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This commit is contained in:
wls2002
2023-06-19 17:32:34 +08:00
parent 5cbe3c14bb
commit 35b095ba74
6 changed files with 428 additions and 42 deletions
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10
configs/configer.py
View File

@@ -2,11 +2,15 @@ import os
import warnings
import configparser
import numpy as np
from .activations import refactor_act
from .aggregations import refactor_agg
# Configuration used in jit-able functions. The change of values will not cause the re-compilation of JAX.
jit_config_keys = [
"input_idx",
"output_idx",
"compatibility_disjoint",
"compatibility_weight",
"conn_add_prob",
@@ -88,10 +92,14 @@ class Configer:
refactor_act(config)
refactor_agg(config)
input_idx = np.arange(config['num_inputs'])
output_idx = np.arange(config['num_inputs'], config['num_inputs'] + config['num_outputs'])
config['input_idx'] = input_idx
config['output_idx'] = output_idx
return config
@classmethod
def create_jit_config(cls, config):
jit_config = {k: config[k] for k in jit_config_keys}
return jit_config

17
neat/genome/crossover_.py
View File

@@ -1,14 +1,17 @@
from functools import partial
"""
Crossover two genomes to generate a new genome.
The calculation method is the same as the crossover operation in NEAT-python.
See https://neat-python.readthedocs.io/en/latest/_modules/genome.html#DefaultGenome.configure_crossover
"""
from typing import Tuple
import jax
from jax import jit, vmap, Array
from jax import jit, Array
from jax import numpy as jnp
@jit
def crossover(randkey: Array, nodes1: Array, cons1: Array, nodes2: Array, cons2: Array) \
-> Tuple[Array, Array]:
def crossover(randkey: Array, nodes1: Array, cons1: Array, nodes2: Array, cons2: Array) -> Tuple[Array, Array]:
"""
use genome1 and genome2 to generate a new genome
notice that genome1 should have higher fitness than genome2 (genome1 is winner!)
@@ -23,7 +26,11 @@ def crossover(randkey: Array, nodes1: Array, cons1: Array, nodes2: Array, cons2:
# crossover nodes
keys1, keys2 = nodes1[:, 0], nodes2[:, 0]
# make homologous genes align in nodes2 align with nodes1
nodes2 = align_array(keys1, keys2, nodes2, 'node')
# For not homologous genes, use the value of nodes1(winner)
# For homologous genes, use the crossover result between nodes1 and nodes2
new_nodes = jnp.where(jnp.isnan(nodes1) | jnp.isnan(nodes2), nodes1, crossover_gene(randkey_1, nodes1, nodes2))
# crossover connections
@@ -34,7 +41,6 @@ def crossover(randkey: Array, nodes1: Array, cons1: Array, nodes2: Array, cons2:
return new_nodes, new_cons
# @partial(jit, static_argnames=['gene_type'])
def align_array(seq1: Array, seq2: Array, ar2: Array, gene_type: str) -> Array:
"""
After I review this code, I found that it is the most difficult part of the code. Please never change it!
@@ -62,7 +68,6 @@ def align_array(seq1: Array, seq2: Array, ar2: Array, gene_type: str) -> Array:
return refactor_ar2
# @jit
def crossover_gene(rand_key: Array, g1: Array, g2: Array) -> Array:
"""
crossover two genes

66
neat/genome/distance_.py
View File

@@ -1,6 +1,7 @@
"""
Calculate the distance between two genomes.
The calculation method is the same as the distance calculation in NEAT-python.
See https://github.com/CodeReclaimers/neat-python/blob/master/neat/genome.py
"""
from typing import Dict
@@ -14,6 +15,13 @@ from .utils import EMPTY_NODE, EMPTY_CON
def distance(nodes1: Array, cons1: Array, nodes2: Array, cons2: Array, jit_config: Dict) -> Array:
"""
Calculate the distance between two genomes.
args:
nodes1: Array(N, 5)
cons1: Array(C, 4)
nodes2: Array(N, 5)
cons2: Array(C, 4)
returns:
distance: Array(, )
"""
nd = node_distance(nodes1, nodes2, jit_config) # node distance
cd = connection_distance(cons1, cons2, jit_config) # connection distance
@@ -23,13 +31,15 @@ def distance(nodes1: Array, cons1: Array, nodes2: Array, cons2: Array, jit_confi
@jit
def node_distance(nodes1: Array, nodes2: Array, jit_config: Dict):
"""
Calculate the distance between two nodes.
Calculate the distance between nodes of two genomes.
"""
# statistics nodes count of two genomes
node_cnt1 = jnp.sum(~jnp.isnan(nodes1[:, 0]))
node_cnt2 = jnp.sum(~jnp.isnan(nodes2[:, 0]))
max_cnt = jnp.maximum(node_cnt1, node_cnt2)
# align homologous nodes
# this process is similar to np.intersect1d.
nodes = jnp.concatenate((nodes1, nodes2), axis=0)
keys = nodes[:, 0]
sorted_indices = jnp.argsort(keys, axis=0)
@@ -37,21 +47,28 @@ def node_distance(nodes1: Array, nodes2: Array, jit_config: Dict):
nodes = jnp.concatenate([nodes, EMPTY_NODE], axis=0) # add a nan row to the end
fr, sr = nodes[:-1], nodes[1:] # first row, second row
# flag location of homologous nodes
intersect_mask = (fr[:, 0] == sr[:, 0]) & ~jnp.isnan(nodes[:-1, 0])
# calculate the count of non_homologous of two genomes
non_homologous_cnt = node_cnt1 + node_cnt2 - 2 * jnp.sum(intersect_mask)
nd = batch_homologous_node_distance(fr, sr)
nd = jnp.where(jnp.isnan(nd), 0, nd)
homologous_distance = jnp.sum(nd * intersect_mask)
val = non_homologous_cnt * disjoint_coe + homologous_distance * compatibility_coe
return jnp.where(max_cnt == 0, 0, val / max_cnt)
# calculate the distance of homologous nodes
hnd = vmap(homologous_node_distance)(fr, sr)
hnd = jnp.where(jnp.isnan(hnd), 0, hnd)
homologous_distance = jnp.sum(hnd * intersect_mask)
val = non_homologous_cnt * jit_config['compatibility_disjoint'] + homologous_distance * jit_config[
'compatibility_weight']
return jnp.where(max_cnt == 0, 0, val / max_cnt) # avoid zero division
@jit
def connection_distance(cons1, cons2, disjoint_coe=1., compatibility_coe=0.5):
def connection_distance(cons1: Array, cons2: Array, jit_config: Dict):
"""
Calculate the distance between two connections.
Calculate the distance between connections of two genomes.
Similar process as node_distance.
"""
con_cnt1 = jnp.sum(~jnp.isnan(cons1[:, 0]))
con_cnt2 = jnp.sum(~jnp.isnan(cons2[:, 0]))
@@ -68,37 +85,34 @@ def connection_distance(cons1, cons2, disjoint_coe=1., compatibility_coe=0.5):
intersect_mask = jnp.all(fr[:, :2] == sr[:, :2], axis=1) & ~jnp.isnan(fr[:, 0])
non_homologous_cnt = con_cnt1 + con_cnt2 - 2 * jnp.sum(intersect_mask)
cd = batch_homologous_connection_distance(fr, sr)
cd = jnp.where(jnp.isnan(cd), 0, cd)
homologous_distance = jnp.sum(cd * intersect_mask)
hcd = vmap(homologous_connection_distance)(fr, sr)
hcd = jnp.where(jnp.isnan(hcd), 0, hcd)
homologous_distance = jnp.sum(hcd * intersect_mask)
val = non_homologous_cnt * disjoint_coe + homologous_distance * compatibility_coe
val = non_homologous_cnt * jit_config['compatibility_disjoint'] + homologous_distance * jit_config[
'compatibility_weight']
return jnp.where(max_cnt == 0, 0, val / max_cnt)
@vmap
def batch_homologous_node_distance(b_n1, b_n2):
return homologous_node_distance(b_n1, b_n2)
@vmap
def batch_homologous_connection_distance(b_c1, b_c2):
return homologous_connection_distance(b_c1, b_c2)
@jit
def homologous_node_distance(n1, n2):
def homologous_node_distance(n1: Array, n2: Array):
"""
Calculate the distance between two homologous nodes.
"""
d = 0
d += jnp.abs(n1[1] - n2[1]) # bias
d += jnp.abs(n1[2] - n2[2]) # response
d += n1[3] != n2[3] # activation
d += n1[4] != n2[4]
d += n1[4] != n2[4] # aggregation
return d
@jit
def homologous_connection_distance(c1, c2):
def homologous_connection_distance(c1: Array, c2: Array):
"""
Calculate the distance between two homologous connections.
"""
d = 0
d += jnp.abs(c1[2] - c2[2]) # weight
d += c1[3] != c2[3] # enable

11
neat/genome/genome_.py
View File

@@ -17,10 +17,7 @@ from jax import jit, numpy as jnp
from .utils import fetch_first
def initialize_genomes(N: int,
C: int,
config: Dict) \
-> Tuple[NDArray, NDArray, NDArray, NDArray]:
def initialize_genomes(N: int, C: int, config: Dict) -> Tuple[NDArray, NDArray]:
"""
Initialize genomes with default values.
@@ -41,8 +38,8 @@ def initialize_genomes(N: int,
pop_nodes = np.full((config['pop_size'], N, 5), np.nan)
pop_cons = np.full((config['pop_size'], C, 4), np.nan)
input_idx = np.arange(config['num_inputs'])
output_idx = np.arange(config['num_inputs'], config['num_inputs'] + config['num_outputs'])
input_idx = config['input_idx']
output_idx = config['output_idx']
pop_nodes[:, input_idx, 0] = input_idx
pop_nodes[:, output_idx, 0] = output_idx
@@ -61,7 +58,7 @@ def initialize_genomes(N: int,
pop_cons[:, :p, 2] = config['weight_init_mean']
pop_cons[:, :p, 3] = 1
return pop_nodes, pop_cons, input_idx, output_idx
return pop_nodes, pop_cons
def expand_single(nodes: NDArray, cons: NDArray, new_N: int, new_C: int) -> Tuple[NDArray, NDArray]:

362
neat/genome/mutate_.py Normal file
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@@ -0,0 +1,362 @@
"""
Mutate a genome.
The calculation method is the same as the mutation operation in NEAT-python.
See https://neat-python.readthedocs.io/en/latest/_modules/genome.html#DefaultGenome.mutate
"""
from typing import Tuple, Dict
from functools import partial
import jax
from jax import numpy as jnp
from jax import jit, Array
from .utils import fetch_random, fetch_first, I_INT, unflatten_connections
from .genome_ import add_node, delete_node_by_idx, delete_connection_by_idx, add_connection
from .graph import check_cycles
@jit
def mutate(rand_key: Array, nodes: Array, connections: Array, new_node_key: int, jit_config: Dict):
"""
:param rand_key:
:param nodes: (N, 5)
:param connections: (2, N, N)
:param new_node_key:
:param jit_config:
:return:
"""
def m_add_node(rk, n, c):
return mutate_add_node(rk, n, c, new_node_key, jit_config['bias_init_mean'], jit_config['response_init_mean'],
jit_config['activation_default'], jit_config['aggregation_default'])
def m_add_connection(rk, n, c):
return mutate_add_connection(rk, n, c, jit_config['input_idx'], jit_config['output_idx'])
def m_delete_node(rk, n, c):
return mutate_delete_node(rk, n, c, jit_config['input_idx'], jit_config['output_idx'])
def m_delete_connection(rk, n, c):
return mutate_delete_connection(rk, n, c)
r1, r2, r3, r4, rand_key = jax.random.split(rand_key, 5)
# structural mutations
# mutate add node
r = rand(r1)
aux_nodes, aux_connections = m_add_node(r1, nodes, connections)
nodes = jnp.where(r < jit_config['node_add_prob'], aux_nodes, nodes)
connections = jnp.where(r < jit_config['node_add_prob'], aux_connections, connections)
# mutate add connection
r = rand(r2)
aux_nodes, aux_connections = m_add_connection(r3, nodes, connections)
nodes = jnp.where(r < jit_config['conn_add_prob'], aux_nodes, nodes)
connections = jnp.where(r < jit_config['conn_add_prob'], aux_connections, connections)
# mutate delete node
r = rand(r3)
aux_nodes, aux_connections = m_delete_node(r2, nodes, connections)
nodes = jnp.where(r < jit_config['node_delete_prob'], aux_nodes, nodes)
connections = jnp.where(r < jit_config['node_delete_prob'], aux_connections, connections)
# mutate delete connection
r = rand(r4)
aux_nodes, aux_connections = m_delete_connection(r4, nodes, connections)
nodes = jnp.where(r < jit_config['conn_delete_prob'], aux_nodes, nodes)
connections = jnp.where(r < jit_config['conn_delete_prob'], aux_connections, connections)
# value mutations
nodes, connections = mutate_values(rand_key, nodes, connections, jit_config)
return nodes, connections
@jit
def mutate_values(rand_key: Array, nodes: Array, cons: Array, jit_config: Dict) -> Tuple[Array, Array]:
"""
Mutate values of nodes and connections.
Args:
rand_key: A random key for generating random values.
nodes: A 2D array representing nodes.
cons: A 3D array representing connections.
jit_config: A dict containing configuration for jit-able functions.
Returns:
A tuple containing mutated nodes and connections.
"""
k1, k2, k3, k4, k5, rand_key = jax.random.split(rand_key, num=6)
bias_new = mutate_float_values(k1, nodes[:, 1], bias_mean, bias_std,
bias_mutate_strength, bias_mutate_rate, bias_replace_rate)
response_new = mutate_float_values(k2, nodes[:, 2], response_mean, response_std,
response_mutate_strength, response_mutate_rate, response_replace_rate)
weight_new = mutate_float_values(k3, cons[:, 2], weight_mean, weight_std,
weight_mutate_strength, weight_mutate_rate, weight_replace_rate)
act_new = mutate_int_values(k4, nodes[:, 3], act_list, act_replace_rate)
agg_new = mutate_int_values(k5, nodes[:, 4], agg_list, agg_replace_rate)
# mutate enabled
r = jax.random.uniform(rand_key, cons[:, 3].shape)
enabled_new = jnp.where(r < enabled_reverse_rate, 1 - cons[:, 3], cons[:, 3])
enabled_new = jnp.where(~jnp.isnan(cons[:, 3]), enabled_new, jnp.nan)
nodes = nodes.at[:, 1].set(bias_new)
nodes = nodes.at[:, 2].set(response_new)
nodes = nodes.at[:, 3].set(act_new)
nodes = nodes.at[:, 4].set(agg_new)
cons = cons.at[:, 2].set(weight_new)
cons = cons.at[:, 3].set(enabled_new)
return nodes, cons
@jit
def mutate_float_values(rand_key: Array, old_vals: Array, mean: float, std: float,
mutate_strength: float, mutate_rate: float, replace_rate: float) -> Array:
"""
Mutate float values of a given array.
Args:
rand_key: A random key for generating random values.
old_vals: A 1D array of float values to be mutated.
mean: Mean of the values.
std: Standard deviation of the values.
mutate_strength: Strength of the mutation.
mutate_rate: Rate of the mutation.
replace_rate: Rate of the replacement.
Returns:
A mutated 1D array of float values.
"""
k1, k2, k3, rand_key = jax.random.split(rand_key, num=4)
noise = jax.random.normal(k1, old_vals.shape) * mutate_strength
replace = jax.random.normal(k2, old_vals.shape) * std + mean
r = jax.random.uniform(k3, old_vals.shape)
new_vals = old_vals
new_vals = jnp.where(r < mutate_rate, new_vals + noise, new_vals)
new_vals = jnp.where(
jnp.logical_and(mutate_rate < r, r < mutate_rate + replace_rate),
replace,
new_vals
)
new_vals = jnp.where(~jnp.isnan(old_vals), new_vals, jnp.nan)
return new_vals
@jit
def mutate_int_values(rand_key: Array, old_vals: Array, val_list: Array, replace_rate: float) -> Array:
"""
Mutate integer values (act, agg) of a given array.
Args:
rand_key: A random key for generating random values.
old_vals: A 1D array of integer values to be mutated.
val_list: List of the integer values.
replace_rate: Rate of the replacement.
Returns:
A mutated 1D array of integer values.
"""
k1, k2, rand_key = jax.random.split(rand_key, num=3)
replace_val = jax.random.choice(k1, val_list, old_vals.shape)
r = jax.random.uniform(k2, old_vals.shape)
new_vals = old_vals
new_vals = jnp.where(r < replace_rate, replace_val, new_vals)
new_vals = jnp.where(~jnp.isnan(old_vals), new_vals, jnp.nan)
return new_vals
@jit
def mutate_add_node(rand_key: Array, nodes: Array, cons: Array, new_node_key: int,
default_bias: float = 0, default_response: float = 1,
default_act: int = 0, default_agg: int = 0) -> Tuple[Array, Array]:
"""
Randomly add a new node from splitting a connection.
:param rand_key:
:param new_node_key:
:param nodes:
:param cons:
:param default_bias:
:param default_response:
:param default_act:
:param default_agg:
:return:
"""
# randomly choose a connection
i_key, o_key, idx = choice_connection_key(rand_key, nodes, cons)
def nothing(): # there is no connection to split
return nodes, cons
def successful_add_node():
# disable the connection
new_nodes, new_cons = nodes, cons
new_cons = new_cons.at[idx, 3].set(False)
# add a new node
new_nodes, new_cons = \
add_node(new_nodes, new_cons, new_node_key,
bias=default_bias, response=default_response, act=default_act, agg=default_agg)
# add two new connections
w = new_cons[idx, 2]
new_nodes, new_cons = add_connection(new_nodes, new_cons, i_key, new_node_key, weight=1, enabled=True)
new_nodes, new_cons = add_connection(new_nodes, new_cons, new_node_key, o_key, weight=w, enabled=True)
return new_nodes, new_cons
# if from_idx == I_INT, that means no connection exist, do nothing
nodes, cons = jax.lax.cond(idx == I_INT, nothing, successful_add_node)
return nodes, cons
# TODO: Need we really need to delete a node?
@jit
def mutate_delete_node(rand_key: Array, nodes: Array, cons: Array,
input_keys: Array, output_keys: Array) -> Tuple[Array, Array]:
"""
Randomly delete a node. Input and output nodes are not allowed to be deleted.
:param rand_key:
:param nodes:
:param cons:
:param input_keys:
:param output_keys:
:return:
"""
# randomly choose a node
node_key, node_idx = choice_node_key(rand_key, nodes, input_keys, output_keys,
allow_input_keys=False, allow_output_keys=False)
def nothing():
return nodes, cons
def successful_delete_node():
# delete the node
aux_nodes, aux_cons = delete_node_by_idx(nodes, cons, node_idx)
# delete all connections
aux_cons = jnp.where(((aux_cons[:, 0] == node_key) | (aux_cons[:, 1] == node_key))[:, jnp.newaxis],
jnp.nan, aux_cons)
return aux_nodes, aux_cons
nodes, cons = jax.lax.cond(node_idx == I_INT, nothing, successful_delete_node)
return nodes, cons
@jit
def mutate_add_connection(rand_key: Array, nodes: Array, cons: Array,
input_keys: Array, output_keys: Array) -> Tuple[Array, Array]:
"""
Randomly add a new connection. The output node is not allowed to be an input node. If in feedforward networks,
cycles are not allowed.
:param rand_key:
:param nodes:
:param cons:
:param input_keys:
:param output_keys:
:return:
"""
# randomly choose two nodes
k1, k2 = jax.random.split(rand_key, num=2)
i_key, from_idx = choice_node_key(k1, nodes, input_keys, output_keys,
allow_input_keys=True, allow_output_keys=True)
o_key, to_idx = choice_node_key(k2, nodes, input_keys, output_keys,
allow_input_keys=False, allow_output_keys=True)
con_idx = fetch_first((cons[:, 0] == i_key) & (cons[:, 1] == o_key))
def successful():
new_nodes, new_cons = add_connection(nodes, cons, i_key, o_key, weight=1, enabled=True)
return new_nodes, new_cons
def already_exist():
new_cons = cons.at[con_idx, 3].set(True)
return nodes, new_cons
def cycle():
return nodes, cons
is_already_exist = con_idx != I_INT
unflattened = unflatten_connections(nodes, cons)
is_cycle = check_cycles(nodes, unflattened, from_idx, to_idx)
choice = jnp.where(is_already_exist, 0, jnp.where(is_cycle, 1, 2))
nodes, cons = jax.lax.switch(choice, [already_exist, cycle, successful])
return nodes, cons
@jit
def mutate_delete_connection(rand_key: Array, nodes: Array, cons: Array):
"""
Randomly delete a connection.
:param rand_key:
:param nodes:
:param cons:
:return:
"""
# randomly choose a connection
i_key, o_key, idx = choice_connection_key(rand_key, nodes, cons)
def nothing():
return nodes, cons
def successfully_delete_connection():
return delete_connection_by_idx(nodes, cons, idx)
nodes, cons = jax.lax.cond(idx == I_INT, nothing, successfully_delete_connection)
return nodes, cons
@partial(jit, static_argnames=('allow_input_keys', 'allow_output_keys'))
def choice_node_key(rand_key: Array, nodes: Array,
input_keys: Array, output_keys: Array,
allow_input_keys: bool = False, allow_output_keys: bool = False) -> Tuple[Array, Array]:
"""
Randomly choose a node key from the given nodes. It guarantees that the chosen node not be the input or output node.
:param rand_key:
:param nodes:
:param input_keys:
:param output_keys:
:param allow_input_keys:
:param allow_output_keys:
:return: return its key and position(idx)
"""
node_keys = nodes[:, 0]
mask = ~jnp.isnan(node_keys)
if not allow_input_keys:
mask = jnp.logical_and(mask, ~jnp.isin(node_keys, input_keys))
if not allow_output_keys:
mask = jnp.logical_and(mask, ~jnp.isin(node_keys, output_keys))
idx = fetch_random(rand_key, mask)
key = jnp.where(idx != I_INT, nodes[idx, 0], jnp.nan)
return key, idx
@jit
def choice_connection_key(rand_key: Array, nodes: Array, cons: Array) -> Tuple[Array, Array, Array]:
"""
Randomly choose a connection key from the given connections.
:param rand_key:
:param nodes:
:param cons:
:return: i_key, o_key, idx
"""
idx = fetch_random(rand_key, ~jnp.isnan(cons[:, 0]))
i_key = jnp.where(idx != I_INT, cons[idx, 0], jnp.nan)
o_key = jnp.where(idx != I_INT, cons[idx, 1], jnp.nan)
return i_key, o_key, idx
@jit
def rand(rand_key):
return jax.random.uniform(rand_key, ())

4
neat/pipeline_.py
View File

@@ -21,7 +21,7 @@ class Pipeline:
self.generation = 0
self.best_genome = None
self.pop_nodes, self.pop_cons, self.input_idx, self.output_idx = initialize_genomes(self.N, self.C, self.config)
self.pop_nodes, self.pop_cons = initialize_genomes(self.N, self.C, self.config)
print(self.pop_nodes, self.pop_cons, self.input_idx, self.output_idx, sep='\n')
print(self.pop_nodes, self.pop_cons, sep='\n')
print(self.jit_config)