second

genetic_calibration.py

@@ -5,11 +5,14 @@ from typing import List, Tuple
 
 from simulation_model import SimulationModel
 
+# set year
+year = json.load(open('year.json', 'r', encoding='utf-8'))['year']
+filename = f"{year}"
 
 # Decision variables:
 #  - month1, month2, month3, month4 efficiencies (hour/blade)
 #  - factory-specific new-factory factors (from data/factory_mapping.json)
-FACTORY_MAPPING = json.loads(Path("data/factory_mapping.json").read_text(encoding="utf-8"))
+FACTORY_MAPPING = json.loads(Path(f"data/{filename}/factory_mapping.json").read_text(encoding="utf-8"))
 FACTORY_IDS = list(FACTORY_MAPPING.values())
 
 # Bounds for genes: (min, max)
@@ -17,7 +20,7 @@ MONTH_BOUNDS = (30.0, 250.0)
 FACTOR_BOUNDS = (0.8, 3.0)
 
 POP_SIZE = 20
-GENERATIONS = 200
+GENERATIONS = 2
 MUTATION_RATE = 0.2
 MUTATION_STD = 5.0  # hours for months; factors mutate separately
 
@@ -73,7 +76,7 @@ def crossover(p1: List[float], p2: List[float]) -> Tuple[List[float], List[float
 def init_population() -> List[List[float]]:
     pop = []
     # Warm start from best params if available
-    best_path = Path("data") / "ga_best_params.json"
+    best_path = Path("data") / filename / "ga_best_params.json"
     seed_indiv = None
     if best_path.exists():
         try:
@@ -140,8 +143,7 @@ def main():
     for fid, val in zip(FACTORY_IDS, best_genes[4:]):
         result[f"factor_{fid}"] = val
 
-    out_path = Path("output") / "ga_best_params.json"
-    data_path = Path("data") / "ga_best_params.json"
+    data_path = Path("data") / filename / "ga_best_params.json"
     data_path.parent.mkdir(exist_ok=True)
     # Only overwrite if better
     if data_path.exists():