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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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algorithms/neat/population.py
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from functools import partial
import jax
import jax.numpy as jnp
from jax import jit, vmap
from jax import Array
from .genome import distance, mutate, crossover
from .genome.utils import I_INT, fetch_first, argmin_with_mask
@jit
def create_next_generation_then_speciate(rand_key, pop_nodes, pop_cons, winner_part, loser_part, elite_mask,
new_node_keys,
pre_spe_center_nodes, pre_spe_center_cons, species_keys, new_species_key_start,
species_kwargs, mutate_kwargs):
# create next generation
pop_nodes, pop_cons = create_next_generation(rand_key, pop_nodes, pop_cons, winner_part, loser_part, elite_mask,
new_node_keys, **mutate_kwargs)
# speciate
idx2specie, spe_center_nodes, spe_center_cons, species_keys = speciate(pop_nodes, pop_cons, pre_spe_center_nodes,
pre_spe_center_cons, species_keys,
new_species_key_start, **species_kwargs)
return pop_nodes, pop_cons, idx2specie, spe_center_nodes, spe_center_cons, species_keys
@jit
def speciate(pop_nodes: Array, pop_cons: Array, spe_center_nodes: Array, spe_center_cons: Array,
species_keys, new_species_key_start,
disjoint_coe: float = 1., compatibility_coe: float = 0.5, compatibility_threshold=3.0
):
"""
args:
pop_nodes: (pop_size, N, 5)
pop_cons: (pop_size, C, 4)
spe_center_nodes: (species_size, N, 5)
spe_center_cons: (species_size, C, 4)
"""
pop_size, species_size = pop_nodes.shape[0], spe_center_nodes.shape[0]
# prepare distance functions
distance_with_args = partial(distance, disjoint_coe=disjoint_coe, compatibility_coe=compatibility_coe)
o2p_distance_func = vmap(distance_with_args, in_axes=(None, None, 0, 0))
s2p_distance_func = vmap(
o2p_distance_func, in_axes=(0, 0, None, None)
)
# idx to specie key
idx2specie = jnp.full((pop_size,), I_INT, dtype=jnp.int32) # I_INT means not assigned to any species
# part 1: find new centers
# the distance between each species' center and each genome in population
s2p_distance = s2p_distance_func(spe_center_nodes, spe_center_cons, pop_nodes, pop_cons)
def find_new_centers(i, carry):
i2s, scn, scc = carry
# find new center
idx = argmin_with_mask(s2p_distance[i], mask=i2s == I_INT)
# check species[i] exist or not
# if not exist, set idx and i to I_INT, jax will not do array value assignment
idx = jnp.where(species_keys[i] != I_INT, idx, I_INT)
i = jnp.where(species_keys[i] != I_INT, i, I_INT)
i2s = i2s.at[idx].set(species_keys[i])
scn = scn.at[i].set(pop_nodes[idx])
scc = scc.at[i].set(pop_cons[idx])
return i2s, scn, scc
idx2specie, spe_center_nodes, spe_center_cons = jax.lax.fori_loop(0, species_size, find_new_centers, (idx2specie, spe_center_nodes, spe_center_cons))
def continue_execute_while(carry):
i, i2s, scn, scc, sk, ck = carry # sk is short for species_keys, ck is short for current key
not_all_assigned = ~jnp.all(i2s != I_INT)
not_reach_species_upper_bounds = i < species_size
return not_all_assigned & not_reach_species_upper_bounds
def deal_with_each_center_genome(carry):
i, i2s, scn, scc, sk, ck = carry # scn is short for spe_center_nodes, scc is short for spe_center_cons
center_nodes, center_cons = spe_center_nodes[i], spe_center_cons[i]
i2s, scn, scc, sk, ck = jax.lax.cond(
jnp.all(jnp.isnan(center_nodes)), # whether the center genome is valid
create_new_specie, # if not valid, create a new specie
update_exist_specie, # if valid, update the specie
(i, i2s, scn, scc, sk, ck)
)
return i + 1, i2s, scn, scc, sk, ck
def create_new_specie(carry):
i, i2s, scn, scc, sk, ck = carry
# pick the first one who has not been assigned to any species
idx = fetch_first(i2s == I_INT)
# assign it to new specie
sk = sk.at[i].set(ck)
i2s = i2s.at[idx].set(ck)
# update center genomes
scn = scn.at[i].set(pop_nodes[idx])
scc = scc.at[i].set(pop_cons[idx])
i2s, scn, scc = speciate_by_threshold((i, i2s, scn, scc, sk))
return i2s, scn, scc, sk, ck + 1 # change to next new speciate key
def update_exist_specie(carry):
i, i2s, scn, scc, sk, ck = carry
i2s, scn, scc = speciate_by_threshold((i, i2s, scn, scc, sk))
return i2s, scn, scc, sk, ck
def speciate_by_threshold(carry):
i, i2s, scn, scc, sk = carry
# distance between such center genome and ppo genomes
o2p_distance = o2p_distance_func(scn[i], scc[i], pop_nodes, pop_cons)
close_enough_mask = o2p_distance < compatibility_threshold
# when it is close enough, assign it to the species, remember not to update genome has already been assigned
i2s = jnp.where(close_enough_mask & (i2s == I_INT), sk[i], i2s)
return i2s, scn, scc
current_new_key = new_species_key_start
# update idx2specie
_, idx2specie, spe_center_nodes, spe_center_cons, species_keys, new_species_key_start = jax.lax.while_loop(
continue_execute_while,
deal_with_each_center_genome,
(0, idx2specie, spe_center_nodes, spe_center_cons, species_keys, current_new_key)
)
# if there are still some pop genomes not assigned to any species, add them to the last genome
# this condition seems to be only happened when the number of species is reached species upper bounds
idx2specie = jnp.where(idx2specie == I_INT, species_keys[-1], idx2specie)
return idx2specie, spe_center_nodes, spe_center_cons, species_keys
@jit
def create_next_generation(rand_key, pop_nodes, pop_cons, winner_part, loser_part, elite_mask, new_node_keys,
**mutate_kwargs):
# prepare functions
batch_crossover = vmap(crossover)
mutate_with_args = vmap(partial(mutate, **mutate_kwargs))
pop_size = pop_nodes.shape[0]
k1, k2 = jax.random.split(rand_key, 2)
crossover_rand_keys = jax.random.split(k1, pop_size)
mutate_rand_keys = jax.random.split(k2, pop_size)
# batch crossover
wpn = pop_nodes[winner_part] # winner pop nodes
wpc = pop_cons[winner_part] # winner pop connections
lpn = pop_nodes[loser_part] # loser pop nodes
lpc = pop_cons[loser_part] # loser pop connections
npn, npc = batch_crossover(crossover_rand_keys, wpn, wpc, lpn, lpc) # new pop nodes, new pop connections
m_npn, m_npc = mutate_with_args(mutate_rand_keys, npn, npc, new_node_keys) # mutate_new_pop_nodes
# elitism don't mutate
pop_nodes = jnp.where(elite_mask[:, None, None], npn, m_npn)
pop_cons = jnp.where(elite_mask[:, None, None], npc, m_npc)
return pop_nodes, pop_cons