576 lines
23 KiB
Python
576 lines
23 KiB
Python
from typing import Tuple
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from functools import partial
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import jax
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import numpy as np
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from jax import numpy as jnp
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from jax import jit, vmap, Array
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from .utils import fetch_random, fetch_first, I_INT
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from .genome import add_node, add_connection_by_idx, delete_node_by_idx, delete_connection_by_idx
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from .graph import check_cycles
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from .activations import act_name2key
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from .aggregations import agg_name2key
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def create_mutate_function(N, config, batch: bool, debug: bool = False):
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"""
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create mutate function for different situations
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:param N:
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:param config:
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:param batch: mutate for population or not
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:param debug:
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:return:
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"""
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num_inputs = config.basic.num_inputs
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num_outputs = config.basic.num_outputs
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input_idx = np.arange(num_inputs)
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output_idx = np.arange(num_inputs, num_inputs + num_outputs)
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bias = config.neat.gene.bias
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bias_default = bias.init_mean
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bias_mean = bias.init_mean
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bias_std = bias.init_stdev
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bias_mutate_strength = bias.mutate_power
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bias_mutate_rate = bias.mutate_rate
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bias_replace_rate = bias.replace_rate
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response = config.neat.gene.response
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response_default = response.init_mean
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response_mean = response.init_mean
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response_std = response.init_stdev
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response_mutate_strength = response.mutate_power
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response_mutate_rate = response.mutate_rate
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response_replace_rate = response.replace_rate
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weight = config.neat.gene.weight
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weight_mean = weight.init_mean
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weight_std = weight.init_stdev
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weight_mutate_strength = weight.mutate_power
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weight_mutate_rate = weight.mutate_rate
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weight_replace_rate = weight.replace_rate
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activation = config.neat.gene.activation
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act_default = act_name2key[activation.default]
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act_list = np.array([act_name2key[name] for name in activation.options])
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act_replace_rate = activation.mutate_rate
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aggregation = config.neat.gene.aggregation
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agg_default = agg_name2key[aggregation.default]
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agg_list = np.array([agg_name2key[name] for name in aggregation.options])
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agg_replace_rate = aggregation.mutate_rate
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enabled = config.neat.gene.enabled
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enabled_reverse_rate = enabled.mutate_rate
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genome = config.neat.genome
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add_node_rate = genome.node_add_prob
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delete_node_rate = genome.node_delete_prob
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add_connection_rate = genome.conn_add_prob
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delete_connection_rate = genome.conn_delete_prob
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single_structure_mutate = genome.single_structural_mutation
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def mutate_with_args(rand_key, nodes, connections, new_node_key):
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return mutate(rand_key, nodes, connections, new_node_key, input_idx, output_idx,
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bias_default, bias_mean, bias_std, bias_mutate_strength, bias_mutate_rate,
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bias_replace_rate, response_default, response_mean, response_std,
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response_mutate_strength, response_mutate_rate, response_replace_rate,
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weight_mean, weight_std, weight_mutate_strength, weight_mutate_rate,
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weight_replace_rate, act_default, act_list, act_replace_rate,
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agg_default, agg_list, agg_replace_rate, enabled_reverse_rate,
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add_node_rate, delete_node_rate, add_connection_rate, delete_connection_rate,
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single_structure_mutate)
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if not batch:
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rand_key_lower = jnp.zeros((2,), dtype=jnp.uint32)
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nodes_lower = jnp.zeros((N, 5))
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connections_lower = jnp.zeros((2, N, N))
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new_node_key_lower = jnp.zeros((), dtype=jnp.int32)
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res_func = jit(mutate_with_args).lower(rand_key_lower, nodes_lower,
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connections_lower, new_node_key_lower).compile()
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if debug:
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return lambda *args: res_func(*args)
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else:
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return res_func
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else:
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pop_size = config.neat.population.pop_size
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rand_key_lower = jnp.zeros((pop_size, 2), dtype=jnp.uint32)
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nodes_lower = jnp.zeros((pop_size, N, 5))
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connections_lower = jnp.zeros((pop_size, 2, N, N))
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new_node_key_lower = jnp.zeros((pop_size,), dtype=jnp.int32)
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batched_mutate_func = jit(vmap(mutate_with_args)).lower(rand_key_lower, nodes_lower,
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connections_lower, new_node_key_lower).compile()
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if debug:
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return lambda *args: batched_mutate_func(*args)
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else:
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return batched_mutate_func
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def mutate(rand_key: Array,
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nodes: Array,
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connections: Array,
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new_node_key: int,
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input_idx: Array,
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output_idx: Array,
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bias_mean: float = 0,
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bias_std: float = 1,
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bias_mutate_strength: float = 0.5,
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bias_mutate_rate: float = 0.7,
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bias_replace_rate: float = 0.1,
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response_mean: float = 1.,
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response_std: float = 0.,
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response_mutate_strength: float = 0.,
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response_mutate_rate: float = 0.,
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response_replace_rate: float = 0.,
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weight_mean: float = 0.,
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weight_std: float = 1.,
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weight_mutate_strength: float = 0.5,
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weight_mutate_rate: float = 0.7,
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weight_replace_rate: float = 0.1,
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act_default: int = 0,
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act_list: Array = None,
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act_replace_rate: float = 0.1,
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agg_default: int = 0,
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agg_list: Array = None,
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agg_replace_rate: float = 0.1,
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enabled_reverse_rate: float = 0.1,
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add_node_rate: float = 0.2,
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delete_node_rate: float = 0.2,
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add_connection_rate: float = 0.4,
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delete_connection_rate: float = 0.4,
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single_structure_mutate: bool = True):
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"""
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:param output_idx:
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:param input_idx:
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:param agg_default:
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:param act_default:
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:param rand_key:
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:param nodes: (N, 5)
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:param connections: (2, N, N)
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:param new_node_key:
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:param bias_mean:
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:param bias_std:
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:param bias_mutate_strength:
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:param bias_mutate_rate:
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:param bias_replace_rate:
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:param response_mean:
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:param response_std:
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:param response_mutate_strength:
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:param response_mutate_rate:
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:param response_replace_rate:
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:param weight_mean:
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:param weight_std:
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:param weight_mutate_strength:
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:param weight_mutate_rate:
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:param weight_replace_rate:
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:param act_list:
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:param act_replace_rate:
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:param agg_list:
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:param agg_replace_rate:
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:param enabled_reverse_rate:
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:param add_node_rate:
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:param delete_node_rate:
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:param add_connection_rate:
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:param delete_connection_rate:
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:param single_structure_mutate: a genome is structurally mutate at most once
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:return:
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"""
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# mutate_structure
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def nothing(rk, n, c):
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return n, c
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def m_add_node(rk, n, c):
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return mutate_add_node(rk, new_node_key, n, c, bias_mean, response_mean, act_default, agg_default)
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def m_delete_node(rk, n, c):
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return mutate_delete_node(rk, n, c, input_idx, output_idx)
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def m_add_connection(rk, n, c):
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return mutate_add_connection(rk, n, c, input_idx, output_idx)
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def m_delete_connection(rk, n, c):
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return mutate_delete_connection(rk, n, c)
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mutate_structure_li = [nothing, m_add_node, m_delete_node, m_add_connection, m_delete_connection]
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if single_structure_mutate:
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r1, r2, rand_key = jax.random.split(rand_key, 3)
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d = jnp.maximum(1, add_node_rate + delete_node_rate + add_connection_rate + delete_connection_rate)
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# shorten variable names for beauty
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anr, dnr = add_node_rate / d, delete_node_rate / d
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acr, dcr = add_connection_rate / d, delete_connection_rate / d
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r = rand(r1)
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branch = 0
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branch = jnp.where(r <= anr, 1, branch)
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branch = jnp.where((anr < r) & (r <= anr + dnr), 2, branch)
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branch = jnp.where((anr + dnr < r) & (r <= anr + dnr + acr), 3, branch)
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branch = jnp.where((anr + dnr + acr) < r & r <= (anr + dnr + acr + dcr), 4, branch)
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nodes, connections = jax.lax.switch(branch, mutate_structure_li, (r2, nodes, connections))
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else:
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r1, r2, r3, r4, rand_key = jax.random.split(rand_key, 5)
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# mutate add node
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aux_nodes, aux_connections = m_add_node(r1, nodes, connections)
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nodes = jnp.where(rand(r1) < add_node_rate, aux_nodes, nodes)
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connections = jnp.where(rand(r1) < add_node_rate, aux_connections, connections)
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# mutate delete node
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aux_nodes, aux_connections = m_delete_node(r2, nodes, connections)
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nodes = jnp.where(rand(r2) < delete_node_rate, aux_nodes, nodes)
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connections = jnp.where(rand(r2) < delete_node_rate, aux_connections, connections)
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# mutate add connection
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aux_nodes, aux_connections = m_add_connection(r3, nodes, connections)
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nodes = jnp.where(rand(r3) < add_connection_rate, aux_nodes, nodes)
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connections = jnp.where(rand(r3) < add_connection_rate, aux_connections, connections)
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# mutate delete connection
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aux_nodes, aux_connections = m_delete_connection(r4, nodes, connections)
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nodes = jnp.where(rand(r4) < delete_connection_rate, aux_nodes, nodes)
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connections = jnp.where(rand(r4) < delete_connection_rate, aux_connections, connections)
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nodes, connections = mutate_values(rand_key, nodes, connections, bias_mean, bias_std, bias_mutate_strength,
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bias_mutate_rate, bias_replace_rate, response_mean, response_std,
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response_mutate_strength, response_mutate_rate, response_replace_rate,
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weight_mean, weight_std, weight_mutate_strength,
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weight_mutate_rate, weight_replace_rate, act_list, act_replace_rate, agg_list,
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agg_replace_rate, enabled_reverse_rate)
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return nodes, connections
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def mutate_values(rand_key: Array,
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nodes: Array,
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connections: Array,
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bias_mean: float = 0,
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bias_std: float = 1,
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bias_mutate_strength: float = 0.5,
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bias_mutate_rate: float = 0.7,
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bias_replace_rate: float = 0.1,
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response_mean: float = 1.,
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response_std: float = 0.,
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response_mutate_strength: float = 0.,
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response_mutate_rate: float = 0.,
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response_replace_rate: float = 0.,
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weight_mean: float = 0.,
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weight_std: float = 1.,
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weight_mutate_strength: float = 0.5,
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weight_mutate_rate: float = 0.7,
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weight_replace_rate: float = 0.1,
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act_list: Array = None,
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act_replace_rate: float = 0.1,
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agg_list: Array = None,
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agg_replace_rate: float = 0.1,
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enabled_reverse_rate: float = 0.1) -> Tuple[Array, Array]:
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"""
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Mutate values of nodes and connections.
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Args:
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rand_key: A random key for generating random values.
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nodes: A 2D array representing nodes.
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connections: A 3D array representing connections.
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bias_mean: Mean of the bias values.
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bias_std: Standard deviation of the bias values.
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bias_mutate_strength: Strength of the bias mutation.
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bias_mutate_rate: Rate of the bias mutation.
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bias_replace_rate: Rate of the bias replacement.
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response_mean: Mean of the response values.
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response_std: Standard deviation of the response values.
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response_mutate_strength: Strength of the response mutation.
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response_mutate_rate: Rate of the response mutation.
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response_replace_rate: Rate of the response replacement.
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weight_mean: Mean of the weight values.
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weight_std: Standard deviation of the weight values.
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weight_mutate_strength: Strength of the weight mutation.
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weight_mutate_rate: Rate of the weight mutation.
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weight_replace_rate: Rate of the weight replacement.
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act_list: List of the activation function values.
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act_replace_rate: Rate of the activation function replacement.
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agg_list: List of the aggregation function values.
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agg_replace_rate: Rate of the aggregation function replacement.
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enabled_reverse_rate: Rate of reversing enabled state of connections.
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Returns:
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A tuple containing mutated nodes and connections.
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"""
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k1, k2, k3, k4, k5, rand_key = jax.random.split(rand_key, num=6)
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bias_new = mutate_float_values(k1, nodes[:, 1], bias_mean, bias_std,
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bias_mutate_strength, bias_mutate_rate, bias_replace_rate)
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response_new = mutate_float_values(k2, nodes[:, 2], response_mean, response_std,
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response_mutate_strength, response_mutate_rate, response_replace_rate)
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weight_new = mutate_float_values(k3, connections[0, :, :], weight_mean, weight_std,
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weight_mutate_strength, weight_mutate_rate, weight_replace_rate)
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act_new = mutate_int_values(k4, nodes[:, 3], act_list, act_replace_rate)
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agg_new = mutate_int_values(k5, nodes[:, 4], agg_list, agg_replace_rate)
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# refactor enabled
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r = jax.random.uniform(rand_key, connections[1, :, :].shape)
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enabled_new = connections[1, :, :] == 1
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enabled_new = jnp.where(r < enabled_reverse_rate, ~enabled_new, enabled_new)
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enabled_new = jnp.where(~jnp.isnan(connections[0, :, :]), enabled_new, jnp.nan)
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nodes = nodes.at[:, 1].set(bias_new)
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nodes = nodes.at[:, 2].set(response_new)
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nodes = nodes.at[:, 3].set(act_new)
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nodes = nodes.at[:, 4].set(agg_new)
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connections = connections.at[0, :, :].set(weight_new)
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connections = connections.at[1, :, :].set(enabled_new)
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return nodes, connections
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def mutate_float_values(rand_key: Array, old_vals: Array, mean: float, std: float,
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mutate_strength: float, mutate_rate: float, replace_rate: float) -> Array:
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"""
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Mutate float values of a given array.
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Args:
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rand_key: A random key for generating random values.
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old_vals: A 1D array of float values to be mutated.
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mean: Mean of the values.
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std: Standard deviation of the values.
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mutate_strength: Strength of the mutation.
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mutate_rate: Rate of the mutation.
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replace_rate: Rate of the replacement.
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Returns:
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A mutated 1D array of float values.
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"""
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k1, k2, k3, rand_key = jax.random.split(rand_key, num=4)
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noise = jax.random.normal(k1, old_vals.shape) * mutate_strength
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replace = jax.random.normal(k2, old_vals.shape) * std + mean
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r = jax.random.uniform(k3, old_vals.shape)
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new_vals = old_vals
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new_vals = jnp.where(r < mutate_rate, new_vals + noise, new_vals)
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new_vals = jnp.where(
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jnp.logical_and(mutate_rate < r, r < mutate_rate + replace_rate),
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replace,
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new_vals
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)
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new_vals = jnp.where(~jnp.isnan(old_vals), new_vals, jnp.nan)
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return new_vals
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def mutate_int_values(rand_key: Array, old_vals: Array, val_list: Array, replace_rate: float) -> Array:
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"""
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Mutate integer values (act, agg) of a given array.
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Args:
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rand_key: A random key for generating random values.
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old_vals: A 1D array of integer values to be mutated.
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val_list: List of the integer values.
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replace_rate: Rate of the replacement.
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Returns:
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A mutated 1D array of integer values.
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"""
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k1, k2, rand_key = jax.random.split(rand_key, num=3)
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replace_val = jax.random.choice(k1, val_list, old_vals.shape)
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r = jax.random.uniform(k2, old_vals.shape)
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new_vals = old_vals
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new_vals = jnp.where(r < replace_rate, replace_val, new_vals)
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new_vals = jnp.where(~jnp.isnan(old_vals), new_vals, jnp.nan)
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return new_vals
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def mutate_add_node(rand_key: Array, new_node_key: int, nodes: Array, connections: Array,
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default_bias: float = 0, default_response: float = 1,
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default_act: int = 0, default_agg: int = 0) -> Tuple[Array, Array]:
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"""
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Randomly add a new node from splitting a connection.
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:param rand_key:
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:param new_node_key:
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:param nodes:
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:param connections:
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:param default_bias:
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:param default_response:
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:param default_act:
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:param default_agg:
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:return:
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"""
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# randomly choose a connection
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from_key, to_key, from_idx, to_idx = choice_connection_key(rand_key, nodes, connections)
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def nothing():
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return nodes, connections
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def successful_add_node():
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# disable the connection
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new_nodes, new_connections = nodes, connections
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new_connections = new_connections.at[1, from_idx, to_idx].set(False)
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# add a new node
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new_nodes, new_connections = \
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add_node(new_node_key, new_nodes, new_connections,
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bias=default_bias, response=default_response, act=default_act, agg=default_agg)
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new_idx = fetch_first(new_nodes[:, 0] == new_node_key)
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# add two new connections
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weight = new_connections[0, from_idx, to_idx]
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new_nodes, new_connections = add_connection_by_idx(from_idx, new_idx,
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new_nodes, new_connections, weight=1., enabled=True)
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new_nodes, new_connections = add_connection_by_idx(new_idx, to_idx,
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new_nodes, new_connections, weight=weight, enabled=True)
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return new_nodes, new_connections
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# if from_idx == I_INT, that means no connection exist, do nothing
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nodes, connections = jax.lax.cond(from_idx == I_INT, nothing, successful_add_node)
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return nodes, connections
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def mutate_delete_node(rand_key: Array, nodes: Array, connections: Array,
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input_keys: Array, output_keys: Array) -> Tuple[Array, Array]:
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"""
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Randomly delete a node. Input and output nodes are not allowed to be deleted.
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:param rand_key:
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:param nodes:
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:param connections:
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:param input_keys:
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:param output_keys:
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:return:
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"""
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|
# randomly choose a node
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|
node_key, node_idx = choice_node_key(rand_key, nodes, input_keys, output_keys,
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|
allow_input_keys=False, allow_output_keys=False)
|
|
|
|
def nothing():
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|
return nodes, connections
|
|
|
|
def successful_delete_node():
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|
# 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)
|
|
|
|
return aux_nodes, aux_connections
|
|
|
|
nodes, connections = jax.lax.cond(node_idx == I_INT, nothing, successful_delete_node)
|
|
|
|
return nodes, connections
|
|
|
|
|
|
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
|
|
|
|
|
|
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)
|
|
|
|
def nothing():
|
|
return nodes, connections
|
|
|
|
def successfully_delete_connection():
|
|
return delete_connection_by_idx(from_idx, to_idx, nodes, connections)
|
|
|
|
nodes, connections = jax.lax.cond(from_idx == I_INT, nothing, successfully_delete_connection)
|
|
|
|
return nodes, connections
|
|
|
|
|
|
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
|
|
|
|
|
|
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)
|
|
|
|
def nothing():
|
|
return jnp.nan, jnp.nan, I_INT, I_INT
|
|
|
|
def has_connection():
|
|
f_idx = fetch_random(k1, has_connections_row)
|
|
col = connection[0, f_idx, :]
|
|
t_idx = fetch_random(k2, ~jnp.isnan(col))
|
|
f_key, t_key = nodes[f_idx, 0], nodes[t_idx, 0]
|
|
return f_key, t_key, f_idx, t_idx
|
|
|
|
from_key, to_key, from_idx, to_idx = jax.lax.cond(jnp.any(has_connections_row), has_connection, nothing)
|
|
return from_key, to_key, from_idx, to_idx
|
|
|
|
|
|
def rand(rand_key):
|
|
return jax.random.uniform(rand_key, ())
|