prepare for experiment

examples/final_design_experiement.py

@@ -0,0 +1,56 @@
+import numpy as np
+import jax
+from utils import Configer
+from algorithms.neat import Pipeline
+from time_utils import using_cprofile
+from algorithms.neat.function_factory import FunctionFactory
+from problems import EnhanceLogic
+import time
+
+
+def evaluate(problem, func):
+    outs = func(problem.inputs)
+    outs = jax.device_get(outs)
+    fitnesses = -np.mean((problem.outputs - outs) ** 2, axis=(1, 2))
+    return fitnesses
+
+
+def main():
+    config = Configer.load_config()
+    problem = EnhanceLogic("xor", n=3)
+    problem.refactor_config(config)
+    function_factory = FunctionFactory(config)
+    evaluate_func = lambda func: evaluate(problem, func)
+
+    # precompile
+    pipeline = Pipeline(config, function_factory, seed=114514)
+    pipeline.auto_run(evaluate_func)
+
+    for r in range(10):
+        print(f"running: {r}/{10}")
+        tic = time.time()
+
+        pipeline = Pipeline(config, function_factory, seed=r)
+        pipeline.auto_run(evaluate_func)
+
+        total_time = time.time() - tic
+        evaluate_time = pipeline.evaluate_time
+        total_it = pipeline.generation
+        print(f"total time: {total_time:.2f}s, evaluate time: {evaluate_time:.2f}s, total_it: {total_it}")
+
+        if total_it >= 500:
+            res = "fail"
+        else:
+            res = "success"
+
+        with open("log", "wb") as f:
+            f.write(f"{res}, total time: {total_time:.2f}s, evaluate time: {evaluate_time:.2f}s, total_it: {total_it}\n".encode("utf-8"))
+            f.write(str(pipeline.generation_time_list).encode("utf-8"))
+
+    compile_time = function_factory.compile_time
+
+    print("total_compile_time:", compile_time)
+
+
+if __name__ == '__main__':
+    main()