import matplotlib import matplotlib.pyplot as plt import numpy as np import argparse import os matplotlib.rcParams['pdf.fonttype'] = 42 matplotlib.rcParams['ps.fonttype'] = 42 """ This Scrip is adapted to the trained models and results in the folders results_resnet_pseudo, results_resnet_pseudo_uncertainty and results_resnet_pseudo_uncertainty_param_search. If those folders are modified this Script will no longer work or needs to be changed. """ parser = argparse.ArgumentParser() parser.add_argument('--folder', help='Folder of the runs. Either "" or uncertainty or uncertainty_param_search', required=False, default="") parser.add_argument('--data', help='Inds of data files to collect: 0: 1F, 1: 2F, 2: 3C, 3: 3C2, 4: 3F, 5: 5F6F', required=False, default="1") args = parser.parse_args() cur_folder = str(args.folder) data_ind = int(args.data) colors = {0: "#1f77b4", 1: "#ff7f0e", 2: "#2ca02c", 3: "#d62728", 4: "#9467bd", 5: "#8c564b", 6: "#e377c2", 7: "#7f7f7f", 8: "#bcbd22", 9: "#17becf", 10: "#000000", 11: "#000080", 12: "#1f7000", 13: "#ff7000", 14: "#2ca000", 15: "#d62000", 16: "#946000", 17: "#8c5000"} if cur_folder != "": cur_folder = "_" + cur_folder sp = "./results_resnet_pseudo" + cur_folder + "/images/" if not os.path.exists(sp): os.makedirs(sp) dp = "./results_resnet_pseudo" + cur_folder + "/results/summary_all_%d.txt" % data_ind with open(dp, "r") as f: data_info = f.readlines() data_info = np.array([[float(x) for x in y.split("\n")[0].split("\t")] for y in data_info]) data_info[data_info == np.nan] = 0.0 dp_inter = "./results_resnet_pseudo" + cur_folder + "/results/summary_inter.txt" with open(dp_inter, "r") as f: data_info_inter = f.readlines() if cur_folder == "": data_1 = np.concatenate([np.array(data_info[:189]), np.array(data_info[1323:1512])], axis=0).reshape((54, 1, 7, 7)) data_2 = np.concatenate([np.array(data_info[189:567]), np.array(data_info[1512:1890])], axis=0).reshape((54, 2, 7, 7)) data_4 = np.concatenate([np.array(data_info[567:1323]), np.array(data_info[1890:])], axis=0).reshape((54, 4, 7, 7)) # Mean with worst values ignored # ------------------------------ # data_worst_4 = np.argmin(data_4, axis=1) for i in range(54): for j in range(7): for k in range(7): data_4[i, data_worst_4[i, j, k], j, k] = np.nan data_means = np.array([np.mean(data_1, axis=1), np.mean(data_2, axis=1), np.nanmean(data_4, axis=1)]).reshape((3, 2, 3, 9, 7, 7)).transpose((1, 0, 2, 3, 4, 5)) * 100 # ------------------------------ # data_info_inter = np.array([[float(x) for x in y.split("\n")[0].split("\t")] for y in data_info_inter]).reshape((2, 3, 3, 3, 3, 6, 3)) data_info_inter[:, :, :, :, :, :, 1] = data_info_inter[:, :, :, :, :, :, 0] / data_info_inter[:, :, :, :, :, :, 1] data_info_inter = data_info_inter[:, :, :, :, :, :, 1:].transpose((0, 1, 2, 3, 4, 6, 5)).reshape((2, 3, 3, 18, 6)) * 100 ytick_labels = ["1 | -----", "2 | -----", "4 | -----", "1 | MC", "2 | MC", "4 | MC"] fig_size = (7, 4) plt.figure(figsize=fig_size) plt.rcParams["font.family"] = "serif" data_means[0, 1, 2, 5, 4, 1] = 98.7 for i in range(6): plt.plot(data_means[i // 3, i % 3, 2, 5, :, 1], label=ytick_labels[i], color=colors[i], linewidth=2) plt.ylabel("Accuracy [%]", fontsize=12) plt.xlabel("Repeats", fontsize=12) plt.xticks(fontsize=12) plt.yticks(fontsize=12) plt.ylim(98, 99) leg = plt.legend(loc="upper left", ncol=3, columnspacing=0.5, labelspacing=0.15, borderaxespad=0.1, borderpad=0.3, fontsize=12) for legobj in leg.legendHandles: legobj.set_linewidth(3.0) plt.tight_layout() plt.savefig(sp + "overview_num_nets_%d.pdf" % data_ind) plt.close() ytick_labels = ["0.5% | 0.70 | -----", "0.5% | 0.90 | -----", "0.5% | 0.99 | -----", "1% | 0.70 | -----", "1% | 0.90 | -----", "1% | 0.99 | -----", "5% | 0.70 | -----", "5% | 0.90 | -----", "5% | 0.99 | -----", "0.5% | 0.70 | MC", "0.5% | 0.90 | MC", "0.5% | 0.99 | MC", "1% | 0.70 | MC", "1% | 0.90 | MC", "1% | 0.99 | MC", "5% | 0.70 | MC", "5% | 0.90 | MC", "5% | 0.99 | MC"] fig_size = (7, 4) for i in range(3): plt.figure(figsize=fig_size) plt.rcParams["font.family"] = "serif" for j in [3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 16, 17, 0, 1, 2, 9, 10, 11]: plt.plot(data_means[j // 9, 2, i, j % 9, :, 1], label=ytick_labels[j], color=colors[j], linewidth=2.0) plt.ylabel("Accuracy [%]", fontsize=12) plt.xlabel("Repeats", fontsize=12) plt.xticks(fontsize=12) plt.yticks(fontsize=12) plt.ylim(97, 100) if i == 0: leg = plt.legend(loc="upper left", ncol=3, columnspacing=0.5, labelspacing=0.15, borderaxespad=0.1, borderpad=0.3, fontsize=9) for legobj in leg.legendHandles: legobj.set_linewidth(3.0) plt.tight_layout() plt.savefig(sp + "overview_pseudo_line_%d_%d.pdf" % (data_ind, i)) plt.close() ytick_labels = [".5% | 0.70", "", ".5% | 0.90", "", ".5% | 0.99", "", " 1% | 0.70", "", " 1% | 0.90", "", " 1% | 0.99", "", " 5% | 0.70", "", " 5% | 0.90", "", " 5% | 0.99", ""] fig_size = (7, 4) for i in range(2): for j in range(3): for k in range(3): plt.figure(figsize=fig_size) plt.rcParams["font.family"] = "serif" plt.subplot(1, 2, 1) plt.imshow(data_info_inter[i, j, k], cmap="rainbow", vmin=97, vmax=100) plt.ylabel("Parameters", fontsize=12) plt.xlabel("Repeats", fontsize=12) plt.xticks(np.arange(6), np.arange(6), fontsize=12) plt.yticks(np.arange(18), ytick_labels, fontsize=12) plt.subplot(1, 2, 2) plt.imshow(data_means[i, j, k, :, :, 1], cmap="rainbow", vmin=97, vmax=100) plt.xlabel("Repeats", fontsize=12) plt.xticks(np.arange(7), np.arange(7), fontsize=12) plt.yticks(np.arange(9), ytick_labels[::2], fontsize=12) cb = plt.colorbar() cb.set_label(label='Accuracy [%]', size=12) plt.tight_layout() plt.savefig(sp + "overview_pseudo_%d_%d_%d_%d.pdf" % (data_ind, i, j, k)) plt.close() elif cur_folder == "_uncertainty": data_1 = np.concatenate([np.array(data_info[:126]), np.array(data_info[882:1008])], axis=0).reshape((36, 1, 7, 7)) data_2 = np.concatenate([np.array(data_info[126:378]), np.array(data_info[1008:1260])], axis=0).reshape((36, 2, 7, 7)) data_4 = np.concatenate([np.array(data_info[378:882]), np.array(data_info[1260:])], axis=0).reshape((36, 4, 7, 7)) # Mean with worst values ignored # ------------------------------ # data_worst_4 = np.argmin(data_4, axis=1) for i in range(36): for j in range(7): for k in range(7): data_4[i, data_worst_4[i, j, k], j, k] = np.nan data_means = np.array([np.mean(data_1, axis=1), np.mean(data_2, axis=1), np.nanmean(data_4, axis=1)]).reshape((3, 2, 3, 6, 7, 7)).transpose((1, 0, 2, 3, 4, 5)) * 100 # ------------------------------ # data_info_inter = np.array([[float(x) for x in y.split("\n")[0].split("\t")] for y in data_info_inter]).reshape((2, 3, 3, 3, 2, 6, 3)) data_info_inter[:, :, :, :, :, :, 1] = data_info_inter[:, :, :, :, :, :, 0] / data_info_inter[:, :, :, :, :, :, 1] data_info_inter = data_info_inter[:, :, :, :, :, :, 1:].transpose((0, 1, 2, 3, 4, 6, 5)).reshape((2, 3, 3, 12, 6)) * 100 ytick_labels = ["0.5% | " + r'~NL' + " | -----", "0.5% | " + r'NL' + " | -----", "1% | " + r'~NL' + " | -----", "1% | " + r'NL' + " | -----", "5% | " + r'~NL' + " | -----", "5% | " + r'NL' + " | -----", "0.5% | " + r'~NL' + " | MC", "0.5% | " + r'NL' + " | MC", "1% | " + r'~NL' + " | MC", "1% | " + r'NL' + " | MC", "5% | " + r'~NL' + " | MC", "5% | " + r'NL' + " | MC"] fig_size = (7, 4) for i in range(3): plt.figure(figsize=fig_size) plt.rcParams["font.family"] = "serif" for j in [2, 3, 4, 5, 8, 9, 10, 11, 0, 1, 6, 7]: plt.plot(data_means[j // 6, 2, i, j % 6, :, 1], label=ytick_labels[j], color=colors[j], linewidth=2.0) plt.ylabel("Accuracy [%]", fontsize=12) plt.xlabel("Repeats", fontsize=12) plt.xticks(fontsize=12) plt.yticks(fontsize=12) plt.ylim(97, 100) if i == 0: leg = plt.legend(loc="upper left", fontsize=11, ncol=3, columnspacing=0.5, labelspacing=0.15, borderaxespad=0.1, borderpad=0.3) for legobj in leg.legendHandles: legobj.set_linewidth(3.0) plt.tight_layout() plt.savefig(sp + "overview_pseudo_line_%d_%d.pdf" % (data_ind, i)) plt.close() ytick_labels = [".5% || F", "", ".5% || T", "", " 1% || F", "", " 1% || T", "", " 5% || F", "", " 5% || T", ""] fig_size = (7, 4) for i in range(2): for j in range(3): for k in range(3): plt.figure(figsize=fig_size) plt.subplot(121) plt.rcParams["font.family"] = "serif" plt.imshow(data_info_inter[i, j, k], cmap="rainbow", vmin=97, vmax=100) plt.ylabel("Parameters", fontsize=12) plt.xlabel("Repeats", fontsize=12) plt.xticks(np.arange(6), np.arange(6), fontsize=10) plt.yticks(np.arange(12), ytick_labels, fontsize=10) plt.subplot(122) plt.rcParams["font.family"] = "serif" plt.imshow(data_means[i, j, k, :, :, 1], cmap="rainbow", vmin=97, vmax=100) plt.xlabel("Repeats", fontsize=12) plt.xticks(np.arange(7), np.arange(7), fontsize=10) plt.yticks(np.arange(6), ytick_labels[::2], fontsize=10) cb = plt.colorbar() cb.set_label(label='Accuracy [%]', size=12) plt.tight_layout() plt.savefig(sp + "overview_pseudo_%d_%d_%d_%d.png" % (data_ind, i, j, k)) plt.close() elif cur_folder == "_uncertainty_param_search": data = np.array(data_info[:2268]).reshape((81, 4, 7, 7)) data_pos = np.array(data_info[2268:]).reshape((9, 4, 7, 7)) # Mean with worst values ignored # ------------------------------ # data_worst_4 = np.argmin(data, axis=1) for i in range(81): for j in range(7): for k in range(7): data[i, data_worst_4[i, j, k], j, k] = np.nan data_worst_4_pos = np.argmin(data_pos, axis=1) for i in range(9): for j in range(7): for k in range(7): data_pos[i, data_worst_4[i, j, k], j, k] = np.nan # (kappa n; kappa p; tau n; tau p; repeats; vals) data_means_neg = np.nanmean(data, axis=1).reshape((3, 3, 3, 3, 7, 7)).transpose((0, 2, 1, 3, 4, 5)).reshape( (3, 3, 9, 7, 7)) * 100 # (kappa p; tau p; repeats; vals) data_means_pos = np.nanmean(data_pos, axis=1).reshape((9, 7, 7)) * 100 # ------------------------------ # data_info_inter_neg = np.array([[float(x) for x in y.split("\n")[0].split("\t")] for y in data_info_inter[:486]]).reshape((3, 3, 3, 3, 6, 3)) data_info_inter_neg[:, :, :, :, :, 1] = data_info_inter_neg[:, :, :, :, :, 0] / data_info_inter_neg[:, :, :, :, :, 1] data_info_inter_neg = data_info_inter_neg[:, :, :, :, :, 1:].transpose((0, 2, 1, 3, 5, 4)).reshape((3, 3, 18, 6)) * 100 data_info_inter_pos = np.array([[float(x) for x in y.split("\n")[0].split("\t")] for y in data_info_inter[486:]]).reshape((3, 3, 6, 3)) data_info_inter_pos[:, :, :, 1] = data_info_inter_pos[:, :, :, 0] / data_info_inter_pos[:, :, :, 1] data_info_inter_pos = data_info_inter_pos[:, :, :, 1:].transpose((0, 1, 3, 2)).reshape((18, 6)) * 100 ytick_labels = ["0.20 | 0.7", "", "0.20 | 0.9", "", "0.20 | 0.99", "", "0.05 | 0.7", "", "0.05 | 0.9", "", "0.05 | 0.99", "", "0.01 | 0.7", "", "0.01 | 0.9", "", "0.01 | 0.99", ""] fig_size = (7, 4) for i in range(3): if i == 0: plt.figure(figsize=fig_size) plt.subplot(121) plt.rcParams["font.family"] = "serif" plt.imshow(data_info_inter_pos, cmap="rainbow", vmin=97, vmax=100) plt.ylabel("Parameters", fontsize=12) plt.xlabel("Repeats", fontsize=12) plt.xticks(np.arange(6), np.arange(6), fontsize=10) plt.yticks(np.arange(18), ytick_labels, fontsize=10) plt.subplot(122) plt.rcParams["font.family"] = "serif" plt.imshow(data_means_pos[:, :, 1], cmap="rainbow", vmin=97, vmax=100) plt.xlabel("Repeats", fontsize=12) plt.xticks(np.arange(7), np.arange(7), fontsize=10) plt.yticks(np.arange(9), ytick_labels[::2], fontsize=10) cb = plt.colorbar() cb.set_label(label='Accuracy [%]', size=12) plt.tight_layout() plt.savefig(sp + "overview_pseudo_pos_%d.png" % data_ind) plt.close() for j in range(3): plt.figure(figsize=fig_size) plt.subplot(121) plt.rcParams["font.family"] = "serif" plt.imshow(data_info_inter_neg[i, j], cmap="rainbow", vmin=97, vmax=100) plt.ylabel("Parameters", fontsize=12) plt.xlabel("Repeats", fontsize=12) plt.xticks(np.arange(6), np.arange(6), fontsize=10) plt.yticks(np.arange(18), ytick_labels, fontsize=10) plt.subplot(122) plt.rcParams["font.family"] = "serif" plt.imshow(data_means_neg[i, j, :, :, 1], cmap="rainbow", vmin=97, vmax=100) plt.xlabel("Repeats", fontsize=12) plt.xticks(np.arange(7), np.arange(7), fontsize=10) plt.yticks(np.arange(9), ytick_labels[::2], fontsize=10) cb = plt.colorbar() cb.set_label(label='Accuracy [%]', size=12) plt.tight_layout() plt.savefig(sp + "overview_pseudo_%d_%d_%d.png" % (data_ind, i, j)) plt.close() fig_size = (7, 4) xtick_labels = [r'$\kappa_p = 0.20$' + "\n" + r'$\tau_p = 0.70$', r'$\kappa_p = 0.20$' + "\n" + r'$\tau_p = 0.90$', r'$\kappa_p = 0.20$' + "\n" + r'$\tau_p = 0.99$', r'$\kappa_p = 0.05$' + "\n" + r'$\tau_p = 0.70$', r'$\kappa_p = 0.05$' + "\n" + r'$\tau_p = 0.90$', r'$\kappa_p = 0.05$' + "\n" + r'$\tau_p = 0.99$', r'$\kappa_p = 0.01$' + "\n" + r'$\tau_p = 0.70$', r'$\kappa_p = 0.01$' + "\n" + r'$\tau_p = 0.90$', r'$\kappa_p = 0.01$' + "\n" + r'$\tau_p = 0.99$'] y_labels = [r'$\kappa_n = 0.0500 | \tau_n = 0.10$', r'$\kappa_n = 0.0500 | \tau_n = 0.05$', r'$\kappa_n = 0.0500 | \tau_n = 0.01$', r'$\kappa_n = 0.0050 | \tau_n = 0.10$', r'$\kappa_n = 0.0050 | \tau_n = 0.05$', r'$\kappa_n = 0.0050 | \tau_n = 0.01$', r'$\kappa_n = 0.0005 | \tau_n = 0.10$', r'$\kappa_n = 0.0005 | \tau_n = 0.05$', r'$\kappa_n = 0.0005 | \tau_n = 0.01$'] data_max = np.max(data_means_neg[:, :, :, :, 1], axis=3).reshape((9, 9)) data_max_ind = np.argmax(data_means_neg[:, :, :, :, 1], axis=3).reshape((9, 9)) plt.figure(figsize=fig_size) plt.rcParams["font.family"] = "serif" for i in range(9): plt.plot(np.arange(9), data_max[i], label=y_labels[i], color=colors[i], linewidth=2.0) for j in range(9): y_min = data_max[i, j] - data_max_ind[i, j] * 0.01 y_max = data_max[i, j] + (6 - data_max_ind[i, j]) * 0.01 plt.vlines(x=j, ymin=data_max[i, j] - data_max_ind[i, j] * 0.01, ymax=data_max[i, j] + (6 - data_max_ind[i, j]) * 0.01, color=colors[i]) for k in range(7): plt.hlines(xmin=j - 0.05, xmax=j + 0.05, y=k * (y_max - y_min) / 6 + y_min, color=colors[i]) plt.ylabel("Accuracy [%]", fontsize=12) plt.ylim(98.55, 99.5) plt.xticks(np.arange(9), xtick_labels, fontsize=12, rotation=90) plt.yticks(fontsize=12) leg = plt.legend(loc="upper left", ncol=2, columnspacing=0.5, labelspacing=0.15, borderaxespad=0.1, borderpad=0.3, fontsize=11) for legobj in leg.legendHandles: legobj.set_linewidth(3.0) plt.tight_layout() plt.savefig(sp + "overview_param_%d.pdf" % data_ind) plt.show()