refactor names;
delete useless
This commit is contained in:
wls2002
131
aaai_exp.py
131
aaai_exp.py
@@ -1,131 +0,0 @@
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from typing import Callable
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from time import time
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import jax
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from jax import numpy as jnp, vmap, jit
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import gymnax
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import numpy as np
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from config import *
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from algorithm import NEAT
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from algorithm.neat.gene import NormalGene, NormalGeneConfig
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from problem.rl_env import GymNaxConfig, GymNaxEnv
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def conf_cartpole():
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return Config(
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basic=BasicConfig(
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seed=42,
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fitness_target=500,
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generation_limit=150,
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pop_size=10000
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),
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neat=NeatConfig(
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inputs=3,
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outputs=1,
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),
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gene=NormalGeneConfig(
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activation_default=Act.tanh,
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activation_options=(Act.tanh,),
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),
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problem=GymNaxConfig(
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env_name='Pendulum-v1',
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output_transform=lambda out: out * 2 # the action of pendulum is [-2, 2]
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)
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)
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def batch_evaluate(
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key,
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alg_state,
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genomes,
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env_params,
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batch_transform: Callable,
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batch_act: Callable,
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batch_reset: Callable,
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batch_step: Callable,
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):
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alg_time, env_time, forward_time = 0, 0, 0
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pop_size = genomes.nodes.shape[0]
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alg_tic = time()
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genomes_transform = batch_transform(alg_state, genomes)
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alg_time += time() - alg_tic
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reset_keys = jax.random.split(key, pop_size)
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observations, states = batch_reset(reset_keys, env_params)
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done = np.zeros(pop_size, dtype=bool)
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fitnesses = np.zeros(pop_size)
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while not np.all(done):
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key, _ = jax.random.split(key)
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vmap_keys = jax.random.split(key, pop_size)
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forward_tic = time()
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actions = batch_act(alg_state, observations, genomes_transform).block_until_ready()
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forward_time += time() - forward_tic
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env_tic = time()
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observations, states, reward, current_done, _ = batch_step(vmap_keys, states, actions, env_params)
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reward, current_done = jax.device_get([reward, current_done])
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env_time += time() - env_tic
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fitnesses += reward * np.logical_not(done)
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done = np.logical_or(done, current_done)
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return fitnesses, alg_time, env_time, forward_time
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def main():
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conf = conf_cartpole()
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algorithm = NEAT(conf, NormalGene)
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def act(state, inputs, genome):
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res = algorithm.act(state, inputs, genome)
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return conf.problem.output_transform(res)
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batch_transform = jit(vmap(algorithm.transform, in_axes=(None, 0)))
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# (state, obs, genome_transform) -> action
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batch_act = jit(vmap(act, in_axes=(None, 0, 0)))
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env, env_params = gymnax.make(conf.problem.env_name)
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# (seed, params) -> (ini_obs, ini_state)
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batch_reset = jit(vmap(env.reset, in_axes=(0, None)))
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# (seed, state, action, params) -> (obs, state, reward, done, info)
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batch_step = jit(vmap(env.step, in_axes=(0, 0, 0, None)))
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key = jax.random.PRNGKey(conf.basic.seed)
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alg_key, pro_key = jax.random.split(key)
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alg_state = algorithm.setup(alg_key)
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for i in range(conf.basic.generation_limit):
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total_tic = time()
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pro_key, _ = jax.random.split(pro_key)
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fitnesses, a1, env_time, forward_time = batch_evaluate(
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pro_key,
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alg_state,
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algorithm.ask(alg_state),
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env_params,
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batch_transform,
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batch_act,
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batch_reset,
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batch_step
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)
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alg_tic = time()
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alg_state = algorithm.tell(alg_state, fitnesses)
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alg_state = jax.tree_map(lambda x: x.block_until_ready(), alg_state)
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a2 = time() - alg_tic
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alg_time = a1 + a2
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total_time = time() - total_tic
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print(f"generation: {i}, alg_time: {alg_time:.2f}, env_time: {env_time:.2f}, forward_time: {forward_time:.2f}, total_time: {total_time: .2f}, "
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f"max_fitness: {np.max(fitnesses):.2f}", f"avg_fitness: {np.mean(fitnesses):.2f}")
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if __name__ == '__main__':
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main()
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@@ -1,33 +0,0 @@
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import jax.random
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import numpy as np
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import jax.numpy as jnp
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import time
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def random_array(key):
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return jax.random.normal(key, (1000,))
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def random_array_np():
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return np.random.normal(size=(1000,))
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def t_jax():
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key = jax.random.PRNGKey(42)
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max_li = []
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tic = time.time()
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for _ in range(100):
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key, sub_key = jax.random.split(key)
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array = random_array(sub_key)
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array = jax.device_get(array)
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max_li.append(max(array))
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print(max_li, time.time() - tic)
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def t_np():
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max_li = []
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tic = time.time()
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for _ in range(100):
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max_li.append(max(random_array_np()))
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print(max_li, time.time() - tic)
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if __name__ == '__main__':
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t_np()
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@@ -1,5 +1,5 @@
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from config import *
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from pipeline_jitable_env import Pipeline
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from pipeline import Pipeline
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from algorithm import NEAT
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from algorithm.neat.gene import NormalGene, NormalGeneConfig
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from problem.func_fit import XOR, FuncFitConfig
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@@ -1,5 +1,5 @@
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from config import *
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from pipeline_jitable_env import Pipeline
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from pipeline import Pipeline
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from algorithm.neat import NormalGene, NormalGeneConfig
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from algorithm.hyperneat import HyperNEAT, NormalSubstrate, NormalSubstrateConfig
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from problem.func_fit import XOR3d, FuncFitConfig
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@@ -1,5 +1,5 @@
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from config import *
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from pipeline_jitable_env import Pipeline
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from pipeline import Pipeline
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from algorithm import NEAT
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from algorithm.neat.gene import RecurrentGene, RecurrentGeneConfig
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from problem.func_fit import XOR3d, FuncFitConfig
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@@ -1,7 +1,7 @@
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import jax.numpy as jnp
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from config import *
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from pipeline_jitable_env import Pipeline
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from pipeline import Pipeline
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from algorithm import NEAT
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from algorithm.neat.gene import NormalGene, NormalGeneConfig
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from problem.rl_env import GymNaxConfig, GymNaxEnv
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@@ -1,7 +1,7 @@
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import jax.numpy as jnp
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from config import *
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from pipeline_jitable_env import Pipeline
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from pipeline import Pipeline
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from algorithm import NEAT
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from algorithm.neat.gene import NormalGene, NormalGeneConfig
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from problem.rl_env import GymNaxConfig, GymNaxEnv
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@@ -1,7 +1,7 @@
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import jax.numpy as jnp
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from config import *
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from pipeline_jitable_env import Pipeline
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from pipeline import Pipeline
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from algorithm import NEAT
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from algorithm.neat.gene import NormalGene, NormalGeneConfig
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from algorithm.hyperneat import HyperNEAT, NormalSubstrateConfig, NormalSubstrate
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@@ -1,7 +1,7 @@
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import jax.numpy as jnp
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from config import *
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from pipeline_jitable_env import Pipeline
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from pipeline import Pipeline
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from algorithm import NEAT
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from algorithm.neat.gene import NormalGene, NormalGeneConfig
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from problem.rl_env import GymNaxConfig, GymNaxEnv
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@@ -1,7 +1,7 @@
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import jax.numpy as jnp
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from config import *
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from pipeline_jitable_env import Pipeline
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from pipeline import Pipeline
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from algorithm import NEAT
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from algorithm.neat.gene import NormalGene, NormalGeneConfig
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from problem.rl_env import GymNaxConfig, GymNaxEnv
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@@ -1,7 +1,7 @@
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import jax.numpy as jnp
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from config import *
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from pipeline_jitable_env import Pipeline
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from pipeline import Pipeline
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from algorithm import NEAT
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from algorithm.neat.gene import NormalGene, NormalGeneConfig
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from problem.rl_env import GymNaxConfig, GymNaxEnv
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@@ -1,5 +1,5 @@
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from config import *
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from pipeline_jitable_env import Pipeline
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from pipeline import Pipeline
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from algorithm import NEAT
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from algorithm.neat.gene import NormalGene, NormalGeneConfig
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from problem.rl_env import GymNaxConfig, GymNaxEnv
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@@ -1,53 +0,0 @@
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import time
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import jax.numpy as jnp
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from config import *
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from pipeline_jitable_env import Pipeline
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from algorithm import NEAT
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from algorithm.neat.gene import NormalGene, NormalGeneConfig
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from problem.rl_env import GymNaxConfig, GymNaxEnv
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def conf_with_seed(seed):
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return Config(
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basic=BasicConfig(
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seed=seed,
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fitness_target=501,
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pop_size=10000,
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generation_limit=100
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),
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neat=NeatConfig(
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inputs=4,
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outputs=1,
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max_species=10000
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),
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gene=NormalGeneConfig(
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activation_default=Act.sigmoid,
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activation_options=(Act.sigmoid,),
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),
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problem=GymNaxConfig(
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env_name='CartPole-v1',
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output_transform=lambda out: jnp.where(out[0] > 0.5, 1, 0) # the action of cartpole is {0, 1}
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)
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)
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if __name__ == '__main__':
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times = []
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for seed in range(10):
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conf = conf_with_seed(seed)
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algorithm = NEAT(conf, NormalGene)
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pipeline = Pipeline(conf, algorithm, GymNaxEnv)
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state = pipeline.setup()
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pipeline.pre_compile(state)
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tic = time.time()
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state, best = pipeline.auto_run(state)
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time_cost = time.time() - tic
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times.append(time_cost)
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print(times)
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print(f"total_times: {times}")
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@@ -1,301 +0,0 @@
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mean_totaltime,Average Fitness
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16.23358095,-1531.406262
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34.16262472,-1469.014171
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52.56341431,-1429.524865
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68.70164006,-1398.485718
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87.61529462,-1369.068788
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105.4480726,-1338.810888
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120.3260972,-1308.414005
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134.2059872,-1280.022147
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148.1975725,-1258.681994
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161.0129987,-1248.612688
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174.4836712,-1242.987934
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
5935.963855,-1219.789742
|
||||
5990.031264,-1220.348486
|
||||
6042.056406,-1221.695037
|
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6094.931383,-1222.573256
|
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6149.866284,-1220.799972
|
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6201.410016,-1223.62492
|
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6254.863233,-1221.173897
|
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6307.299802,-1218.384144
|
||||
6358.419653,-1221.949964
|
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6410.39371,-1220.695003
|
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6462.294865,-1221.354209
|
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6514.29837,-1220.013649
|
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6566.028445,-1221.057066
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6619.112563,-1220.250728
|
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6724.056905,-1220.696191
|
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6834.066508,-1220.67166
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|
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|
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6993.728177,-1220.293248
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7645.189188,-1219.780434
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8537.958919,-1221.428118
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8760.582589,-1218.94684
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8874.317387,-1219.76819
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8988.303494,-1220.058048
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14030.04214,-1219.768839
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|
||||
14218.77685,-1218.442359
|
||||
14281.016,-1219.142937
|
||||
14346.43126,-1218.540456
|
||||
14409.63096,-1220.27516
|
||||
14475.35352,-1220.047078
|
||||
14541.72266,-1217.934667
|
||||
14605.84911,-1219.121418
|
||||
14673.27209,-1219.250748
|
||||
14737.62863,-1220.368594
|
||||
14802.66072,-1220.611695
|
||||
14867.97276,-1221.085498
|
||||
14935.65309,-1216.312149
|
||||
15001.18007,-1218.441571
|
||||
|
@@ -1,22 +0,0 @@
|
||||
from matplotlib import pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
# 使用 genfromtxt 函数读取 CSV 文件
|
||||
data = np.genfromtxt('neatpython.csv', delimiter=',', skip_header=1) # 假设有一个头部行
|
||||
mean_time, fitness_mean = data.T
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
ax.plot(mean_time, fitness_mean, color='green', label='NEAT-Python', linestyle=':')
|
||||
ax.set_xlabel('Time (s)')
|
||||
ax.set_ylabel('Average Fitness')
|
||||
ax.set_xlim(0, 500)
|
||||
ax.set_ylim(-2000, -1000)
|
||||
ax.legend()
|
||||
|
||||
|
||||
# ci = 1.96 * neatax_sem
|
||||
# lower_bound = neatax_mean - ci
|
||||
# upper_bound = neatax_mean + ci
|
||||
# plt.plot(mean_time, fitness_mean, color='r', label='NEAT-Python')
|
||||
# plt.fill_between(x_axis, lower_bound, upper_bound, color='red', alpha=0.2)
|
||||
fig.show()
|
||||
111
pipeline_time.py
111
pipeline_time.py
@@ -1,111 +0,0 @@
|
||||
from typing import Type
|
||||
|
||||
import jax
|
||||
import time
|
||||
import numpy as np
|
||||
|
||||
from algorithm import NEAT, HyperNEAT
|
||||
from config import Config
|
||||
from core import State, Algorithm, Problem
|
||||
|
||||
|
||||
class Pipeline:
|
||||
|
||||
def __init__(self, config: Config, algorithm: Algorithm, problem_type: Type[Problem]):
|
||||
self.config = config
|
||||
self.algorithm = algorithm
|
||||
self.problem = problem_type(config.problem)
|
||||
|
||||
if isinstance(algorithm, NEAT):
|
||||
assert config.neat.inputs == self.problem.input_shape[-1]
|
||||
|
||||
elif isinstance(algorithm, HyperNEAT):
|
||||
assert config.hyperneat.inputs == self.problem.input_shape[-1]
|
||||
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
self.act_func = self.algorithm.act
|
||||
|
||||
for _ in range(len(self.problem.input_shape) - 1):
|
||||
self.act_func = jax.vmap(self.act_func, in_axes=(None, 0, None))
|
||||
|
||||
self.best_genome = None
|
||||
self.best_fitness = float('-inf')
|
||||
self.generation_timestamp = None
|
||||
|
||||
def setup(self):
|
||||
key = jax.random.PRNGKey(self.config.basic.seed)
|
||||
algorithm_key, evaluate_key = jax.random.split(key, 2)
|
||||
state = State()
|
||||
state = self.algorithm.setup(algorithm_key, state)
|
||||
return state.update(
|
||||
evaluate_key=evaluate_key
|
||||
)
|
||||
|
||||
def step(self, state):
|
||||
|
||||
key, sub_key = jax.random.split(state.evaluate_key)
|
||||
keys = jax.random.split(key, self.config.basic.pop_size)
|
||||
|
||||
pop = self.algorithm.ask(state)
|
||||
|
||||
pop_transformed = jax.vmap(self.algorithm.transform, in_axes=(None, 0))(state, pop)
|
||||
|
||||
fitnesses = jax.vmap(self.problem.evaluate, in_axes=(0, None, None, 0))(keys, state, self.act_func,
|
||||
pop_transformed)
|
||||
|
||||
state = self.algorithm.tell(state, fitnesses)
|
||||
|
||||
return state.update(evaluate_key=sub_key), fitnesses
|
||||
|
||||
def auto_run(self, ini_state):
|
||||
state = ini_state
|
||||
for _ in range(self.config.basic.generation_limit):
|
||||
|
||||
self.generation_timestamp = time.time()
|
||||
|
||||
previous_pop = self.algorithm.ask(state)
|
||||
|
||||
state, fitnesses = self.step(state)
|
||||
|
||||
fitnesses = jax.device_get(fitnesses)
|
||||
|
||||
self.analysis(state, previous_pop, fitnesses)
|
||||
|
||||
if max(fitnesses) >= self.config.basic.fitness_target:
|
||||
print("Fitness limit reached!")
|
||||
return state, self.best_genome
|
||||
|
||||
print("Generation limit reached!")
|
||||
return state, self.best_genome
|
||||
|
||||
def analysis(self, state, pop, fitnesses):
|
||||
|
||||
max_f, min_f, mean_f, std_f = max(fitnesses), min(fitnesses), np.mean(fitnesses), np.std(fitnesses)
|
||||
|
||||
new_timestamp = time.time()
|
||||
|
||||
cost_time = new_timestamp - self.generation_timestamp
|
||||
|
||||
max_idx = np.argmax(fitnesses)
|
||||
if fitnesses[max_idx] > self.best_fitness:
|
||||
self.best_fitness = fitnesses[max_idx]
|
||||
self.best_genome = pop[max_idx]
|
||||
|
||||
member_count = jax.device_get(state.species_info.member_count)
|
||||
species_sizes = [int(i) for i in member_count if i > 0]
|
||||
|
||||
print(f"Generation: {state.generation}",
|
||||
f"species: {len(species_sizes)}, {species_sizes}",
|
||||
f"fitness: {max_f:.6f}, {min_f:.6f}, {mean_f:.6f}, {std_f:.6f}, Cost time: {cost_time * 1000:.6f}ms")
|
||||
|
||||
def show(self, state, genome):
|
||||
transformed = self.algorithm.transform(state, genome)
|
||||
self.problem.show(state.evaluate_key, state, self.act_func, transformed)
|
||||
|
||||
def pre_compile(self, state):
|
||||
tic = time.time()
|
||||
print("start compile")
|
||||
self.step.lower(self, state).compile()
|
||||
print(f"compile finished, cost time: {time.time() - tic}s")
|
||||
Reference in New Issue
Block a user