remove create_func....
This commit is contained in:
wls2002
@@ -1,20 +1,18 @@
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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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from jax import numpy as jnp
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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 SpeciesInfo, update_species, create_speciate
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from .ga import create_next_generation
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from .species import SpeciesInfo, update_species, 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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def __init__(self, config: Config, gene: Gene):
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self.config = config
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self.gene_type = gene_type
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self.gene = gene
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self.forward_func = None
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self.tell_func = None
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@@ -31,8 +29,8 @@ class NEAT(Algorithm):
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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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NL=1 + len(self.gene.node_attrs), # node length = (key) + attributes
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CL=3 + len(self.gene.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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@@ -46,7 +44,7 @@ class NEAT(Algorithm):
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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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state = self.gene.setup(state)
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pop_genomes = self._initialize_genomes(state)
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species_info = SpeciesInfo.initialize(state)
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@@ -74,26 +72,32 @@ class NEAT(Algorithm):
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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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def ask_algorithm(self, state: State):
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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 tell_algorithm(self, state: State, fitness):
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k1, k2, randkey = jax.random.split(state.randkey, 3)
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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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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(self.config.neat, self.gene, state, k2, winner, loser, elite_mask)
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state = speciate(self.gene, state)
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return state
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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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return self.gene.forward_transform(state, genome)
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def forward(self, state: State, inputs, genome: Genome):
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return self.gene.forward(state, inputs, 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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@@ -106,80 +110,21 @@ class NEAT(Algorithm):
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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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o_nodes[np.concatenate([input_idx, output_idx]), 1:] = self.gene.new_node_attrs(state)
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o_nodes[new_node_key, 1:] = self.gene.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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o_conns[input_idx, 3:] = self.gene.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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o_conns[output_idx, 3:] = self.gene.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 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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