350 lines
12 KiB
Python
350 lines
12 KiB
Python
"""
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Mutate a genome.
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The calculation method is the same as the mutation operation in NEAT-python.
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See https://neat-python.readthedocs.io/en/latest/_modules/genome.html#DefaultGenome.mutate
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"""
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from typing import Tuple, Dict
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import jax
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from jax import numpy as jnp, jit, Array
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from .utils import fetch_random, fetch_first, I_INT, unflatten_connections
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from .genome import add_node, delete_node_by_idx, delete_connection_by_idx, add_connection
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from .graph import check_cycles
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@jit
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def mutate(rand_key: Array, nodes: Array, connections: Array, new_node_key: int, jit_config: Dict):
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"""
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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 jit_config:
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:return:
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"""
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r1, r2, r3, r4, rand_key = jax.random.split(rand_key, 5)
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# structural mutations
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# mutate add node
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r = rand(r1)
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aux_nodes, aux_connections = mutate_add_node(r1, nodes, connections, new_node_key, jit_config)
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nodes = jnp.where(r < jit_config['node_add_prob'], aux_nodes, nodes)
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connections = jnp.where(r < jit_config['node_add_prob'], aux_connections, connections)
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# mutate add connection
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r = rand(r2)
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aux_nodes, aux_connections = mutate_add_connection(r3, nodes, connections, jit_config)
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nodes = jnp.where(r < jit_config['conn_add_prob'], aux_nodes, nodes)
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connections = jnp.where(r < jit_config['conn_add_prob'], aux_connections, connections)
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# mutate delete node
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r = rand(r3)
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aux_nodes, aux_connections = mutate_delete_node(r2, nodes, connections, jit_config)
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nodes = jnp.where(r < jit_config['node_delete_prob'], aux_nodes, nodes)
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connections = jnp.where(r < jit_config['node_delete_prob'], aux_connections, connections)
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# mutate delete connection
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r = rand(r4)
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aux_nodes, aux_connections = mutate_delete_connection(r4, nodes, connections)
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nodes = jnp.where(r < jit_config['conn_delete_prob'], aux_nodes, nodes)
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connections = jnp.where(r < jit_config['conn_delete_prob'], aux_connections, connections)
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# value mutations
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nodes, connections = mutate_values(rand_key, nodes, connections, jit_config)
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return nodes, connections
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def mutate_values(rand_key: Array, nodes: Array, cons: Array, jit_config: Dict) -> 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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cons: A 3D array representing connections.
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jit_config: A dict containing configuration for jit-able functions.
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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
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bias_new = mutate_float_values(k1, nodes[:, 1], jit_config['bias_init_mean'], jit_config['bias_init_std'],
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jit_config['bias_mutate_power'], jit_config['bias_mutate_rate'],
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jit_config['bias_replace_rate'])
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# response
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response_new = mutate_float_values(k2, nodes[:, 2], jit_config['response_init_mean'],
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jit_config['response_init_std'], jit_config['response_mutate_power'],
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jit_config['response_mutate_rate'], jit_config['response_replace_rate'])
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# weight
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weight_new = mutate_float_values(k3, cons[:, 2], jit_config['weight_init_mean'], jit_config['weight_init_std'],
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jit_config['weight_mutate_power'], jit_config['weight_mutate_rate'],
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jit_config['weight_replace_rate'])
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# activation
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act_new = mutate_int_values(k4, nodes[:, 3], jit_config['activation_options'],
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jit_config['activation_replace_rate'])
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# aggregation
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agg_new = mutate_int_values(k5, nodes[:, 4], jit_config['aggregation_options'],
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jit_config['aggregation_replace_rate'])
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# enabled
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r = jax.random.uniform(rand_key, cons[:, 3].shape)
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enabled_new = jnp.where(r < jit_config['enable_mutate_rate'], 1 - cons[:, 3], cons[:, 3])
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# merge
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nodes = jnp.column_stack([nodes[:, 0], bias_new, response_new, act_new, agg_new])
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cons = jnp.column_stack([cons[:, 0], cons[:, 1], weight_new, enabled_new])
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return nodes, cons
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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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# default
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new_vals = old_vals
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# r in [0, mutate_rate), mutate
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new_vals = jnp.where(r < mutate_rate, new_vals + noise, new_vals)
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# r in [mutate_rate, mutate_rate + replace_rate), replace
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new_vals = jnp.where(
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(mutate_rate < r) & (r < mutate_rate + replace_rate),
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replace + new_vals * 0.0, # in case of nan replace to values
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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 = jnp.where(r < replace_rate, replace_val + old_vals * 0.0, old_vals) # in case of nan replace to values
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return new_vals
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def mutate_add_node(rand_key: Array, nodes: Array, cons: Array, new_node_key: int,
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jit_config: Dict) -> 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 cons:
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:param jit_config:
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:return:
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"""
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# randomly choose a connection
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i_key, o_key, idx = choice_connection_key(rand_key, nodes, cons)
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def nothing(): # there is no connection to split
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return nodes, cons
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def successful_add_node():
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# disable the connection
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new_nodes, new_cons = nodes, cons
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# set enable to false
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new_cons = new_cons.at[idx, 3].set(False)
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# add a new node
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new_nodes, new_cons = add_node(new_nodes, new_cons, new_node_key, bias=0, response=1,
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act=jit_config['activation_default'], agg=jit_config['aggregation_default'])
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# add two new connections
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w = new_cons[idx, 2]
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new_nodes, new_cons = add_connection(new_nodes, new_cons, i_key, new_node_key, weight=1, enabled=True)
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new_nodes, new_cons = add_connection(new_nodes, new_cons, new_node_key, o_key, weight=w, enabled=True)
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return new_nodes, new_cons
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# if from_idx == I_INT, that means no connection exist, do nothing
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nodes, cons = jax.lax.cond(idx == I_INT, nothing, successful_add_node)
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return nodes, cons
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# TODO: Do we really need to delete a node?
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def mutate_delete_node(rand_key: Array, nodes: Array, cons: Array, jit_config: Dict) -> 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 cons:
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:param jit_config:
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:return:
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"""
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# randomly choose a node
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key, idx = choice_node_key(rand_key, nodes, jit_config['input_idx'], jit_config['output_idx'],
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allow_input_keys=False, allow_output_keys=False)
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def nothing():
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return nodes, cons
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def successful_delete_node():
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# delete the node
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aux_nodes, aux_cons = delete_node_by_idx(nodes, cons, idx)
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# delete all connections
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aux_cons = jnp.where(((aux_cons[:, 0] == key) | (aux_cons[:, 1] == key))[:, None],
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jnp.nan, aux_cons)
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return aux_nodes, aux_cons
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nodes, cons = jax.lax.cond(idx == I_INT, nothing, successful_delete_node)
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return nodes, cons
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def mutate_add_connection(rand_key: Array, nodes: Array, cons: Array, jit_config: Dict) -> Tuple[Array, Array]:
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"""
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Randomly add a new connection. The output node is not allowed to be an input node. If in feedforward networks,
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cycles are not allowed.
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:param rand_key:
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:param nodes:
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:param cons:
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:param jit_config:
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:return:
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"""
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# randomly choose two nodes
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k1, k2 = jax.random.split(rand_key, num=2)
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i_key, from_idx = choice_node_key(k1, nodes, jit_config['input_idx'], jit_config['output_idx'],
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allow_input_keys=True, allow_output_keys=True)
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o_key, to_idx = choice_node_key(k2, nodes, jit_config['input_idx'], jit_config['output_idx'],
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allow_input_keys=False, allow_output_keys=True)
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con_idx = fetch_first((cons[:, 0] == i_key) & (cons[:, 1] == o_key))
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def successful():
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new_nodes, new_cons = add_connection(nodes, cons, i_key, o_key, weight=1, enabled=True)
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return new_nodes, new_cons
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def already_exist():
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new_cons = cons.at[con_idx, 3].set(True)
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return nodes, new_cons
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def cycle():
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return nodes, cons
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is_already_exist = con_idx != I_INT
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u_cons = unflatten_connections(nodes, cons)
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is_cycle = check_cycles(nodes, u_cons, from_idx, to_idx)
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choice = jnp.where(is_already_exist, 0, jnp.where(is_cycle, 1, 2))
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nodes, cons = jax.lax.switch(choice, [already_exist, cycle, successful])
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return nodes, cons
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def mutate_delete_connection(rand_key: Array, nodes: Array, cons: Array):
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"""
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Randomly delete a connection.
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:param rand_key:
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:param nodes:
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:param cons:
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:return:
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"""
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# randomly choose a connection
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i_key, o_key, idx = choice_connection_key(rand_key, nodes, cons)
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def nothing():
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return nodes, cons
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def successfully_delete_connection():
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return delete_connection_by_idx(nodes, cons, idx)
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nodes, cons = jax.lax.cond(idx == I_INT, nothing, successfully_delete_connection)
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return nodes, cons
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def choice_node_key(rand_key: Array, nodes: Array,
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input_keys: Array, output_keys: Array,
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allow_input_keys: bool = False, allow_output_keys: bool = False) -> Tuple[Array, Array]:
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"""
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Randomly choose a node key from the given nodes. It guarantees that the chosen node not be the input or output node.
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:param rand_key:
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:param nodes:
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:param input_keys:
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:param output_keys:
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:param allow_input_keys:
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:param allow_output_keys:
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:return: return its key and position(idx)
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"""
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node_keys = nodes[:, 0]
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mask = ~jnp.isnan(node_keys)
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if not allow_input_keys:
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mask = jnp.logical_and(mask, ~jnp.isin(node_keys, input_keys))
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if not allow_output_keys:
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mask = jnp.logical_and(mask, ~jnp.isin(node_keys, output_keys))
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idx = fetch_random(rand_key, mask)
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key = jnp.where(idx != I_INT, nodes[idx, 0], jnp.nan)
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return key, idx
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def choice_connection_key(rand_key: Array, nodes: Array, cons: Array) -> Tuple[Array, Array, Array]:
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"""
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Randomly choose a connection key from the given connections.
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:param rand_key:
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:param nodes:
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:param cons:
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:return: i_key, o_key, idx
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"""
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idx = fetch_random(rand_key, ~jnp.isnan(cons[:, 0]))
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i_key = jnp.where(idx != I_INT, cons[idx, 0], jnp.nan)
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o_key = jnp.where(idx != I_INT, cons[idx, 1], jnp.nan)
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return i_key, o_key, idx
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def rand(rand_key):
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return jax.random.uniform(rand_key, ())
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