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Module: utils.py
Description:
This module provides utility functions for the MODDA framework including:
- Evaluation metric calculation (ROC, AUPR, Accuracy, F1, etc.)
- Setting random seeds for reproducibility.
- Early stopping mechanism during training.
- Plotting ROC and Precision-Recall curves.
"""
import datetime
import numpy as np
import torch
import random
# import seaborn
import os
from sklearn.metrics import roc_curve, roc_auc_score, precision_recall_curve, average_precision_score
import matplotlib.pyplot as plt
def get_metrics_auc(real_score, predict_score):
"""
Compute the Area Under the ROC Curve (AUC) and the Average Precision (AUPR).
Parameters:
real_score (array-like): True binary labels.
predict_score (array-like): Predicted scores.
Returns:
tuple: (AUC, AUPR)
"""
auc = roc_auc_score(real_score, predict_score)
aupr = average_precision_score(real_score, predict_score)
return auc, aupr
def get_metrics(real_score, predict_score):
"""
Calculate various performance metrics including AUC, AUPR, Accuracy, F1-Score, Precision, Recall, and Specificity.
The implementation is based on the method described in:
Yu Z, Huang F, Zhao X et al. Predicting drug-disease associations through layer attention graph convolutional network,
Brief Bioinform 2021;22.
Parameters:
real_score (array-like): True labels.
predict_score (array-like): Predicted scores.
Returns:
tuple: (AUC, AUPR, Accuracy, F1-Score, Precision, Recall, Specificity)
"""
# Obtain sorted unique predicted scores to generate thresholds
sorted_predict_score = np.array(sorted(list(set(np.array(predict_score).flatten()))))
sorted_predict_score_num = len(sorted_predict_score)
thresholds = sorted_predict_score[np.int32(sorted_predict_score_num * np.arange(1, 1000) / 1000)]
thresholds = np.asmatrix(thresholds)
thresholds_num = thresholds.shape[1]
# Create a prediction matrix for all thresholds
predict_score_matrix = np.tile(predict_score, (thresholds_num, 1))
negative_index = np.where(predict_score_matrix < thresholds.T)
positive_index = np.where(predict_score_matrix >= thresholds.T)
predict_score_matrix[negative_index] = 0
predict_score_matrix[positive_index] = 1
# Calculate TP, FP, FN, and TN for each threshold
TP = predict_score_matrix.dot(real_score.T)
FP = predict_score_matrix.sum(axis=1) - TP
FN = real_score.sum() - TP
TN = len(real_score.T) - TP - FP - FN
fpr = FP / (FP + TN)
tpr = TP / (TP + FN)
ROC_dot_matrix = np.mat(sorted(np.column_stack((fpr, tpr)).tolist())).T
ROC_dot_matrix.T[0] = [0, 0]
ROC_dot_matrix = np.c_[ROC_dot_matrix, [1, 1]]
x_ROC = ROC_dot_matrix[0].T
y_ROC = ROC_dot_matrix[1].T
auc = 0.5 * (x_ROC[1:] - x_ROC[:-1]).T * (y_ROC[:-1] + y_ROC[1:])
recall_list = tpr
precision_list = TP / (TP + FP)
PR_dot_matrix = np.mat(sorted(np.column_stack((recall_list, precision_list)).tolist())).T
PR_dot_matrix.T[0] = [0, 1]
PR_dot_matrix = np.c_[PR_dot_matrix, [1, 0]]
x_PR = PR_dot_matrix[0].T
y_PR = PR_dot_matrix[1].T
aupr = 0.5 * (x_PR[1:] - x_PR[:-1]).T * (y_PR[:-1] + y_PR[1:])
f1_score_list = 2 * TP / (len(real_score.T) + TP - TN)
accuracy_list = (TP + TN) / len(real_score.T)
specificity_list = TN / (TN + FP)
max_index = np.argmax(f1_score_list)
f1_score = f1_score_list[max_index]
accuracy = accuracy_list[max_index]
specificity = specificity_list[max_index]
recall = recall_list[max_index]
precision = precision_list[max_index]
return auc[0, 0], aupr[0, 0], accuracy, f1_score, precision, recall, specificity
def set_seed(seed=0):
"""
Set the random seed for Python, NumPy, and PyTorch for reproducibility.
Parameters:
seed (int): The random seed to set.
"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
class EarlyStopping(object):
"""
Early stopping utility to halt training when the validation performance stops improving.
"""
def __init__(self, patience=10, saved_path="."):
"""
Parameters:
patience (int): Number of epochs with no improvement after which training is stopped.
saved_path (str): Directory path to save the model checkpoint.
"""
dt = datetime.datetime.now()
self.filename = os.path.join(
saved_path, "early_stop_{}_{}-{}-{}.pth".format(dt.date(), dt.hour, dt.minute, dt.second)
)
self.patience = patience
self.counter = 0
self.best_acc = None
self.best_loss = None
self.early_stop = False
def step(self, loss, acc, model):
"""
Check if early stopping condition is met based on current loss and accuracy.
Parameters:
loss (float): Current loss value.
acc (float): Current accuracy value.
model (torch.nn.Module): The model being trained.
Returns:
bool: True if early stopping condition is met, otherwise False.
"""
if self.best_loss is None:
self.best_acc = acc
self.best_loss = loss
self.save_checkpoint(model)
elif (loss > self.best_loss) and (acc < self.best_acc):
self.counter += 1
# Uncomment the following line for debugging
# print(f"EarlyStopping counter: {self.counter} out of {self.patience}")
if self.counter >= self.patience:
self.early_stop = True
else:
if (loss <= self.best_loss) and (acc >= self.best_acc):
self.save_checkpoint(model)
self.best_loss = np.min((loss, self.best_loss))
self.best_acc = np.max((acc, self.best_acc))
self.counter = 0
return self.early_stop
def save_checkpoint(self, model):
"""Save the current model state as a checkpoint."""
torch.save(model.state_dict(), self.filename)
def load_checkpoint(self, model):
"""Load the best model checkpoint."""
model.load_state_dict(torch.load(self.filename))
def plot_result_auc(args, label, predict, auc):
"""
Plot and save the Receiver Operating Characteristic (ROC) curve.
Parameters:
args: Argument object containing saved_path.
label (array-like): True labels.
predict (array-like): Predicted scores.
auc (float): Computed AUC value.
"""
# seaborn.set_style()
fpr, tpr, _ = roc_curve(label, predict)
plt.figure(figsize=(8, 8))
lw = 2
plt.plot(fpr, tpr, color="darkorange", lw=lw, label="ROC curve (area = %0.4f)" % auc)
plt.plot([0, 1], [0, 1], color="navy", lw=lw, linestyle="--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver Operating Characteristic")
plt.legend(loc="lower right")
plt.savefig(os.path.join(args.saved_path, "result_auc.png"))
plt.clf()
def plot_result_aupr(args, label, predict, aupr):
"""
Plot and save the Precision-Recall (PR) curve.
Parameters:
args: Argument object containing saved_path.
label (array-like): True labels.
predict (array-like): Predicted scores.
aupr (float): Computed AUPR value.
"""
# seaborn.set_style()
precision, recall, _ = precision_recall_curve(label, predict)
plt.figure(figsize=(8, 8))
lw = 2
plt.plot(precision, recall, color="darkorange", lw=lw, label="AUPR (area = %0.4f)" % aupr)
plt.xlabel("Recall")
plt.ylabel("Precision")
plt.title("Precision-Recall Curve")
plt.legend(loc="lower right")
plt.savefig(os.path.join(args.saved_path, "result_aupr.png"))
plt.clf()
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