methods.py

import pandas as pd
import torch
from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
from transformers import BertTokenizer, BertForSequenceClassification, AdamW, get_linear_schedule_with_warmup
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import numpy as np
from torch.optim import AdamW
from torch.optim import AdamW as TorchAdamW 
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
import torch.nn.functional as F
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay

def evaluate_model_with_roc_and_confusion_matrix(model, data_loader, device, class_names):
    model.eval()
    predictions, true_labels = [], []
    total_loss = 0
    all_probs = []

    with torch.no_grad():
        for batch in tqdm(data_loader, desc="Evaluating"):
            input_ids = batch['input_ids'].to(device)
            attention_mask = batch['attention_mask'].to(device)
            labels = batch['labels'].to(device)

            outputs = model(input_ids, attention_mask=attention_mask)
            logits = outputs.logits

            loss = F.cross_entropy(logits, labels)
            total_loss += loss.item()

            probs = F.softmax(logits, dim=1)
            all_probs.extend(probs.cpu().numpy())
            _, preds = torch.max(logits, dim=1)
            predictions.extend(preds.cpu().numpy())
            true_labels.extend(labels.cpu().numpy())

    avg_loss = total_loss / len(data_loader)

    accuracy = accuracy_score(true_labels, predictions)
    precision = precision_score(true_labels, predictions, average='weighted')
    recall = recall_score(true_labels, predictions, average='weighted')
    f1 = f1_score(true_labels, predictions, average='weighted')

    print("Evaluation Metrics:")
    print(f"Loss: {avg_loss:.4f}")
    print(f"Accuracy: {accuracy:.4f}")
    print(f"Precision: {precision:.4f}")
    print(f"Recall: {recall:.4f}")
    print(f"F1 Score: {f1:.4f}")

    conf_matrix = confusion_matrix(true_labels, predictions)
    disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix, display_labels=class_names)
    disp.plot(cmap='Blues', xticks_rotation=45)
    plt.title("Confusion Matrix")
    plt.show()

    all_probs = np.array(all_probs) 
    plot_roc_auc(true_labels, all_probs, class_names)

    return {
        'loss': avg_loss,
        'accuracy': accuracy,
        'precision': precision,
        'recall': recall,
        'f1_score': f1,
        'predictions': predictions,
        'true_labels': true_labels,
        'probabilities': all_probs,
        'confusion_matrix': conf_matrix  
    }

def evaluate_model(model, data_loader, device):
    model.eval()
    predictions, true_labels = [], []
    total_loss = 0

    with torch.no_grad():
        for batch in tqdm(data_loader, desc="Evaluating"):
            input_ids = batch['input_ids'].to(device)
            attention_mask = batch['attention_mask'].to(device)
            labels = batch['labels'].to(device)

            outputs = model(input_ids, attention_mask=attention_mask)
            logits = outputs.logits

            loss = F.cross_entropy(logits, labels)
            total_loss += loss.item()

            _, preds = torch.max(logits, dim=1)
            predictions.extend(preds.cpu().numpy())
            true_labels.extend(labels.cpu().numpy())

    avg_loss = total_loss / len(data_loader)

    accuracy = accuracy_score(true_labels, predictions)
    precision = precision_score(true_labels, predictions, average='weighted')
    recall = recall_score(true_labels, predictions, average='weighted')
    f1 = f1_score(true_labels, predictions, average='weighted')

    print("Evaluation Metrics:")
    print(f"Loss: {avg_loss:.4f}")
    print(f"Accuracy: {accuracy:.4f}")
    print(f"Precision: {precision:.4f}")
    print(f"Recall: {recall:.4f}")
    print(f"F1 Score: {f1:.4f}")

    return {
        'loss': avg_loss,
        'accuracy': accuracy,
        'precision': precision,
        'recall': recall,
        'f1_score': f1
    }