file cleanup.

2025-09-28 19:12:51 +02:00
parent 90e67652ab
commit d742a0c0ce
28 changed files with 36 additions and 36 deletions
--- a/experiments/stochastic_hillclimber/trainer.py
+++ b/experiments/stochastic_hillclimber/trainer.py
@@ -0,0 +1,112 @@
+import asyncio
+import os
+import time
+from typing import Any, Dict, List, Tuple, Optional
+
+import morphology
+from genotype import construct, save_genotype, print_genotype
+from exoself import Exoself
+
+
+class Trainer:
+    def __init__(
+            self,
+            morphology_spec=morphology,
+            hidden_layer_densities: List[int] = None,
+            *,
+            max_attempts: int = 5,
+            eval_limit: float = float("inf"),
+            fitness_target: float = float("inf"),
+            experimental_file: Optional[str] = "experimental.json",
+            best_file: Optional[str] = "best.json",
+            exoself_steps_per_eval: int = 0,
+    ):
+        self.morphology_spec = morphology_spec
+        self.hds = hidden_layer_densities or []
+        self.max_attempts = max_attempts
+        self.eval_limit = eval_limit
+        self.fitness_target = fitness_target
+        self.experimental_file = experimental_file
+        self.best_file = best_file
+        self.exoself_steps_per_eval = exoself_steps_per_eval
+
+        self.best_fitness = float("-inf")
+        self.best_genotype: Optional[Dict[str, Any]] = None
+
+        self.eval_acc = 0
+        self.cycle_acc = 0
+        self.time_acc = 0.0
+
+    async def _run_one_attempt(self) -> Tuple[float, int, int, float]:
+        print("constructing genotype...")
+        geno = construct(
+            self.morphology_spec,
+            self.hds,
+            file_name=self.experimental_file,  # <-- schreibt Startnetz nach experimental.json
+            add_bias=True
+        )
+        fitness, evals, cycles, elapsed = await self._evaluate_with_exoself(geno)
+        return fitness, evals, cycles, elapsed
+
+    async def _evaluate_with_exoself(self, genotype: Dict[str, Any]) -> Tuple[float, int, int, float]:
+        print("creating exoself...")
+        ex = Exoself(genotype, file_name=self.experimental_file)
+        best_fitness, evals, cycles, elapsed = await ex.train_until_stop()
+        return best_fitness, evals, cycles, elapsed
+
+    async def go(self):
+        attempt = 1
+        while True:
+            print(".........")
+            print("current attempt: ", attempt)
+            print(".........")
+
+            if attempt > self.max_attempts or self.eval_acc >= self.eval_limit or self.best_fitness >= self.fitness_target:
+                # Abschlussausgabe wie im Buch
+                if self.best_file and os.path.exists(self.best_file):
+                    print_genotype(self.best_file)
+                print(
+                    f" Morphology: {getattr(self.morphology_spec, '__name__', str(self.morphology_spec))} | "
+                    f"Best Fitness: {self.best_fitness} | EvalAcc: {self.eval_acc}"
+                )
+                return {
+                    "best_fitness": self.best_fitness,
+                    "eval_acc": self.eval_acc,
+                    "cycle_acc": self.cycle_acc,
+                    "time_acc": self.time_acc,
+                    "best_file": self.best_file,
+                }
+
+            print("RUN ONE ATTEMPT!")
+            fitness, evals, cycles, elapsed = await self._run_one_attempt()
+
+            print("update akkus...")
+
+            self.eval_acc += evals
+            self.cycle_acc += cycles
+            self.time_acc += elapsed
+
+            # Besser als bisher?
+            if fitness > self.best_fitness:
+                self.best_fitness = fitness
+                if self.best_file and self.experimental_file and os.path.exists(self.experimental_file):
+                    os.replace(self.experimental_file, self.best_file)
+                attempt = 1
+            else:
+                attempt += 1
+
+
+
+if __name__ == "__main__":
+    trainer = Trainer(
+        morphology_spec=morphology,
+        hidden_layer_densities=[2],
+        max_attempts=200,
+        eval_limit=float("inf"),
+        fitness_target=99.9,
+        experimental_file="experimental.json",
+        best_file="best.json",
+        exoself_steps_per_eval=0,
+    )
+
+    asyncio.run(trainer.go())