)] + ux <- unique(x) + tab <- tabulate(match(x, ux)) + mode <- ux[tab == max(tab)] + ifelse(length(mode) > 1, sample(mode, 1), mode) + } + + df <- df %>% + mutate(across(c(gender_bg, burgstat_2020, oplmet_2020, cf20m454), ~replace_na(., my_mode(.))), + across(c(gender_bg, burgstat_2020, oplmet_2020, cf20m454), as.factor), + across(birthyear_bg, ~replace_na(., median(., na.rm=TRUE)))) + + return(df) +} + + + +# if necessary, edit the function so it returns predicted classes (1/0), not probabilities +predict_outcomes <- function(df, model_path="./model.rds"){ + # preprocess the holdout data + df <- clean_df(df) + ids <- select(df, nomem_encr) + + # Load the model + model <- readRDS(model_path) + + # !if necessary, make edits to produce predicted classes + # E.g. if you used glm() function to train a model, add 'type="response"' to get probabilities + pred <- predict(model, df, type="response") + #and then transform them into predicted classes + pred <- ifelse(pred>0.5, 1, 0) + + # adding prediction column to id column + ids$prediction<- pred + + return(ids) +} + + +# ######## do not edit this ############################ +# df <- read.csv(args[1]) +# predictions <- predict_holdout(df) +# write.csv(predictions,"predictions.csv", row.names = FALSE) diff --git a/submission.py b/submission.py index 2d5c9abb..77ada969 100644 --- a/submission.py +++ b/submission.py @@ -65,8 +65,8 @@ def predict_outcomes(df): df = df.loc[:, keepcols] - # Load your trained model from the models directory - model_path = os.path.join(os.path.dirname(__file__), "models", "model.joblib") + # Load your trained model + model_path = os.path.join(os.path.dirname(__file__), "model.joblib") model = load(model_path) # Use your trained model for prediction