sems = [
np.sqrt(
np.var([ave_prec[seed][id]
for seed in seed_list]) / (len(seed_list) - 1))
for id in ids
]
cis = [
1.96 * np.sqrt(
np.var([ave_prec[seed][id]
for seed in seed_list]) / (len(seed_list) - 1))
for id in ids
]
figure = visual_plt.plot_bar(means,
names=names,
colors=colors,
ylabel="average precision (after all tasks)",
title=title,
yerr=cis if len(seed_list) > 1 else None,
ylim=(0, 1))
figure_list.append(figure)
# print results to screen
print("\n\n" + "#" * 60 + "\nSUMMARY RESULTS: {}\n".format(title) +
"-" * 60)
for i, name in enumerate(names):
if len(seed_list) > 1:
print("{:12s} {:.2f} (+/- {:.2f}), n={}".format(
name, 100 * means[i], 100 * sems[i], len(seed_list)))
else:
print("{:12s} {:.2f}".format(name, 100 * means[i]))
print("#" * 60)
sems = [
np.sqrt(
np.var([ave_prec[seed][id]
for seed in seed_list]) / (len(seed_list) - 1))
for id in ids
]
cis = [
1.96 * np.sqrt(
np.var([ave_prec[seed][id]
for seed in seed_list]) / (len(seed_list) - 1))
for id in ids
]
figure = visual_plt.plot_bar(means,
names=names,
colors=colors,
ylabel="average precision (after all tasks)",
title=title,
yerr=cis if len(seed_list) > 1 else None,
ylim=(0, 1))
figure_list.append(figure)
# print results to screen
print("\n\n" + "#" * 60 + "\nSUMMARY RESULTS: {}\n".format(title) +
"-" * 60)
for i, name in enumerate(names):
if len(seed_list) > 1:
print("{:19s} {:.2f} (+/- {:.2f}), n={}".format(
name, 100 * means[i], 100 * sems[i], len(seed_list)))
else:
print("{:19s} {:.2f}".format(name, 100 * means[i]))
if i == 1:
