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from typing import Tuple
from functools import partial
import jax
from jax import numpy as jnp
from jax import jit, vmap, Array
from .utils import fetch_random, fetch_first, I_INT
from .genome import add_node, add_connection_by_idx, delete_node_by_idx, delete_connection_by_idx
from .graph import check_cycles
def create_mutate_function(config, input_keys, output_keys, batch: bool):
"""
create mutate function for different situations
:param output_keys:
:param input_keys:
:param config:
:param batch: mutate for population or not
:return:
"""
bias = config.neat.gene.bias
bias_default = bias.init_mean
bias_mean = bias.init_mean
bias_std = bias.init_stdev
bias_mutate_strength = bias.mutate_power
bias_mutate_rate = bias.mutate_rate
bias_replace_rate = bias.replace_rate
response = config.neat.gene.response
response_default = response.init_mean
response_mean = response.init_mean
response_std = response.init_stdev
response_mutate_strength = response.mutate_power
response_mutate_rate = response.mutate_rate
response_replace_rate = response.replace_rate
weight = config.neat.gene.weight
weight_mean = weight.init_mean
weight_std = weight.init_stdev
weight_mutate_strength = weight.mutate_power
weight_mutate_rate = weight.mutate_rate
weight_replace_rate = weight.replace_rate
activation = config.neat.gene.activation
# act_default = activation.default
act_default = 0
act_range = len(activation.options)
act_replace_rate = activation.mutate_rate
aggregation = config.neat.gene.aggregation
# agg_default = aggregation.default
agg_default = 0
agg_range = len(aggregation.options)
agg_replace_rate = aggregation.mutate_rate
enabled = config.neat.gene.enabled
enabled_reverse_rate = enabled.mutate_rate
genome = config.neat.genome
add_node_rate = genome.node_add_prob
delete_node_rate = genome.node_delete_prob
add_connection_rate = genome.conn_add_prob
delete_connection_rate = genome.conn_delete_prob
single_structure_mutate = genome.single_structural_mutation
if not batch:
return lambda rand_key, nodes, connections, new_node_key: \
mutate(rand_key, nodes, connections, new_node_key, input_keys, output_keys,
bias_default, bias_mean, bias_std, bias_mutate_strength, bias_mutate_rate,
bias_replace_rate, response_default, response_mean, response_std,
response_mutate_strength, response_mutate_rate, response_replace_rate,
weight_mean, weight_std, weight_mutate_strength, weight_mutate_rate,
weight_replace_rate, act_default, act_range, act_replace_rate,
agg_default, agg_range, agg_replace_rate, enabled_reverse_rate,
add_node_rate, delete_node_rate, add_connection_rate, delete_connection_rate,
single_structure_mutate)
else:
batched_mutate = vmap(mutate, in_axes=(0, 0, 0, 0, *(None,) * 31))
return lambda rand_keys, pop_nodes, pop_connections, new_node_keys: \
batched_mutate(rand_keys, pop_nodes, pop_connections, new_node_keys, input_keys, output_keys,
bias_default, bias_mean, bias_std, bias_mutate_strength, bias_mutate_rate,
bias_replace_rate, response_default, response_mean, response_std,
response_mutate_strength, response_mutate_rate, response_replace_rate,
weight_mean, weight_std, weight_mutate_strength, weight_mutate_rate,
weight_replace_rate, act_default, act_range, act_replace_rate,
agg_default, agg_range, agg_replace_rate, enabled_reverse_rate,
add_node_rate, delete_node_rate, add_connection_rate, delete_connection_rate,
single_structure_mutate)
@partial(jit, static_argnames=["single_structure_mutate"])
def mutate(rand_key: Array,
nodes: Array,
connections: Array,
new_node_key: int,
input_keys: Array,
output_keys: Array,
bias_default: float = 0,
bias_mean: float = 0,
bias_std: float = 1,
bias_mutate_strength: float = 0.5,
bias_mutate_rate: float = 0.7,
bias_replace_rate: float = 0.1,
response_default: float = 1,
response_mean: float = 1.,
response_std: float = 0.,
response_mutate_strength: float = 0.,
response_mutate_rate: float = 0.,
response_replace_rate: float = 0.,
weight_mean: float = 0.,
weight_std: float = 1.,
weight_mutate_strength: float = 0.5,
weight_mutate_rate: float = 0.7,
weight_replace_rate: float = 0.1,
act_default: int = 0,
act_range: int = 5,
act_replace_rate: float = 0.1,
agg_default: int = 0,
agg_range: int = 5,
agg_replace_rate: float = 0.1,
enabled_reverse_rate: float = 0.1,
add_node_rate: float = 0.2,
delete_node_rate: float = 0.2,
add_connection_rate: float = 0.4,
delete_connection_rate: float = 0.4,
single_structure_mutate: bool = True):
"""
:param output_keys:
:param input_keys:
:param agg_default:
:param act_default:
:param response_default:
:param bias_default:
:param rand_key:
:param nodes: (N, 5)
:param connections: (2, N, N)
:param new_node_key:
:param bias_mean:
:param bias_std:
:param bias_mutate_strength:
:param bias_mutate_rate:
:param bias_replace_rate:
:param response_mean:
:param response_std:
:param response_mutate_strength:
:param response_mutate_rate:
:param response_replace_rate:
:param weight_mean:
:param weight_std:
:param weight_mutate_strength:
:param weight_mutate_rate:
:param weight_replace_rate:
:param act_range:
:param act_replace_rate:
:param agg_range:
:param agg_replace_rate:
:param enabled_reverse_rate:
:param add_node_rate:
:param delete_node_rate:
:param add_connection_rate:
:param delete_connection_rate:
:param single_structure_mutate: a genome is structurally mutate at most once
:return:
"""
# mutate_structure
def nothing(rk, n, c):
return n, c
def m_add_node(rk, n, c):
return mutate_add_node(rk, new_node_key, n, c, bias_default, response_default, act_default, agg_default)
def m_delete_node(rk, n, c):
return mutate_delete_node(rk, n, c, input_keys, output_keys)
def m_add_connection(rk, n, c):
return mutate_add_connection(rk, n, c, input_keys, output_keys)
def m_delete_connection(rk, n, c):
return mutate_delete_connection(rk, n, c)
mutate_structure_li = [nothing, m_add_node, m_delete_node, m_add_connection, m_delete_connection]
if single_structure_mutate:
r1, r2, rand_key = jax.random.split(rand_key, 3)
d = jnp.maximum(1, add_node_rate + delete_node_rate + add_connection_rate + delete_connection_rate)
# shorten variable names for beauty
anr, dnr = add_node_rate / d, delete_node_rate / d
acr, dcr = add_connection_rate / d, delete_connection_rate / d
r = rand(r1)
branch = 0
branch = jnp.where(r <= anr, 1, branch)
branch = jnp.where((anr < r) & (r <= anr + dnr), 2, branch)
branch = jnp.where((anr + dnr < r) & (r <= anr + dnr + acr), 3, branch)
branch = jnp.where((anr + dnr + acr) < r & r <= (anr + dnr + acr + dcr), 4, branch)
nodes, connections = jax.lax.switch(branch, mutate_structure_li, (r2, nodes, connections))
else:
r1, r2, r3, r4, rand_key = jax.random.split(rand_key, 5)
# mutate add node
aux_nodes, aux_connections = m_add_node(r1, nodes, connections)
nodes = jnp.where(rand(r1) < add_node_rate, aux_nodes, nodes)
connections = jnp.where(rand(r1) < add_node_rate, aux_connections, connections)
# mutate delete node
aux_nodes, aux_connections = m_delete_node(r2, nodes, connections)
nodes = jnp.where(rand(r2) < delete_node_rate, aux_nodes, nodes)
connections = jnp.where(rand(r2) < delete_node_rate, aux_connections, connections)
# mutate add connection
aux_nodes, aux_connections = m_add_connection(r3, nodes, connections)
nodes = jnp.where(rand(r3) < add_connection_rate, aux_nodes, nodes)
connections = jnp.where(rand(r3) < add_connection_rate, aux_connections, connections)
# mutate delete connection
aux_nodes, aux_connections = m_delete_connection(r4, nodes, connections)
nodes = jnp.where(rand(r4) < delete_connection_rate, aux_nodes, nodes)
connections = jnp.where(rand(r4) < delete_connection_rate, aux_connections, connections)
nodes, connections = mutate_values(rand_key, nodes, connections, bias_mean, bias_std, bias_mutate_strength,
bias_mutate_rate, bias_replace_rate, response_mean, response_std,
response_mutate_strength, response_mutate_rate, response_replace_rate,
weight_mean, weight_std, weight_mutate_strength,
weight_mutate_rate, weight_replace_rate, act_range, act_replace_rate, agg_range,
agg_replace_rate, enabled_reverse_rate)
return nodes, connections
@jit
def mutate_values(rand_key: Array,
nodes: Array,
connections: Array,
bias_mean: float = 0,
bias_std: float = 1,
bias_mutate_strength: float = 0.5,
bias_mutate_rate: float = 0.7,
bias_replace_rate: float = 0.1,
response_mean: float = 1.,
response_std: float = 0.,
response_mutate_strength: float = 0.,
response_mutate_rate: float = 0.,
response_replace_rate: float = 0.,
weight_mean: float = 0.,
weight_std: float = 1.,
weight_mutate_strength: float = 0.5,
weight_mutate_rate: float = 0.7,
weight_replace_rate: float = 0.1,
act_range: int = 5,
act_replace_rate: float = 0.1,
agg_range: int = 5,
agg_replace_rate: float = 0.1,
enabled_reverse_rate: float = 0.1) -> 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.
connections: A 3D array representing connections.
bias_mean: Mean of the bias values.
bias_std: Standard deviation of the bias values.
bias_mutate_strength: Strength of the bias mutation.
bias_mutate_rate: Rate of the bias mutation.
bias_replace_rate: Rate of the bias replacement.
response_mean: Mean of the response values.
response_std: Standard deviation of the response values.
response_mutate_strength: Strength of the response mutation.
response_mutate_rate: Rate of the response mutation.
response_replace_rate: Rate of the response replacement.
weight_mean: Mean of the weight values.
weight_std: Standard deviation of the weight values.
weight_mutate_strength: Strength of the weight mutation.
weight_mutate_rate: Rate of the weight mutation.
weight_replace_rate: Rate of the weight replacement.
act_range: Range of the activation function values.
act_replace_rate: Rate of the activation function replacement.
agg_range: Range of the aggregation function values.
agg_replace_rate: Rate of the aggregation function replacement.
enabled_reverse_rate: Rate of reversing enabled state of connections.
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, connections[0, :, :], weight_mean, weight_std,
weight_mutate_strength, weight_mutate_rate, weight_replace_rate)
act_new = mutate_int_values(k4, nodes[:, 3], act_range, act_replace_rate)
agg_new = mutate_int_values(k5, nodes[:, 4], agg_range, agg_replace_rate)
# refactor enabled
r = jax.random.uniform(rand_key, connections[1, :, :].shape)
enabled_new = connections[1, :, :] == 1
enabled_new = jnp.where(r < enabled_reverse_rate, ~enabled_new, enabled_new)
enabled_new = jnp.where(~jnp.isnan(connections[0, :, :]), 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)
connections = connections.at[0, :, :].set(weight_new)
connections = connections.at[1, :, :].set(enabled_new)
return nodes, connections
@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, range: int, 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.
range: Range 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.randint(k1, old_vals.shape, 0, range)
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, new_node_key: int, nodes: Array, connections: Array,
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 connections:
:param default_bias:
:param default_response:
:param default_act:
:param default_agg:
:return:
"""
# randomly choose a connection
from_key, to_key, from_idx, to_idx = choice_connection_key(rand_key, nodes, connections)
# disable the connection
connections = connections.at[1, from_idx, to_idx].set(False)
# add a new node
nodes, connections = add_node(new_node_key, nodes, connections,
bias=default_bias, response=default_response, act=default_act, agg=default_agg)
new_idx = fetch_first(nodes[:, 0] == new_node_key)
# add two new connections
weight = connections[0, from_idx, to_idx]
nodes, connections = add_connection_by_idx(from_idx, new_idx, nodes, connections, weight=0, enabled=True)
nodes, connections = add_connection_by_idx(new_idx, to_idx, nodes, connections, weight=weight, enabled=True)
return nodes, connections
@jit
def mutate_delete_node(rand_key: Array, nodes: Array, connections: 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 connections:
: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)
# delete the node
aux_nodes, aux_connections = delete_node_by_idx(node_idx, nodes, connections)
# delete connections
aux_connections = aux_connections.at[:, node_idx, :].set(jnp.nan)
aux_connections = aux_connections.at[:, :, node_idx].set(jnp.nan)
# check node_key valid
nodes = jnp.where(jnp.isnan(node_key), nodes, aux_nodes) # if node_key is nan, do not delete the node
connections = jnp.where(jnp.isnan(node_key), connections, aux_connections)
return nodes, connections
@jit
def mutate_add_connection(rand_key: Array, nodes: Array, connections: 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 connections:
:param input_keys:
:param output_keys:
:return:
"""
# randomly choose two nodes
k1, k2 = jax.random.split(rand_key, num=2)
from_key, from_idx = choice_node_key(k1, nodes, input_keys, output_keys,
allow_input_keys=True, allow_output_keys=True)
to_key, to_idx = choice_node_key(k2, nodes, input_keys, output_keys,
allow_input_keys=False, allow_output_keys=True)
def successful():
new_nodes, new_connections = add_connection_by_idx(from_idx, to_idx, nodes, connections)
return new_nodes, new_connections
def already_exist():
new_connections = connections.at[1, from_idx, to_idx].set(True)
return nodes, new_connections
def cycle():
return nodes, connections
is_already_exist = ~jnp.isnan(connections[0, from_idx, to_idx])
is_cycle = check_cycles(nodes, connections, from_idx, to_idx)
choice = jnp.where(is_already_exist, 0, jnp.where(is_cycle, 1, 2))
nodes, connections = jax.lax.switch(choice, [already_exist, cycle, successful])
return nodes, connections
@jit
def mutate_delete_connection(rand_key: Array, nodes: Array, connections: Array):
"""
Randomly delete a connection.
:param rand_key:
:param nodes:
:param connections:
:return:
"""
# randomly choose a connection
from_key, to_key, from_idx, to_idx = choice_connection_key(rand_key, nodes, connections)
nodes, connections = delete_connection_by_idx(from_idx, to_idx, nodes, connections)
return nodes, connections
@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, connection: Array) -> Tuple[Array, Array, Array, Array]:
"""
Randomly choose a connection key from the given connections.
:param rand_key:
:param nodes:
:param connection:
:return: from_key, to_key, from_idx, to_idx
"""
k1, k2 = jax.random.split(rand_key, num=2)
has_connections_row = jnp.any(~jnp.isnan(connection[0, :, :]), axis=1)
from_idx = fetch_random(k1, has_connections_row)
col = connection[0, from_idx, :]
to_idx = fetch_random(k2, ~jnp.isnan(col))
from_key, to_key = nodes[from_idx, 0], nodes[to_idx, 0]
return from_key, to_key, from_idx, to_idx
@jit
def rand(rand_key):
return jax.random.uniform(rand_key, ())