NEAT_visual.py

import warnings

import graphviz
import matplotlib.pyplot as plt
import numpy as np


# def plot_stats(statistics, ylog=False, view=False, filename='avg_fitness.svg'):
#     """ Plots the population's average and best fitness. """
#     if plt is None:
#         warnings.warn("This display is not available due to a missing optional dependency (matplotlib)")
#         return

#     generation = range(len(statistics.most_fit_genomes))
#     best_fitness = [c.fitness for c in statistics.most_fit_genomes]
#     avg_fitness = np.array(statistics.get_fitness_mean())
#     stdev_fitness = np.array(statistics.get_fitness_stdev())

#     plt.plot(generation, avg_fitness, 'b-', label="average")
#     plt.plot(generation, avg_fitness - stdev_fitness, 'g-.', label="-1 sd")
#     plt.plot(generation, avg_fitness + stdev_fitness, 'g-.', label="+1 sd")
#     plt.plot(generation, best_fitness, 'r-', label="best")

#     plt.title("Population's average and best fitness")
#     plt.xlabel("Generations")
#     plt.ylabel("Fitness")
#     plt.grid()
#     plt.legend(loc="best")
#     if ylog:
#         plt.gca().set_yscale('symlog')

#     plt.savefig(filename)
#     if view:
#         plt.show()

#     plt.close()


# def plot_spikes(spikes, view=False, filename=None, title=None):
#     """ Plots the trains for a single spiking neuron. """
#     t_values = [t for t, I, v, u, f in spikes]
#     v_values = [v for t, I, v, u, f in spikes]
#     u_values = [u for t, I, v, u, f in spikes]
#     I_values = [I for t, I, v, u, f in spikes]
#     f_values = [f for t, I, v, u, f in spikes]

#     fig = plt.figure()
#     plt.subplot(4, 1, 1)
#     plt.ylabel("Potential (mv)")
#     plt.xlabel("Time (in ms)")
#     plt.grid()
#     plt.plot(t_values, v_values, "g-")

#     if title is None:
#         plt.title("Izhikevich's spiking neuron model")
#     else:
#         plt.title("Izhikevich's spiking neuron model ({0!s})".format(title))

#     plt.subplot(4, 1, 2)
#     plt.ylabel("Fired")
#     plt.xlabel("Time (in ms)")
#     plt.grid()
#     plt.plot(t_values, f_values, "r-")

#     plt.subplot(4, 1, 3)
#     plt.ylabel("Recovery (u)")
#     plt.xlabel("Time (in ms)")
#     plt.grid()
#     plt.plot(t_values, u_values, "r-")

#     plt.subplot(4, 1, 4)
#     plt.ylabel("Current (I)")
#     plt.xlabel("Time (in ms)")
#     plt.grid()
#     plt.plot(t_values, I_values, "r-o")

#     if filename is not None:
#         plt.savefig(filename)

#     if view:
#         plt.show()
#         plt.close()
#         fig = None

# #     return fig


# def plot_species(statistics, view=False, filename='speciation.svg'):
#     """ Visualizes speciation throughout evolution. """
#     if plt is None:
#         warnings.warn("This display is not available due to a missing optional dependency (matplotlib)")
#         return

#     species_sizes = statistics.get_species_sizes()
#     num_generations = len(species_sizes)
#     curves = np.array(species_sizes).T

#     fig, ax = plt.subplots()
#     ax.stackplot(range(num_generations), *curves)

#     plt.title("Speciation")
#     plt.ylabel("Size per Species")
#     plt.xlabel("Generations")

#     plt.savefig(filename)

#     if view:
#         plt.show()

#     plt.close()


def draw_net(config, genome, view=False, filename=None, node_names=None, show_disabled=True, prune_unused=False,
             node_colors=None, fmt='pdf'):

    node_names = {}
    node_colors = {}

    node_attrs = {
        'shape': 'circle',
        'fontsize': '9',
        'height': '0.2',
        'width': '0.2'}

    dot = graphviz.Digraph(format=fmt, node_attr=node_attrs)

    inputs = set()
    for k in config.genome_config.input_keys:
        inputs.add(k)
        name = node_names.get(k, str(k))
        input_attrs = {'style': 'filled', 'shape': 'box', 'fillcolor': node_colors.get(k, 'lightgray')}
        dot.node(name, _attributes=input_attrs)

    outputs = set()
    for k in config.genome_config.output_keys:
        outputs.add(k)
        name = node_names.get(k, str(k))
        node_attrs = {'style': 'filled', 'fillcolor': node_colors.get(k, 'lightblue')}

        dot.node(name, _attributes=node_attrs)

    used_nodes = set(genome.nodes.keys())
    for n in used_nodes:
        if n in inputs or n in outputs:
            continue

        attrs = {'style': 'filled',
                 'fillcolor': node_colors.get(n, 'white')}
        dot.node(str(n), _attributes=attrs)

    for cg in genome.connections.values():
        if cg.enabled or show_disabled:
            input, output = cg.key
            a = node_names.get(input, str(input))
            b = node_names.get(output, str(output))
            style = 'solid'
            color = 'green' if cg.theta > 0 else 'red'
            width = str(0.1 + abs(cg.theta / 5.0))
            dot.edge(a, b, _attributes={'style': style, 'color': color, 'penwidth': width})

    dot.render(filename, view=view)

    return dot