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