#Copyright (c) 2007-2011, cesar.gomes and mirrorballu2
#Copyright (c) 2015-2017, CodeReclaimers, LLC
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from __future__ import print_function
import copy
import warnings
import random
import argparse
import os
import graphviz
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
import matplotlib.patches as mpatches
import numpy as np
import geometry
import agent
import maze_environment as maze
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_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, directory=None, node_names=None, show_disabled=True, prune_unused=False,
node_colors=None, fmt='svg'):
""" Receives a genome and draws a neural network with arbitrary topology. """
# Attributes for network nodes.
if graphviz is None:
warnings.warn("This display is not available due to a missing optional dependency (graphviz)")
return
if node_names is None:
node_names = {}
assert type(node_names) is dict
if node_colors is None:
node_colors = {}
assert type(node_colors) is dict
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)
if prune_unused:
connections = set()
for cg in genome.connections.values():
if cg.enabled or show_disabled:
connections.add((cg.in_node_id, cg.out_node_id))
used_nodes = copy.copy(outputs)
pending = copy.copy(outputs)
while pending:
new_pending = set()
for a, b in connections:
if b in pending and a not in used_nodes:
new_pending.add(a)
used_nodes.add(a)
pending = new_pending
else:
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:
#if cg.input not in used_nodes or cg.output not in used_nodes:
# continue
input, output = cg.key
a = node_names.get(input, str(input))
b = node_names.get(output, str(output))
style = 'solid' if cg.enabled else 'dotted'
color = 'green' if cg.weight > 0 else 'red'
width = str(0.1 + abs(cg.weight / 5.0))
dot.edge(a, b, _attributes={'style': style, 'color': color, 'penwidth': width})
dot.render(filename, directory, view=view)
return dot
def draw_maze_records(maze_env, records, best_threshold=0.8, filename=None, view=False, show_axes=False, width=400, height=400, fig_height=7):
"""
The function to draw maze with recorded agents positions.
Arguments:
maze_env: The maze environment configuration.
records: The records of solver agents collected during NEAT execution.
best_threshold: The minimal fitness of maze solving agent's species to be included into the best ones.
view: The flag to indicate whether to view plot.
width: The width of drawing in pixels
height: The height of drawing in pixels
fig_height: The plot figure height in inches
"""
# find the distance threshold for the best species
dist_threshold = maze_env.agent_distance_to_exit() * (1.0 - best_threshold)
# generate color palette and find the best species IDS
max_sid = 0
for r in records:
if r.species_id > max_sid:
max_sid = r.species_id
colors = [None] * (max_sid + 1)
sp_idx = [False] * (max_sid + 1)
best_sp_idx = [0] * (max_sid + 1)
for r in records:
if not sp_idx[r.species_id]:
sp_idx[r.species_id] = True
rgb = (random.random(), random.random(), random.random())
colors[r.species_id] = rgb
if maze_env.exit_point.distance(geometry.Point(r.x, r.y)) <= dist_threshold:
best_sp_idx[r.species_id] += 1
# initialize plotting
fig = plt.figure()
fig.set_dpi(100)
fig_width = fig_height * (float(width)/float(2.0 * height )) - 0.2
print("Plot figure width: %.1f, height: %.1f" % (fig_width, fig_height))
fig.set_size_inches(fig_width, fig_height)
ax1, ax2 = fig.subplots(2, 1, sharex=True)
ax1.set_xlim(0, width)
ax1.set_ylim(0, height)
ax2.set_xlim(0, width)
ax2.set_ylim(0, height)
# draw species
n_best_species = 0
for i, v in enumerate(best_sp_idx):
if v > 0:
n_best_species += 1
_draw_species_(records=records, sid=i, colors=colors, ax=ax1)
else:
_draw_species_(records=records, sid=i, colors=colors, ax=ax2)
ax1.set_title('fitness >= %.1f, species: %d' % (best_threshold, n_best_species))
ax2.set_title('fitness < %.1f' % best_threshold)
# draw maze
_draw_maze_(maze_env, ax1)
_draw_maze_(maze_env, ax2)
# turn off axis rendering
if not show_axes:
ax1.axis('off')
ax2.axis('off')
# Invert Y axis to have coordinates origin at the top left
ax1.invert_yaxis()
ax2.invert_yaxis()
# Save figure to file
if filename is not None:
plt.savefig(filename)
if view:
plt.show()
plt.close()
def _draw_species_(records, sid, colors, ax):
"""
The function to draw specific species from the records with
particular color.
Arguments:
records: The records of solver agents collected during NEAT execution.
sid: The species ID
colors: The colors table by species ID
ax: The figure axis instance
"""
for r in records:
if r.species_id == sid:
circle = plt.Circle((r.x, r.y), 2.0, facecolor=colors[r.species_id])
ax.add_patch(circle)
def _draw_maze_(maze_env, ax):
"""
The function to draw maze environment
Arguments:
maze_env: The maze environment configuration.
ax: The figure axis instance
"""
# draw maze walls
for wall in maze_env.walls:
line = plt.Line2D((wall.a.x, wall.b.x), (wall.a.y, wall.b.y), lw=1.5)
ax.add_line(line)
# draw start point
start_circle = plt.Circle((maze_env.agent.location.x, maze_env.agent.location.y),
radius=2.5, facecolor=(0.6, 1.0, 0.6), edgecolor='w')
ax.add_patch(start_circle)
# draw exit point
exit_circle = plt.Circle((maze_env.exit_point.x, maze_env.exit_point.y),
radius=2.5, facecolor=(1.0, 0.2, 0.0), edgecolor='w')
ax.add_patch(exit_circle)
if __name__ == '__main__':
# read command line parameters
parser = argparse.ArgumentParser(description="The maze experiment visualizer.")
parser.add_argument('-m', '--maze', default='medium', help='The maze configuration to use.')
parser.add_argument('-r', '--records', help='The records file.')
parser.add_argument('-o', '--output', help='The file to store the plot.')
parser.add_argument('--width', type=int, default=400, help='The width of the subplot')
parser.add_argument('--height', type=int, default=400, help='The height of the subplot')
parser.add_argument('--fig_height', type=float, default=7, help='The height of the plot figure')
parser.add_argument('--show_axes', type=bool, default=False, help='The flag to indicate whether to show plot axes.')
args = parser.parse_args()
local_dir = os.path.dirname(__file__)
if not (args.maze == 'medium' or args.maze == 'hard'):
print('Unsupported maze configuration: %s' % args.maze)
exit(1)
# read maze environment
maze_env_config = os.path.join(local_dir, '%s_maze.txt' % args.maze)
maze_env = maze.read_environment(maze_env_config)
# read agents records
rs = agent.AgentRecordStore()
rs.load(args.records)
# render visualization
random.seed(42)
draw_maze_records(maze_env,
rs.records,
width=args.width,
height=args.height,
fig_height=args.fig_height,
view=True,
show_axes=args.show_axes,
filename=args.output)
