Optimizing One-Way partial AUC on Imbalanced CIFAR10 Dataset (SOPA)
Author: Dixian Zhu, Zhuoning Yuan, Gang Li, Tianbao Yang
Version: 1.3.1
Introduction
In this tutorial, we will learn how to quickly train a ResNet18 model by optimizing one way partial AUC (OPAUC) score using our novel pAUC_CVaR_Loss and SOPA optimizer [Ref] method on a binary image classification task on Cifar10. After completion of this tutorial, you should be able to use LibAUC to train your own models on your own datasets.
Reference:
If you find this tutorial helpful in your work, please cite our [library paper] and the following papers:
@inproceedings{zhu2022auc,
title={When auc meets dro: Optimizing partial auc for deep learning with non-convex convergence guarantee},
author={Zhu, Dixian and Li, Gang and Wang, Bokun and Wu, Xiaodong and Yang, Tianbao},
booktitle={International Conference on Machine Learning},
pages={27548--27573},
year={2022},
organization={PMLR}
}
Install LibAUC
Let’s start with install our library here. In this tutorial, we will use the lastest version for LibAUC by using pip install -U.
!pip install -U libauc
Importing LibAUC
Import required libraries to use
from libauc.models import resnet18
from libauc.datasets import CIFAR10
from libauc.utils import ImbalancedDataGenerator
from libauc.losses import pAUC_CVaR_Loss
from libauc.optimizers import SOPA
from libauc.utils import ImbalancedDataGenerator
from libauc.sampler import DualSampler # data resampling (for binary class)
from libauc.metrics import pauc_roc_score
import torch
from PIL import Image
import numpy as np
import torchvision.transforms as transforms
from torch.utils.data import Dataset
Reproducibility
The following function set_all_seeds limits the number of sources
of randomness behaviors, such as model intialization, data shuffling,
etcs. However, completely reproducible results are not guaranteed
across PyTorch releases
[Ref].
def set_all_seeds(SEED):
# REPRODUCIBILITY
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
set_all_seeds(2023)
Image Dataset
Now we define the data input pipeline such as data augmentations. In this tutorial, we use RandomCrop, RandomHorizontalFlip. The pos_index_map helps map global index to local index for reducing memory cost in loss function since we only need to track the indices for positive samples.
class ImageDataset(Dataset):
def __init__(self, images, targets, image_size=32, crop_size=30, mode='train'):
self.images = images.astype(np.uint8)
self.targets = targets
self.mode = mode
self.transform_train = transforms.Compose([
transforms.ToTensor(),
transforms.RandomCrop((crop_size, crop_size), padding=None),
transforms.RandomHorizontalFlip(),
transforms.Resize((image_size, image_size)),
])
self.transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Resize((image_size, image_size)),
])
# for loss function
self.pos_indices = np.flatnonzero(targets==1)
self.pos_index_map = {}
for i, idx in enumerate(self.pos_indices):
self.pos_index_map[idx] = i
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
image = self.images[idx]
target = self.targets[idx]
image = Image.fromarray(image.astype('uint8'))
if self.mode == 'train':
idx = self.pos_index_map[idx] if idx in self.pos_indices else -1
image = self.transform_train(image)
else:
image = self.transform_test(image)
return image, target, idx
HyperParameters
# HyperParameters
batch_size = 64
total_epochs = 60
weight_decay = 2e-4 # regularization weight decay
lr = 1e-3 # learning rate
eta = 1.0 # learning rate for control negative samples weights
decay_epochs = [30, 45]
decay_factor = 10
beta = 0.1 # upper bound for FPR
load_pretrain = False
Loading datasets
# load data as numpy arrays
train_data, train_targets = CIFAR10(root='./data', train=True).as_array()
test_data, test_targets = CIFAR10(root='./data', train=False).as_array()
# generate imbalanced data
imratio = 0.2
generator = ImbalancedDataGenerator(shuffle=True, verbose=True, random_seed=2023)
(train_images, train_labels) = generator.transform(train_data, train_targets, imratio=imratio)
(test_images, test_labels) = generator.transform(test_data, test_targets, imratio=imratio)
# data augmentations
trainDataset = ImageDataset(train_images, train_labels)
testDataset = ImageDataset(test_images, test_labels, mode='test')
Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
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Extracting ./data/cifar-10-python.tar.gz to ./data
Files already downloaded and verified
#SAMPLES: 31250, CLASS 0.0 COUNT: 25000, CLASS RATIO: 0.8000
#SAMPLES: 31250, CLASS 1.0 COUNT: 6250, CLASS RATIO: 0.2000
#SAMPLES: 6250, CLASS 0.0 COUNT: 5000, CLASS RATIO: 0.8000
#SAMPLES: 6250, CLASS 1.0 COUNT: 1250, CLASS RATIO: 0.2000
Pretraining (Recommended)
Following the original paper, it’s recommended to start from a pretrained checkpoint with cross-entropy loss to significantly boost models’ performance. It includes a pre-training step with standard cross-entropy loss, and a Partial AUC maximization step that maximizes a Partial AUC surrogate loss of the pre-trained model.
from torch.optim import Adam
import warnings
warnings.filterwarnings('ignore')
load_pretrain = True
model = resnet18(pretrained=False, num_classes=1, last_activation=None)
model = model.cuda()
loss_fn = torch.nn.BCELoss()
optimizer =Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
trainloader = torch.utils.data.DataLoader(trainDataset, batch_size=batch_size, shuffle=True, num_workers=2)
testloader = torch.utils.data.DataLoader(testDataset, batch_size=batch_size, shuffle=False, num_workers=2)
best_test = 0
for epoch in range(total_epochs):
if epoch in decay_epochs:
for param_group in optimizer.param_groups:
param_group['lr'] = 0.1 * param_group['lr']
model.train()
for idx, (data, targets, index) in enumerate(trainloader):
data, targets, index = data.cuda(), targets.cuda(), index.cuda()
y_pred = model(data)
y_prob = torch.sigmoid(y_pred)
loss = loss_fn(y_prob, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
######***evaluation***####
# evaluation on test sets
model.eval()
test_pred_list, test_true_list = [], []
with torch.no_grad():
for j, data in enumerate(testloader):
test_data, test_targets, _ = data
test_data = test_data.cuda()
y_pred = model(test_data)
y_prob = torch.sigmoid(y_pred)
test_pred_list.append(y_prob.cpu().detach().numpy())
test_true_list.append(test_targets.numpy())
test_true = np.concatenate(test_true_list)
test_pred = np.concatenate(test_pred_list)
test_pauc = pauc_roc_score(test_true, test_pred, max_fpr = 0.3)
if best_test < test_pauc:
best_test = test_pauc
torch.save(model.state_dict(), 'ce_pretrained_model_sopa.pth')
model.train()
print("epoch: %s, test_pauc: %.4f, best_test_pauc: %.4f, lr: %.4f"%(epoch, test_pauc, best_test, optimizer.param_groups[0]['lr'] ))
epoch: 0, test_pauc: 0.6030, best_test_pauc: 0.6030, lr: 0.0010
epoch: 1, test_pauc: 0.6749, best_test_pauc: 0.6749, lr: 0.0010
epoch: 2, test_pauc: 0.6666, best_test_pauc: 0.6749, lr: 0.0010
epoch: 3, test_pauc: 0.6917, best_test_pauc: 0.6917, lr: 0.0010
epoch: 4, test_pauc: 0.6525, best_test_pauc: 0.6917, lr: 0.0010
epoch: 5, test_pauc: 0.7070, best_test_pauc: 0.7070, lr: 0.0010
epoch: 6, test_pauc: 0.7555, best_test_pauc: 0.7555, lr: 0.0010
epoch: 7, test_pauc: 0.7448, best_test_pauc: 0.7555, lr: 0.0010
epoch: 8, test_pauc: 0.7583, best_test_pauc: 0.7583, lr: 0.0010
epoch: 9, test_pauc: 0.7545, best_test_pauc: 0.7583, lr: 0.0010
epoch: 10, test_pauc: 0.7258, best_test_pauc: 0.7583, lr: 0.0010
epoch: 11, test_pauc: 0.7956, best_test_pauc: 0.7956, lr: 0.0010
epoch: 12, test_pauc: 0.8150, best_test_pauc: 0.8150, lr: 0.0010
epoch: 13, test_pauc: 0.8036, best_test_pauc: 0.8150, lr: 0.0010
epoch: 14, test_pauc: 0.7952, best_test_pauc: 0.8150, lr: 0.0010
epoch: 15, test_pauc: 0.8003, best_test_pauc: 0.8150, lr: 0.0010
epoch: 16, test_pauc: 0.7778, best_test_pauc: 0.8150, lr: 0.0010
epoch: 17, test_pauc: 0.8249, best_test_pauc: 0.8249, lr: 0.0010
epoch: 18, test_pauc: 0.8038, best_test_pauc: 0.8249, lr: 0.0010
epoch: 19, test_pauc: 0.8295, best_test_pauc: 0.8295, lr: 0.0010
epoch: 20, test_pauc: 0.8236, best_test_pauc: 0.8295, lr: 0.0010
epoch: 21, test_pauc: 0.8370, best_test_pauc: 0.8370, lr: 0.0010
epoch: 22, test_pauc: 0.8464, best_test_pauc: 0.8464, lr: 0.0010
epoch: 23, test_pauc: 0.8088, best_test_pauc: 0.8464, lr: 0.0010
epoch: 24, test_pauc: 0.8337, best_test_pauc: 0.8464, lr: 0.0010
epoch: 25, test_pauc: 0.8365, best_test_pauc: 0.8464, lr: 0.0010
epoch: 26, test_pauc: 0.8448, best_test_pauc: 0.8464, lr: 0.0010
epoch: 27, test_pauc: 0.8076, best_test_pauc: 0.8464, lr: 0.0010
epoch: 28, test_pauc: 0.8388, best_test_pauc: 0.8464, lr: 0.0010
epoch: 29, test_pauc: 0.8477, best_test_pauc: 0.8477, lr: 0.0010
epoch: 30, test_pauc: 0.8617, best_test_pauc: 0.8617, lr: 0.0001
epoch: 31, test_pauc: 0.8637, best_test_pauc: 0.8637, lr: 0.0001
epoch: 32, test_pauc: 0.8625, best_test_pauc: 0.8637, lr: 0.0001
epoch: 33, test_pauc: 0.8640, best_test_pauc: 0.8640, lr: 0.0001
epoch: 34, test_pauc: 0.8611, best_test_pauc: 0.8640, lr: 0.0001
epoch: 35, test_pauc: 0.8651, best_test_pauc: 0.8651, lr: 0.0001
epoch: 36, test_pauc: 0.8583, best_test_pauc: 0.8651, lr: 0.0001
epoch: 37, test_pauc: 0.8665, best_test_pauc: 0.8665, lr: 0.0001
epoch: 38, test_pauc: 0.8643, best_test_pauc: 0.8665, lr: 0.0001
epoch: 39, test_pauc: 0.8634, best_test_pauc: 0.8665, lr: 0.0001
epoch: 40, test_pauc: 0.8619, best_test_pauc: 0.8665, lr: 0.0001
epoch: 41, test_pauc: 0.8612, best_test_pauc: 0.8665, lr: 0.0001
epoch: 42, test_pauc: 0.8610, best_test_pauc: 0.8665, lr: 0.0001
epoch: 43, test_pauc: 0.8600, best_test_pauc: 0.8665, lr: 0.0001
epoch: 44, test_pauc: 0.8612, best_test_pauc: 0.8665, lr: 0.0001
epoch: 45, test_pauc: 0.8629, best_test_pauc: 0.8665, lr: 0.0000
epoch: 46, test_pauc: 0.8630, best_test_pauc: 0.8665, lr: 0.0000
epoch: 47, test_pauc: 0.8625, best_test_pauc: 0.8665, lr: 0.0000
epoch: 48, test_pauc: 0.8627, best_test_pauc: 0.8665, lr: 0.0000
epoch: 49, test_pauc: 0.8642, best_test_pauc: 0.8665, lr: 0.0000
epoch: 50, test_pauc: 0.8619, best_test_pauc: 0.8665, lr: 0.0000
epoch: 51, test_pauc: 0.8629, best_test_pauc: 0.8665, lr: 0.0000
epoch: 52, test_pauc: 0.8627, best_test_pauc: 0.8665, lr: 0.0000
epoch: 53, test_pauc: 0.8637, best_test_pauc: 0.8665, lr: 0.0000
epoch: 54, test_pauc: 0.8598, best_test_pauc: 0.8665, lr: 0.0000
epoch: 55, test_pauc: 0.8605, best_test_pauc: 0.8665, lr: 0.0000
epoch: 56, test_pauc: 0.8606, best_test_pauc: 0.8665, lr: 0.0000
epoch: 57, test_pauc: 0.8622, best_test_pauc: 0.8665, lr: 0.0000
epoch: 58, test_pauc: 0.8618, best_test_pauc: 0.8665, lr: 0.0000
epoch: 59, test_pauc: 0.8610, best_test_pauc: 0.8665, lr: 0.0000
Optimizing pAUC Loss with SOPA
# oversampling minority class, you can tune it in (0, 0.5]
# e.g., sampling_rate=0.5 is that num of positive samples in mini-batch is sampling_rate*batch_size=32
sampling_rate = 0.5
# dataloaders
sampler = DualSampler(trainDataset, batch_size, sampling_rate=sampling_rate)
trainloader = torch.utils.data.DataLoader(trainDataset, batch_size, sampler=sampler, shuffle=False, num_workers=2)
testloader = torch.utils.data.DataLoader(testDataset, batch_size=batch_size, shuffle=False, num_workers=2)
Model, Loss and Optimizer
# You can include sigmoid/l2 activations on model's outputs before computing loss
model = resnet18(pretrained=False, num_classes=1, last_activation=None)
model = model.cuda()
# load pretrained model
if load_pretrain:
PATH = 'ce_pretrained_model_sopa.pth'
state_dict = torch.load(PATH)
filtered = {k:v for k,v in state_dict.items() if 'fc' not in k}
msg = model.load_state_dict(filtered, False)
print(msg)
model.fc.reset_parameters()
# Initialize the loss function and optimizer
# When we don't have mapping function for index, please provide data_len = the length of the dataset.
loss_fn = pAUC_CVaR_Loss(pos_len=sampler.pos_len, data_len=sampler.pos_len, beta=beta, eta=eta)
optimizer = SOPA(model.parameters(), loss_fn=loss_fn, mode='adam', lr=lr, weight_decay=weight_decay)
Training
Now it’s time for training. And we evaluate partial AUC performance with False Positive Rate(FPR) less than or equal to 0.3, i.e., FPR ≤ 0.3.
print ('Start Training')
print ('-'*30)
tr_pAUC=[]
te_pAUC=[]
best_test = 0
for epoch in range(total_epochs):
if epoch in decay_epochs:
optimizer.update_lr(decay_factor=decay_factor)
train_loss = 0
model.train()
for idx, data in enumerate(trainloader):
train_data, train_labels, index = data
train_data, train_labels = train_data.cuda(), train_labels.cuda()
y_pred = model(train_data)
y_prob = torch.sigmoid(y_pred)
loss = loss_fn(y_prob, train_labels, index.cuda())
train_loss = train_loss + loss.cpu().detach().numpy()
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss = train_loss/(idx+1)
# evaluation
model.eval()
with torch.no_grad():
train_pred = []
train_true = []
for jdx, data in enumerate(trainloader):
train_data, train_labels, _ = data
train_data = train_data.cuda()
y_pred = model(train_data)
y_prob = torch.sigmoid(y_pred)
train_pred.append(y_prob.cpu().detach().numpy())
train_true.append(train_labels.numpy())
train_true = np.concatenate(train_true)
train_pred = np.concatenate(train_pred)
single_train_auc = pauc_roc_score(train_true, train_pred, max_fpr = 0.3)
test_pred = []
test_true = []
for jdx, data in enumerate(testloader):
test_data, test_labels, index = data
test_data = test_data.cuda()
y_pred = model(test_data)
test_pred.append(y_pred.cpu().detach().numpy())
test_true.append(test_labels.numpy())
test_true = np.concatenate(test_true)
test_pred = np.concatenate(test_pred)
single_test_auc = pauc_roc_score(test_true, test_pred, max_fpr = 0.3)
if best_test < single_test_auc:
best_test = single_test_auc
print('Epoch=%s, Loss=%.4f, Train_pAUC(0.3)=%.4f, Test_pAUC(0.3)=%.4f, Best_test_pAUC(0.3): %.4f, lr=%.4f'%(epoch, train_loss, single_train_auc, single_test_auc, best_test, optimizer.lr))
tr_pAUC.append(single_train_auc)
te_pAUC.append(single_test_auc)
Start Training
------------------------------
Epoch=0, Loss=2.1513, Train_pAUC(0.3)=0.9137, Test_pAUC(0.3)=0.8342, Best_test_pAUC(0.3): 0.8342, lr=0.0010
Epoch=1, Loss=1.6701, Train_pAUC(0.3)=0.9518, Test_pAUC(0.3)=0.8392, Best_test_pAUC(0.3): 0.8392, lr=0.0010
Epoch=2, Loss=1.5509, Train_pAUC(0.3)=0.9506, Test_pAUC(0.3)=0.8440, Best_test_pAUC(0.3): 0.8440, lr=0.0010
Epoch=3, Loss=1.3958, Train_pAUC(0.3)=0.9447, Test_pAUC(0.3)=0.8415, Best_test_pAUC(0.3): 0.8440, lr=0.0010
Epoch=4, Loss=1.3125, Train_pAUC(0.3)=0.9449, Test_pAUC(0.3)=0.8231, Best_test_pAUC(0.3): 0.8440, lr=0.0010
Epoch=5, Loss=1.2588, Train_pAUC(0.3)=0.9553, Test_pAUC(0.3)=0.8417, Best_test_pAUC(0.3): 0.8440, lr=0.0010
Epoch=6, Loss=1.1675, Train_pAUC(0.3)=0.9615, Test_pAUC(0.3)=0.8526, Best_test_pAUC(0.3): 0.8526, lr=0.0010
Epoch=7, Loss=1.0579, Train_pAUC(0.3)=0.9674, Test_pAUC(0.3)=0.8456, Best_test_pAUC(0.3): 0.8526, lr=0.0010
Epoch=8, Loss=1.0299, Train_pAUC(0.3)=0.9713, Test_pAUC(0.3)=0.8517, Best_test_pAUC(0.3): 0.8526, lr=0.0010
Epoch=9, Loss=0.9694, Train_pAUC(0.3)=0.9685, Test_pAUC(0.3)=0.8538, Best_test_pAUC(0.3): 0.8538, lr=0.0010
Epoch=10, Loss=0.9354, Train_pAUC(0.3)=0.9690, Test_pAUC(0.3)=0.8413, Best_test_pAUC(0.3): 0.8538, lr=0.0010
Epoch=11, Loss=0.8698, Train_pAUC(0.3)=0.9769, Test_pAUC(0.3)=0.8498, Best_test_pAUC(0.3): 0.8538, lr=0.0010
Epoch=12, Loss=0.8315, Train_pAUC(0.3)=0.9787, Test_pAUC(0.3)=0.8652, Best_test_pAUC(0.3): 0.8652, lr=0.0010
Epoch=13, Loss=0.8237, Train_pAUC(0.3)=0.9726, Test_pAUC(0.3)=0.8311, Best_test_pAUC(0.3): 0.8652, lr=0.0010
Epoch=14, Loss=0.7782, Train_pAUC(0.3)=0.9819, Test_pAUC(0.3)=0.8569, Best_test_pAUC(0.3): 0.8652, lr=0.0010
Epoch=15, Loss=0.7882, Train_pAUC(0.3)=0.9825, Test_pAUC(0.3)=0.8619, Best_test_pAUC(0.3): 0.8652, lr=0.0010
Epoch=16, Loss=0.7176, Train_pAUC(0.3)=0.9802, Test_pAUC(0.3)=0.8576, Best_test_pAUC(0.3): 0.8652, lr=0.0010
Epoch=17, Loss=0.6888, Train_pAUC(0.3)=0.9760, Test_pAUC(0.3)=0.8499, Best_test_pAUC(0.3): 0.8652, lr=0.0010
Epoch=18, Loss=0.6799, Train_pAUC(0.3)=0.9861, Test_pAUC(0.3)=0.8571, Best_test_pAUC(0.3): 0.8652, lr=0.0010
Epoch=19, Loss=0.6335, Train_pAUC(0.3)=0.9480, Test_pAUC(0.3)=0.8047, Best_test_pAUC(0.3): 0.8652, lr=0.0010
Epoch=20, Loss=0.6215, Train_pAUC(0.3)=0.9743, Test_pAUC(0.3)=0.8350, Best_test_pAUC(0.3): 0.8652, lr=0.0010
Epoch=21, Loss=0.6178, Train_pAUC(0.3)=0.9861, Test_pAUC(0.3)=0.8637, Best_test_pAUC(0.3): 0.8652, lr=0.0010
Epoch=22, Loss=0.5869, Train_pAUC(0.3)=0.9880, Test_pAUC(0.3)=0.8518, Best_test_pAUC(0.3): 0.8652, lr=0.0010
Epoch=23, Loss=0.5867, Train_pAUC(0.3)=0.9909, Test_pAUC(0.3)=0.8610, Best_test_pAUC(0.3): 0.8652, lr=0.0010
Epoch=24, Loss=0.5764, Train_pAUC(0.3)=0.9840, Test_pAUC(0.3)=0.8686, Best_test_pAUC(0.3): 0.8686, lr=0.0010
Epoch=25, Loss=0.5766, Train_pAUC(0.3)=0.9887, Test_pAUC(0.3)=0.8611, Best_test_pAUC(0.3): 0.8686, lr=0.0010
Epoch=26, Loss=0.5154, Train_pAUC(0.3)=0.9921, Test_pAUC(0.3)=0.8613, Best_test_pAUC(0.3): 0.8686, lr=0.0010
Epoch=27, Loss=0.5410, Train_pAUC(0.3)=0.9889, Test_pAUC(0.3)=0.8616, Best_test_pAUC(0.3): 0.8686, lr=0.0010
Epoch=28, Loss=0.5087, Train_pAUC(0.3)=0.9898, Test_pAUC(0.3)=0.8650, Best_test_pAUC(0.3): 0.8686, lr=0.0010
Epoch=29, Loss=0.4854, Train_pAUC(0.3)=0.9877, Test_pAUC(0.3)=0.8601, Best_test_pAUC(0.3): 0.8686, lr=0.0010
Reducing lr to 0.00010 @ T=23430!
Epoch=30, Loss=0.3000, Train_pAUC(0.3)=0.9971, Test_pAUC(0.3)=0.8674, Best_test_pAUC(0.3): 0.8686, lr=0.0001
Epoch=31, Loss=0.2111, Train_pAUC(0.3)=0.9979, Test_pAUC(0.3)=0.8721, Best_test_pAUC(0.3): 0.8721, lr=0.0001
Epoch=32, Loss=0.1833, Train_pAUC(0.3)=0.9983, Test_pAUC(0.3)=0.8729, Best_test_pAUC(0.3): 0.8729, lr=0.0001
Epoch=33, Loss=0.1408, Train_pAUC(0.3)=0.9983, Test_pAUC(0.3)=0.8710, Best_test_pAUC(0.3): 0.8729, lr=0.0001
Epoch=34, Loss=0.1289, Train_pAUC(0.3)=0.9986, Test_pAUC(0.3)=0.8710, Best_test_pAUC(0.3): 0.8729, lr=0.0001
Epoch=35, Loss=0.1250, Train_pAUC(0.3)=0.9987, Test_pAUC(0.3)=0.8733, Best_test_pAUC(0.3): 0.8733, lr=0.0001
Epoch=36, Loss=0.1123, Train_pAUC(0.3)=0.9989, Test_pAUC(0.3)=0.8695, Best_test_pAUC(0.3): 0.8733, lr=0.0001
Epoch=37, Loss=0.1028, Train_pAUC(0.3)=0.9988, Test_pAUC(0.3)=0.8704, Best_test_pAUC(0.3): 0.8733, lr=0.0001
Epoch=38, Loss=0.1021, Train_pAUC(0.3)=0.9988, Test_pAUC(0.3)=0.8709, Best_test_pAUC(0.3): 0.8733, lr=0.0001
Epoch=39, Loss=0.0860, Train_pAUC(0.3)=0.9990, Test_pAUC(0.3)=0.8684, Best_test_pAUC(0.3): 0.8733, lr=0.0001
Epoch=40, Loss=0.0888, Train_pAUC(0.3)=0.9990, Test_pAUC(0.3)=0.8742, Best_test_pAUC(0.3): 0.8742, lr=0.0001
Epoch=41, Loss=0.0884, Train_pAUC(0.3)=0.9990, Test_pAUC(0.3)=0.8760, Best_test_pAUC(0.3): 0.8760, lr=0.0001
Epoch=42, Loss=0.0835, Train_pAUC(0.3)=0.9992, Test_pAUC(0.3)=0.8743, Best_test_pAUC(0.3): 0.8760, lr=0.0001
Epoch=43, Loss=0.0721, Train_pAUC(0.3)=0.9990, Test_pAUC(0.3)=0.8738, Best_test_pAUC(0.3): 0.8760, lr=0.0001
Epoch=44, Loss=0.0658, Train_pAUC(0.3)=0.9991, Test_pAUC(0.3)=0.8751, Best_test_pAUC(0.3): 0.8760, lr=0.0001
Reducing lr to 0.00001 @ T=35145!
Epoch=45, Loss=0.0658, Train_pAUC(0.3)=0.9992, Test_pAUC(0.3)=0.8752, Best_test_pAUC(0.3): 0.8760, lr=0.0000
Epoch=46, Loss=0.0588, Train_pAUC(0.3)=0.9992, Test_pAUC(0.3)=0.8745, Best_test_pAUC(0.3): 0.8760, lr=0.0000
Epoch=47, Loss=0.0578, Train_pAUC(0.3)=0.9992, Test_pAUC(0.3)=0.8742, Best_test_pAUC(0.3): 0.8760, lr=0.0000
Epoch=48, Loss=0.0570, Train_pAUC(0.3)=0.9993, Test_pAUC(0.3)=0.8751, Best_test_pAUC(0.3): 0.8760, lr=0.0000
Epoch=49, Loss=0.0555, Train_pAUC(0.3)=0.9993, Test_pAUC(0.3)=0.8736, Best_test_pAUC(0.3): 0.8760, lr=0.0000
Epoch=50, Loss=0.0506, Train_pAUC(0.3)=0.9995, Test_pAUC(0.3)=0.8742, Best_test_pAUC(0.3): 0.8760, lr=0.0000
Epoch=51, Loss=0.0459, Train_pAUC(0.3)=0.9993, Test_pAUC(0.3)=0.8733, Best_test_pAUC(0.3): 0.8760, lr=0.0000
Epoch=52, Loss=0.0489, Train_pAUC(0.3)=0.9991, Test_pAUC(0.3)=0.8733, Best_test_pAUC(0.3): 0.8760, lr=0.0000
Epoch=53, Loss=0.0558, Train_pAUC(0.3)=0.9994, Test_pAUC(0.3)=0.8731, Best_test_pAUC(0.3): 0.8760, lr=0.0000
Epoch=54, Loss=0.0543, Train_pAUC(0.3)=0.9994, Test_pAUC(0.3)=0.8746, Best_test_pAUC(0.3): 0.8760, lr=0.0000
Epoch=55, Loss=0.0448, Train_pAUC(0.3)=0.9993, Test_pAUC(0.3)=0.8734, Best_test_pAUC(0.3): 0.8760, lr=0.0000
Epoch=56, Loss=0.0556, Train_pAUC(0.3)=0.9994, Test_pAUC(0.3)=0.8735, Best_test_pAUC(0.3): 0.8760, lr=0.0000
Epoch=57, Loss=0.0497, Train_pAUC(0.3)=0.9993, Test_pAUC(0.3)=0.8738, Best_test_pAUC(0.3): 0.8760, lr=0.0000
Epoch=58, Loss=0.0474, Train_pAUC(0.3)=0.9992, Test_pAUC(0.3)=0.8735, Best_test_pAUC(0.3): 0.8760, lr=0.0000
Epoch=59, Loss=0.0467, Train_pAUC(0.3)=0.9991, Test_pAUC(0.3)=0.8747, Best_test_pAUC(0.3): 0.8760, lr=0.0000
Visualization
Now, let’s see the learning curves for optimizing pAUC from scratch and from a pretrained model with cross entropy loss.
import matplotlib.pyplot as plt
import numpy as np
tr_pAUC= [0.9136682184609737, 0.9517632978422141, 0.9506051189223815, 0.9446549060621192, 0.9448721741895119, 0.9553083587887938, 0.9614520149623798, 0.9673729312719279, 0.9712668124473427, 0.9684614213627522, 0.969031741760386, 0.9768724826395, 0.9787389748857618, 0.972618762105704, 0.9819383784116936, 0.9824749153089939, 0.9801847539971296, 0.9759544325932434, 0.9861215766419662, 0.94802891637734, 0.9742945431398482, 0.9860951032311058, 0.9879519023728125, 0.9909198847148448, 0.9839547896495817, 0.9886518771869768, 0.9920652092915674, 0.9889322806164584, 0.9898229531439064, 0.9876859668679878, 0.9970812494750148, 0.9978576703833218, 0.9982887449720146, 0.9983248235880461, 0.9985837497448606, 0.9987383051150596, 0.9988528329894066, 0.9987962399128876, 0.9987518635950197, 0.998998087663014, 0.9990350738401836, 0.9989643098133549, 0.999234712490438, 0.9989916964360283, 0.9990960559212947, 0.9991606569456097, 0.999160259514824, 0.9992489302157771, 0.9993013370841397, 0.9992642981673392, 0.9994508690045507, 0.9992692635407481, 0.9990622670842126, 0.9994277263524861, 0.9994164206081635, 0.9993182658776183, 0.9993755668581037, 0.999262087183909, 0.9992265686125232, 0.9990694836236276]
te_pAUC = [0.8341716078431373, 0.8391899607843137, 0.8440392156862745, 0.8414500392156863, 0.823107137254902, 0.8416680784313725, 0.8525552941176469, 0.8455767843137254, 0.8516702745098039, 0.8538396862745098, 0.8413189019607843, 0.84976, 0.8651912156862744, 0.8311450980392157, 0.8569256470588236, 0.8618810980392155, 0.8575761568627451, 0.8499369411764706, 0.8571290980392157, 0.8046785882352943, 0.8349985882352942, 0.8637057254901961, 0.8517753725490196, 0.8610061176470587, 0.8685794509803921, 0.8611127843137256, 0.8612837647058823, 0.8616470588235294, 0.8649800784313726, 0.8601126274509804, 0.8673957647058823, 0.8721054117647059, 0.8729427450980392, 0.8710494117647058, 0.871019294117647, 0.873260862745098, 0.8694773333333333, 0.8703607843137255, 0.8709029019607842, 0.8683535686274511, 0.8742487843137254, 0.8759701960784314, 0.8742820392156863, 0.8738189803921568, 0.8751306666666666, 0.8751614117647059, 0.8745226666666668, 0.8741687843137256, 0.875124862745098, 0.8735847843137254, 0.8742079999999999, 0.8733223529411764, 0.8733339607843138, 0.873078588235294, 0.8745618823529412, 0.8733992156862744, 0.8734832941176471, 0.8738170980392157, 0.8735460392156862, 0.8747134117647057]
tr_pAUC_scratch = [0.6356903545832213, 0.7210205775802815, 0.767468625535477, 0.8005981139567835, 0.8209403917942953, 0.8345637945534954, 0.866052684924753, 0.9098821439963045, 0.8835058544209279, 0.9209312117584438, 0.8899414743626047, 0.9227558077051679, 0.9452859542605039, 0.9493837033022419, 0.9454534120045476, 0.9611925315862568, 0.9671769438992941, 0.9710854728791765, 0.9614036021792345, 0.9776061414393387, 0.9568010818218176, 0.9689421085611798, 0.982109825532059, 0.9753092539410508, 0.9705543687916516, 0.9829754175399547, 0.9796734335495003, 0.9845552466648984, 0.9712936892535897, 0.9818976169559868, 0.9968739993659712, 0.9974880619502038, 0.9977731378597904, 0.9977342995170321, 0.9978620468308591, 0.9986271378225131, 0.9980927098825727, 0.9990495706460671, 0.9987034002697288, 0.999049182946999, 0.9989652575570687, 0.9989112537452571, 0.9990108289926309, 0.9992897108209923, 0.9991501498299746, 0.9992343982501406, 0.9992116979203324, 0.9992235294913262, 0.9993363505479946, 0.999393357065544, 0.9992819075531902, 0.999232876962947, 0.9993267506795718, 0.999304969840104, 0.9992699284368018, 0.9993589300161501, 0.9992910836210124, 0.9993503431881259, 0.9994803711770379, 0.999233425392317]
te_pAUC_scratch = [0.6298767058823529, 0.704208, 0.7717273725490196, 0.7719074509803923, 0.769153568627451, 0.7769876078431373, 0.8074996078431373, 0.8232696470588234, 0.8086773333333332, 0.8277976470588235, 0.8243394509803922, 0.8191604705882353, 0.8410145882352941, 0.8182732549019607, 0.841843137254902, 0.8387259607843137, 0.8451968627450981, 0.8460429803921569, 0.8420614901960783, 0.8492727843137255, 0.8310625882352942, 0.8534343529411765, 0.8542500392156863, 0.841534431372549, 0.8540028235294117, 0.8544090980392156, 0.8401998431372548, 0.8388323137254903, 0.8542553725490196, 0.8457979607843136, 0.8640969411764705, 0.8666164705882353, 0.8648514509803922, 0.8604235294117646, 0.8607930980392158, 0.8636122352941176, 0.8637712941176471, 0.8689094901960784, 0.8665320784313726, 0.8636730980392155, 0.8627203137254902, 0.8693690980392157, 0.8601540392156861, 0.8665207843137255, 0.8607909019607842, 0.862432, 0.8640138039215686, 0.8628445490196077, 0.8631764705882352, 0.8634083137254902, 0.8630629019607843, 0.8632549019607842, 0.8623711372549019, 0.8618704313725489, 0.8638996078431372, 0.8631716078431373, 0.8630183529411763, 0.864654431372549, 0.8645311372549019, 0.862983843137255]
fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(12,5))
plt.suptitle('CIFAR-10 (20% imbalanced)',fontsize=25)
x=np.arange(len(tr_pAUC))
ax0.plot(x, tr_pAUC_scratch, label='From Scratch', linewidth=3)
ax0.plot(x, tr_pAUC, label='From Pretraining', linewidth=3)
ax0.set_title('Training',fontsize=25)
ax1.plot(x, te_pAUC_scratch, label='From Scratch', linewidth=3)
ax1.plot(x, te_pAUC, label='From Pretraining', linewidth=3)
ax1.set_title('Testing',fontsize=25)
ax0.legend(fontsize=15)
ax1.legend(fontsize=15)
ax0.set_ylabel('OPAUC(0.3)', fontsize=20)
ax0.set_xlabel('Epoch', fontsize=20)
ax1.set_ylabel('OPAUC(0.3)', fontsize=20)
ax1.set_xlabel('Epoch', fontsize=20)
plt.tight_layout()
