def load_exp(pipeline, resync='n'):
exp = Experiment.load(pipeline)
if resync == 'y':
exp.wait(retry_interval=5)
exp.queue.sync(exp.pipeline_path,
exp.pipeline_path,
sync_to=sge.SyncTo.LOCAL,
recursive=True)
return exp
def main():
exp = Experiment.load('color_fix.10')
plot_with_conf2(exp,
'regier_cost',
'term_usage',
'com_noise',
z_label='com $\sigma^2$')
plot_with_conf2(exp, 'gibson_cost', 'term_usage', 'com_noise')
plot_with_conf2(exp, 'wellformedness', 'term_usage', 'com_noise')
def main():
consensus_iters = 10
e = com_enviroments.make('wcs')
k = 3
sims = []
# human maps
human_maps = list(e.human_mode_maps.values())
# robo maps
exp = Experiment.load('color_fix.1')
robo_maps = exp.reshape('agent_language_map')
human_rand = evaluate.mean_rand_index(human_maps)
exp.log.info('mean rand for all human maps = {:.3f}'.format(human_rand))
robo_rand = evaluate.mean_rand_index(robo_maps)
exp.log.info('mean rand for all agent maps = {:.3f}'.format(robo_rand))
cross_rand = evaluate.mean_rand_index(human_maps, robo_maps)
exp.log.info(
'mean rand cross human and robot maps = {:.3f}'.format(cross_rand))
for k in range(3, 12):
cielab_map = evaluate.compute_cielab_map(e,
k,
iter=consensus_iters,
bw_boost=1)
e.plot_with_colors(cielab_map,
save_to_path=exp.pipeline_path + 'cielab_map_' +
str(k) + '.png')
human_consensus_map = Correlation_Clustering.compute_consensus_map(
human_maps, k=k, iter=consensus_iters)
e.plot_with_colors(human_consensus_map,
save_to_path=exp.pipeline_path +
'human_consensus_language_map_' + str(k) + '.png')
robo_consensus_map = Correlation_Clustering.compute_consensus_map(
robo_maps, k=k, iter=consensus_iters)
e.plot_with_colors(robo_consensus_map,
save_to_path=exp.pipeline_path +
'consensus_language_map_' + str(k) + '.png')
# compare human and robo maps
rand_i = adjusted_rand_score(human_consensus_map, robo_consensus_map)
print('rand i between human consensus and agent consensus = {:.3f}'.
format(rand_i))
sims += [rand_i]
sims = np.array(sims)
print(sims.mean())
def visualize(pipeline_name):
print('plot results')
exp = Experiment.load(pipeline_name)
viz.plot_with_std(exp,
'gibson_cost', 'com_noise', 'msg_dim',
measure_label='Gibson communication efficiency',
x_label='Communication noise',
z_label='terms')
viz.plot_with_std(exp, 'gibson_cost', 'perception_noise', 'msg_dim')
viz.plot_with_std(exp, 'gibson_cost', 'com_noise', 'perception_noise')
viz.plot_with_std(exp, 'regier_cost', 'com_noise', 'msg_dim')
viz.plot_with_std(exp, 'wellformedness', 'com_noise', 'msg_dim')
def main():
args = exp_shared.parse_script_arguments()
# Run experiment
if args.pipeline == '':
exp = run(args.host_name)
else:
# Load existing experiment
exp = Experiment.load(args.pipeline)
if args.resync == 'y':
exp.wait(retry_interval=5)
exp.queue.sync(exp.pipeline_path, exp.pipeline_path, sync_to=sge.SyncTo.LOCAL, recursive=True)
cluster_ensemble = exp.get_flattened_results('agent_language_map')
consensus = Correlation_Clustering.compute_consensus_map(cluster_ensemble, k=10, iter=100)
print(consensus.values())
# Visualize experiment
visualize(exp)