Add ops dashboards for training data and model coverage
This commit is contained in:
2569718930@qq.com
@@ -13,6 +13,8 @@ from src.database.runtime_state import (
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ProbabilitySnapshotRepository,
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STATE_STORAGE_FILE,
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STATE_STORAGE_SQLITE,
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TrainingFeatureRecordRepository,
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TruthRecordRepository,
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get_state_storage_mode,
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)
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@@ -288,23 +290,93 @@ def build_training_samples(
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snapshot_index: Optional[Dict[Tuple[str, str], Dict[str, Any]]] = None,
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) -> List[Dict[str, Any]]:
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if isinstance(history_data, dict):
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data = history_data
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runtime_history = history_data
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elif get_state_storage_mode() == STATE_STORAGE_SQLITE:
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data = DailyRecordRepository().load_all()
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runtime_history = DailyRecordRepository().load_all()
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else:
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data = load_history(_history_file_path())
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runtime_history = load_history(_history_file_path())
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if get_state_storage_mode() == STATE_STORAGE_SQLITE:
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truth_history = TruthRecordRepository().load_all()
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training_feature_history = TrainingFeatureRecordRepository().load_all()
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else:
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truth_history = runtime_history
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training_feature_history = {}
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snapshots = snapshot_index if isinstance(snapshot_index, dict) else load_snapshot_index()
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samples: List[Dict[str, Any]] = []
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excluded_keys: set[tuple[str, str]] = set()
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for city_name, city_records in (data or {}).items():
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for (city_name, date_str), snapshot in (snapshots or {}).items():
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if not isinstance(snapshot, dict):
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continue
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truth_row = ((truth_history.get(city_name) or {}).get(str(date_str)) or {})
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target = _sf(truth_row.get("actual_high"))
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if target is None:
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runtime_record = ((runtime_history.get(city_name) or {}).get(str(date_str)) or {})
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target = _sf(runtime_record.get("actual_high"))
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if target is None:
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continue
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observation = snapshot.get("observation") if isinstance(snapshot.get("observation"), dict) else {}
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current_forecasts = snapshot.get("multi_model") if isinstance(snapshot.get("multi_model"), dict) else {}
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local_hour = _sf(observation.get("local_hour"))
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if local_hour is None:
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timestamp = _parse_timestamp(snapshot.get("timestamp"))
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local_hour = float(timestamp.hour) if timestamp is not None else 12.0
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feature_map, meta = build_runtime_feature_map(
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city_name=city_name,
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current_forecasts=current_forecasts,
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deb_prediction=_sf(snapshot.get("deb_prediction")) or _sf(snapshot.get("raw_mu")),
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current_temp=_sf(observation.get("current_temp")),
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max_so_far=_sf(snapshot.get("max_so_far")),
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humidity=_sf(observation.get("humidity")),
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wind_speed_kt=_sf(observation.get("wind_speed_kt")),
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visibility_mi=_sf(observation.get("visibility_mi")),
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local_hour=int(local_hour),
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local_date=str(date_str),
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peak_status=str(snapshot.get("peak_status") or "before"),
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history_data=truth_history,
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)
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if not feature_map:
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continue
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samples.append(
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{
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"city": _normalized_city_key(city_name),
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"date": str(date_str),
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"target": float(target),
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"features": feature_map,
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"vector": _features_to_vector(feature_map),
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"history_count": int(meta.get("history_count") or 0),
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"deb_prediction": _sf(snapshot.get("deb_prediction")) or _sf(snapshot.get("raw_mu")),
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"forecasts": {
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key: _sf(value)
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for key, value in current_forecasts.items()
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if _sf(value) is not None
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},
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"sample_source": "snapshot",
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"settlement_source": truth_row.get("settlement_source"),
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"settlement_station_code": truth_row.get("settlement_station_code"),
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"truth_version": truth_row.get("truth_version"),
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"truth_updated_by": truth_row.get("updated_by"),
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"truth_updated_at": truth_row.get("truth_updated_at"),
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}
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)
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excluded_keys.add((_normalized_city_key(city_name), str(date_str)))
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training_source = training_feature_history or runtime_history or {}
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for city_name, city_records in training_source.items():
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if not isinstance(city_records, dict):
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continue
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ordered_dates = sorted(city_records.keys())
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for date_str in ordered_dates:
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normalized_city = _normalized_city_key(city_name)
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if (normalized_city, str(date_str)) in excluded_keys:
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continue
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record = city_records.get(date_str)
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if not isinstance(record, dict):
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continue
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target = _sf(record.get("actual_high"))
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truth_row = ((truth_history.get(normalized_city) or {}).get(str(date_str)) or {})
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target = _sf(truth_row.get("actual_high"))
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if target is None:
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target = _sf(((runtime_history.get(normalized_city) or {}).get(str(date_str)) or {}).get("actual_high"))
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forecasts = record.get("forecasts") if isinstance(record.get("forecasts"), dict) else {}
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if target is None or not forecasts:
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continue
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@@ -321,7 +393,7 @@ def build_training_samples(
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local_hour=12,
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local_date=str(date_str),
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peak_status="before",
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history_data=data,
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history_data=truth_history,
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)
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if not feature_map:
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continue
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@@ -361,6 +433,12 @@ def build_training_samples(
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for key, value in forecasts.items()
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if _sf(value) is not None
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},
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"sample_source": "daily_record",
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"settlement_source": truth_row.get("settlement_source"),
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"settlement_station_code": truth_row.get("settlement_station_code"),
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"truth_version": truth_row.get("truth_version"),
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"truth_updated_by": truth_row.get("updated_by"),
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"truth_updated_at": truth_row.get("truth_updated_at"),
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}
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)
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samples.sort(key=lambda row: (row["date"], row["city"]))
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