| from typing import Any, Callable, Dict, List, Optional |
| import abc |
| from .decorators import EntryPoint |
| from .registry import ParamRegistry |
|
|
| class BaseOptimizer(abc.ABC): |
| |
| |
| |
| |
| |
| |
|
|
| def __init__( |
| self, |
| registry: ParamRegistry, |
| program: Callable[..., Dict[str, Any]] = None, |
| evaluator: Optional[Callable[..., Any]] = None, |
| ): |
| """ |
| Abstract base class for optimization routines. |
| |
| Parameters: |
| - registry (ParamRegistry): parameter access layer |
| - evaluator (Callable): function that evaluates the result dict and returns a float |
| """ |
| self.program = program |
| self.registry = registry |
| self.program = program |
| self.evaluator = evaluator |
| |
| def get_param(self, name: str) -> Any: |
| """Retrieve the current value of a parameter by name.""" |
| return self.registry.get(name) |
|
|
| def set_param(self, name: str, value: Any): |
| """Set the value of a parameter by name.""" |
| self.registry.set(name, value) |
|
|
| def param_names(self) -> List[str]: |
| """Return the list of all registered parameter names.""" |
| return self.registry.names() |
| |
| def get_current_cfg(self) -> Dict[str, Any]: |
| """Return current config as a dictionary.""" |
| return {name: self.get_param(name) for name in self.param_names()} |
|
|
| def apply_cfg(self, cfg: Dict[str, Any]): |
| """Apply a configuration dictionary to the registered parameters.""" |
| for k, v in cfg.items(): |
| if k in self.registry.fields: |
| self.registry.set(k, v) |
|
|
| @abc.abstractmethod |
| def optimize(self): |
| """ |
| Abstract optimization loop. Should be implemented by subclasses. |
| |
| Parameters: |
| - program_entry: callable that runs the program and returns output dict |
| |
| Returns: |
| - (best_cfg, history): best config found and full search history |
| """ |
| if self.program is None: |
| self.program = EntryPoint.get_entry() |
| if self.program is None: |
| raise RuntimeError("No entry function provided or registered.") |
| print(f"Starting optimization from entry: {self.program.__name__}") |
| raise NotImplementedError |
