import torch import pandas as pd from sklearn.metrics import accuracy_score, f1_score, roc_auc_score from model import LSTMModel def load_model(model_path, vocab_size): model = LSTMModel(vocab_size) model.load_state_dict(torch.load(model_path)) model.eval() return model def predict(model, titles, texts, device): titles, texts = titles.to(device), texts.to(device) model.to(device) with torch.no_grad(): outputs = model(titles, texts).squeeze() return outputs def evaluate_model(model, data_loader, device, labels): model.to(device) model.eval() predictions = [] labels = torch.tensor(labels).to(device) for titles, texts in data_loader: titles, texts = titles.to(device), texts.to(device) outputs = predict(model, titles, texts, device) predictions.extend(outputs.cpu().numpy()) labels = labels.cpu() # Calculate metrics predicted_labels = [1 if p > 0.5 else 0 for p in predictions] accuracy = accuracy_score(labels, predicted_labels) f1 = f1_score(labels, predicted_labels) auc_roc = roc_auc_score(labels, predictions) return accuracy, f1, auc_roc