| """Check submission.csv before you submit: the same checks the challenge Space runs. |
| |
| Usage: python code/check_submission.py [path/to/submission.csv] |
| Needs pandas and huggingface_hub (to read the list of cities from the training dataset). |
| """ |
|
|
| import sys |
|
|
| import pandas as pd |
|
|
| TRAIN = "hf://datasets/CMU-OSPO/protostar-climate-train-to-2018/city_info" |
| COLUMNS = ["city_id", "year", "metric", "predicted_value"] |
| YEARS = set(range(2019, 2026)) |
| METRICS = {"annual_avg_temp", "total_precip_in", "hottest_day_temp", "cdd"} |
|
|
| path = sys.argv[1] if len(sys.argv) > 1 else "submission.csv" |
| cities = set(pd.read_parquet(TRAIN)["ID"]) |
| df = pd.read_csv(path, dtype={"city_id": str, "metric": str}) |
|
|
| if list(df.columns) != COLUMNS: |
| sys.exit(f"Columns must be exactly: {','.join(COLUMNS)} (found: {','.join(df.columns)})") |
|
|
| year = pd.to_numeric(df["year"], errors="coerce") |
| value = pd.to_numeric(df["predicted_value"], errors="coerce") |
| checks = { |
| "unknown city_id": ~df["city_id"].isin(cities), |
| "year outside 2019-2025": ~year.isin(YEARS), |
| "unknown metric": ~df["metric"].isin(METRICS), |
| "missing or non-numeric predicted_value": value.isna() | value.abs().eq(float("inf")), |
| "duplicate row": df.duplicated(["city_id", "year", "metric"]), |
| } |
| problems = [f"{mask.sum()} rows with {what}, e.g. line {mask.idxmax() + 2}" for what, mask in checks.items() if mask.any()] |
| expected = len(cities) * len(YEARS) * len(METRICS) |
| if not problems and len(df) != expected: |
| problems.append(f"{len(df)} rows; expected {expected}") |
|
|
| if problems: |
| sys.exit("Not ready:\n- " + "\n- ".join(problems)) |
| print(f"OK: {path} has all {expected} forecasts and can be scored.") |
|
|
