136 lines
5.3 KiB
Python
136 lines
5.3 KiB
Python
import time
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from typing import Union, Callable
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import numpy as np
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import jax
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from jax import jit, vmap
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from configs import Configer
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from algorithms.neat import initialize_genomes
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from algorithms.neat.population import create_next_generation, speciate, update_species
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from algorithms.neat import unflatten_connections, topological_sort, create_forward_function
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class Pipeline:
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"""
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Neat algorithm pipeline.
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"""
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def __init__(self, config, seed=42):
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self.randkey = jax.random.PRNGKey(seed)
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np.random.seed(seed)
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self.config = config # global config
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self.jit_config = Configer.create_jit_config(config) # config used in jit-able functions
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self.P = config['pop_size']
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self.N = config['init_maximum_nodes']
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self.C = config['init_maximum_connections']
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self.S = config['init_maximum_species']
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self.generation = 0
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self.best_genome = None
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self.pop_nodes, self.pop_cons = initialize_genomes(self.N, self.C, self.config)
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self.species_info = np.full((self.S, 3), np.nan)
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self.species_info[0, :] = 0, -np.inf, 0
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self.idx2species = np.zeros(self.P, dtype=np.float32)
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self.center_nodes = np.full((self.S, self.N, 5), np.nan)
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self.center_cons = np.full((self.S, self.C, 4), np.nan)
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self.center_nodes[0, :, :] = self.pop_nodes[0, :, :]
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self.center_cons[0, :, :] = self.pop_cons[0, :, :]
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self.best_fitness = float('-inf')
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self.best_genome = None
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self.generation_timestamp = time.time()
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self.evaluate_time = 0
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self.pop_unflatten_connections = jit(vmap(unflatten_connections))
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self.pop_topological_sort = jit(vmap(topological_sort))
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self.forward = create_forward_function(config)
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def ask(self):
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"""
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Creates a function that receives a genome and returns a forward function.
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There are 3 types of config['forward_way']: {'single', 'pop', 'common'}
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single:
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Create pop_size number of forward functions.
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Each function receive (batch_size, input_size) and returns (batch_size, output_size)
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e.g. RL task
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pop:
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Create a single forward function, which use only once calculation for the population.
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The function receives (pop_size, batch_size, input_size) and returns (pop_size, batch_size, output_size)
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common:
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Special case of pop. The population has the same inputs.
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The function receives (batch_size, input_size) and returns (pop_size, batch_size, output_size)
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e.g. numerical regression; Hyper-NEAT
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"""
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u_pop_cons = self.pop_unflatten_connections(self.pop_nodes, self.pop_cons)
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pop_seqs = self.pop_topological_sort(self.pop_nodes, u_pop_cons)
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# only common mode is supported currently
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assert self.config['forward_way'] == 'common'
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return lambda x: self.forward(x, pop_seqs, self.pop_nodes, u_pop_cons)
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def tell(self, fitnesses):
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self.generation += 1
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k1, k2, self.randkey = jax.random.split(self.randkey, 3)
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self.species_info, self.center_nodes, self.center_cons, winner, loser, elite_mask = \
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update_species(k1, fitnesses, self.species_info, self.idx2species, self.center_nodes,
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self.center_cons, self.generation, self.jit_config)
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self.pop_nodes, self.pop_cons = create_next_generation(k2, self.pop_nodes, self.pop_cons, winner, loser,
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elite_mask, self.generation, self.jit_config)
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self.idx2species, self.center_nodes, self.center_cons, self.species_info = speciate(
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self.pop_nodes, self.pop_cons, self.species_info, self.center_nodes, self.center_cons, self.generation,
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self.jit_config)
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def auto_run(self, fitness_func, analysis: Union[Callable, str] = "default"):
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for _ in range(self.config['generation_limit']):
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forward_func = self.ask()
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tic = time.time()
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fitnesses = fitness_func(forward_func)
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self.evaluate_time += time.time() - tic
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assert np.all(~np.isnan(fitnesses)), "fitnesses should not be nan!"
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if analysis is not None:
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if analysis == "default":
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self.default_analysis(fitnesses)
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else:
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assert callable(analysis), f"What the fuck you passed in? A {analysis}?"
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analysis(fitnesses)
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if max(fitnesses) >= self.config['fitness_threshold']:
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print("Fitness limit reached!")
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return self.best_genome
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self.tell(fitnesses)
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print("Generation limit reached!")
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return self.best_genome
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def default_analysis(self, fitnesses):
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max_f, min_f, mean_f, std_f = max(fitnesses), min(fitnesses), np.mean(fitnesses), np.std(fitnesses)
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new_timestamp = time.time()
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cost_time = new_timestamp - self.generation_timestamp
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self.generation_timestamp = new_timestamp
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max_idx = np.argmax(fitnesses)
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if fitnesses[max_idx] > self.best_fitness:
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self.best_fitness = fitnesses[max_idx]
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self.best_genome = (self.pop_nodes[max_idx], self.pop_cons[max_idx])
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print(f"Generation: {self.generation}",
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f"fitness: {max_f}, {min_f}, {mean_f}, {std_f}, Cost time: {cost_time}")
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