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change project structure and using .ini as config file

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wls2002
2023-06-15 11:05:26 +08:00
parent 47fb0151f4
commit acedd67617
30 changed files with 97 additions and 301 deletions
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158
algorithms/neat/pipeline.py
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@@ -1,158 +0,0 @@
from typing import List, Union, Tuple, Callable
import time
import jax
import numpy as np
from .species import SpeciesController
from .genome import expand, expand_single
from .function_factory import FunctionFactory
from .population import *
class Pipeline:
"""
Neat algorithm pipeline.
"""
def __init__(self, config, function_factory, seed=42):
self.time_dict = {}
self.function_factory = function_factory
self.randkey = jax.random.PRNGKey(seed)
np.random.seed(seed)
self.config = config
self.N = config.basic.init_maximum_nodes
self.C = config.basic.init_maximum_connections
self.S = config.basic.init_maximum_species
self.expand_coe = config.basic.expands_coe
self.pop_size = config.neat.population.pop_size
self.species_controller = SpeciesController(config)
self.initialize_func = self.function_factory.create_initialize(self.N, self.C)
self.pop_nodes, self.pop_cons, self.input_idx, self.output_idx = self.initialize_func()
self.create_and_speciate = self.function_factory.create_update_speciate(self.N, self.C, self.S)
self.generation = 0
self.generation_time_list = []
self.species_controller.init_speciate(self.pop_nodes, self.pop_cons)
self.best_fitness = float('-inf')
self.best_genome = None
self.generation_timestamp = time.time()
self.evaluate_time = 0
def ask(self):
"""
Create a forward function for the population.
:return:
Algorithm gives the population a forward function, then environment gives back the fitnesses.
"""
return self.function_factory.ask_pop_batch_forward(self.pop_nodes, self.pop_cons)
def tell(self, fitnesses):
self.generation += 1
winner_part, loser_part, elite_mask, pre_spe_center_nodes, pre_spe_center_cons, pre_species_keys, new_species_key_start = self.species_controller.ask(
fitnesses,
self.generation,
self.S, self.N, self.C)
new_node_keys = np.arange(self.generation * self.pop_size, self.generation * self.pop_size + self.pop_size)
self.pop_nodes, self.pop_cons, idx2specie, new_center_nodes, new_center_cons, new_species_keys = self.create_and_speciate(
self.randkey, self.pop_nodes, self.pop_cons, winner_part, loser_part, elite_mask,
new_node_keys,
pre_spe_center_nodes, pre_spe_center_cons, pre_species_keys, new_species_key_start)
self.pop_nodes, self.pop_cons, idx2specie, new_center_nodes, new_center_cons, new_species_keys = \
jax.device_get([self.pop_nodes, self.pop_cons, idx2specie, new_center_nodes, new_center_cons, new_species_keys])
self.species_controller.tell(idx2specie, new_center_nodes, new_center_cons, new_species_keys, self.generation)
self.expand()
def auto_run(self, fitness_func, analysis: Union[Callable, str] = "default"):
for _ in range(self.config.neat.population.generation_limit):
forward_func = self.ask()
tic = time.time()
fitnesses = fitness_func(forward_func)
self.evaluate_time += time.time() - tic
assert np.all(~np.isnan(fitnesses)), "fitnesses should not be nan!"
if analysis is not None:
if analysis == "default":
self.default_analysis(fitnesses)
else:
assert callable(analysis), f"What the fuck you passed in? A {analysis}?"
analysis(fitnesses)
if max(fitnesses) >= self.config.neat.population.fitness_threshold:
print("Fitness limit reached!")
return self.best_genome
self.tell(fitnesses)
print("Generation limit reached!")
return self.best_genome
def expand(self):
"""
Expand the population if needed.
:return:
when the maximum node number of the population >= N
the population will expand
"""
pop_node_keys = self.pop_nodes[:, :, 0]
pop_node_sizes = np.sum(~np.isnan(pop_node_keys), axis=1)
max_node_size = np.max(pop_node_sizes)
if max_node_size >= self.N:
self.N = int(self.N * self.expand_coe)
# self.C = int(self.C * self.expand_coe)
print(f"node expand to {self.N}!")
self.pop_nodes, self.pop_cons = expand(self.pop_nodes, self.pop_cons, self.N, self.C)
# don't forget to expand representation genome in species
for s in self.species_controller.species.values():
s.representative = expand_single(*s.representative, self.N, self.C)
pop_con_keys = self.pop_cons[:, :, 0]
pop_node_sizes = np.sum(~np.isnan(pop_con_keys), axis=1)
max_con_size = np.max(pop_node_sizes)
if max_con_size >= self.C:
# self.N = int(self.N * self.expand_coe)
self.C = int(self.C * self.expand_coe)
print(f"connections expand to {self.C}!")
self.pop_nodes, self.pop_cons = expand(self.pop_nodes, self.pop_cons, self.N, self.C)
# don't forget to expand representation genome in species
for s in self.species_controller.species.values():
s.representative = expand_single(*s.representative, self.N, self.C)
self.create_and_speciate = self.function_factory.create_update_speciate(self.N, self.C, self.S)
def default_analysis(self, fitnesses):
max_f, min_f, mean_f, std_f = max(fitnesses), min(fitnesses), np.mean(fitnesses), np.std(fitnesses)
species_sizes = [len(s.members) for s in self.species_controller.species.values()]
new_timestamp = time.time()
cost_time = new_timestamp - self.generation_timestamp
self.generation_time_list.append(cost_time)
self.generation_timestamp = new_timestamp
max_idx = np.argmax(fitnesses)
if fitnesses[max_idx] > self.best_fitness:
self.best_fitness = fitnesses[max_idx]
self.best_genome = (self.pop_nodes[max_idx], self.pop_cons[max_idx])
print(f"Generation: {self.generation}",
f"fitness: {max_f}, {min_f}, {mean_f}, {std_f}, Species sizes: {species_sizes}, Cost time: {cost_time}")