rpotter6298
c8e3016fd6
- Implement `sigen_daemon.py` to poll Sigenergy plant metrics and store snapshots. - Create `web_daemon.py` for serving a web interface with various endpoints. - Add debug scripts: - `debug_duplicates.py` to find duplicate target times in forecast data. - `debug_energy_forecast.py` to print baseline energy forecast curves. - `debug_oracle_evaluations.py` to run the oracle evaluator. - `debug_sigen.py` to inspect stored Sigenergy plant snapshots. - `debug_weather.py` to trace resolved truth data. - `modbus_test.py` for exploring Sigenergy plants or inverters over Modbus TCP. - Introduce `oracle_evaluator.py` for evaluating stored oracle predictions against actuals. - Add TCN training scripts in `tcn` directory for training usage sequence models.
103 lines
3.1 KiB
Python
103 lines
3.1 KiB
Python
from __future__ import annotations
|
|
|
|
import argparse
|
|
from datetime import timedelta
|
|
|
|
from gibil.classes.env_loader import EnvLoader
|
|
from gibil.classes.oracle.store import OracleStore
|
|
|
|
|
|
def main() -> None:
|
|
EnvLoader().load()
|
|
args = parse_args()
|
|
store = OracleStore.from_env()
|
|
|
|
if args.evaluate:
|
|
evaluated_count = store.evaluate_due_forecasts(
|
|
actual_window=timedelta(minutes=args.actual_window_minutes),
|
|
lookback=timedelta(hours=args.lookback_hours),
|
|
limit=args.limit,
|
|
)
|
|
print(f"evaluated_oracle_forecasts={evaluated_count}")
|
|
|
|
if args.summary:
|
|
rows = store.load_evaluation_summary(
|
|
lookback=timedelta(hours=args.lookback_hours)
|
|
)
|
|
print(f"oracle_evaluation_summary_rows={len(rows)}")
|
|
for row in rows:
|
|
print(
|
|
" ".join(
|
|
[
|
|
f"kind={row['kind']}",
|
|
f"model={row['model_version']}",
|
|
f"horizon={row.get('horizon_label') or _format_horizon(row)}",
|
|
f"n={row['evaluated_count']}",
|
|
f"bias={_format_w(row['mean_error_w'])}",
|
|
f"mae={_format_w(row['mean_absolute_error_w'])}",
|
|
f"median_ae={_format_w(row['median_absolute_error_w'])}",
|
|
f"mape={_format_pct(row['mean_absolute_pct_error'])}",
|
|
f"coverage={_format_pct(row['interval_coverage'])}",
|
|
]
|
|
)
|
|
)
|
|
|
|
|
|
def parse_args() -> argparse.Namespace:
|
|
parser = argparse.ArgumentParser(
|
|
description="Evaluate stored Astrape oracle predictions against Sigen actuals."
|
|
)
|
|
parser.add_argument(
|
|
"--evaluate",
|
|
action="store_true",
|
|
help="Evaluate stored predictions whose target time has passed.",
|
|
)
|
|
parser.add_argument(
|
|
"--summary",
|
|
action="store_true",
|
|
help="Print evaluation quality by kind/model/horizon bucket.",
|
|
)
|
|
parser.add_argument(
|
|
"--actual-window-minutes",
|
|
type=float,
|
|
default=5,
|
|
help="Minutes after each target timestamp to average as realized actuals.",
|
|
)
|
|
parser.add_argument(
|
|
"--lookback-hours",
|
|
type=float,
|
|
default=168,
|
|
help="Only evaluate/summarize predictions with target times this recent.",
|
|
)
|
|
parser.add_argument(
|
|
"--limit",
|
|
type=int,
|
|
default=1000,
|
|
help="Maximum unevaluated predictions to process.",
|
|
)
|
|
args = parser.parse_args()
|
|
if not args.evaluate and not args.summary:
|
|
args.evaluate = True
|
|
args.summary = True
|
|
return args
|
|
|
|
|
|
def _format_w(value: object) -> str:
|
|
if value is None:
|
|
return "n/a"
|
|
return f"{float(value):.0f}W"
|
|
|
|
|
|
def _format_horizon(row: dict[str, object]) -> str:
|
|
return f"{row['min_horizon_minutes']}-{row['max_horizon_minutes']}m"
|
|
|
|
|
|
def _format_pct(value: object) -> str:
|
|
if value is None:
|
|
return "n/a"
|
|
return f"{float(value) * 100:.1f}%"
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|