competitions/compute_metrics.py

from functools import partial

import pandas as pd
from huggingface_hub import hf_hub_download
from sklearn import metrics


def compute_metrics(params):
    solution_file = hf_hub_download(
        repo_id=params.competition_id,
        filename="solution.csv",
        token=params.token,
        repo_type="dataset",
    )

    solution_df = pd.read_csv(solution_file)

    submission_filename = f"submissions/{params.team_id}-{params.submission_id}.csv"
    submission_file = hf_hub_download(
        repo_id=params.competition_id,
        filename=submission_filename,
        token=params.token,
        repo_type="dataset",
    )
    submission_df = pd.read_csv(submission_file)

    public_ids = solution_df[solution_df.split == "public"][params.submission_id_col].values
    private_ids = solution_df[solution_df.split == "private"][params.submission_id_col].values

    public_solution_df = solution_df[solution_df[params.submission_id_col].isin(public_ids)]
    public_submission_df = submission_df[submission_df[params.submission_id_col].isin(public_ids)]

    private_solution_df = solution_df[solution_df[params.submission_id_col].isin(private_ids)]
    private_submission_df = submission_df[submission_df[params.submission_id_col].isin(private_ids)]

    public_solution_df = public_solution_df.sort_values(params.submission_id_col).reset_index(drop=True)
    public_submission_df = public_submission_df.sort_values(params.submission_id_col).reset_index(drop=True)

    private_solution_df = private_solution_df.sort_values(params.submission_id_col).reset_index(drop=True)
    private_submission_df = private_submission_df.sort_values(params.submission_id_col).reset_index(drop=True)

    if params.metric == "f1-macro":
        _metric = partial(metrics.f1_score, average="macro")
        target_cols = [col for col in solution_df.columns if col not in [params.submission_id_col, "split"]]
        public_score = _metric(public_solution_df[target_cols], public_submission_df[target_cols])
        private_score = _metric(private_solution_df[target_cols], private_submission_df[target_cols])
    else:
        _metric = getattr(metrics, params.metric)
        target_cols = [col for col in solution_df.columns if col not in [params.submission_id_col, "split"]]
        public_score = _metric(public_solution_df[target_cols], public_submission_df[target_cols])
        private_score = _metric(private_solution_df[target_cols], private_submission_df[target_cols])

    # scores can also be dictionaries for multiple metrics
    evaluation = {
        "public_score": public_score,
        "private_score": private_score,
    }
    return evaluation