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visualization.py
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import matplotlib.pyplot as plt
import matplotlib.style as style
from data.cifar10_dataset import Cifar10Dataset
from data.config.cifar10_config import set_cifar_configs
from data.config.isic_config import set_isic_configs
from data.config.retinopathy_config import set_retinopathy_configs
from data.isic_dataset import ISICDataset
from data.jurkat_dataset import JurkatDataset
from data.matek_dataset import MatekDataset
from data.plasmodium_dataset import PlasmodiumDataset
from data.config.matek_config import set_matek_configs
from data.config.jurkat_config import set_jurkat_configs
from data.config.plasmodium_config import set_plasmodium_configs
from data.retinopathy_dataset import RetinopathyDataset
from results import ratio_metrics
from options.visualization_options import get_arguments
import os
import numpy as np
"""
plot the accuracy vs data proportion being used, graph
credits to: Alex Olteanu (https://www.dataquest.io/blog/making-538-plots/) for the plot style
:return: None
"""
datasets = {'matek': MatekDataset, 'cifar10': Cifar10Dataset, 'plasmodium': PlasmodiumDataset,
'jurkat': JurkatDataset, 'isic': ISICDataset, 'retinopathy': RetinopathyDataset}
configs = {'matek': set_matek_configs, 'jurkat': set_jurkat_configs,
'plasmodium': set_plasmodium_configs, 'cifar10': set_cifar_configs, 'isic': set_isic_configs,
'retinopathy': set_retinopathy_configs}
plot_configs = {'matek': (2, 5),
'jurkat': (2, 4),
'plasmodium': (1, 2),
'cifar10': (2, 5),
'isic': (2, 4)
}
fully_supervised = {
'matek': {'recall': 0.9121, 'f1-score': 0.8348, 'precision': 0.8912, 'accuracy': 0.9652},
'jurkat': {'recall': 0.7151, 'f1-score': 0.6338, 'precision': 0.8156, 'accuracy': 0.8265},
'plasmodium': 0.9763,
'cifar10': 0.75,
'isic': {'recall': 0.6852, 'f1-score': 0.6802, 'precision': 0.6807, 'accuracy': 0.8194},
'retinopathy': {'recall': 0.6752, 'f1-score': 0.6702, 'precision': 0.7507, 'accuracy': 0.8255}
}
fully_supervised_std = {
'matek': {'recall': 0.0318, 'f1-score': 0.0224, 'precision': 0.0103, 'accuracy': 0.0249},
'jurkat': {'recall': 0.0326, 'f1-score': 0.0326, 'precision': 0.0216, 'accuracy': 0.0265},
'plasmodium': 0.9763,
'cifar10': 0.75,
'isic': {'recall': 0.0159, 'f1-score': 0.0165, 'precision': 0.0119, 'accuracy': 0.0356},
'retinopathy': {'recall': 0.0128, 'f1-score': 0.0265, 'precision': 0.0325, 'accuracy': 0.0287},
}
methods_default = [
'random_sampling',
'mc_dropout',
'entropy_based',
'augmentations_based',
]
dataset_rep = {
'matek': 'White blood cell dataset',
'isic': 'Skin lesion dataset',
'jurkat': 'Cell cycle dataset',
'retinopathy': 'Retinopathy dataset',
}
def plot_ratio_class_wise_metrics(metric, classes, label_y, prop, plot_config):
fig = plt.figure(figsize=(20, 7))
style.use('fivethirtyeight')
colors = [[0, 0, 0, 1], [230 / 255, 159 / 255, 0, 1], [86 / 255, 180 / 255, 233 / 255, 1],
[0, 158 / 255, 115 / 255, 1], [213 / 255, 94 / 255, 0, 1], [238 / 255, 136 / 255, 102 / 255, 1],
[93 / 255, 58 / 255, 155 / 255, 1], [153 / 255, 79 / 255, 0, 1], [211 / 255, 95 / 255, 183 / 255, 1],
[238 / 255, 136 / 255, 102 / 255, 1]]
ax_main = fig.add_subplot(111)
for i, cls in enumerate(classes):
ax = fig.add_subplot(plot_config[0], plot_config[1], i + 1)
for j, method in enumerate(methods_default):
if len(metric[j]) == 0:
continue
linestyle = '-'
ax.errorbar(prop, metric[j][i][1], yerr=(metric[j][i][0] - metric[j][i][2]) / 2,
color=colors[j % len(colors)],
label=methods_default[j], linewidth=2, linestyle=linestyle, marker='o', capsize=3)
# ax.fill_between(prop, metric[j][i][0], metric[j][i][2], color=colors[i % len(colors)], alpha=0.05)
ax.set_title(classes[i])
ax_main.spines['top'].set_color('none')
ax_main.spines['bottom'].set_color('none')
ax_main.spines['left'].set_color('none')
ax_main.spines['right'].set_color('none')
ax_main.tick_params(labelcolor='w', top=False, bottom=False, left=False, right=False)
ax_main.set_xlabel("Active Learning Cycles", fontsize=20, weight='bold', alpha=.75)
ax_main.set_ylabel(label_y, fontsize=20, weight='bold', alpha=.75)
plt.show()
def plot_ratio_metrics(prop, metric, label_y, fully_supervised_metric, save_path, methods, title,
fully_supervised_std_metric):
plt.rcParams["font.family"] = "Times New Roman"
plt.rcParams["font.weight"] = "ultralight"
plt.rcParams["axes.labelweight"] = "ultralight"
plt.figure(figsize=(14, 10))
plt.rc('xtick', labelsize=45)
plt.rc('ytick', labelsize=45)
# plt.grid(color='black')
style.use(['science', 'no-latex'])
colors = [[86 / 255, 180 / 255, 233 / 255, 1], [230 / 255, 159 / 255, 0, 1], [212 / 255, 16 / 255, 16 / 255, 1],
[0, 158 / 255, 115 / 255, 1], [213 / 255, 94 / 255, 0, 1], [238 / 255, 136 / 255, 102 / 255, 1],
[93 / 255, 58 / 255, 155 / 255, 1], [153 / 255, 79 / 255, 0, 1], [211 / 255, 95 / 255, 183 / 255, 1],
[238 / 255, 136 / 255, 102 / 255, 1]]
if 'Recall' in label_y:
plt.errorbar(prop, [fully_supervised_metric['recall']] * len(prop),
yerr=[fully_supervised_std_metric['recall']] * len(prop),
color=[0, 0, 0, 1], label='fully_supervised', linewidth=2, linestyle='--', marker=',', capsize=3)
plt.fill_between(prop,
np.array([fully_supervised_metric['recall']] * len(prop)) - fully_supervised_std_metric[
'recall'],
np.array([fully_supervised_metric['recall']] * len(prop)) + fully_supervised_std_metric[
'recall'],
color=[0, 0, 0, 1], alpha=0.05)
elif 'Precision' in label_y:
plt.errorbar(prop, [fully_supervised_metric['precision']] * len(prop),
yerr=[fully_supervised_std_metric['precision']] * len(prop), color=[0, 0, 0, 1],
label='fully_supervised', linewidth=2, linestyle='--', marker=',', capsize=3)
plt.fill_between(prop,
np.array([fully_supervised_metric['precision']] * len(prop)) - fully_supervised_std_metric[
'precision'],
np.array([fully_supervised_metric['precision']] * len(prop)) + fully_supervised_std_metric[
'precision'],
color=[0, 0, 0, 1], alpha=0.05)
elif 'F1-score' in label_y:
plt.errorbar(prop, [fully_supervised_metric['f1-score']] * len(prop),
yerr=[fully_supervised_std_metric['f1-score']] * len(prop), color=[0, 0, 0, 1],
label='fully_supervised', linewidth=2, linestyle='--', marker=',', capsize=3)
plt.fill_between(prop,
np.array([fully_supervised_metric['f1-score']] * len(prop)) - fully_supervised_std_metric[
'f1-score'],
np.array([fully_supervised_metric['f1-score']] * len(prop)) + fully_supervised_std_metric[
'f1-score'],
color=[0, 0, 0, 1], alpha=0.05)
else:
plt.errorbar(prop, [fully_supervised_metric['accuracy']] * len(prop),
yerr=[fully_supervised_std_metric['accuracy']] * len(prop), color=[0, 0, 0, 1],
label='fully_supervised', linewidth=2, linestyle='--', marker=',', capsize=3)
plt.fill_between(prop,
np.array([fully_supervised_metric['accuracy']] * len(prop)) - fully_supervised_std_metric[
'accuracy'],
np.array([fully_supervised_metric['accuracy']] * len(prop)) + fully_supervised_std_metric[
'accuracy'],
color=[0, 0, 0, 1], alpha=0.05)
for i, method in enumerate(methods):
if len(metric[i]) == 0:
continue
if 'Semi-supervised' in method:
linestyle = '-'
else:
linestyle = '--'
if 'Entropy Based' in method:
c = colors[3]
elif 'MC Dropout' in method:
c = colors[1]
elif 'Augmentations Based' in method:
c = colors[2]
else:
c = colors[0]
if 'SimCLR' in method:
marker = 's'
elif 'Autoencoder' in method:
marker = 'o'
elif 'ImageNet' in method:
marker = '^'
else:
marker = ','
plt.errorbar(prop, metric[i][1], yerr=(metric[i][0] - metric[i][2]) / 2, color=c, markersize=15,
label=method, linewidth=2, linestyle=linestyle, marker=marker, capsize=3)
plt.fill_between(prop, metric[i][0], metric[i][2], color=c, alpha=0.05)
plt.xlabel("Added annotated data (%)", fontsize=45)
plt.ylabel(label_y, fontsize=45)
plt.legend(loc='lower right', fontsize=18)
# plt.title(title, fontsize=45, weight='bold', alpha=.75)
plt.xticks(ticks=prop)
plt.yticks(ticks=np.arange(0.10, 1.0, step=0.10))
plt.savefig(save_path)
def plot_epoch_class_wise_loss(values, classes, label_y, epochs, plot_config):
fig = plt.figure(figsize=(20, 7))
style.use('fivethirtyeight')
colors = [[0, 0, 0, 1], [230 / 255, 159 / 255, 0, 1], [86 / 255, 180 / 255, 233 / 255, 1],
[0, 158 / 255, 115 / 255, 1], [213 / 255, 94 / 255, 0, 1], [0, 114 / 255, 178 / 255, 1],
[93 / 255, 58 / 255, 155 / 255, 1], [153 / 255, 79 / 255, 0, 1], [211 / 255, 95 / 255, 183 / 255, 1],
[238 / 255, 136 / 255, 102 / 255, 1]]
ax_main = fig.add_subplot(111)
for i, cls in enumerate(classes):
ax = fig.add_subplot(plot_config[0], plot_config[1], i + 1)
if len(values[i]) == 0:
continue
linestyle = '-'
ax.plot(epochs, values[i][0], color=colors[0], label='Train Loss',
linewidth=2, linestyle=linestyle)
ax.plot(epochs, values[i][1], color=colors[1], label='Valid Loss',
linewidth=2, linestyle=linestyle)
ax.set_title(classes[i])
ax_main.spines['top'].set_color('none')
ax_main.spines['bottom'].set_color('none')
ax_main.spines['left'].set_color('none')
ax_main.spines['right'].set_color('none')
ax_main.tick_params(labelcolor='w', top=False, bottom=False, left=False, right=False)
ax_main.set_xlabel("Epochs", fontsize=20, weight='bold')
ax_main.set_ylabel(label_y, fontsize=15, weight='bold')
plt.show()
def plot_ae_loss(losses, logs, epochs):
plt.figure(figsize=(15, 10))
style.use('fivethirtyeight')
colors = [[0, 0, 0, 1], [230 / 255, 159 / 255, 0, 1], [86 / 255, 180 / 255, 233 / 255, 1],
[0, 158 / 255, 115 / 255, 1], [213 / 255, 94 / 255, 0, 1], [0, 114 / 255, 178 / 255, 1],
[93 / 255, 58 / 255, 155 / 255, 1], [153 / 255, 79 / 255, 0, 1], [211 / 255, 95 / 255, 183 / 255, 1],
[238 / 255, 136 / 255, 102 / 255, 1]]
for i, log in enumerate(logs):
if i >= len(losses):
break
plt.plot(epochs, log, color=colors[i], label=losses[i], linewidth=2)
plt.xlabel("Epochs", fontsize=20, weight='bold', alpha=.75)
plt.ylabel("Loss Value", fontsize=20, weight='bold', alpha=.75)
plt.legend(loc='lower right', fontsize=18)
plt.show()
def main(args):
args = configs[args.dataset](args)
num = [i for i in range(0, 21, 5)]
if 'macro' in args.metric_ratio:
y_label = f'Macro {args.metric.capitalize()}'
else:
y_label = 'Accuracy'
dataset_title = {'matek': 'White blood cells', 'jurkat': 'Jurkat cell cycle', 'isic': 'Skin lesions',
'plasmodium': 'Red blood cells'}
'''
ratio_class_wise_metrics_log = ratio_class_wise_metrics(args.metric, dataset.classes, args.dataset)
plot_ratio_class_wise_metrics(ratio_class_wise_metrics_log, dataset.classes, y_label, num,
plot_configs[args.dataset])
'''
ratio_metrics_logs = ratio_metrics(args.metric, args.dataset, cls=args.metric_ratio,
methods=args.methods_default_results)
plot_ratio_metrics(num[:5], ratio_metrics_logs, y_label, fully_supervised[args.dataset],
save_path=args.save_path, methods=args.methods_default,
title=dataset_title[args.dataset],
fully_supervised_std_metric=fully_supervised_std[args.dataset])
'''
epoch_class_wise_log = epoch_class_wise_loss(dataset.classes, methods[args.method_id], args.dataset)
plot_epoch_class_wise_loss(epoch_class_wise_log, dataset.classes, y_label_alt,
list(range(len(epoch_class_wise_log[0][0]))), plot_configs[args.dataset])
ae_loss_logs = ae_loss(args.dataset)
plot_ae_loss(losses=['bce', 'l1', 'l2', 'ssim'], logs=ae_loss_logs, epochs=list(range(len(ae_loss_logs[0]))))
'''
if __name__ == '__main__':
"""
root_vis = '/home/ahmad/thesis/visualization'
arguments = get_arguments()
methods_states = {
'a': ['Augmentations Based + Random + Supervised',
'MC Dropout + Random + Supervised',
'Entropy Based + Random + Supervised',
'Random Sampling + Random + Supervised',
'Least Confidence + Random + Supervised',
'Margin Confidence + Random + Supervised',
'Learning Loss + Random + Supervised',
'Badge + Random + Supervised'],
'd': ['Augmentations Based + ImageNet + Supervised',
'Augmentations Based + SimCLR + Supervised',
'Learning Loss + ImageNet + Supervised',
'Learning Loss + SimCLR + Supervised',
'Badge + ImageNet + Supervised',
'Badge + SimCLR + Supervised',
'Random Sampling + Random + Supervised'],
'i': ['Augmentations Based + ImageNet + FixMatch',
'Augmentations Based + SimCLR + FixMatch',
'Augmentations Based + ImageNet + Supervised',
'Augmentations Based + SimCLR + Supervised',
'Learning Loss + ImageNet + FixMatch',
'Learning Loss + SimCLR + FixMatch',
'Learning Loss + ImageNet + Supervised',
'Learning Loss + SimCLR + Supervised',
'Badge + ImageNet + FixMatch',
'Badge + SimCLR + FixMatch',
'Badge + ImageNet + Supervised',
'Badge + SimCLR + Supervised',
'Augmentations Based + SimCLR + Pseudo',
'Learning Loss + SimCLR + Pseudo',
'Badge + SimCLR + Pseudo',
'Random Sampling + Random + Supervised']
}
methods_states_results = {
'a': ['augmentations_based', 'mc_dropout', 'entropy_based',
'random_sampling', 'least_confidence', 'margin_confidence', 'learning_loss', 'badge'],
'd': ['augmentations_based_pretrained',
'simclr_with_al_augmentations_based',
'learning_loss_pretrained',
'simclr_with_al_learning_loss',
'badge_pretrained',
'simclr_with_al_badge',
'random_sampling'],
'i': ['fixmatch_with_al_augmentations_based_pretrained_pretrained',
'fixmatch_with_al_augmentations_based_pretrained_simclr',
'augmentations_based_pretrained',
'simclr_with_al_augmentations_based',
'fixmatch_with_al_learning_loss_pretrained_pretrained',
'fixmatch_with_al_learning_loss_pretrained_simclr',
'learning_loss_pretrained',
'simclr_with_al_learning_loss',
'fixmatch_with_al_badge_pretrained_pretrained',
'fixmatch_with_al_badge_pretrained_simclr',
'badge_pretrained',
'simclr_with_al_badge',
'pseudo_label_with_al_augmentations_based_pretrained_simclr',
'pseudo_label_with_al_learning_loss_pretrained_simclr',
'pseudo_label_with_al_badge_pretrained_simclr',
'random_sampling']
}
dataset = 'isic'
plt.rcParams["font.family"] = "Times New Roman"
plt.rcParams["font.weight"] = "ultralight"
plt.rcParams["axes.labelweight"] = "ultralight"
plt.rc('xtick', labelsize=30)
plt.rc('ytick', labelsize=30)
plt.rcParams['legend.fontsize'] = 25
fig, ax = plt.subplots(3, 2, figsize=(20, 20))
fig.suptitle(dataset_rep[dataset], fontsize=45)
for itera, (k, method_state) in enumerate(methods_states.items()):
if arguments.run_batch:
states = [
# (dataset, 'recall', 'accuracy'),
# (dataset, 'precision', 'macro avg'),
(dataset, 'recall', 'macro avg'),
(dataset, 'f1-score', 'macro avg'),
]
for j, (d, m, r) in enumerate(states):
root_path = os.path.join(root_vis, d, k)
if not os.path.exists(root_path):
os.makedirs(root_path)
arguments.dataset = d
arguments.metric = m
arguments.metric_ratio = r
arguments.methods_default = method_state
arguments.methods_default_results = methods_states_results[k]
arguments.save_path = os.path.join(root_path, f'{m}_{r}.png')
args = configs[arguments.dataset](arguments)
num = [i for i in range(0, 41, 5)]
if 'macro' in args.metric_ratio:
y_label = f'Macro {args.metric.capitalize()}'
else:
y_label = 'Accuracy'
dataset_title = {'matek': 'White blood cells', 'jurkat': 'Jurkat cell cycle',
'isic': 'Skin lesions',
'plasmodium': 'Red blood cells', 'retinopathy': 'Retina'}
'''
ratio_class_wise_metrics_log = ratio_class_wise_metrics(args.metric, dataset.classes, args.dataset)
plot_ratio_class_wise_metrics(ratio_class_wise_metrics_log, dataset.classes, y_label, num,
plot_configs[args.dataset])
'''
ratio_metrics_logs = ratio_metrics(args.metric, args.dataset, cls=args.metric_ratio,
methods=args.methods_default_results)
prop = num[:9]
metric = ratio_metrics_logs
label_y = y_label
fully_supervised_metric = fully_supervised[args.dataset]
save_path = args.save_path
methods = args.methods_default
title = dataset_title[args.dataset]
fully_supervised_std_metric = fully_supervised_std[args.dataset]
# plt.figure(figsize=(14, 10))
# plt.grid(color='black')
style.use(['science', 'no-latex'])
colors = [[86 / 255, 180 / 255, 233 / 255, 1], [230 / 255, 159 / 255, 0, 1],
[212 / 255, 16 / 255, 16 / 255, 1],
[0, 158 / 255, 115 / 255, 1], [213 / 255, 94 / 255, 0, 1], [0, 114 / 255, 178 / 255, 1],
[93 / 255, 58 / 255, 155 / 255, 1], [153 / 255, 79 / 255, 0, 1],
[211 / 255, 95 / 255, 183 / 255, 1],
[238 / 255, 136 / 255, 102 / 255, 1]]
if 'Recall' in label_y:
ax[itera, j].errorbar(prop, [fully_supervised_metric['recall']] * len(prop),
yerr=[fully_supervised_std_metric['recall']] * len(prop),
color=[0, 0, 0, 1], label='Fully Supervised', linewidth=2, linestyle='--',
marker=',',
capsize=3)
ax[itera, j].fill_between(prop,
np.array([fully_supervised_metric['recall']] * len(prop)) -
fully_supervised_std_metric[
'recall'],
np.array([fully_supervised_metric['recall']] * len(prop)) +
fully_supervised_std_metric[
'recall'],
color=[0, 0, 0, 1], alpha=0.05)
elif 'Precision' in label_y:
ax[itera, j].errorbar(prop, [fully_supervised_metric['precision']] * len(prop),
yerr=[fully_supervised_std_metric['precision']] * len(prop),
color=[0, 0, 0, 1],
label='Fully Supervised', linewidth=2, linestyle='--', marker=',',
capsize=3)
ax[itera, j].fill_between(prop,
np.array([fully_supervised_metric['precision']] * len(prop)) -
fully_supervised_std_metric[
'precision'],
np.array([fully_supervised_metric['precision']] * len(prop)) +
fully_supervised_std_metric[
'precision'],
color=[0, 0, 0, 1], alpha=0.05)
elif 'F1-score' in label_y:
ax[itera, j].errorbar(prop, [fully_supervised_metric['f1-score']] * len(prop),
yerr=[fully_supervised_std_metric['f1-score']] * len(prop),
color=[0, 0, 0, 1],
label='Fully Supervised', linewidth=2, linestyle='--', marker=',',
capsize=3)
ax[itera, j].fill_between(prop,
np.array([fully_supervised_metric['f1-score']] * len(prop)) -
fully_supervised_std_metric[
'f1-score'],
np.array([fully_supervised_metric['f1-score']] * len(prop)) +
fully_supervised_std_metric[
'f1-score'],
color=[0, 0, 0, 1], alpha=0.05)
else:
ax[itera, j].errorbar(prop, [fully_supervised_metric['accuracy']] * len(prop),
yerr=[fully_supervised_std_metric['accuracy']] * len(prop),
color=[0, 0, 0, 1],
label='Fully Supervised', linewidth=2, linestyle='--', marker=',',
capsize=3)
ax[itera, j].fill_between(prop,
np.array([fully_supervised_metric['accuracy']] * len(prop)) -
fully_supervised_std_metric[
'accuracy'],
np.array([fully_supervised_metric['accuracy']] * len(prop)) +
fully_supervised_std_metric[
'accuracy'],
color=[0, 0, 0, 1], alpha=0.05)
for i, method in enumerate(methods):
if len(metric[i]) == 0:
continue
if 'FixMatch' in method:
linestyle = '-'
elif 'Pseudo' in method:
linestyle = 'dotted'
else:
linestyle = '--'
if 'Entropy Based' in method:
c = colors[3]
elif 'MC Dropout' in method:
c = colors[1]
elif 'Augmentations Based' in method:
c = colors[2]
elif 'Least Confidence' in method:
c = colors[4]
elif 'Margin Confidence' in method:
c = colors[5]
elif 'Learning Loss' in method:
c = colors[6]
elif 'Badge' in method:
c = colors[7]
else:
c = colors[0]
if 'SimCLR' in method:
marker = 's'
elif 'Autoencoder' in method:
marker = 'o'
elif 'ImageNet' in method:
marker = '^'
else:
marker = ','
ax[itera, j].errorbar(prop, metric[i][1], yerr=(metric[i][0] - metric[i][2]) / 2, color=c,
markersize=10,
label=method, linewidth=2, linestyle=linestyle, marker=marker, capsize=3)
ax[itera, j].fill_between(prop, metric[i][0], metric[i][2], color=c, alpha=0.05)
# ax[itera, j].set_legend(loc='lower right', fontsize=18)
# plt.title(title, fontsize=30, weight='bold', alpha=.75)
ax[itera, j].set_xticks(ticks=prop)
ax[itera, j].set_yticks(ticks=np.arange(0.10, 1.0, step=0.10))
ax[itera, j].xaxis.set_ticklabels([])
ax[itera, j].yaxis.set_ticklabels([])
# ax[itera, j].savefig(save_path)
else:
main(args=arguments)
ax[2, 0].set_xlabel("Added annotated data (%)", fontsize=30)
ax[2, 1].set_xlabel("Added annotated data (%)", fontsize=30)
# ax[2, 2].set_xlabel("Added annotated data (%)", fontsize=30)
# ax[2, 3].set_xlabel("Added annotated data (%)", fontsize=30)
ax[0, 0].set_title('Macro Recall', fontsize=30)
ax[0, 1].set_title('Macro F1-Score', fontsize=30)
# ax[0, 2].set_title('Macro Recall', fontsize=30)
# ax[0, 3].set_title('Macro F1-Score', fontsize=30)
ax[2, 0].set_xticks(ticks=prop)
ax[2, 1].set_xticks(ticks=prop)
# ax[2, 2].set_xticks(ticks=prop)
# ax[2, 3].set_xticks(ticks=prop)
ax[0, 0].set_yticks(ticks=np.arange(0.10, 1.0, step=0.10))
ax[1, 0].set_yticks(ticks=np.arange(0.10, 1.0, step=0.10))
ax[2, 0].set_yticks(ticks=np.arange(0.10, 1.0, step=0.10))
ax[2, 0].xaxis.set_ticklabels([str(pr)[:3] for pr in prop])
ax[2, 1].xaxis.set_ticklabels([str(pr)[:3] for pr in prop])
# ax[2, 2].xaxis.set_ticklabels([str(pr)[:3] for pr in prop])
# ax[2, 3].xaxis.set_ticklabels([str(pr)[:3] for pr in prop])
ax[0, 0].yaxis.set_ticklabels(np.round(np.arange(0.10, 1.0, step=0.10), decimals=1))
ax[1, 0].yaxis.set_ticklabels(np.round(np.arange(0.10, 1.0, step=0.10), decimals=1))
ax[2, 0].yaxis.set_ticklabels(np.round(np.arange(0.10, 1.0, step=0.10), decimals=1))
fig.subplots_adjust(right=0.8, wspace=0.2, hspace=0.2)
handles, labels = ax[2, 1].get_legend_handles_labels()
lgd1 = fig.legend(handles, labels, bbox_to_anchor=(1.141, 0.27))
handles, labels = ax[1, 1].get_legend_handles_labels()
lgd2 = fig.legend(handles, labels, bbox_to_anchor=(1.164, 0.55))
handles, labels = ax[0, 1].get_legend_handles_labels()
lgd3 = fig.legend(handles, labels, bbox_to_anchor=(1.138, 0.82))
handles, labels = ax[1, 1].get_legend_handles_labels()
lgd4 = fig.legend(handles, ["" for lbl in labels], bbox_to_anchor=(1.25, 0.82))
fig.savefig(f'Fig 2 - {dataset_rep[dataset]}.png', dpi=fig.dpi)
"""
"""
states = [
'random_sampling',
'mc_dropout',
'entropy_based',
'augmentations_based',
'least_confidence',
'margin_confidence',
'learning_loss',
'badge',
'random_sampling_pretrained',
'mc_dropout_pretrained',
'entropy_based_pretrained',
'augmentations_based_pretrained',
'least_confidence_pretrained',
'margin_confidence_pretrained',
'learning_loss_pretrained',
'badge_pretrained',
'auto_encoder',
'auto_encoder_with_al_mc_dropout',
'auto_encoder_with_al_entropy_based',
'auto_encoder_with_al_augmentations_based',
'auto_encoder_with_al_least_confidence',
'auto_encoder_with_al_margin_confidence',
'auto_encoder_with_al_learning_loss',
'auto_encoder_with_al_badge',
'simclr',
'simclr_with_al_mc_dropout',
'simclr_with_al_entropy_based',
'simclr_with_al_augmentations_based',
'simclr_with_al_least_confidence',
'simclr_with_al_margin_confidence',
'simclr_with_al_learning_loss',
'simclr_with_al_badge',
'fixmatch',
'fixmatch_with_al_mc_dropout',
'fixmatch_with_al_entropy_based',
'fixmatch_with_al_augmentations_based',
'fixmatch_with_al_least_confidence',
'fixmatch_with_al_margin_confidence',
'fixmatch_with_al_learning_loss',
'fixmatch_with_al_badge',
'fixmatch_pretrained',
'fixmatch_with_al_mc_dropout_pretrained_pretrained',
'fixmatch_with_al_entropy_based_pretrained_pretrained',
'fixmatch_with_al_augmentations_based_pretrained_pretrained',
'fixmatch_with_al_least_confidence_pretrained_pretrained',
'fixmatch_with_al_margin_confidence_pretrained_pretrained',
'fixmatch_with_al_learning_loss_pretrained_pretrained',
'fixmatch_with_al_badge_pretrained_pretrained',
'fixmatch_pretrained_autoencoder',
'fixmatch_with_al_mc_dropout_pretrained_autoencoder',
'fixmatch_with_al_entropy_based_pretrained_autoencoder',
'fixmatch_with_al_augmentations_based_pretrained_autoencoder',
'fixmatch_with_al_least_confidence_pretrained_autoencoder',
'fixmatch_with_al_margin_confidence_pretrained_autoencoder',
'fixmatch_with_al_learning_loss_pretrained_autoencoder',
'fixmatch_with_al_badge_pretrained_autoencoder',
'fixmatch_pretrained_simclr',
'fixmatch_with_al_mc_dropout_pretrained_simclr',
'fixmatch_with_al_entropy_based_pretrained_simclr',
'fixmatch_with_al_augmentations_based_pretrained_simclr',
'fixmatch_with_al_least_confidence_pretrained_simclr',
'fixmatch_with_al_margin_confidence_pretrained_simclr',
'fixmatch_with_al_learning_loss_pretrained_simclr',
'fixmatch_with_al_badge_pretrained_simclr',
'pseudo_label',
'pseudo_label_with_al_mc_dropout',
'pseudo_label_with_al_entropy_based',
'pseudo_label_with_al_augmentations_based',
'pseudo_label_with_al_least_confidence',
'pseudo_label_with_al_margin_confidence',
'pseudo_label_with_al_learning_loss',
'pseudo_label_with_al_badge',
'pseudo_label_pretrained_pretrained',
'pseudo_label_with_al_mc_dropout_pretrained_pretrained',
'pseudo_label_with_al_entropy_based_pretrained_pretrained',
'pseudo_label_with_al_augmentations_based_pretrained_pretrained',
'pseudo_label_with_al_least_confidence_pretrained_pretrained',
'pseudo_label_with_al_margin_confidence_pretrained_pretrained',
'pseudo_label_with_al_learning_loss_pretrained_pretrained',
'pseudo_label_with_al_badge_pretrained_pretrained',
'pseudo_label_pretrained_autoencoder',
'pseudo_label_with_al_mc_dropout_pretrained_autoencoder',
'pseudo_label_with_al_entropy_based_pretrained_autoencoder',
'pseudo_label_with_al_augmentations_based_pretrained_autoencoder',
'pseudo_label_with_al_least_confidence_pretrained_autoencoder',
'pseudo_label_with_al_margin_confidence_pretrained_autoencoder',
'pseudo_label_with_al_learning_loss_pretrained_autoencoder',
'pseudo_label_with_al_badge_pretrained_autoencoder',
'pseudo_label_pretrained_simclr',
'pseudo_label_with_al_mc_dropout_pretrained_simclr',
'pseudo_label_with_al_entropy_based_pretrained_simclr',
'pseudo_label_with_al_augmentations_based_pretrained_simclr',
'pseudo_label_with_al_least_confidence_pretrained_simclr',
'pseudo_label_with_al_margin_confidence_pretrained_simclr',
'pseudo_label_with_al_learning_loss_pretrained_simclr',
'pseudo_label_with_al_badge_pretrained_simclr',
]
datasets = ['isic', 'matek', 'jurkat', 'retinopathy']
datasets_rep = ['Skin Lesions', 'White blood cells', 'Jurkat cells cycle', 'Retina']
inits = ['Random', 'ImageNet', 'Autoencoder', 'SimCLR']
trainings = ['Supervised', 'Semi-supervised (FixMatch)', 'Semi-supervised (Pseudo)']
uncertainty_samplings = ['Random', 'MC dropout', 'Entropy Based', 'Augmentations Based', 'Least Confidence',
'Margin Confidence', 'Learning Loss', 'Badge']
metrics = ['recall', 'precision', 'f1-score', 'accuracy']
metrics_rep = ['Recall', 'Precision', 'F1-score', 'Accuracy']
rows = []
methods = []
for dataset, dataset_rep in zip(datasets, datasets_rep):
i = 0
for training in trainings:
for init in inits:
for uncertainty_sampling in uncertainty_samplings:
row = {'Dataset': dataset_rep, 'Network Initialization': init, 'Training Method': training,
'Uncertainty Sampling': uncertainty_sampling}
for metric, metric_rep in zip(metrics, metrics_rep):
if metric == 'accuracy':
ratio_metrics_logs = ratio_metrics('recall', dataset, cls='accuracy', methods=[states[i]])
else:
print(i)
ratio_metrics_logs = ratio_metrics(metric, dataset, cls='macro avg', methods=[states[i]])
if len(ratio_metrics_logs[0]) == 0:
print(row, states[i])
continue
for iterations in range(len(ratio_metrics_logs[0][1][:9])):
row.update({f'{metric_rep} {iterations}': ratio_metrics_logs[0][1][iterations],
f'{metric_rep} STD. {iterations}': ratio_metrics_logs[0][1][iterations] -
ratio_metrics_logs[0][0][iterations]})
i += 1
if len(ratio_metrics_logs[0]) == 0:
continue
else:
methods.append(states[i-1])
rows.append(row)
import pandas as pd
df = pd.DataFrame(rows)
df['Method'] = methods
df.to_csv('results.csv')
"""
"""
datasets = ['matek', 'isic', 'jurkat', 'retinopathy']
dataset_title = {'matek': 'White blood cell', 'jurkat': 'Cell cycle',
'isic': 'Skin lesion',
'plasmodium': 'Red blood cells', 'retinopathy': 'Retinopathy'}
limits = {'matek': [0.1, 0.9], 'jurkat': [0.1, 0.7],
'isic': [0.15, 0.75],
'plasmodium': 'Red blood cells', 'retinopathy': [0.0, 0.7]}
plt.rcParams["font.family"] = "Arial"
plt.rcParams["font.weight"] = "ultralight"
plt.rcParams["axes.labelweight"] = "ultralight"
plt.rc('xtick', labelsize=30)
plt.rc('ytick', labelsize=30)
plt.rcParams['legend.fontsize'] = 25
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
# fig.suptitle(dataset_rep[dataset], fontsize=45)
# fig.subplots_adjust(top=0.8)
plt.subplots_adjust(wspace=0.02, hspace=0.07)
import pandas as pd
cm = plt.get_cmap('Dark2')
df = pd.read_csv('./results/mean.csv')
data_rows = []
data_rows_std = []
for i in range(4):
data_rows.append(df.iloc[:, i+1].values)
for i in range(4):
data_rows_std.append(df.iloc[:, i+5].values)
prop = df.iloc[:, 0].values
for i, row in enumerate(data_rows):
ax.errorbar(prop, row, yerr=data_rows_std[i], color=cm([i])[0].tolist(), label=dataset_title[datasets[i]])
ax.fill_between(prop, row - data_rows_std[i], row + data_rows_std[i], alpha=0.05, zorder=-300, color=cm([i])[0].tolist())
ax.plot([25 for x in range(11)], np.arange(0, 110, step=10), linestyle="dotted", color=[0, 0, 0, 1])
ax.plot(prop, [80 for x in prop], linestyle="dotted", color=[0, 0, 0, 1])
ax.plot(prop, [90 for x in prop], linestyle="dotted", color=[0, 0, 0, 1])
ax.set_xticks(ticks=prop)
ax.set_yticks(ticks=np.arange(0, 110, step=10))
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.set_xlabel("Added annotated data (%)", fontsize=30)
ax.set_ylabel("Macro F1-score ratio to fully supervised", fontsize=30)
ax.set_xticks(ticks=prop)
ax.xaxis.set_ticklabels([str(pr)[:3] for pr in prop])
ax.yaxis.set_ticklabels([str(round(x, 1)) for x in np.arange(0.0, 1.1, step=0.10)])
ax.legend(loc="lower right", prop={'size': 18})
ax.set_title(f'Our recommended strategy', fontsize=30)
# fig.savefig(f'results/{dataset_rep[dataset]}.png', dpi=150, bbox_extra_artists=(lgd1,), bbox_inches='tight')
fig.savefig(f'results/fig7.png', dpi=150, bbox_inches='tight')
"""
root_vis = '/home/ahmad/thesis/visualization'
arguments = get_arguments()
methods_states = {
'i': [
'Supervised + Random sampling + Random initialization',
'Supervised + MC-dropout + Random initialization',
'Supervised + Entropy-based + Random initialization',
'Supervised + Augmentation-based + Random initialization',
'Supervised + Least confidence + Random initialization',
'Supervised + Margin confidence + Random initialization',
'Supervised + Learning loss + Random initialization',
'Supervised + BADGE + Random initialization',
'Supervised + Random sampling + ImageNet',
'Supervised + MC-dropout + ImageNet',
'Supervised + Entropy-based + ImageNet',
'Supervised + Augmentation-based + ImageNet',
'Supervised + Least confidence + ImageNet',
'Supervised + Margin confidence + ImageNet',
'Supervised + Learning loss + ImageNet',
'Supervised + BADGE + ImageNet',
'Supervised + Random sampling + Autoencoder',
'Supervised + MC-dropout + Autoencoder',
'Supervised + Entropy-based + Autoencoder',
'Supervised + Augmentation-based + Autoencoder',
'Supervised + Least confidence + Autoencoder',
'Supervised + Margin confidence + Autoencoder',
'Supervised + Learning loss + Autoencoder',
'Supervised + BADGE + Autoencoder',
'Supervised + Random sampling + SimCLR',
'Supervised + MC-dropout + SimCLR',
'Supervised + Entropy-based + SimCLR',
'Supervised + Augmentation-based + SimCLR',
'Supervised + Least confidence + SimCLR',
'Supervised + Margin confidence + SimCLR',
'Supervised + Learning loss + SimCLR',
'Supervised + BADGE + SimCLR',
'FixMatch + Random sampling + Random initialization',
'FixMatch + MC-dropout + Random initialization',
'FixMatch + Entropy-based + Random initialization',
'FixMatch + Augmentation-based + Random initialization',
'FixMatch + Least confidence + Random initialization',
'FixMatch + Margin confidence + Random initialization',
'FixMatch + Learning loss + Random initialization',
'FixMatch + BADGE + Random initialization',
'FixMatch + Random sampling + ImageNet',
'FixMatch + MC-dropout + ImageNet',
'FixMatch + Entropy-based + ImageNet',
'FixMatch + Augmentation-based + ImageNet',
'FixMatch + Least confidence + ImageNet',
'FixMatch + Margin confidence + ImageNet',
'FixMatch + Learning loss + ImageNet',
'FixMatch + BADGE + ImageNet',
'FixMatch + Random sampling + Autoencoder',
'FixMatch + MC-dropout + Autoencoder',
'FixMatch + Entropy-based + Autoencoder',
'FixMatch + Augmentation-based + Autoencoder',
'FixMatch + Least confidence + Autoencoder',
'FixMatch + Margin confidence + Autoencoder',
'FixMatch + Learning loss + Autoencoder',
'FixMatch + BADGE + Autoencoder',
'FixMatch + Random sampling + SimCLR',
'FixMatch + MC-dropout + SimCLR',
'FixMatch + Entropy-based + SimCLR',
'FixMatch + Augmentation-based + SimCLR',
'FixMatch + Least confidence + SimCLR',
'FixMatch + Margin confidence + SimCLR',
'FixMatch + Learning loss + SimCLR',
'FixMatch + BADGE + SimCLR',
'Pseudo labeling + Random sampling + Random initialization',
'Pseudo labeling + MC-dropout + Random initialization',
'Pseudo labeling + Entropy-based + Random initialization',
'Pseudo labeling + Augmentation-based + Random initialization',
'Pseudo labeling + Least confidence + Random initialization',
'Pseudo labeling + Margin confidence + Random initialization',
'Pseudo labeling + Learning loss + Random initialization',
'Pseudo labeling + BADGE + Random initialization',
'Pseudo labeling + Random sampling + ImageNet',
'Pseudo labeling + MC-dropout + ImageNet',
'Pseudo labeling + Entropy-based + ImageNet',
'Pseudo labeling + Augmentation-based + ImageNet',
'Pseudo labeling + Least confidence + ImageNet',
'Pseudo labeling + Margin confidence + ImageNet',
'Pseudo labeling + Learning loss + ImageNet',
'Pseudo labeling + BADGE + ImageNet',
'Pseudo labeling + Random sampling + Autoencoder',
'Pseudo labeling + MC-dropout + Autoencoder',
'Pseudo labeling + Entropy-based + Autoencoder',
'Pseudo labeling + Augmentation-based + Autoencoder',
'Pseudo labeling + Least confidence + Autoencoder',
'Pseudo labeling + Margin confidence + Autoencoder',
'Pseudo labeling + Learning loss + Autoencoder',
'Pseudo labeling + BADGE + Autoencoder',
'Pseudo labeling + Random sampling + SimCLR',
'Pseudo labeling + MC-dropout + SimCLR',
'Pseudo labeling + Entropy-based + SimCLR',
'Pseudo labeling + Augmentation-based + SimCLR',
'Pseudo labeling + Least confidence + SimCLR',
'Pseudo labeling + Margin confidence + SimCLR',
'Pseudo labeling + Learning loss + SimCLR',
'Pseudo labeling + BADGE + SimCLR'
]
}
methods_states_results = {
'i': [
'random_sampling',
'mc_dropout',
'entropy_based',
'augmentations_based',
'least_confidence',
'margin_confidence',
'learning_loss',
'badge',
'random_sampling_pretrained',
'mc_dropout_pretrained',
'entropy_based_pretrained',
'augmentations_based_pretrained',
'least_confidence_pretrained',
'margin_confidence_pretrained',
'learning_loss_pretrained',
'badge_pretrained',
'auto_encoder',
'auto_encoder_with_al_mc_dropout',
'auto_encoder_with_al_entropy_based',
'auto_encoder_with_al_augmentations_based',
'auto_encoder_with_al_least_confidence',
'auto_encoder_with_al_margin_confidence',
'auto_encoder_with_al_learning_loss',
'auto_encoder_with_al_badge',
'simclr',
'simclr_with_al_mc_dropout',
'simclr_with_al_entropy_based',
'simclr_with_al_augmentations_based',
'simclr_with_al_least_confidence',
'simclr_with_al_margin_confidence',
'simclr_with_al_learning_loss',
'simclr_with_al_badge',
'fixmatch',
'fixmatch_with_al_mc_dropout',
'fixmatch_with_al_entropy_based',
'fixmatch_with_al_augmentations_based',
'fixmatch_with_al_least_confidence',
'fixmatch_with_al_margin_confidence',
'fixmatch_with_al_learning_loss',
'fixmatch_with_al_badge',
'fixmatch_pretrained',
'fixmatch_with_al_mc_dropout_pretrained_pretrained',
'fixmatch_with_al_entropy_based_pretrained_pretrained',
'fixmatch_with_al_augmentations_based_pretrained_pretrained',
'fixmatch_with_al_least_confidence_pretrained_pretrained',
'fixmatch_with_al_margin_confidence_pretrained_pretrained',
'fixmatch_with_al_learning_loss_pretrained_pretrained',
'fixmatch_with_al_badge_pretrained_pretrained',
'fixmatch_pretrained_autoencoder',
'fixmatch_with_al_mc_dropout_pretrained_autoencoder',
'fixmatch_with_al_entropy_based_pretrained_autoencoder',
'fixmatch_with_al_augmentations_based_pretrained_autoencoder',
'fixmatch_with_al_least_confidence_pretrained_autoencoder',
'fixmatch_with_al_margin_confidence_pretrained_autoencoder',
'fixmatch_with_al_learning_loss_pretrained_autoencoder',
'fixmatch_with_al_badge_pretrained_autoencoder',
'fixmatch_pretrained_simclr',
'fixmatch_with_al_mc_dropout_pretrained_simclr',
'fixmatch_with_al_entropy_based_pretrained_simclr',
'fixmatch_with_al_augmentations_based_pretrained_simclr',
'fixmatch_with_al_least_confidence_pretrained_simclr',
'fixmatch_with_al_margin_confidence_pretrained_simclr',
'fixmatch_with_al_learning_loss_pretrained_simclr',
'fixmatch_with_al_badge_pretrained_simclr',
'pseudo_label',
'pseudo_label_with_al_mc_dropout',
'pseudo_label_with_al_entropy_based',
'pseudo_label_with_al_augmentations_based',
'pseudo_label_with_al_least_confidence',
'pseudo_label_with_al_margin_confidence',
'pseudo_label_with_al_learning_loss',
'pseudo_label_with_al_badge',
'pseudo_label_pretrained',
'pseudo_label_with_al_mc_dropout_pretrained_pretrained',
'pseudo_label_with_al_entropy_based_pretrained_pretrained',
'pseudo_label_with_al_augmentations_based_pretrained_pretrained',
'pseudo_label_with_al_least_confidence_pretrained_pretrained',
'pseudo_label_with_al_margin_confidence_pretrained_pretrained',
'pseudo_label_with_al_learning_loss_pretrained_pretrained',
'pseudo_label_with_al_badge_pretrained_pretrained',
'pseudo_label_pretrained_autoencoder',
'pseudo_label_with_al_mc_dropout_pretrained_autoencoder',
'pseudo_label_with_al_entropy_based_pretrained_autoencoder',
'pseudo_label_with_al_augmentations_based_pretrained_autoencoder',
'pseudo_label_with_al_least_confidence_pretrained_autoencoder',
'pseudo_label_with_al_margin_confidence_pretrained_autoencoder',
'pseudo_label_with_al_learning_loss_pretrained_autoencoder',
'pseudo_label_with_al_badge_pretrained_autoencoder',
'pseudo_label_pretrained_simclr',
'pseudo_label_with_al_mc_dropout_pretrained_simclr',
'pseudo_label_with_al_entropy_based_pretrained_simclr',
'pseudo_label_with_al_augmentations_based_pretrained_simclr',
'pseudo_label_with_al_least_confidence_pretrained_simclr',
'pseudo_label_with_al_margin_confidence_pretrained_simclr',
'pseudo_label_with_al_learning_loss_pretrained_simclr',
'pseudo_label_with_al_badge_pretrained_simclr'
]
}
datasets = ['matek', 'isic', 'jurkat', 'retinopathy']
dataset_title_1 = {'matek': 'White blood cells', 'jurkat': 'Jurkat cells cycle',
'isic': 'Skin Lesions',
'plasmodium': 'Red blood cells', 'retinopathy': 'Retina'}
limits = {'matek': [0.0, 0.9], 'jurkat': [0.0, 0.7],
'isic': [0.0, 0.8],
'plasmodium': 'Red blood cells', 'retinopathy': [0.0, 0.7]}
plt.rcParams["font.family"] = "Arial"
plt.rcParams["font.weight"] = "ultralight"
plt.rcParams["axes.labelweight"] = "ultralight"