plot_results.py

import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from pylab import setp
import argparse
import os
from glob import glob
import pickle as pkl
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42

parser = argparse.ArgumentParser()
parser.add_argument('--folder', help='Folder of the runs. Either resnet or resnet_augment',
                    required=False, default="resnet")
parser.add_argument('--repeats', help='The number of times each model was trained', required=False, default="10")
args = parser.parse_args()
cur_folder = str(args.folder)
repeats = int(args.repeats)

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"}

# Plot Accuracy and F2-Score overview
dp = "./results_" + cur_folder + "/results/summary.txt"
dp2 = "./results_" + cur_folder + "/results/summary_rev.txt"
sp = "./results_" + cur_folder + "/images/"
if not os.path.exists("./results_" + cur_folder + "/images/"):
    os.makedirs("./results_" + cur_folder + "/images/")
with open(dp, "r") as f:
    data = f.readlines()
data = np.array([[float(x) for x in y.split("\n")[0].split("\t")] for y in data])
data[np.isnan(data)] = 0.0
data = data.reshape((6, repeats, 6, 7))
data_mean = np.mean(data, axis=1)

with open(dp2, "r") as f:
    data2 = f.readlines()
data2 = np.array([[float(x) for x in y.split("\n")[0].split("\t")] for y in data2])
data2[np.isnan(data2)] = 0.0
data2 = data2.reshape((6, repeats, 6, 5))
data_mean2 = np.mean(data2, axis=1)

poses = np.arange(3, 36, 6)
w = 0.5
poses = np.array([[x - 3*w, x - 1.8*w, x - 0.6*w, x + 0.6*w, x + 1.8*w, x + 3*w] for x in poses]).flatten()
data_sum = np.zeros((6, repeats, 6, 2))
data_sum[:, :, :, 0] = data[:, :, :, 0]
data_sum[:, :, :, 1] = (data[:, :, :, 3] + data2[:, :, :, 1]) / 2.
data_sum = data_sum.transpose((1, 0, 2, 3)).reshape((repeats, 36, 2)) * 100
plt.figure(figsize=(10, 4))
plt.rcParams["font.family"] = "serif"
bp = plt.boxplot(data_sum[:, :, 0], positions=poses, widths=w)
for i in range(6):
    if i < 3:
        cur_col = colors[i]
    else:
        cur_col = colors[i - 1]
    for j in range(6):
        setp(bp['boxes'][j*6 + i], color=cur_col)
        setp(bp['caps'][(j*6 + i) * 2], color=cur_col)
        setp(bp['caps'][(j*6 + i) * 2 + 1], color=cur_col)
        setp(bp['whiskers'][(j*6 + i) * 2], color=cur_col)
        setp(bp['whiskers'][(j*6 + i) * 2 + 1], color=cur_col)
        setp(bp['fliers'][j*6 + i], markeredgecolor=cur_col)
plt.ylabel("Accuracy [%]", fontsize=12)
plt.xticks(np.arange(3, 36, 6), ["Real-world\nHighway 1", "Real-world\nHighway 2",
                                 "Controlled\nlarge-scale 1+2\n[low]", "Controlled\nlarge-scale 2\n[high]",
                                 "Controlled\n[large+small]-scale\n[high]", "Combined"])
for tick_label, tick_color in zip(plt.gca().get_xticklabels(), [colors[x] for x in range(5)] + ["black"]):
    tick_label.set_color(tick_color)
plt.tight_layout()
plt.savefig(sp + "/overview_accuracy.pdf")
plt.close()
plt.figure(figsize=(10, 4))
plt.rcParams["font.family"] = "serif"
bp = plt.boxplot(data_sum[:, :, 1], positions=poses, widths=w)
for i in range(6):
    if i < 3:
        cur_col = colors[i]
    else:
        cur_col = colors[i - 1]
    for j in range(6):
        setp(bp['boxes'][j*6 + i], color=cur_col)
        setp(bp['caps'][(j*6 + i) * 2], color=cur_col)
        setp(bp['caps'][(j*6 + i) * 2 + 1], color=cur_col)
        setp(bp['whiskers'][(j*6 + i) * 2], color=cur_col)
        setp(bp['whiskers'][(j*6 + i) * 2 + 1], color=cur_col)
        setp(bp['fliers'][j*6 + i], markeredgecolor=cur_col)
plt.ylabel("F2-Score", fontsize=12)
plt.xticks(np.arange(3, 36, 6), ["Real-world\nHighway 1", "Real-world\nHighway 2",
                                 "Controlled\nlarge-scale 1+2\n[low]", "Controlled\nlarge-scale 2\n[high]",
                                 "Controlled\n[large+small]-scale\n[high]", "Combined"])
for tick_label, tick_color in zip(plt.gca().get_xticklabels(), [colors[x] for x in range(5)] + ["black"]):
    tick_label.set_color(tick_color)
plt.tight_layout()
plt.savefig(sp + "overview_f2.pdf")
plt.close()

# Plot Confusion Matrices and Uncertainty
base_path = "./results_" + cur_folder + "/uncertainties/"
dp = base_path + "*_confusion.pkl"
sp = "./results_" + cur_folder + "/images/%06d_%02d_plot.pdf"
files = glob(dp)
files.sort()
files = [x.replace("\\", "/") for x in files]
nums = [int(x.split("/")[-1].split("_")[0]) for x in files]
classes = ["0", "1", "2", "3", "4", "5", "6", "7", "8"]

font = {'family': 'normal',
        'weight': 'bold',
        'size': 10}
data_keys = ["T0", "T1", "T2", "T3", "T4", "T5"]
for cur_num in nums:
    with open(base_path + "%06d_aleatoric_diag.pkl" % cur_num, "rb") as f:
        aleatoric = pkl.load(f)
    with open(base_path + "%06d_epistemic_diag.pkl" % cur_num, "rb") as f:
        epistemic = pkl.load(f)
    with open(base_path + "%06d_confusion.pkl" % cur_num, "rb") as f:
        confusion_mat = pkl.load(f)
    for ik, key in enumerate(data_keys):
        aleatoric[key][np.isnan(aleatoric[key])] = 0.0
        epistemic[key][np.isnan(epistemic[key])] = 0.0
        plt.figure(figsize=(9, 3))
        plt.rcParams["font.family"] = "serif"
        num_c = len(confusion_mat[key])
        plt.subplot(1, 3, 1)
        plt.imshow(confusion_mat[key], cmap="rainbow", norm=matplotlib.colors.LogNorm(vmin=1.0))
        for ci, c in enumerate(confusion_mat[key].flatten()):
            cii = ci % len(confusion_mat[key])
            cij = ci // len(confusion_mat[key])
            plt.text(cii, cij, np.minimum(int(c), 9999), ha='center', va='center', size=8)
        plt.xlabel("Class [Prediction]", fontsize=12)
        plt.ylabel("Class [Ground Truth]", fontsize=12)
        plt.xticks(np.arange(num_c), classes, fontsize=12)
        plt.yticks(np.arange(num_c), classes, fontsize=12)
        plt.colorbar(shrink=0.7)
        plt.subplot(1, 3, 2)
        plt.imshow(aleatoric[key], cmap="rainbow", vmin=0.0, vmax=0.25)
        plt.xlabel("Class [Prediction]", fontsize=12)
        plt.xticks(np.arange(num_c), classes, fontsize=12)
        plt.yticks(np.arange(num_c), classes, fontsize=12)
        plt.colorbar(shrink=0.7)
        plt.subplot(1, 3, 3)
        plt.imshow(epistemic[key], cmap="rainbow", vmin=0.0, vmax=np.max(epistemic[key]))
        plt.xlabel("Class [Prediction]", fontsize=12)
        plt.xticks(np.arange(num_c), classes, fontsize=12)
        plt.yticks(np.arange(num_c), classes, fontsize=12)
        plt.colorbar(shrink=0.7)
        plt.tight_layout()
        plt.savefig(sp % (cur_num, ik))
        plt.close()