| # |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # |
| |
| import os |
| |
| from matplotlib import pyplot as plt |
| import seaborn as sns |
| import numpy as np |
| import palettable |
| from matplotlib.ticker import MaxNLocator |
| import numpy |
| from src.common.constant import Config |
| import matplotlib |
| |
| # lines' mark size |
| set_marker_size = 15 |
| # points' mark size |
| set_marker_point = 14 |
| # points' mark size |
| set_font_size = 40 |
| set_lgend_size = 15 |
| set_tick_size = 20 |
| |
| frontinsidebox = 23 |
| |
| # update tick size |
| matplotlib.rc('xtick', labelsize=set_tick_size) |
| matplotlib.rc('ytick', labelsize=set_tick_size) |
| |
| plt.rcParams['axes.labelsize'] = set_tick_size |
| |
| mark_list = ["o", "*", "<", "^", "s", "d", "D", ">", "h"] |
| mark_size_list = [set_marker_size, set_marker_size + 1, set_marker_size + 1, set_marker_size, |
| set_marker_size, set_marker_size, set_marker_size, set_marker_size + 1, set_marker_size + 2] |
| line_shape_list = ['-.', '--', '-', ':'] |
| |
| |
| # this is for draw figure3 only |
| def get_plot_compare_with_base_line_cfg(search_space, dataset, if_with_phase1=False): |
| if search_space == Config.NB201: |
| run_range_ = range(0, 100, 1) |
| if if_with_phase1: |
| draw_graph = draw_anytime_result_with_p1 |
| else: |
| draw_graph = draw_anytime_result |
| # min, this is for plot only |
| if dataset == Config.c10: |
| # C10 array |
| budget_array = [0.017, 0.083] + list(range(1, 350, 4)) |
| sub_graph_y1 = [91, 94.5] |
| sub_graph_y2 = [53.5, 55] |
| sub_graph_split = 60 |
| elif dataset == Config.c100: |
| # C10 array |
| budget_array = [0.017, 0.083] + list(range(1, 350, 4)) |
| |
| sub_graph_y1 = [64, 73.5] |
| sub_graph_y2 = [15, 16] |
| sub_graph_split = 20 |
| else: |
| # ImgNet X array |
| budget_array = [0.017, 0.083] + list(range(1, 350, 4)) |
| sub_graph_y1 = [33, 48] |
| sub_graph_y2 = [15.5, 17] |
| sub_graph_split = 34 |
| else: |
| # this is NB101 + C10, because only 101 has 20 run. others have 100 run. |
| run_range_ = range(0, 20, 1) |
| if if_with_phase1: |
| draw_graph = draw_anytime_result_one_graph_with_p1 |
| # budget_array = list(range(1, 16, 1)) |
| budget_array = numpy.arange(0.02, 15, 0.02).tolist() |
| else: |
| draw_graph = draw_anytime_result_one_graph |
| budget_array = [0.017, 0.083] + list(range(1, 2000, 8)) |
| |
| if dataset == Config.c10: |
| # C10 array |
| # budget_array = list(range(0, 2000, 1)) |
| sub_graph_y1 = [90, 94.5] |
| sub_graph_y2 = [52, 55] |
| sub_graph_split = 60 |
| else: |
| raise Exception |
| |
| return run_range_, budget_array, sub_graph_y1, sub_graph_y2, sub_graph_split, draw_graph |
| |
| |
| def draw_anytime_result(result_dir, y_acc_list_arr, x_T_list, |
| x_acc_train, y_acc_train_l, y_acc_train_m, y_acc_train_h, |
| annotations, lv, |
| name_img, dataset, |
| x1_lim=[], x2_lim=[], |
| ): |
| fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, dpi=100, gridspec_kw={'height_ratios': [4, 1]}) |
| exp = np.array(y_acc_list_arr) |
| sys_acc_h = np.quantile(exp, .75, axis=0) |
| sys_acc_m = np.quantile(exp, .5, axis=0) |
| sys_acc_l = np.quantile(exp, .25, axis=0) |
| |
| # plot simulate result of system |
| ax1.fill_between(x_T_list, sys_acc_l, sys_acc_h, alpha=0.1) |
| ax1.plot(x_T_list, sys_acc_m, mark_list[-1], label="TRAILS") |
| ax2.fill_between(x_T_list, sys_acc_l, sys_acc_h, alpha=0.1) |
| |
| # plot simulate result of train-based line |
| ax1.fill_between(x_acc_train, y_acc_train_l, y_acc_train_h, alpha=0.3) |
| ax1.plot(x_acc_train, y_acc_train_m, mark_list[-2], label="Training-based MS") |
| ax2.fill_between(x_acc_train, y_acc_train_l, y_acc_train_h, alpha=0.3) |
| |
| for i in range(len(annotations)): |
| ele = annotations[i] |
| if ele[1] < lv: |
| # convert to mins |
| ax2.plot(ele[2] / 60, ele[1], mark_list[i], label=ele[0], fontsize=set_marker_size) |
| else: |
| ax1.plot(ele[2] / 60, ele[1], mark_list[i], label=ele[0], fontsize=set_marker_size) |
| # ax2.scatter(ele[2]/60, ele[1]* 0.01, s=100, color="red") |
| # ax2.annotate(ele[0], (ele[2]/60, ele[1] * 0.01)) |
| |
| if len(x1_lim) > 0 and len(x2_lim) > 0: |
| ax1.set_ylim(x1_lim[0], x1_lim[1]) # 子图1设置y轴范围，只显示部分图 |
| ax2.set_ylim(x2_lim[0], x2_lim[1]) # 子图2设置y轴范围，只显示部分图 |
| |
| ax1.spines['bottom'].set_visible(False) # 关闭子图1中底部脊 |
| ax2.spines['top'].set_visible(False) ##关闭子图2中顶部脊 |
| ax2.set_xticks(range(0, 31, 1)) |
| |
| d = .85 # 设置倾斜度 |
| # 绘制断裂处的标记 |
| kwargs = dict(marker=[(-1, -d), (1, d)], markersize=set_marker_size, |
| linestyle='none', color='r', mec='r', mew=1, clip_on=False) |
| ax1.plot([0, 1], [0, 0], transform=ax1.transAxes, **kwargs) |
| ax2.plot([0, 1], [1, 1], transform=ax2.transAxes, **kwargs) |
| |
| plt.tight_layout() |
| plt.xscale("symlog") |
| ax1.grid() |
| ax2.grid() |
| plt.xlabel("Time Budget given by user (min)", fontsize=set_font_size) |
| ax1.set_ylabel(f"Test accuracy on {dataset}", fontsize=set_font_size) |
| ax1.legend(ncol=1, fontsize=set_lgend_size) |
| ax2.legend(fontsize=set_lgend_size) |
| # plt.show() |
| plt.savefig(f"{result_dir}/any_time_{name_img}.pdf", bbox_inches='tight') |
| |
| |
| def draw_anytime_result_one_graph(y_acc_list_arr, x_T_list, |
| x_acc_train, y_acc_train_l, y_acc_train_m, y_acc_train_h, |
| annotations, lv, |
| name_img, dataset, |
| x1_lim=[], x2_lim=[], |
| ): |
| # fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, dpi=100, gridspec_kw={'height_ratios': [5, 1]}) |
| exp = np.array(y_acc_list_arr) * 100 |
| sys_acc_h = np.quantile(exp, .75, axis=0) |
| sys_acc_m = np.quantile(exp, .5, axis=0) |
| sys_acc_l = np.quantile(exp, .25, axis=0) |
| |
| # exp_time = np.array(real_time_used_arr) |
| # time_mean = np.quantile(exp_time, .5, axis=0) |
| time_mean = x_T_list |
| |
| # plot simulate result of system |
| plt.fill_between(time_mean, sys_acc_l, sys_acc_h, alpha=0.1) |
| plt.plot(time_mean, sys_acc_m, "o-", label="TRAILS") |
| # plt.plot(time_mean, sys_acc_m, label="TRAILS") |
| |
| # plot simulate result of train-based line |
| plt.fill_between(x_acc_train, y_acc_train_l, y_acc_train_h, alpha=0.3) |
| plt.plot(x_acc_train, y_acc_train_m, "o-", label="Training-based MS") |
| # plt.plot(x_acc_train, y_acc_train_m, label="Training-based MS") |
| |
| if len(x1_lim) > 0: |
| plt.ylim(x1_lim[0], x1_lim[1]) # 子图1设置y轴范围，只显示部分图 |
| |
| d = .85 # 设置倾斜度 |
| # 绘制断裂处的标记 |
| kwargs = dict(marker=[(-1, -d), (1, d)], markersize=set_marker_size, |
| linestyle='none', color='r', mec='r', mew=1, clip_on=False) |
| # plt.plot([0, 1], [0, 0], transform=ax1.transAxes, **kwargs) |
| # plt.plot([0, 1], [1, 1], transform=ax2.transAxes, **kwargs) |
| |
| plt.tight_layout() |
| # plt.xscale("symlog") |
| plt.grid() |
| plt.xlabel("Time Budget given by user (min)", fontsize=set_font_size) |
| plt.ylabel(f"Test accuracy on {dataset}", fontsize=set_font_size) |
| plt.legend(ncol=1, fontsize=set_lgend_size) |
| plt.show() |
| # plt.savefig(f"amy_time_{name_img}.pdf", bbox_inches='tight') |
| |
| |
| # those two function will plot phase 1 and phase 2 |
| def draw_anytime_result_with_p1(result_dir, y_acc_list_arr, x_T_list, y_acc_list_arr_p1, x_T_list_p1, |
| x_acc_train, y_acc_train_l, y_acc_train_m, y_acc_train_h, |
| annotations, lv, |
| name_img, dataset, max_value, |
| x1_lim=[], x2_lim=[], |
| ): |
| fig, (ax1, ax2) = plt.subplots( |
| 2, 1, |
| sharex=True, |
| dpi=100, |
| gridspec_kw={'height_ratios': [6, 1]}) |
| |
| shade_degree = 0.2 |
| |
| # plot simulate result of train-based line |
| ax1.plot(x_acc_train, y_acc_train_m, mark_list[-3] + line_shape_list[0], label="Training-Based MS", |
| markersize=mark_size_list[-3]) |
| ax1.fill_between(x_acc_train, y_acc_train_l, y_acc_train_h, alpha=shade_degree) |
| ax2.fill_between(x_acc_train, y_acc_train_l, y_acc_train_h, alpha=shade_degree) |
| |
| # plot simulate result of system |
| exp = np.array(y_acc_list_arr_p1) |
| sys_acc_p1_h = np.quantile(exp, .75, axis=0) |
| sys_acc_p1_m = np.quantile(exp, .5, axis=0) |
| sys_acc_p1_l = np.quantile(exp, .25, axis=0) |
| ax1.plot(x_T_list_p1, sys_acc_p1_m, mark_list[-2] + line_shape_list[1], label="Training-Free MS", |
| markersize=mark_size_list[-2]) |
| ax1.fill_between(x_T_list_p1, sys_acc_p1_l, sys_acc_p1_h, alpha=shade_degree) |
| ax2.fill_between(x_T_list_p1, sys_acc_p1_l, sys_acc_p1_h, alpha=shade_degree) |
| |
| # plot simulate result of system |
| exp = np.array(y_acc_list_arr) |
| sys_acc_h = np.quantile(exp, .75, axis=0) |
| sys_acc_m = np.quantile(exp, .5, axis=0) |
| sys_acc_l = np.quantile(exp, .25, axis=0) |
| ax1.plot(x_T_list, sys_acc_m, mark_list[-1] + line_shape_list[2], label="2Phase-MS", markersize=mark_size_list[-1]) |
| ax1.fill_between(x_T_list, sys_acc_l, sys_acc_h, alpha=shade_degree) |
| ax2.fill_between(x_T_list, sys_acc_l, sys_acc_h, alpha=shade_degree) |
| |
| print(f"speed-up on {dataset} = {x_acc_train[-1] / x_T_list[-2]}, " |
| f"t_train = {x_acc_train[-1]}, t_f = {x_T_list[-2]}") |
| |
| for i in range(len(annotations)): |
| ele = annotations[i] |
| if ele[1] < lv: |
| # convert to mins |
| ax2.plot(ele[2] / 60, ele[1], mark_list[i], label=ele[0], markersize=set_marker_point) |
| else: |
| ax1.plot(ele[2] / 60, ele[1], mark_list[i], label=ele[0], markersize=set_marker_point) |
| # ax2.scatter(ele[2]/60, ele[1]* 0.01, s=100, color="red") |
| # ax2.annotate(ele[0], (ele[2]/60, ele[1] * 0.01)) |
| |
| if len(x1_lim) > 0 and len(x2_lim) > 0: |
| ax1.set_ylim(x1_lim[0], x1_lim[1]) # 子图1设置y轴范围，只显示部分图 |
| ax2.set_ylim(x2_lim[0], x2_lim[1]) # 子图2设置y轴范围，只显示部分图 |
| |
| ax1.spines['bottom'].set_visible(False) # 关闭子图1中底部脊 |
| ax2.spines['top'].set_visible(False) ##关闭子图2中顶部脊 |
| ax2.set_xticks(range(0, 31, 1)) |
| |
| d = .85 # 设置倾斜度 |
| # 绘制断裂处的标记 |
| kwargs = dict(marker=[(-1, -d), (1, d)], markersize=set_marker_size, |
| linestyle='none', color='r', mec='r', mew=1, clip_on=False) |
| ax1.plot([0, 1], [0, 0], transform=ax1.transAxes, **kwargs) |
| ax2.plot([0, 1], [1, 1], transform=ax2.transAxes, **kwargs) |
| |
| plt.xscale("log") |
| ax1.grid() |
| ax2.grid() |
| plt.xlabel(r"Response Time Threshold $T_{max}$ (min)", fontsize=set_font_size) |
| ax1.set_ylabel(f"Test Acc on {'In-16'}", fontsize=set_font_size) |
| # ax1.legend(ncol=1, fontsize=set_lgend_size) |
| # ax2.legend(fontsize=set_lgend_size) |
| |
| ax1.xaxis.label.set_size(set_tick_size) |
| ax1.yaxis.label.set_size(set_tick_size) |
| # ax1.set_xticks([]) |
| |
| ax2.xaxis.label.set_size(set_tick_size) |
| ax2.yaxis.label.set_size(set_tick_size) |
| |
| ax1.yaxis.set_major_locator(MaxNLocator(nbins=4, integer=True)) |
| |
| ax1.axhline(max_value, color='r', linestyle='-', label='Global Best Accuracy') |
| |
| tick_values = [0.01, 0.1, 1, 10, 100, 1000] |
| ax2.set_xticks(tick_values) |
| ax2.set_xticklabels([f'$10^{{{int(np.log10(val))}}}$' for val in tick_values]) |
| |
| # this is for unique hash |
| export_legend( |
| fig, |
| colnum=3, |
| unique_labels=['TE-NAS (Training-Free)', 'ENAS (Weight sharing)', |
| 'KNAS (Training-Free)', 'DARTS-V1 (Weight sharing)', 'DARTS-V2 (Weight sharing)', |
| 'Training-Based MS', 'Training-Free MS', '2Phase-MS', 'Global Best Accuracy']) |
| plt.tight_layout() |
| fig.savefig(f"{result_dir}/any_time_{name_img}_p1_from_0.1_sec.pdf", bbox_inches='tight') |
| |
| |
| def export_legend(ori_fig, filename="any_time_legend", colnum=9, unique_labels=[]): |
| fig2 = plt.figure(figsize=(5, 0.3)) |
| lines_labels = [ax.get_legend_handles_labels() for ax in ori_fig.axes] |
| lines, labels = [sum(lol, []) for lol in zip(*lines_labels)] |
| # grab unique labels |
| if len(unique_labels) == 0: |
| unique_labels = set(labels) |
| # assign labels and legends in dict |
| legend_dict = dict(zip(labels, lines)) |
| # query dict based on unique labels |
| unique_lines = [legend_dict[x] for x in unique_labels] |
| fig2.legend(unique_lines, unique_labels, loc='center', |
| ncol=colnum, |
| fancybox=True, |
| shadow=True, scatterpoints=1, fontsize=set_lgend_size) |
| fig2.tight_layout() |
| fig2.savefig(f"{filename}.pdf", bbox_inches='tight') |
| |
| |
| def draw_anytime_result_one_graph_with_p1(y_acc_list_arr, x_T_list, y_acc_list_arr_p1, x_T_list_p1, |
| x_acc_train, y_acc_train_l, y_acc_train_m, y_acc_train_h, |
| annotations, lv, |
| name_img, dataset, |
| x1_lim=[], x2_lim=[], |
| ): |
| # fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, dpi=100, gridspec_kw={'height_ratios': [5, 1]}) |
| |
| # plot simulate result of system |
| exp = np.array(y_acc_list_arr_p1) * 100 |
| sys_acc_p1_h = np.quantile(exp, .75, axis=0) |
| sys_acc_p1_m = np.quantile(exp, .5, axis=0) |
| sys_acc_p1_l = np.quantile(exp, .25, axis=0) |
| |
| plt.fill_between(x_T_list_p1, sys_acc_p1_l, sys_acc_p1_h, alpha=0.1) |
| plt.plot(x_T_list_p1, sys_acc_p1_m, "o-", label="TRAILS-P1") |
| # plt.fill_between(x_T_list_p1, sys_acc_p1_l, sys_acc_p1_h, alpha=0.1) |
| |
| exp = np.array(y_acc_list_arr) * 100 |
| sys_acc_h = np.quantile(exp, .75, axis=0) |
| sys_acc_m = np.quantile(exp, .5, axis=0) |
| sys_acc_l = np.quantile(exp, .25, axis=0) |
| |
| # exp_time = np.array(real_time_used_arr) |
| # time_mean = np.quantile(exp_time, .5, axis=0) |
| time_mean = x_T_list |
| |
| # plot simulate result of system |
| plt.fill_between(time_mean, sys_acc_l, sys_acc_h, alpha=0.1) |
| plt.plot(time_mean, sys_acc_m, "o-", label="TRAILS") |
| # plt.plot(time_mean, sys_acc_m, label="TRAILS") |
| |
| # plot simulate result of train-based line |
| plt.fill_between(x_acc_train, y_acc_train_l, y_acc_train_h, alpha=0.3) |
| plt.plot(x_acc_train, y_acc_train_m, "o-", label="Training-based MS") |
| # plt.plot(x_acc_train, y_acc_train_m, label="Training-based MS") |
| |
| if len(x1_lim) > 0: |
| plt.ylim(x1_lim[0], x1_lim[1]) # 子图1设置y轴范围，只显示部分图 |
| |
| d = .85 # 设置倾斜度 |
| # 绘制断裂处的标记 |
| kwargs = dict(marker=[(-1, -d), (1, d)], markersize=set_marker_size, |
| linestyle='none', color='r', mec='r', mew=1, clip_on=False) |
| # plt.plot([0, 1], [0, 0], transform=ax1.transAxes, **kwargs) |
| # plt.plot([0, 1], [1, 1], transform=ax2.transAxes, **kwargs) |
| |
| plt.tight_layout() |
| plt.xscale("symlog") |
| plt.grid() |
| plt.xlabel("Time Budget given by user (min)", fontsize=set_font_size) |
| plt.ylabel(f"Test accuracy on {dataset}", fontsize=set_font_size) |
| plt.legend(ncol=1, fontsize=set_lgend_size) |
| # plt.show() |
| plt.savefig(f"amy_time_{name_img}.pdf", bbox_inches='tight') |
| |
| |
| # for K, U N trade-off |
| def draw_grid_graph_with_budget( |
| acc, bt, b1, b2, |
| img_name: str, y_array: list, x_array: list): |
| """ |
| :param acc: Two array list |
| :param bt: Two array list |
| :param img_name: img name string |
| :return: |
| """ |
| |
| acc_new = np.array(acc) |
| acc = acc_new.tolist() |
| |
| mask = np.array(acc) |
| mask[mask > 0] = 0 |
| mask[mask < 0] = 1 |
| |
| bt = np.round(np.array(bt), 2).tolist() |
| mask2 = np.array(bt) |
| mask2[mask2 > 0] = 0 |
| mask2[mask2 < 0] = 1 |
| |
| mask3 = np.array(b1) |
| mask3[mask3 > 0] = 0 |
| mask3[mask3 < 0] = 1 |
| |
| mask4 = np.array(b2) |
| mask4[mask4 > 0] = 0 |
| mask4[mask4 < 0] = 1 |
| |
| fig, ax = plt.subplots(2, 2, figsize=(15, 14)) |
| |
| linewidths = 0.5 |
| sns.set(font_scale=3) |
| sns.heatmap( |
| data=acc, |
| vmax=99, |
| vmin=93, |
| cmap=palettable.cmocean.diverging.Curl_10.mpl_colors, |
| annot=True, |
| fmt=".2f", |
| annot_kws={'size': frontinsidebox, 'weight': 'normal', 'color': 'w', 'va': 'bottom'}, |
| mask=mask, |
| square=True, linewidths=linewidths, # 每个方格外框显示，外框宽度设置 |
| cbar_kws={"shrink": .5}, |
| ax=ax[0, 0] |
| ) |
| |
| sns.heatmap( |
| data=bt, |
| # vmax=, |
| vmin=-9, |
| cmap=palettable.cmocean.diverging.Curl_10.mpl_colors, |
| annot=True, |
| fmt=".2f", |
| annot_kws={'size': frontinsidebox, 'weight': 'normal', 'color': 'w', 'va': 'top'}, |
| mask=mask2, |
| square=True, linewidths=linewidths, # 每个方格外框显示，外框宽度设置 |
| cbar_kws={"shrink": .5}, |
| ax=ax[0, 1] |
| ) |
| |
| sns.heatmap( |
| data=b1, |
| vmax=17000, |
| vmin=15000, |
| cmap=palettable.cmocean.diverging.Curl_10.mpl_colors, |
| annot=True, |
| fmt=".0f", |
| annot_kws={'size': frontinsidebox, 'weight': 'normal', 'color': 'w', 'va': 'top'}, |
| mask=mask4, |
| square=True, linewidths=linewidths, # 每个方格外框显示，外框宽度设置 |
| cbar_kws={"shrink": .5}, |
| ax=ax[1, 0] |
| ) |
| |
| sns.heatmap( |
| data=b2, |
| # vmax=, |
| # vmin=-9, |
| cmap=palettable.cmocean.diverging.Curl_10.mpl_colors, |
| annot=True, |
| fmt=".0f", |
| annot_kws={'size': frontinsidebox, 'weight': 'normal', 'color': 'w', 'va': 'top'}, |
| mask=mask4, |
| square=True, linewidths=linewidths, # 每个方格外框显示，外框宽度设置 |
| cbar_kws={"shrink": .5}, |
| ax=ax[1, 1] |
| ) |
| |
| plt.tight_layout() |
| plt.xlabel("U (epoch)", fontsize=set_font_size) |
| plt.ylabel("K (# models)", fontsize=set_font_size) |
| |
| for i in [0, 1]: |
| for j in [0, 1]: |
| ax[i, j].set_xticklabels(x_array, fontsize=set_font_size) |
| ax[i, j].set_yticklabels(y_array, fontsize=set_font_size) |
| ax[i, j].set_xlabel("U (# epoch)", fontsize=set_font_size) |
| ax[i, j].set_ylabel("K (# models)", fontsize=set_font_size) |
| |
| ax[0, 0].set_title('Test Accuracy (%)', fontsize=set_font_size) |
| ax[0, 1].set_title(r'Time Budget $T$ (min)', fontsize=set_font_size) |
| ax[1, 0].set_title(r'$N$', fontsize=set_font_size) |
| ax[1, 1].set_title(r"$K \cdot U \cdot \log_{\eta}K$", fontsize=set_font_size) |
| |
| plt.tight_layout() |
| fig.subplots_adjust(wspace=0.001, hspace=0.3) |
| |
| # plt.show() |
| base_dr = os.getcwd() |
| path_gra = os.path.join(base_dr, f"{img_name}.pdf") |
| fig.savefig(path_gra, bbox_inches='tight') |
| |
| |
| def draw_grid_graph_with_budget_only_Acc_and_T( |
| acc, bt, b1, b2, |
| img_name: str, y_array: list, x_array: list): |
| """ |
| :param acc: Two array list |
| :param bt: Two array list |
| :param img_name: img name string |
| :return: |
| """ |
| |
| acc_new = np.array(acc) |
| acc = acc_new.tolist() |
| |
| mask = np.array(acc) |
| mask[mask > 0] = 0 |
| mask[mask < 0] = 1 |
| |
| bt = np.round(np.array(bt), 2).tolist() |
| mask2 = np.array(bt) |
| mask2[mask2 > 0] = 0 |
| mask2[mask2 < 0] = 1 |
| |
| mask3 = np.array(b1) |
| mask3[mask3 > 0] = 0 |
| mask3[mask3 < 0] = 1 |
| |
| mask4 = np.array(b2) |
| mask4[mask4 > 0] = 0 |
| mask4[mask4 < 0] = 1 |
| |
| fig, ax = plt.subplots(1, 2, figsize=(15, 14)) |
| |
| linewidths = 0.5 |
| sns.set(font_scale=2) |
| sns.heatmap( |
| data=acc, |
| vmax=99, |
| vmin=93, |
| cmap=palettable.cmocean.diverging.Curl_10.mpl_colors, |
| annot=True, |
| fmt=".2f", |
| annot_kws={'size': frontinsidebox, 'weight': 'normal', 'color': 'w', 'va': 'bottom'}, |
| mask=mask, |
| square=True, |
| linewidths=linewidths, # 每个方格外框显示，外框宽度设置 |
| cbar_kws={"shrink": .4}, |
| ax=ax[0] |
| ) |
| |
| sns.heatmap( |
| data=bt, |
| vmax=600, |
| # vmin=-9, |
| cmap=palettable.cmocean.diverging.Curl_10.mpl_colors, |
| annot=True, |
| fmt=".2f", |
| annot_kws={'size': frontinsidebox, 'weight': 'normal', 'color': 'w', 'va': 'top'}, |
| mask=mask2, |
| square=True, |
| linewidths=linewidths, # 每个方格外框显示，外框宽度设置 |
| cbar_kws={"shrink": .4}, |
| ax=ax[1] |
| ) |
| |
| plt.tight_layout() |
| plt.xlabel("U (epoch)", fontsize=set_font_size) |
| plt.ylabel("K (# models)", fontsize=set_font_size) |
| |
| for j in [0, 1]: |
| ax[j].set_xticklabels(x_array, fontsize=set_font_size) |
| ax[j].set_yticklabels(y_array, fontsize=set_font_size) |
| ax[j].set_xlabel("U (# epoch)", fontsize=set_font_size) |
| ax[j].set_ylabel("K (# models)", fontsize=set_font_size) |
| |
| ax[0].set_title('Test Accuracy (%)', fontsize=set_font_size) |
| ax[1].set_title(r'Time Budget $T$ (min)', fontsize=set_font_size) |
| |
| plt.tight_layout() |
| fig.subplots_adjust(wspace=0.3, hspace=0.3) |
| |
| # plt.show() |
| base_dr = os.getcwd() |
| path_gra = os.path.join(base_dr, f"{img_name}.pdf") |
| fig.savefig(path_gra, bbox_inches='tight') |
| |
| |
| def draw_grid_graph_with_budget_only_Acc( |
| acc, bt, b1, b2, |
| img_name: str, y_array: list, x_array: list): |
| """ |
| :param acc: Two array list |
| :param bt: Two array list |
| :param img_name: img name string |
| :return: |
| """ |
| |
| acc_new = np.array(acc) |
| acc = acc_new.tolist() |
| |
| mask = np.array(acc) |
| mask[mask > 0] = 0 |
| mask[mask < 0] = 1 |
| |
| fig = plt.figure(figsize=(7, 14)) |
| |
| linewidths = 0.5 |
| sns.set(font_scale=2) |
| sns.heatmap( |
| data=acc, |
| vmax=99, |
| vmin=93, |
| cmap=palettable.cmocean.diverging.Curl_10.mpl_colors, |
| annot=True, |
| fmt=".2f", |
| annot_kws={'size': frontinsidebox, 'weight': 'normal', 'color': 'w', 'va': 'bottom'}, |
| mask=mask, |
| square=True, |
| linewidths=linewidths, # 每个方格外框显示，外框宽度设置 |
| cbar_kws={"shrink": .4}, |
| ax=fig |
| ) |
| |
| plt.tight_layout() |
| plt.xlabel("U (epoch)", fontsize=set_font_size) |
| plt.ylabel("K (# models)", fontsize=set_font_size) |
| |
| plt.xticks(x_array, fontsize=set_font_size) |
| plt.yticks(y_array, fontsize=set_font_size) |
| |
| plt.title('Test Accuracy (%)', fontsize=set_font_size) |
| plt.tight_layout() |
| # fig.subplots_adjust(wspace=0.3, hspace=0.3) |
| # plt.show() |
| base_dr = os.getcwd() |
| path_gra = os.path.join(base_dr, f"{img_name}.pdf") |
| fig.savefig(path_gra, bbox_inches='tight') |
| |
| |
| def draw_grid_graph_with_budget_only_T( |
| acc, bt, b1, b2, |
| img_name: str, y_array: list, x_array: list): |
| """ |
| :param acc: Two array list |
| :param bt: Two array list |
| :param img_name: img name string |
| :return: |
| """ |
| |
| acc_new = np.array(acc) |
| acc = acc_new.tolist() |
| |
| mask = np.array(acc) |
| mask[mask > 0] = 0 |
| mask[mask < 0] = 1 |
| |
| bt = np.round(np.array(bt), 2).tolist() |
| mask2 = np.array(bt) |
| mask2[mask2 > 0] = 0 |
| mask2[mask2 < 0] = 1 |
| |
| mask3 = np.array(b1) |
| mask3[mask3 > 0] = 0 |
| mask3[mask3 < 0] = 1 |
| |
| mask4 = np.array(b2) |
| mask4[mask4 > 0] = 0 |
| mask4[mask4 < 0] = 1 |
| |
| fig, ax = plt.subplots(1, 2, figsize=(15, 14)) |
| |
| linewidths = 0.5 |
| sns.set(font_scale=2) |
| sns.heatmap( |
| data=acc, |
| vmax=99, |
| vmin=93, |
| cmap=palettable.cmocean.diverging.Curl_10.mpl_colors, |
| annot=True, |
| fmt=".2f", |
| annot_kws={'size': frontinsidebox, 'weight': 'normal', 'color': 'w', 'va': 'bottom'}, |
| mask=mask, |
| square=True, |
| linewidths=linewidths, # 每个方格外框显示，外框宽度设置 |
| cbar_kws={"shrink": .4}, |
| ax=ax[0] |
| ) |
| |
| sns.heatmap( |
| data=bt, |
| vmax=600, |
| # vmin=-9, |
| cmap=palettable.cmocean.diverging.Curl_10.mpl_colors, |
| annot=True, |
| fmt=".2f", |
| annot_kws={'size': frontinsidebox, 'weight': 'normal', 'color': 'w', 'va': 'top'}, |
| mask=mask2, |
| square=True, |
| linewidths=linewidths, # 每个方格外框显示，外框宽度设置 |
| cbar_kws={"shrink": .4}, |
| ax=ax[1] |
| ) |
| |
| plt.tight_layout() |
| plt.xlabel("U (epoch)", fontsize=set_font_size) |
| plt.ylabel("K (# models)", fontsize=set_font_size) |
| |
| for j in [0, 1]: |
| ax[j].set_xticklabels(x_array, fontsize=set_font_size) |
| ax[j].set_yticklabels(y_array, fontsize=set_font_size) |
| ax[j].set_xlabel("U (# epoch)", fontsize=set_font_size) |
| ax[j].set_ylabel("K (# models)", fontsize=set_font_size) |
| |
| ax[0].set_title('Test Accuracy (%)', fontsize=set_font_size) |
| ax[1].set_title(r'Time Budget $T$ (min)', fontsize=set_font_size) |
| |
| plt.tight_layout() |
| fig.subplots_adjust(wspace=0.3, hspace=0.3) |
| |
| # plt.show() |
| base_dr = os.getcwd() |
| path_gra = os.path.join(base_dr, f"{img_name}.pdf") |
| fig.savefig(path_gra, bbox_inches='tight') |
