Source code for genome

"""Handles genomes (individuals in the population)."""
from __future__ import division, print_function


from itertools import count
from random import choice, random, shuffle

import sys

from neat.activations import ActivationFunctionSet
from neat.aggregations import AggregationFunctionSet
from neat.config import ConfigParameter, write_pretty_params
from neat.genes import DefaultConnectionGene, DefaultNodeGene
from neat.graphs import creates_cycle
from neat.six_util import iteritems, iterkeys


[docs]class DefaultGenomeConfig(object): """Sets up and holds configuration information for the DefaultGenome class.""" allowed_connectivity = ['unconnected', 'fs_neat_nohidden', 'fs_neat', 'fs_neat_hidden', 'full_nodirect', 'full', 'full_direct', 'partial_nodirect', 'partial', 'partial_direct'] def __init__(self, params): # Create full set of available activation functions. self.activation_defs = ActivationFunctionSet() # ditto for aggregation functions - name difference for backward compatibility self.aggregation_function_defs = AggregationFunctionSet() self.aggregation_defs = self.aggregation_function_defs self._params = [ConfigParameter('num_inputs', int), ConfigParameter('num_outputs', int), ConfigParameter('num_hidden', int), ConfigParameter('feed_forward', bool), ConfigParameter('compatibility_disjoint_coefficient', float), ConfigParameter('compatibility_weight_coefficient', float), ConfigParameter('conn_add_prob', float), ConfigParameter('conn_delete_prob', float), ConfigParameter('node_add_prob', float), ConfigParameter('node_delete_prob', float), ConfigParameter('single_structural_mutation', bool, 'false'), ConfigParameter('structural_mutation_surer', str, 'default'), ConfigParameter('initial_connection', str, 'unconnected')] # Gather configuration data from the gene classes. self.node_gene_type = params['node_gene_type'] self._params += self.node_gene_type.get_config_params() self.connection_gene_type = params['connection_gene_type'] self._params += self.connection_gene_type.get_config_params() # Use the configuration data to interpret the supplied parameters. for p in self._params: setattr(self, p.name, p.interpret(params)) # By convention, input pins have negative keys, and the output # pins have keys 0,1,... self.input_keys = [-i - 1 for i in range(self.num_inputs)] self.output_keys = [i for i in range(self.num_outputs)] self.connection_fraction = None # Verify that initial connection type is valid. # pylint: disable=access-member-before-definition if 'partial' in self.initial_connection: c, p = self.initial_connection.split() self.initial_connection = c self.connection_fraction = float(p) if not (0 <= self.connection_fraction <= 1): raise RuntimeError( "'partial' connection value must be between 0.0 and 1.0, inclusive.") assert self.initial_connection in self.allowed_connectivity # Verify structural_mutation_surer is valid. # pylint: disable=access-member-before-definition if self.structural_mutation_surer.lower() in ['1','yes','true','on']: self.structural_mutation_surer = 'true' elif self.structural_mutation_surer.lower() in ['0','no','false','off']: self.structural_mutation_surer = 'false' elif self.structural_mutation_surer.lower() == 'default': self.structural_mutation_surer = 'default' else: error_string = "Invalid structural_mutation_surer {!r}".format( self.structural_mutation_surer) raise RuntimeError(error_string) self.node_indexer = None
[docs] def add_activation(self, name, func): self.activation_defs.add(name, func)
[docs] def add_aggregation(self, name, func): self.aggregation_function_defs.add(name, func)
[docs] def save(self, f): if 'partial' in self.initial_connection: if not (0 <= self.connection_fraction <= 1): raise RuntimeError( "'partial' connection value must be between 0.0 and 1.0, inclusive.") f.write('initial_connection = {0} {1}\n'.format(self.initial_connection, self.connection_fraction)) else: f.write('initial_connection = {0}\n'.format(self.initial_connection)) assert self.initial_connection in self.allowed_connectivity write_pretty_params(f, self, [p for p in self._params if not 'initial_connection' in p.name])
[docs] def get_new_node_key(self, node_dict): if self.node_indexer is None: self.node_indexer = count(max(list(iterkeys(node_dict))) + 1) new_id = next(self.node_indexer) assert new_id not in node_dict return new_id
[docs] def check_structural_mutation_surer(self): if self.structural_mutation_surer == 'true': return True elif self.structural_mutation_surer == 'false': return False elif self.structural_mutation_surer == 'default': return self.single_structural_mutation else: error_string = "Invalid structural_mutation_surer {!r}".format( self.structural_mutation_surer) raise RuntimeError(error_string)
[docs]class DefaultGenome(object): """ A genome for generalized neural networks. Terminology pin: Point at which the network is conceptually connected to the external world; pins are either input or output. node: Analog of a physical neuron. connection: Connection between a pin/node output and a node's input, or between a node's output and a pin/node input. key: Identifier for an object, unique within the set of similar objects. Design assumptions and conventions. 1. Each output pin is connected only to the output of its own unique neuron by an implicit connection with weight one. This connection is permanently enabled. 2. The output pin's key is always the same as the key for its associated neuron. 3. Output neurons can be modified but not deleted. 4. The input values are applied to the input pins unmodified. """
[docs] @classmethod def parse_config(cls, param_dict): param_dict['node_gene_type'] = DefaultNodeGene param_dict['connection_gene_type'] = DefaultConnectionGene return DefaultGenomeConfig(param_dict)
[docs] @classmethod def write_config(cls, f, config): config.save(f)
def __init__(self, key): # Unique identifier for a genome instance. self.key = key # (gene_key, gene) pairs for gene sets. self.connections = {} self.nodes = {} # Fitness results. self.fitness = None
[docs] def configure_new(self, config): """Configure a new genome based on the given configuration.""" # Create node genes for the output pins. for node_key in config.output_keys: self.nodes[node_key] = self.create_node(config, node_key) # Add hidden nodes if requested. if config.num_hidden > 0: for i in range(config.num_hidden): node_key = config.get_new_node_key(self.nodes) assert node_key not in self.nodes node = self.create_node(config, node_key) self.nodes[node_key] = node # Add connections based on initial connectivity type. if 'fs_neat' in config.initial_connection: if config.initial_connection == 'fs_neat_nohidden': self.connect_fs_neat_nohidden(config) elif config.initial_connection == 'fs_neat_hidden': self.connect_fs_neat_hidden(config) else: if config.num_hidden > 0: print( "Warning: initial_connection = fs_neat will not connect to hidden nodes;", "\tif this is desired, set initial_connection = fs_neat_nohidden;", "\tif not, set initial_connection = fs_neat_hidden", sep='\n', file=sys.stderr); self.connect_fs_neat_nohidden(config) elif 'full' in config.initial_connection: if config.initial_connection == 'full_nodirect': self.connect_full_nodirect(config) elif config.initial_connection == 'full_direct': self.connect_full_direct(config) else: if config.num_hidden > 0: print( "Warning: initial_connection = full with hidden nodes will not do direct input-output connections;", "\tif this is desired, set initial_connection = full_nodirect;", "\tif not, set initial_connection = full_direct", sep='\n', file=sys.stderr); self.connect_full_nodirect(config) elif 'partial' in config.initial_connection: if config.initial_connection == 'partial_nodirect': self.connect_partial_nodirect(config) elif config.initial_connection == 'partial_direct': self.connect_partial_direct(config) else: if config.num_hidden > 0: print( "Warning: initial_connection = partial with hidden nodes will not do direct input-output connections;", "\tif this is desired, set initial_connection = partial_nodirect {0};".format( config.connection_fraction), "\tif not, set initial_connection = partial_direct {0}".format( config.connection_fraction), sep='\n', file=sys.stderr); self.connect_partial_nodirect(config)
[docs] def configure_crossover(self, genome1, genome2, config): """ Configure a new genome by crossover from two parent genomes. """ assert isinstance(genome1.fitness, (int, float)) assert isinstance(genome2.fitness, (int, float)) if genome1.fitness > genome2.fitness: parent1, parent2 = genome1, genome2 else: parent1, parent2 = genome2, genome1 # Inherit connection genes for key, cg1 in iteritems(parent1.connections): cg2 = parent2.connections.get(key) if cg2 is None: # Excess or disjoint gene: copy from the fittest parent. self.connections[key] = cg1.copy() else: # Homologous gene: combine genes from both parents. self.connections[key] = cg1.crossover(cg2) # Inherit node genes parent1_set = parent1.nodes parent2_set = parent2.nodes for key, ng1 in iteritems(parent1_set): ng2 = parent2_set.get(key) assert key not in self.nodes if ng2 is None: # Extra gene: copy from the fittest parent self.nodes[key] = ng1.copy() else: # Homologous gene: combine genes from both parents. self.nodes[key] = ng1.crossover(ng2)
[docs] def mutate(self, config): """ Mutates this genome. """ if config.single_structural_mutation: div = max(1,(config.node_add_prob + config.node_delete_prob + config.conn_add_prob + config.conn_delete_prob)) r = random() if r < (config.node_add_prob/div): self.mutate_add_node(config) elif r < ((config.node_add_prob + config.node_delete_prob)/div): self.mutate_delete_node(config) elif r < ((config.node_add_prob + config.node_delete_prob + config.conn_add_prob)/div): self.mutate_add_connection(config) elif r < ((config.node_add_prob + config.node_delete_prob + config.conn_add_prob + config.conn_delete_prob)/div): self.mutate_delete_connection() else: if random() < config.node_add_prob: self.mutate_add_node(config) if random() < config.node_delete_prob: self.mutate_delete_node(config) if random() < config.conn_add_prob: self.mutate_add_connection(config) if random() < config.conn_delete_prob: self.mutate_delete_connection() # Mutate connection genes. for cg in self.connections.values(): cg.mutate(config) # Mutate node genes (bias, response, etc.). for ng in self.nodes.values(): ng.mutate(config)
[docs] def mutate_add_node(self, config): if not self.connections: if config.check_structural_mutation_surer(): self.mutate_add_connection(config) return # Choose a random connection to split conn_to_split = choice(list(self.connections.values())) new_node_id = config.get_new_node_key(self.nodes) ng = self.create_node(config, new_node_id) self.nodes[new_node_id] = ng # Disable this connection and create two new connections joining its nodes via # the given node. The new node+connections have roughly the same behavior as # the original connection (depending on the activation function of the new node). conn_to_split.enabled = False i, o = conn_to_split.key self.add_connection(config, i, new_node_id, 1.0, True) self.add_connection(config, new_node_id, o, conn_to_split.weight, True)
[docs] def add_connection(self, config, input_key, output_key, weight, enabled): # TODO: Add further validation of this connection addition? assert isinstance(input_key, int) assert isinstance(output_key, int) assert output_key >= 0 assert isinstance(enabled, bool) key = (input_key, output_key) connection = config.connection_gene_type(key) connection.init_attributes(config) connection.weight = weight connection.enabled = enabled self.connections[key] = connection
[docs] def mutate_add_connection(self, config): """ Attempt to add a new connection, the only restriction being that the output node cannot be one of the network input pins. """ possible_outputs = list(iterkeys(self.nodes)) out_node = choice(possible_outputs) possible_inputs = possible_outputs + config.input_keys in_node = choice(possible_inputs) # Don't duplicate connections. key = (in_node, out_node) if key in self.connections: # TODO: Should this be using mutation to/from rates? Hairy to configure... if config.check_structural_mutation_surer(): self.connections[key].enabled = True return # Don't allow connections between two output nodes if in_node in config.output_keys and out_node in config.output_keys: return # No need to check for connections between input nodes: # they cannot be the output end of a connection (see above). # For feed-forward networks, avoid creating cycles. if config.feed_forward and creates_cycle(list(iterkeys(self.connections)), key): return cg = self.create_connection(config, in_node, out_node) self.connections[cg.key] = cg
[docs] def mutate_delete_node(self, config): # Do nothing if there are no non-output nodes. available_nodes = [k for k in iterkeys(self.nodes) if k not in config.output_keys] if not available_nodes: return -1 del_key = choice(available_nodes) connections_to_delete = set() for k, v in iteritems(self.connections): if del_key in v.key: connections_to_delete.add(v.key) for key in connections_to_delete: del self.connections[key] del self.nodes[del_key] return del_key
[docs] def mutate_delete_connection(self): if self.connections: key = choice(list(self.connections.keys())) del self.connections[key]
[docs] def distance(self, other, config): """ Returns the genetic distance between this genome and the other. This distance value is used to compute genome compatibility for speciation. """ # Compute node gene distance component. node_distance = 0.0 if self.nodes or other.nodes: disjoint_nodes = 0 for k2 in iterkeys(other.nodes): if k2 not in self.nodes: disjoint_nodes += 1 for k1, n1 in iteritems(self.nodes): n2 = other.nodes.get(k1) if n2 is None: disjoint_nodes += 1 else: # Homologous genes compute their own distance value. node_distance += n1.distance(n2, config) max_nodes = max(len(self.nodes), len(other.nodes)) node_distance = (node_distance + (config.compatibility_disjoint_coefficient * disjoint_nodes)) / max_nodes # Compute connection gene differences. connection_distance = 0.0 if self.connections or other.connections: disjoint_connections = 0 for k2 in iterkeys(other.connections): if k2 not in self.connections: disjoint_connections += 1 for k1, c1 in iteritems(self.connections): c2 = other.connections.get(k1) if c2 is None: disjoint_connections += 1 else: # Homologous genes compute their own distance value. connection_distance += c1.distance(c2, config) max_conn = max(len(self.connections), len(other.connections)) connection_distance = (connection_distance + (config.compatibility_disjoint_coefficient * disjoint_connections)) / max_conn distance = node_distance + connection_distance return distance
[docs] def size(self): """ Returns genome 'complexity', taken to be (number of nodes, number of enabled connections) """ num_enabled_connections = sum([1 for cg in self.connections.values() if cg.enabled]) return len(self.nodes), num_enabled_connections
[docs] def __str__(self): s = "Key: {0}\nFitness: {1}\nNodes:".format(self.key, self.fitness) for k, ng in iteritems(self.nodes): s += "\n\t{0} {1!s}".format(k, ng) s += "\nConnections:" connections = list(self.connections.values()) connections.sort() for c in connections: s += "\n\t" + str(c) return s
[docs] @staticmethod def create_node(config, node_id): node = config.node_gene_type(node_id) node.init_attributes(config) return node
[docs] @staticmethod def create_connection(config, input_id, output_id): connection = config.connection_gene_type((input_id, output_id)) connection.init_attributes(config) return connection
[docs] def connect_fs_neat_nohidden(self, config): """ Randomly connect one input to all output nodes (FS-NEAT without connections to hidden, if any). Originally connect_fs_neat. """ input_id = choice(config.input_keys) for output_id in config.output_keys: connection = self.create_connection(config, input_id, output_id) self.connections[connection.key] = connection
[docs] def connect_fs_neat_hidden(self, config): """ Randomly connect one input to all hidden and output nodes (FS-NEAT with connections to hidden, if any). """ input_id = choice(config.input_keys) others = [i for i in iterkeys(self.nodes) if i not in config.input_keys] for output_id in others: connection = self.create_connection(config, input_id, output_id) self.connections[connection.key] = connection
[docs] def compute_full_connections(self, config, direct): """ Compute connections for a fully-connected feed-forward genome--each input connected to all hidden nodes (and output nodes if ``direct`` is set or there are no hidden nodes), each hidden node connected to all output nodes. (Recurrent genomes will also include node self-connections.) """ hidden = [i for i in iterkeys(self.nodes) if i not in config.output_keys] output = [i for i in iterkeys(self.nodes) if i in config.output_keys] connections = [] if hidden: for input_id in config.input_keys: for h in hidden: connections.append((input_id, h)) for h in hidden: for output_id in output: connections.append((h, output_id)) if direct or (not hidden): for input_id in config.input_keys: for output_id in output: connections.append((input_id, output_id)) # For recurrent genomes, include node self-connections. if not config.feed_forward: for i in iterkeys(self.nodes): connections.append((i, i)) return connections
[docs] def connect_full_nodirect(self, config): """ Create a fully-connected genome (except without direct input-output unless no hidden nodes). """ for input_id, output_id in self.compute_full_connections(config, False): connection = self.create_connection(config, input_id, output_id) self.connections[connection.key] = connection
[docs] def connect_full_direct(self, config): """ Create a fully-connected genome, including direct input-output connections. """ for input_id, output_id in self.compute_full_connections(config, True): connection = self.create_connection(config, input_id, output_id) self.connections[connection.key] = connection
[docs] def connect_partial_nodirect(self, config): """ Create a partially-connected genome, with (unless no hidden nodes) no direct input-output connections.""" assert 0 <= config.connection_fraction <= 1 all_connections = self.compute_full_connections(config, False) shuffle(all_connections) num_to_add = int(round(len(all_connections) * config.connection_fraction)) for input_id, output_id in all_connections[:num_to_add]: connection = self.create_connection(config, input_id, output_id) self.connections[connection.key] = connection
[docs] def connect_partial_direct(self, config): """ Create a partially-connected genome, including (possibly) direct input-output connections. """ assert 0 <= config.connection_fraction <= 1 all_connections = self.compute_full_connections(config, True) shuffle(all_connections) num_to_add = int(round(len(all_connections) * config.connection_fraction)) for input_id, output_id in all_connections[:num_to_add]: connection = self.create_connection(config, input_id, output_id) self.connections[connection.key] = connection