prepare for experiment

examples/enhane_xor.py

@@ -0,0 +1,44 @@
+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):
+    inputs = problem.ask_for_inputs()
+    pop_predict = jax.device_get(func(inputs))
+    # print(pop_predict)
+    fitnesses = []
+    for predict in pop_predict:
+        f = problem.evaluate_predict(predict)
+        fitnesses.append(f)
+    return np.array(fitnesses)
+
+# @using_cprofile
+# @partial(using_cprofile, root_abs_path='/mnt/e/neatax/', replace_pattern="/mnt/e/neat-jax/")
+def main():
+    tic = time.time()
+    config = Configer.load_config()
+    problem = EnhanceLogic("xor", n=3)
+    problem.refactor_config(config)
+    function_factory = FunctionFactory(config)
+    evaluate_func = lambda func: evaluate(problem, func)
+    pipeline = Pipeline(config, function_factory, seed=33413)
+    print("start run")
+    pipeline.auto_run(evaluate_func)
+
+    total_time = time.time() - tic
+    compile_time = pipeline.function_factory.compile_time
+    total_it = pipeline.generation
+    mean_time_per_it = (total_time - compile_time) / total_it
+    evaluate_time = pipeline.evaluate_time
+    print(f"total time: {total_time:.2f}s, compile time: {compile_time:.2f}s, real_time: {total_time - compile_time:.2f}s, evaluate time: {evaluate_time:.2f}s")
+    print(f"total it: {total_it}, mean time per it: {mean_time_per_it:.2f}s")
+
+
+if __name__ == '__main__':
+    main()

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()

examples/xor.py

@@ -1,19 +1,27 @@
-from functools import partial
-
 from utils import Configer
 from algorithms.neat import Pipeline
 from time_utils import using_cprofile
 from problems import Sin, Xor, DIY
+import time
 
 
-@using_cprofile
+# @using_cprofile
 # @partial(using_cprofile, root_abs_path='/mnt/e/neatax/', replace_pattern="/mnt/e/neat-jax/")
 def main():
+    tic = time.time()
     config = Configer.load_config()
     problem = Xor()
     problem.refactor_config(config)
-    pipeline = Pipeline(config, seed=1)
-    pipeline.auto_run(problem.evaluate)
+    pipeline = Pipeline(config, seed=6)
+    nodes, cons = pipeline.auto_run(problem.evaluate)
+    # print(nodes, cons)
+    total_time = time.time() - tic
+    compile_time = pipeline.function_factory.compile_time
+    total_it = pipeline.generation
+    mean_time_per_it = (total_time - compile_time) / total_it
+    evaluate_time = pipeline.evaluate_time
+    print(f"total time: {total_time:.2f}s, compile time: {compile_time:.2f}s, real_time: {total_time - compile_time:.2f}s, evaluate time: {evaluate_time:.2f}s")
+    print(f"total it: {total_it}, mean time per it: {mean_time_per_it:.2f}s")
 
 
 if __name__ == '__main__':