190 lines
7.8 KiB
Python
190 lines
7.8 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 configs import Configer
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from function_factory import FunctionFactory
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from algorithms.neat import initialize_genomes, expand, expand_single, SpeciesController
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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, function_factory=None, 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.function_factory = function_factory or FunctionFactory(self.config, self.jit_config)
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self.symbols = {
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'P': self.config['pop_size'],
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'N': self.config['init_maximum_nodes'],
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'C': self.config['init_maximum_connections'],
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'S': self.config['init_maximum_species'],
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}
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self.generation = 0
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self.best_genome = None
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self.species_controller = SpeciesController(self.config)
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self.pop_nodes, self.pop_cons = initialize_genomes(self.symbols['N'], self.symbols['C'], self.config)
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self.species_controller.init_speciate(self.pop_nodes, self.pop_cons)
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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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print(self.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.get_func('pop_unflatten_connections')(self.pop_nodes, self.pop_cons)
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pop_seqs = self.get_func('pop_topological_sort')(self.pop_nodes, u_pop_cons)
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if self.config['forward_way'] == 'single':
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forward_funcs = []
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for seq, nodes, cons in zip(pop_seqs, self.pop_nodes, u_pop_cons):
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func = lambda x: self.get_func('forward')(x, seq, nodes, cons)
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forward_funcs.append(func)
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return forward_funcs
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elif self.config['forward_way'] == 'pop':
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func = lambda x: self.get_func('pop_batch_forward')(x, pop_seqs, self.pop_nodes, u_pop_cons)
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return func
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elif self.config['forward_way'] == 'common':
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func = lambda x: self.get_func('common_forward')(x, pop_seqs, self.pop_nodes, u_pop_cons)
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return func
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else:
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raise NotImplementedError
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def tell(self, fitnesses):
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self.generation += 1
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winner, loser, elite_mask, center_nodes, center_cons, species_keys, species_key_start = \
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self.species_controller.ask(fitnesses, self.generation, self.symbols)
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# node keys to be used in the mutation process
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new_node_keys = np.arange(self.generation * self.config['pop_size'],
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self.generation * self.config['pop_size'] + self.config['pop_size'])
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# create the next generation and then speciate the population
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self.pop_nodes, self.pop_cons, idx2specie, center_nodes, center_cons, species_keys = \
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self.get_func('create_next_generation_then_speciate') \
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(self.randkey, self.pop_nodes, self.pop_cons, winner, loser, elite_mask, new_node_keys, center_nodes,
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center_cons, species_keys, species_key_start, self.jit_config)
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# carry data to cpu
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self.pop_nodes, self.pop_cons, idx2specie, center_nodes, center_cons, species_keys = \
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jax.device_get([self.pop_nodes, self.pop_cons, idx2specie, center_nodes, center_cons, species_keys])
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self.species_controller.tell(idx2specie, center_nodes, center_cons, species_keys, self.generation)
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# expand the population if needed
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self.expand()
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# update randkey
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self.randkey = jax.random.split(self.randkey)[0]
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def expand(self):
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"""
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Expand the population if needed.
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when the maximum node number >= N or the maximum connection number of >= C
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the population will expand
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"""
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# analysis nodes
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pop_node_keys = self.pop_nodes[:, :, 0]
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pop_node_sizes = np.sum(~np.isnan(pop_node_keys), axis=1)
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max_node_size = np.max(pop_node_sizes)
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# analysis connections
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pop_con_keys = self.pop_cons[:, :, 0]
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pop_node_sizes = np.sum(~np.isnan(pop_con_keys), axis=1)
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max_con_size = np.max(pop_node_sizes)
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# expand if needed
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if max_node_size >= self.symbols['N'] or max_con_size >= self.symbols['C']:
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if max_node_size > self.symbols['N'] * self.config['pre_expand_threshold']:
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self.symbols['N'] = int(self.symbols['N'] * self.config['expand_coe'])
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print(f"pre node expand to {self.symbols['N']}!")
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if max_con_size > self.symbols['C'] * self.config['pre_expand_threshold']:
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self.symbols['C'] = int(self.symbols['C'] * self.config['expand_coe'])
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print(f"pre connection expand to {self.symbols['C']}!")
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self.pop_nodes, self.pop_cons = expand(self.pop_nodes, self.pop_cons, self.symbols['N'], self.symbols['C'])
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# don't forget to expand representation genome in species
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for s in self.species_controller.species.values():
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s.representative = expand_single(*s.representative, self.symbols['N'], self.symbols['C'])
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def get_func(self, name):
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return self.function_factory.get(name, self.symbols)
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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"Callable is needed here😅😅😅 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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species_sizes = [len(s.members) for s in self.species_controller.species.values()]
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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}, Species sizes: {species_sizes}, Cost time: {cost_time}")
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