<jupyter_start><jupyter_text>---<jupyter_code># plt.figure(figsize=(6, 4), dpi=600)
# x = [256, 512, 1024, 2048, 3200, 6400, 12800, 25600]
# y_rand = [accs_erl_rand[x_].mean() for x_ in x]
# y_easy = [accs_erl_easy[x_].mean() for x_ in x]
# plt.axhline(accs_erl_all[400].mean(), c='C0', linewidth=2)
# plt.axhline(accs_erl_all[200].mean(), c='C1', linewidth=2)
# plt.axhline(accs_erl_all[0].mean(), c='C8', linewidth=2)
# plt.plot(np.log2(x), y_rand, marker='o', c='C2', linewidth=2)
# plt.plot(np.log2(x), y_easy, marker='^', c='C3', linewidth=2)
# plt.xticks(np.log2(np.array(x)), labels=x, fontsize='small')
# plt.yticks(fontsize='small')
# plt.xlabel('Dataset Size')
# plt.ylabel('Avg. Test Acc. Across Sparsities')
# sns.despine()
# plt.show()
# n=2048

# plt.figure(figsize=(7.5, 4), dpi=600)
# ls = []

# x = [0, 20]
# ys = accs_erl_all[400][:, 0]
# y_mean, y_std = ys.mean(0), ys.std(0)
# c='C7'
# plt.axhline(y_mean, c=c, ls='--', alpha=0.8, linewidth=2)
# plt.fill_between(x, [y_mean + y_std]*2, [y_mean - y_std]*2, color=c, alpha=0.2)

# # x = np.arange(21)
# # ys = accs_erl_all[800]
# # y_mean, y_std = ys.mean(0), ys.std(0)
# # c = f'C0'
# # l = plt.plot(x, y_mean, c=c, alpha=0.8, linewidth=2)
# # ls.append(l[0])
# # plt.fill_between(x, y_mean + y_std, y_mean - y_std, color=c, alpha=0.2)

# x = np.arange(21)
# ys = accs_erl_all[200]
# y_mean, y_std = ys.mean(0), ys.std(0)
# c = f'C1'
# l = plt.plot(x, y_mean, c=c, alpha=0.8, linewidth=2)
# ls.append(l[0])
# plt.fill_between(x, y_mean + y_std, y_mean - y_std, color=c, alpha=0.2)

# # x = np.arange(21)
# # ys = accs_erl_all[0]
# # y_mean, y_std = ys.mean(0), ys.std(0)
# # c = f'C8'
# # l = plt.plot(x, y_mean, c=c, alpha=0.8, linewidth=2)
# # ls.append(l[0])
# # plt.fill_between(x, y_mean + y_std, y_mean - y_std, color=c, alpha=0.2)

# x = np.arange(21)
# ys = accs_erl_rand[n]
# y_mean, y_std = ys.mean(0), ys.std(0)
# c = f'C2'
# l = plt.plot(x, y_mean, c=c, alpha=0.8, linewidth=2)
# ls.append(l[0])
# plt.fill_between(x, y_mean + y_std, y_mean - y_std, color=c, alpha=0.1)

# x = np.arange(21)
# ys = accs_erl_hard[n]
# y_mean, y_std = ys.mean(0), ys.std(0)
# c = f'C4'
# l = plt.plot(x, y_mean, c=c, alpha=0.8, linewidth=2)
# ls.append(l[0])
# plt.fill_between(x, y_mean + y_std, y_mean - y_std, color=c, alpha=0.2)

# x = np.arange(21)
# ys = accs_erl_easy[n]
# y_mean, y_std = ys.mean(0), ys.std(0)
# c = f'C3'
# l = plt.plot(x, y_mean, c=c, alpha=0.8, linewidth=2)
# ls.append(l[0])
# plt.fill_between(x, y_mean + y_std, y_mean - y_std, color=c, alpha=0.2)

# plt.xlim(0, 16)
# plt.ylim(0.82, 0.92)
# plt.xticks(np.arange(0, 17, 2), [f'{f*100:.1f}' for f in 0.8**np.arange(0, 17, 2)])
# plt.xlabel('% Weights Remaining')
# plt.ylabel('Final Test Accuracy')
# # # plt.title('CIFAR100 ResNet32')
# sns.despine()
# # # plt.savefig('/home/mansheej/open_lth/figs/0027.svg')
# plt.show()
# plt.figure(figsize=(6.4, 4.8), dpi=600)
# x, y = [], []
# for k, v in accs_erl_all.items():
#     if k != 0:
#         y.append(v.mean(1))
#         x.append(accs_erp_all[k])
# for k, v in accs_erl_rand.items():
#     y.append(v.mean(1))
#     x.append(accs_erp_rand[k])
# for k, v in accs_erl_easy.items():
#     y.append(v.mean(1))
#     x.append(accs_erp_easy[k])
# for k, v in accs_erl_hard.items():
#     y.append(v.mean(1))
#     x.append(accs_erp_hard[k])
# # for k, v in accs_erl_lmc.items():
# #     y.append(v.mean(1))
# #     x.append(accs_erp_lmc[k])
# x, y = np.concatenate(x), np.concatenate(y)
# plt.scatter(x, y)<jupyter_output><empty_output>
StarcoderdataJupyterStructuredCleanDedup