198 lines
7.9 KiB
Python
198 lines
7.9 KiB
Python
from typing import Type
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import jax
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from jax import numpy as jnp, Array, vmap
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import numpy as np
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from config import Config
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from core import Algorithm, State, Gene, Genome
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from .ga import crossover, create_mutate
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from .species import update_species, create_speciate
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class NEAT(Algorithm):
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def __init__(self, config: Config, gene_type: Type[Gene]):
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self.config = config
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self.gene_type = gene_type
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self.forward_func = None
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self.tell_func = None
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def setup(self, randkey, state: State = State()):
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"""initialize the state of the algorithm"""
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input_idx = np.arange(self.config.basic.num_inputs)
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output_idx = np.arange(self.config.basic.num_inputs,
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self.config.basic.num_inputs + self.config.basic.num_outputs)
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state = state.update(
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P=self.config.basic.pop_size,
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N=self.config.neat.maximum_nodes,
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C=self.config.neat.maximum_conns,
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S=self.config.neat.maximum_species,
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NL=1 + len(self.gene_type.node_attrs), # node length = (key) + attributes
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CL=3 + len(self.gene_type.conn_attrs), # conn length = (in, out, key) + attributes
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max_stagnation=self.config.neat.max_stagnation,
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species_elitism=self.config.neat.species_elitism,
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spawn_number_change_rate=self.config.neat.spawn_number_change_rate,
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genome_elitism=self.config.neat.genome_elitism,
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survival_threshold=self.config.neat.survival_threshold,
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compatibility_threshold=self.config.neat.compatibility_threshold,
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compatibility_disjoint=self.config.neat.compatibility_disjoint,
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compatibility_weight=self.config.neat.compatibility_weight,
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input_idx=input_idx,
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output_idx=output_idx,
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)
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state = self.gene_type.setup(self.config.gene, state)
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pop_genomes = self._initialize_genomes(state)
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species_keys = np.full((state.S,), np.nan, dtype=np.float32)
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best_fitness = np.full((state.S,), np.nan, dtype=np.float32)
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last_improved = np.full((state.S,), np.nan, dtype=np.float32)
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member_count = np.full((state.S,), np.nan, dtype=np.float32)
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idx2species = jnp.zeros(state.P, dtype=jnp.float32)
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species_keys[0] = 0
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best_fitness[0] = -np.inf
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last_improved[0] = 0
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member_count[0] = state.P
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center_nodes = jnp.full((state.S, state.N, state.NL), jnp.nan, dtype=jnp.float32)
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center_conns = jnp.full((state.S, state.C, state.CL), jnp.nan, dtype=jnp.float32)
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center_nodes = center_nodes.at[0, :, :].set(pop_genomes.nodes[0, :, :])
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center_conns = center_conns.at[0, :, :].set(pop_genomes.conns[0, :, :])
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center_genomes = vmap(Genome)(center_nodes, center_conns)
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generation = 0
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next_node_key = max(*state.input_idx, *state.output_idx) + 2
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next_species_key = 1
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state = state.update(
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randkey=randkey,
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pop_genomes=pop_genomes,
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species_keys=species_keys,
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best_fitness=best_fitness,
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last_improved=last_improved,
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member_count=member_count,
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idx2species=idx2species,
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center_genomes=center_genomes,
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# avoid jax auto cast from int to float. that would cause re-compilation.
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generation=jnp.asarray(generation, dtype=jnp.int32),
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next_node_key=jnp.asarray(next_node_key, dtype=jnp.float32),
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next_species_key=jnp.asarray(next_species_key, dtype=jnp.float32),
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)
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self.forward_func = self.gene_type.create_forward(state, self.config.gene)
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self.tell_func = self._create_tell()
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return jax.device_put(state)
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def ask(self, state: State):
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"""require the population to be evaluated"""
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return state.pop_genomes
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def tell(self, state: State, fitness):
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"""update the state of the algorithm"""
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return self.tell_func(state, fitness)
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def forward(self, inputs: Array, transformed: Array):
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"""the forward function of a single forward transformation"""
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return self.forward_func(inputs, transformed)
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def forward_transform(self, state: State, genome: Genome):
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"""create the forward transformation of a genome"""
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return self.gene_type.forward_transform(state, genome)
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def _initialize_genomes(self, state):
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o_nodes = np.full((state.N, state.NL), np.nan, dtype=np.float32) # original nodes
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o_conns = np.full((state.C, state.CL), np.nan, dtype=np.float32) # original connections
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input_idx = state.input_idx
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output_idx = state.output_idx
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new_node_key = max([*input_idx, *output_idx]) + 1
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o_nodes[input_idx, 0] = input_idx
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o_nodes[output_idx, 0] = output_idx
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o_nodes[new_node_key, 0] = new_node_key
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o_nodes[np.concatenate([input_idx, output_idx]), 1:] = self.gene_type.new_node_attrs(state)
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o_nodes[new_node_key, 1:] = self.gene_type.new_node_attrs(state)
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input_conns = np.c_[input_idx, np.full_like(input_idx, new_node_key)]
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o_conns[input_idx, 0:2] = input_conns # in key, out key
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o_conns[input_idx, 2] = True # enabled
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o_conns[input_idx, 3:] = self.gene_type.new_conn_attrs(state)
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output_conns = np.c_[np.full_like(output_idx, new_node_key), output_idx]
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o_conns[output_idx, 0:2] = output_conns # in key, out key
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o_conns[output_idx, 2] = True # enabled
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o_conns[output_idx, 3:] = self.gene_type.new_conn_attrs(state)
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# repeat origin genome for P times to create population
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pop_nodes = np.tile(o_nodes, (state.P, 1, 1))
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pop_conns = np.tile(o_conns, (state.P, 1, 1))
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return vmap(Genome)(pop_nodes, pop_conns)
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def _create_tell(self):
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mutate = create_mutate(self.config.neat, self.gene_type)
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def create_next_generation(state, randkey, winner, loser, elite_mask):
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# prepare random keys
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pop_size = state.idx2species.shape[0]
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new_node_keys = jnp.arange(pop_size) + state.next_node_key
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k1, k2 = jax.random.split(randkey, 2)
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crossover_rand_keys = jax.random.split(k1, pop_size)
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mutate_rand_keys = jax.random.split(k2, pop_size)
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# batch crossover
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wpn, wpc = state.pop_genomes.nodes[winner], state.pop_genomes.conns[winner]
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lpn, lpc = state.pop_genomes.nodes[loser], state.pop_genomes.conns[loser]
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n_genomes = vmap(crossover)(crossover_rand_keys, Genome(wpn, wpc), Genome(lpn, lpc))
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# batch mutation
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mutate_func = vmap(mutate, in_axes=(None, 0, 0, 0))
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m_n_genomes = mutate_func(state, mutate_rand_keys, n_genomes, new_node_keys) # mutate_new_pop_nodes
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# elitism don't mutate
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pop_nodes = jnp.where(elite_mask[:, None, None], n_genomes.nodes, m_n_genomes.nodes)
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pop_conns = jnp.where(elite_mask[:, None, None], n_genomes.conns, m_n_genomes.conns)
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# update next node key
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all_nodes_keys = pop_nodes[:, :, 0]
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max_node_key = jnp.max(jnp.where(jnp.isnan(all_nodes_keys), -jnp.inf, all_nodes_keys))
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next_node_key = max_node_key + 1
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return state.update(
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pop_genomes=Genome(pop_nodes, pop_conns),
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next_node_key=next_node_key,
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)
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speciate = create_speciate(self.gene_type)
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def tell(state, fitness):
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"""
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Main update function in NEAT.
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"""
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k1, k2, randkey = jax.random.split(state.randkey, 3)
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state = state.update(
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generation=state.generation + 1,
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randkey=randkey
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)
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state, winner, loser, elite_mask = update_species(state, k1, fitness)
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state = create_next_generation(state, k2, winner, loser, elite_mask)
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state = speciate(state)
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return state
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return tell
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