src/populate.py

import pandas as pd
from datasets import get_dataset_config_names, load_dataset
from datasets.exceptions import DatasetNotFoundError
from tqdm.auto import tqdm

from src.display.utils import AutoEvalColumn
from src.envs import TOKEN
from src.logger import get_logger

logger = get_logger(__name__)


def get_leaderboard_df(results_dataset_name: str) -> pd.DataFrame:
    """
    @brief Creates a dataframe from all the individual experiment results.
    """

    empty_df = pd.DataFrame(
        columns=[
            AutoEvalColumn.system.name,
            AutoEvalColumn.organization.name,
            AutoEvalColumn.success_rate_overall.name,
            AutoEvalColumn.success_rate_tier1.name,
            AutoEvalColumn.success_rate_tier2.name,
            AutoEvalColumn.submitted_on.name,
        ]
    )

    try:
        configs = get_dataset_config_names(
            results_dataset_name,
            token=TOKEN,
        )
    except (DatasetNotFoundError, FileNotFoundError):

        # Return an empty DataFrame with expected columns
        logger.warning("Failed to load configuration", exc_info=True)
        return empty_df

    if configs == ["default"]:
        logger.info("Dataset has only default config — treating as empty")
        return empty_df

    rows = []
    for submission_id in tqdm(
        configs,
        total=len(configs),
        desc="Processing Submission Results",
    ):
        submission_ds = load_dataset(
            results_dataset_name,
            submission_id,
            split="train",
            token=TOKEN,
        )
        submission_df = pd.DataFrame(submission_ds)

        if submission_df.empty or "did_pass" not in submission_df.columns or submission_df.did_pass.isna().any():
            logger.warning(f"Skipping {submission_id} due to invalid did_pass values")
            continue

        assert submission_df["tier"].isin([1, 2]).all(), "Invalid tier values found in submission_df"
        success_rate = 100 * submission_df["did_pass"].mean()
        tier1_success_rate = 100 * submission_df[submission_df["tier"] == 1]["did_pass"].mean()
        tier2_success_rate = 100 * submission_df[submission_df["tier"] == 2]["did_pass"].mean()
        first_row = submission_df.iloc[0]

        rows.append(
            {
                AutoEvalColumn.system.name: first_row["system_name"],
                AutoEvalColumn.organization.name: first_row["organization"],
                AutoEvalColumn.success_rate_overall.name: success_rate,
                AutoEvalColumn.success_rate_tier1.name: tier1_success_rate,
                AutoEvalColumn.success_rate_tier2.name: tier2_success_rate,
                AutoEvalColumn.submitted_on.name: pd.to_datetime(first_row["submission_ts"]).strftime("%Y-%m-%d %H:%M"),
            }
        )

    full_df = pd.DataFrame(rows)

    logger.info(f"Loaded results df with {len(full_df)} entries")

    # Keep only the latest entry per unique (System Name, System Type, Organization) triplet
    final_df = (
        full_df.sort_values("Submitted On", ascending=False)
        .drop_duplicates(subset=[AutoEvalColumn.system.name, AutoEvalColumn.organization.name], keep="first")
        .sort_values(by=[AutoEvalColumn.success_rate_overall.name], ascending=False)
        .reset_index(drop=True)
    )

    cols_to_round = [
        AutoEvalColumn.success_rate_overall.name,
        AutoEvalColumn.success_rate_tier1.name,
        AutoEvalColumn.success_rate_tier2.name,
    ]
    final_df[cols_to_round] = final_df[cols_to_round].round(decimals=2)

    return final_df