min_avgs[env_key] = data['avg'][index]
min_stds[env_key] = data['std'][index]
for env_key in env_keys:
ps[env_key] = sorted(ps[env_key])
avgs[env_key] = np.array([avgs[env_key][p] for p in ps[env_key]])
stds[env_key] = np.array([stds[env_key][p] for p in ps[env_key]])
os.chdir(cwd)
return env_keys, ps, avgs, stds, min_avgs, min_stds
if __name__ == '__main__':
env_keys, ps, avgs, stds, min_avgs, min_stds = load_data('nn')
colors = [graphs.BLUE, graphs.PINK, graphs.GREEN]
graphs.output_file('c4_fig4_2_mbratio_perf.html')
env_displayed = [env_key for env_key in env_keys if env_key[0] == 'kin20_150' and env_key[2] == 10000]
for color, envd in zip(colors, env_displayed):
print(avgs[envd])
y_max = max(avgs[envd] + stds[envd])
graphs.perf_std_discrete(ps[envd], avgs[envd], stds[envd], std_width=0.0035, color=color,
y_range=[0.0, y_max+0.02], plot_width=1000, plot_height=500, title='{} {}'.format(*envd))
graphs.hold(True)
graphs.show()
ratios[tick].append(ratios_[i])
avgs[tick].append(src_data[0]['avg'][index])
stds[tick].append(src_data[0]['std'][index])
y_max = max(y_max, max(avgs[tick]) + max(stds[tick]))
except ValueError:
pass
os.chdir(cwd)
if __name__ == "__main__":
print(' '.join('{:6.3f}|{:6.3f}'.format(a, s)
for a, s in zip(avgs, stds)))
graphs.output_file('c3_fig3_8_unreach_perf.html')
colors = ['#2577B2', '#E84A5F']
for color, tick in zip(colors, [2000, 10000]):
graphs.perf_std_discrete(
ratios[tick],
avgs[tick],
stds[tick],
std_width=0.25,
color=color,
alpha=0.75,
y_range=[0.0, y_max + 0.01], #y_range=[y_min-0.1, y_max+0.1],
plot_width=1000,
plot_height=400,
title='t={}'.format(tick))
graphs.hold(True)
graphs.show()
# for N in Ns:
# for w in windows:
# print('N={}, w={} : {} +- {}'.format(N, w, adapt_avgs[(w, N, 20)], adapt_stds[(w, N, 20)]))
# print()
if __name__ == '__main__':
graphs.output_file('c4_figB_4_adaptg_reach_graph.html')
for res in RESS:
for N in Ns:
y_max = max(np.array(avgs[N]) + np.array(stds[N]))
graphs.perf_std_discrete(ratios[N],
avgs[N],
stds[N],
color='#2577B2',
alpha=0.5,
y_range=[0, y_max * 1.05],
title='adaptg_reach, t={}, res={}'.format(
N, res),
std_width=0.25,
plot_width=1000,
plot_height=300)
graphs.hold(True)
graphs.line([0, 100], adapt_avgs[(W, N, res)],
adapt_stds[(W, N, res)])
graphs.hold(False)
graphs.show()
# for d in disturbs:
# for N in Ns:
# print('N={}: {} +- {}'.format(N, adapt_avgs[(d, res, N)], adapt_stds[(d, res N)]))
if __name__ == '__main__':
graphs.output_file('c4_fig4_5_adapt_graph_{}.html'.format(KIND))
y_ranges = [(0.075, 0.3), (0.0, 0.1), (0.08, 0.14)]
for i, d in enumerate(disturbs):
y_range = y_ranges[i]
for N in Ns:
for res in RESS:
graphs.perf_std_discrete(
[100 * i * 0.05 for i in range(21)],
avgs[('kin20_150', d, N)],
stds[('kin20_150', d, N)],
std_width=0.25,
color='#2577B2',
alpha=0.5,
y_range=y_range, #y_range=[y_min-0.1, y_max+0.1],
plot_width=1000,
plot_height=300,
title='d={} t={} res={}'.format(d, N, res))
graphs.hold(True)
graphs.line([0, 100], adapt_avgs[(d, res, N)],
adapt_stds[(d, res, N)])
graphs.hold(False)
graphs.show()
min_avgs[env_key] = data['avg'][index]
min_stds[env_key] = data['std'][index]
for env_key in env_keys:
ps[env_key] = sorted(ps[env_key])
avgs[env_key] = np.array([avgs[env_key][p] for p in ps[env_key]])
stds[env_key] = np.array([stds[env_key][p] for p in ps[env_key]])
os.chdir(cwd)
return env_keys, ps, avgs, stds, min_avgs, min_stds
if __name__ == '__main__':
env_keys, ps, avgs, stds, min_avgs, min_stds = load_data('nn')
colors = [graphs.BLUE, graphs.PINK, graphs.GREEN]
graphs.output_file('fixed_graph.html')
env_displayed = [env_key for env_key in env_keys if env_key[0] == 'kin20_150' and env_key[2] == 5000]
for color, envd in zip(colors, env_displayed):
print(avgs[envd])
y_max = max(avgs[envd] + stds[envd])
graphs.perf_std_discrete(ps[envd], avgs[envd], stds[envd], std_width=0.0035, color=color,
y_range=[0.0, y_max+0.02], plot_width=1000, plot_height=500, title='{} {}'.format(*envd))
graphs.hold(True)
graphs.show()
batch = jobfactory.make_jobgroup([exp_cfg])
os.chdir(os.path.expanduser(exp_cfg.meta.rootpath))
data = hub.ResultsHub(batch, kind='nn').data()[0]
window = exp_cfg.exploration.explorer.ex_1.window
for N in Ns:
index = data['ticks'].index(N)
adapt_avgs[(window, N)] = data['avg'][index]
adapt_stds[(window, N)] = data['std'][index]
os.chdir(cwd)
for N in Ns:
for w in windows:
print('N={}, w={} : {} +- {}'.format(N, w, adapt_avgs[(w, N)], adapt_stds[(w, N)]))
print()
if __name__ == '__main__':
graphs.output_file('c4_fig4_9_adapt_reach_graph.html')
for N in Ns:
y_max = max(np.array(avgs[N]) + np.array(stds[N]))
graphs.perf_std_discrete(ratios[N], avgs[N], stds[N], std_width=0.25, alpha=0.5,
y_range=[0, y_max*1.05], title='adaptative reach performance, t = {}'.format(N))
graphs.hold(True)
graphs.line([0, 100], adapt_avgs[(W, N)], adapt_stds[(W, N)])
graphs.hold(False)
graphs.show()
ex_name = exp_cfg.exp.explorer_name
dims.setdefault(ex_name, [])
avgs.setdefault(ex_name, [])
stds.setdefault(ex_name, [])
dims[ex_name].append(dim)
avgs[ex_name].append(data['avg'][-1])
stds[ex_name].append(data['std'][-1])
y_max = max(y_max, data['avg'][-1] + data['std'][-1])
os.chdir(cwd)
colors = ['#2577B2', '#E84A5F']
graphs.output_file('c3_fig3_3_eval_perf.html')
for color, ex_name in zip(colors, dims.keys()):
graphs.perf_std_discrete(dims[ex_name],
avgs[ex_name],
stds[ex_name],
std_width=0.25,
color=color,
title=str(ex_name),
x_range=[0.0, 110.0],
y_range=[0.0, y_max + 0.1],
plot_width=1000,
plot_height=300)
graphs.hold(True)
graphs.show()
n_motor = exp_cfg.exploration.explorer.eras[0]
env = env_name
d = exp_cfg.exploration.explorer.ex_1.learner.m_disturb
try:
disturb[env].append(d)
avgs[env][d] = src_data[0]['avg'][-1]
stds[env][d] = src_data[0]['std'][-1]
except ValueError:
pass
env_displayed = ['kin2_150', 'kin7_150', 'kin20_150']
for env_name in env_displayed:
disturb[env_name] = sorted(disturb[env_name])
avgs[env_name] = np.array([avgs[env_name][d] for d in disturb[env_name]])
stds[env_name] = np.array([stds[env_name][d] for d in disturb[env_name]])
y_max = max(y_max, max(avgs[env_name] + stds[env_name]))
os.chdir(cwd)
colors = ['#2577B2', '#E84A5F']
graphs.output_file('c3_fig3_14_inverse_perf.html')
for env_name in env_displayed:
x = [math.log(d) for d in disturb[env_name]]
graphs.perf_std_discrete(x, avgs[env_name], stds[env_name], std_width=0.02,
y_range=[0.0, 0.3], title=str(env_name),
plot_width=1000, plot_height=300)
graphs.show()
for N in Ns:
index = data['ticks'].index(N)
adapt_avgs[(d, N)] = data['avg'][index]
adapt_stds[(d, N)] = data['std'][index]
os.chdir(cwd)
for d in disturbs:
for N in Ns:
print('N={}: {} +- {}'.format(N, adapt_avgs[(d, N)], adapt_stds[(d, N)]))
if __name__ == '__main__':
graphs.output_file('ddmab_graph.html')
y_ranges=[(0.075, 0.3), (0.0, 0.1), (0.08, 0.14)]
for i, d in enumerate(disturbs):
y_range = y_ranges[i]
for N in Ns:
graphs.perf_std_discrete([100*i*0.05 for i in range(21)],
avgs[('kin20_150', d, N)], stds[('kin20_150', d, N)],
std_width=0.25, alpha=0.5, y_range=y_range,
plot_height=300, title='d={} t={}'.format(d, N))
graphs.hold(True)
graphs.line([0, 100], adapt_avgs[(d, N)], adapt_stds[(d, N)])
graphs.hold(False)
graphs.show()
adapt_avgs[(d, N)] = data['avg'][index]
adapt_stds[(d, N)] = data['std'][index]
os.chdir(cwd)
for d in disturbs:
for N in Ns:
print('N={}: {} +- {}'.format(N, adapt_avgs[(d, N)],
adapt_stds[(d, N)]))
if __name__ == '__main__':
graphs.output_file('c4_fig4_5_adapt_graph_{}.html'.format(KIND))
y_ranges = [(0.075, 0.3), (0.0, 0.1), (0.08, 0.14)]
for i, d in enumerate(disturbs):
y_range = y_ranges[i]
for N in Ns:
graphs.perf_std_discrete([100 * i * 0.05 for i in range(21)],
avgs[('kin20_150', d, N)],
stds[('kin20_150', d, N)],
std_width=0.25,
alpha=0.5,
y_range=y_range,
plot_height=300,
title='d={} t={}'.format(d, N))
graphs.hold(True)
graphs.line([0, 100], adapt_avgs[(d, N)], adapt_stds[(d, N)])
graphs.hold(False)
graphs.show()