tasks/utils/train.py

import torch
from torch.cuda.amp import autocast
import numpy as np
import time
import os
import yaml
from matplotlib import pyplot as plt
import glob
from collections import OrderedDict
from tqdm import tqdm
import torch.distributed as dist
import pandas as pd
import xgboost as xgb
from sklearn.metrics import accuracy_score, classification_report, roc_auc_score


from torch.nn import ModuleList
# from inr import INR
# from kan import FasterKAN

class Trainer(object):
    """
    A class that encapsulates the training loop for a PyTorch model.
    """
    def __init__(self, model, optimizer, criterion, train_dataloader, device, world_size=1, output_dim=2,
                 scheduler=None, val_dataloader=None,   max_iter=np.inf, scaler=None,
                  grad_clip=False, exp_num=None, log_path=None, exp_name=None, plot_every=None,
                   cos_inc=False, range_update=None, accumulation_step=1, wandb_log=False, num_quantiles=1,
                   update_func=lambda x: x):
        self.model = model
        self.optimizer = optimizer
        self.criterion = criterion
        self.scaler = scaler
        self.grad_clip = grad_clip
        self.cos_inc = cos_inc
        self.output_dim = output_dim
        self.scheduler = scheduler
        self.train_dl = train_dataloader
        self.val_dl = val_dataloader
        self.train_sampler = self.get_sampler_from_dataloader(train_dataloader)
        self.val_sampler = self.get_sampler_from_dataloader(val_dataloader)
        self.max_iter = max_iter
        self.device = device
        self.world_size = world_size
        self.exp_num = exp_num
        self.exp_name = exp_name
        self.log_path = log_path
        self.best_state_dict = None
        self.plot_every = plot_every
        self.logger = None
        self.range_update = range_update
        self.accumulation_step = accumulation_step
        self.wandb = wandb_log
        self.num_quantiles = num_quantiles
        self.update_func = update_func
        # if log_path is not None:
        #     self.logger =SummaryWriter(f'{self.log_path}/exp{self.exp_num}')
        #     # print(f"logger path: {self.log_path}/exp{self.exp_num}")

        # print("logger is: ", self.logger)
    
    def get_sampler_from_dataloader(self, dataloader):
        if hasattr(dataloader, 'sampler'):
            if isinstance(dataloader.sampler, torch.utils.data.DistributedSampler):
                return dataloader.sampler
            elif hasattr(dataloader.sampler, 'sampler'):
                return dataloader.sampler.sampler
        
        if hasattr(dataloader, 'batch_sampler') and hasattr(dataloader.batch_sampler, 'sampler'):
            return dataloader.batch_sampler.sampler
        
        return None

    def fit(self, num_epochs, device,  early_stopping=None, only_p=False, best='loss', conf=False):
        """
        Fits the model for the given number of epochs.
        """
        min_loss = np.inf
        best_acc = 0
        train_loss, val_loss,  = [], []
        train_acc, val_acc = [], []
        lrs = []
        # self.optim_params['lr_history'] = []
        epochs_without_improvement = 0
        # main_proccess = (torch.distributed.is_initialized() and torch.distributed.get_rank() == 0) or self.device == 'cpu'
        main_proccess = True    # change in a ddp setting
        print(f"Starting training for {num_epochs} epochs")
        print("is main process: ", main_proccess, flush=True)
        global_time = time.time()
        self.epoch = 0
        for epoch in range(num_epochs):
            self.epoch = epoch
            start_time = time.time()
            plot = (self.plot_every is not None) and (epoch % self.plot_every == 0)
            t_loss, t_acc = self.train_epoch(device, epoch=epoch)
            t_loss_mean = np.nanmean(t_loss)
            train_loss.extend(t_loss)
            global_train_accuracy, global_train_loss = self.process_loss(t_acc, t_loss_mean)
            if main_proccess:  # Only perform this on the master GPU
                train_acc.append(global_train_accuracy.mean().item())
                
            v_loss, v_acc = self.eval_epoch(device, epoch=epoch)
            v_loss_mean = np.nanmean(v_loss)
            val_loss.extend(v_loss)
            global_val_accuracy, global_val_loss = self.process_loss(v_acc, v_loss_mean)
            if main_proccess:  # Only perform this on the master GPU                
                val_acc.append(global_val_accuracy.mean().item())
                
                current_objective = global_val_loss if best == 'loss' else global_val_accuracy.mean()
                improved = False
                
                if best == 'loss':
                    if current_objective < min_loss:
                        min_loss = current_objective
                        improved = True
                else:
                    if current_objective > best_acc:
                        best_acc = current_objective
                        improved = True
                
                if improved:
                    model_name = f'{self.log_path}/{self.exp_num}/{self.exp_name}.pth'
                    print(f"saving model at {model_name}...")
                    torch.save(self.model.state_dict(), model_name)
                    self.best_state_dict = self.model.state_dict()
                    epochs_without_improvement = 0
                else:
                    epochs_without_improvement += 1

                current_lr = self.optimizer.param_groups[0]['lr'] if self.scheduler is None \
                            else self.scheduler.get_last_lr()[0]
                
                lrs.append(current_lr)

                print(f'Epoch {epoch}, lr {current_lr}, Train Loss: {global_train_loss:.6f}, Val Loss:'\
                f'{global_val_loss:.6f}, Train Acc: {global_train_accuracy.round(decimals=4).tolist()}, '\
                f'Val Acc: {global_val_accuracy.round(decimals=4).tolist()},'\
                  f'Time: {time.time() - start_time:.2f}s, Total Time: {(time.time() - global_time)/3600} hr', flush=True)
                if epoch % 10 == 0:
                    print(os.system('nvidia-smi'))

                if epochs_without_improvement == early_stopping:
                    print('early stopping!', flush=True)
                    break
                if time.time() - global_time > (23.83 * 3600):
                    print("time limit reached")
                    break 

        return {"num_epochs":num_epochs, "train_loss": train_loss,
                 "val_loss": val_loss, "train_acc": train_acc, "val_acc": val_acc, "lrs": lrs}

    def process_loss(self, acc, loss_mean):
        if  torch.cuda.is_available() and torch.distributed.is_initialized():
            global_accuracy = torch.tensor(acc).cuda()  # Convert accuracy to a tensor on the GPU
            torch.distributed.reduce(global_accuracy, dst=0, op=torch.distributed.ReduceOp.SUM)
            global_loss = torch.tensor(loss_mean).cuda()  # Convert loss to a tensor on the GPU
            torch.distributed.reduce(global_loss, dst=0, op=torch.distributed.ReduceOp.SUM)
            
            # Divide both loss and accuracy by world size
            world_size = torch.distributed.get_world_size()
            global_loss /= world_size
            global_accuracy /= world_size
        else:
            global_loss = torch.tensor(loss_mean)
            global_accuracy = torch.tensor(acc)
        return global_accuracy, global_loss

    def load_best_model(self, to_ddp=True, from_ddp=True):
        data_dir = f'{self.log_path}/exp{self.exp_num}'
        # data_dir = f'{self.log_path}/exp29' # for debugging

        state_dict_files = glob.glob(data_dir + '/*.pth')
        print("loading model from ", state_dict_files[-1])
        
        state_dict = torch.load(state_dict_files[-1]) if to_ddp else torch.load(state_dict_files[0],map_location=self.device)
    
        if from_ddp:
            print("loading distributed model")
            # Remove "module." from keys
            new_state_dict = OrderedDict()
            for key, value in state_dict.items():
                if key.startswith('module.'):
                    while key.startswith('module.'):
                        key = key[7:]
                new_state_dict[key] = value
            state_dict = new_state_dict
        # print("state_dict: ", state_dict.keys())
        # print("model: ", self.model.state_dict().keys())

        self.model.load_state_dict(state_dict, strict=False)

    def check_gradients(self):
        for name, param in self.model.named_parameters():
            if param.grad is not None:
                grad_norm = param.grad.norm().item()
                if grad_norm > 10:
                    print(f"Large gradient in {name}: {grad_norm}")

    def train_epoch(self, device, epoch):
        """
        Trains the model for one epoch.
        """
        if self.train_sampler is not None:
            try:
                self.train_sampler.set_epoch(epoch)
            except AttributeError:
                pass
        self.model.train()
        train_loss = []
        train_acc = 0
        total = 0
        all_accs = torch.zeros(self.output_dim, device=device)
        pbar = tqdm(self.train_dl)
        for i, batch in enumerate(pbar):
            if self.optimizer is not None:
                self.optimizer.zero_grad()
            loss, acc , y = self.train_batch(batch, i, device)
            train_loss.append(loss.item())
            all_accs = all_accs + acc
            total += len(y)
            pbar.set_description(f"train_acc: {acc}, train_loss:  {loss.item()}")      
            if i > self.max_iter:
                break
        print("number of train_accs: ", train_acc)
        return train_loss, all_accs/total
    
    def train_batch(self, batch, batch_idx, device):
        x, fft, y = batch['audio']['array'], batch['audio']['fft_mag'], batch['label']
        # features = torch.stack(batch['audio']['features']).to(device).float()
        # cwt = batch['audio']['cwt_mag']
        x = x.to(device).float()
        fft = fft.to(device).float()
        # cwt = cwt.to(device).float()
        y = y.to(device).float()
        x_fft = torch.cat((x.unsqueeze(dim=1), fft.unsqueeze(dim=1)), dim=1)
        y_pred = self.model(x_fft).squeeze()
        loss = self.criterion(y_pred, y)
        loss.backward()
        self.optimizer.step()
        if self.scheduler is not None:
            self.scheduler.step()
        # get predicted classes
        probs = torch.sigmoid(y_pred)
        cls_pred = (probs > 0.5).float()
        acc = (cls_pred == y).sum()
        return loss, acc, y

    def eval_epoch(self, device, epoch):
        """
        Evaluates the model for one epoch.
        """
        self.model.eval()
        val_loss = []
        val_acc = 0
        total = 0
        all_accs = torch.zeros(self.output_dim, device=device)
        pbar = tqdm(self.val_dl)
        for i,batch in enumerate(pbar):
            loss, acc, y = self.eval_batch(batch, i, device)
            val_loss.append(loss.item())
            all_accs = all_accs + acc
            total += len(y)
            pbar.set_description(f"val_acc: {acc}, val_loss:  {loss.item()}")
            if i > self.max_iter:
                break
        return val_loss, all_accs/total

    def eval_batch(self, batch, batch_idx, device):
        x, fft, y = batch['audio']['array'], batch['audio']['fft_mag'], batch['label']
        # features = torch.stack(batch['audio']['features']).to(device).float()

        # features = batch['audio']['features_arr'].to(device).float()
        x = x.to(device).float()
        fft = fft.to(device).float()
        x_fft = torch.cat((x.unsqueeze(dim=1), fft.unsqueeze(dim=1)), dim=1)
        y = y.to(device).float()
        with torch.no_grad():
            y_pred = self.model(x_fft).squeeze()
        loss = self.criterion(y_pred.squeeze(), y)
        probs = torch.sigmoid(y_pred)
        cls_pred = (probs > 0.5).float()
        acc = (cls_pred == y).sum()
        return loss, acc, y

    def predict(self, test_dataloader, device):
        """
        Returns the predictions of the model on the given dataset.
        """
        self.model.eval()
        total = 0
        all_accs = 0
        predictions = []
        true_labels = []
        pbar = tqdm(test_dataloader)
        for i,batch in enumerate(pbar):
            x, fft, y = batch['audio']['array'], batch['audio']['fft_mag'], batch['label']
            # features = batch['audio']['features']
            x = x.to(device).float()
            fft = fft.to(device).float()
            x_fft = torch.cat((x.unsqueeze(dim=1), fft.unsqueeze(dim=1)), dim=1)
            y = y.to(device).float()
            with torch.no_grad():
                y_pred = self.model(x_fft).squeeze()
            loss = self.criterion(y_pred, y)
            probs = torch.sigmoid(y_pred)
            cls_pred = (probs > 0.5).float()
            acc = (cls_pred == y).sum()
            predictions.extend(cls_pred.cpu().numpy())
            true_labels.extend(y.cpu().numpy().astype(np.int64))
            all_accs += acc
            total += len(y)
            pbar.set_description("acc: {:.4f}".format(acc))
            if i > self.max_iter:
                break
        return predictions, true_labels, all_accs/total


class INRDatabase:
    """Database to store and manage INRs persistently."""

    def __init__(self, save_dir='./inr_database'):
        self.inrs = {}  # Maps sample_id -> INR
        self.optimizers = {}  # Maps sample_id -> optimizer state
        self.save_dir = save_dir
        os.makedirs(save_dir, exist_ok=True)

    def get_or_create_inr(self, sample_id, create_fn, device):
        """Get existing INR or create new one if not exists."""
        if sample_id not in self.inrs:
            # Create new INR
            inr = create_fn().to(device)
            optimizer = torch.optim.Adam(inr.parameters())
            self.inrs[sample_id] = inr
            self.optimizers[sample_id] = optimizer
        return self.inrs[sample_id], self.optimizers[sample_id]

    def set_inr(self, sample_id, inr, optimizer):
        self.inrs[sample_id] = inr
        self.optimizers[sample_id] = optimizer

    def save_state(self):
        """Save all INRs and optimizer states to disk."""
        state = {
            'inrs': {
                sample_id: inr.state_dict()
                for sample_id, inr in self.inrs.items()
            },
            'optimizers': {
                sample_id: opt.state_dict()
                for sample_id, opt in self.optimizers.items()
            }
        }
        torch.save(state, os.path.join(self.save_dir, 'inr_database.pt'))

    def load_state(self, create_fn, device):
        """Load INRs and optimizer states from disk."""
        path = os.path.join(self.save_dir, 'inr_database.pt')
        if os.path.exists(path):
            state = torch.load(path, map_location=device)

            # Restore INRs
            for sample_id, inr_state in state['inrs'].items():
                inr = create_fn().to(device)
                inr.load_state_dict(inr_state)
                self.inrs[sample_id] = inr

            # Restore optimizers
            for sample_id, opt_state in state['optimizers'].items():
                optimizer = torch.optim.Adam(self.inrs[sample_id].parameters())
                optimizer.load_state_dict(opt_state)
                self.optimizers[sample_id] = optimizer


class INRTrainer(Trainer):
    def __init__(self, hidden_features=128, n_layers=3, in_features=1, out_features=1,
                 num_steps=5000, lr=1e-3, inr_criterion=torch.nn.MSELoss(), save_dir='./inr_database', *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.hidden_features = hidden_features
        self.n_layers = n_layers
        self.in_features = in_features
        self.out_features = out_features
        self.num_steps = num_steps
        self.lr = lr
        self.inr_criterion = inr_criterion

        # Initialize INR database
        self.db = INRDatabase(save_dir)

        # Load existing INRs if available
        self.db.load_state(self.create_inr, self.device)

    def create_inr(self):
        """Factory function to create new INR instances."""
        return INR(
            hidden_features=self.hidden_features,
            n_layers=self.n_layers,
            in_features=self.in_features,
            out_features=self.out_features
        )

    def create_kan(self):
        return FasterKAN(layers_hidden=[self.in_features] + [self.hidden_features] * (self.n_layers) + [self.out_features],)

    def get_sample_id(self, batch, idx):
        """Extract unique identifier for a sample in the batch.
        Override this method based on your data structure."""
        # Example: if your batch contains unique IDs
        if 'id' in batch:
            return batch['id'][idx]
        # Fallback: create hash from the sample data
        sample_data = batch['audio']['array'][idx]
        return hash(sample_data.cpu().numpy().tobytes())

    def train_inr(self, optimizer, model, coords, values, num_iters=10, plot=False):
        # pbar = tqdm(range(num_iters))
        for _ in range(num_iters):
            optimizer.zero_grad()
            pred_values = model(coords.to(self.device)).float()
            loss = self.inr_criterion(pred_values.squeeze(), values)
            loss.backward()
            optimizer.step()
            # pbar.set_description(f'loss: {loss.item()}')
        if plot:
            plt.plot(values.cpu().detach().numpy())
            plt.plot(pred_values.cpu().detach().numpy())
            plt.title(loss.item())
            plt.show()
        return model, optimizer

    def train_batch(self, batch, batch_idx, device):
        """Train INRs for each sample in batch, persisting progress."""
        coords = batch['audio']['coords'].to(device)  # [B, N, 1]
        fft = batch['audio']['fft_mag'].to(device)  # [B, N]
        audio = batch['audio']['array'].to(device)  # [B, N]
        y = batch['label'].to(device).float()

        batch_size = coords.shape[0]

        values = audio

        batch_losses = []
        batch_optimizers = []
        batch_inrs = []
        batch_weights = tuple()
        batch_biases = tuple()
        # Training loop
        # pbar = tqdm(range(self.num_steps), desc="Training INRs")
        plot = batch_idx == 0
        for i in range(batch_size):
            sample_id = self.get_sample_id(batch, i)
            inr, optimizer = self.db.get_or_create_inr(sample_id, self.create_inr, device)
            inr, optimizer = self.train_inr(optimizer, inr, coords[i], values[i])
            self.db.set_inr(sample_id, inr, optimizer)
            # pred_values = inr(coords[i]).squeeze()
            # batch_losses.append(self.inr_criterion(pred_values, values[i]))
            # batch_optimizers.append(optimizer)
            state_dict = inr.state_dict()
            weights = tuple(
                [v.permute(1, 0).unsqueeze(-1).unsqueeze(0).to(device) for w, v in state_dict.items() if "weight" in w]
            )
            biases = tuple([v.unsqueeze(-1).unsqueeze(0).to(device) for w, v in state_dict.items() if "bias" in w])
            if not len(batch_weights):
                batch_weights = weights
            else:
                batch_weights = tuple(
                    [torch.cat((weights[i], batch_weights[i]), dim=0) for i in range(len(weights))]
                )
            if not len(batch_biases):
                batch_biases = biases
            else:
                batch_biases = tuple(
                    [torch.cat((biases[i], batch_biases[i]), dim=0) for i in range(len(biases))]
                )
        # loss_preds = torch.tensor([0])
        # acc = 0
        y_pred = self.model(inputs=(batch_weights, batch_biases)).squeeze()
        loss_preds = self.criterion(y_pred, y)
        self.optimizer.zero_grad()
        loss_preds.backward()
        self.optimizer.step()
        # for i in range(batch_size):
        #     batch_optimizers[i].zero_grad()
        #     batch_losses[i] += loss_preds
        #     batch_losses[i].backward()
        #     batch_optimizers[i].step()


        if batch_idx % 10 == 0:  # Adjust frequency as needed
            self.db.save_state()

        probs = torch.sigmoid(y_pred)
        cls_pred = (probs > 0.5).float()
        acc = (cls_pred == y).sum()


        return loss_preds, acc, y

    def eval_batch(self, batch, batch_idx, device):
        """Evaluate INRs for each sample in batch."""
        coords = batch['audio']['coords'].to(device)
        fft = batch['audio']['fft_mag'].to(device)
        audio = batch['audio']['array'].to(device)

        batch_size = coords.shape[0]
        # values = torch.cat((
        #     audio.unsqueeze(-1),
        #     fft.unsqueeze(-1)
        # ), dim=-1)
        values = audio
        # Get INRs for each sample
        batch_inrs = []
        for i in range(batch_size):
            sample_id = self.get_sample_id(batch, i)
            inr, _ = self.db.get_or_create_inr(sample_id, self.create_inr, device)
            batch_inrs.append(inr)

        # Evaluate
        with torch.no_grad():
            all_preds = torch.stack([
                inr(coords[i])
                for i, inr in enumerate(batch_inrs)
            ])

            batch_losses = torch.stack([
                self.criterion(all_preds[i].squeeze(), values[i])
                for i in range(batch_size)
            ])

            avg_loss = batch_losses.mean().item()

        acc = torch.zeros(self.output_dim, device=device)
        y = values

        return torch.tensor(avg_loss), acc, y


def verify_parallel_gradient_isolation(trainer, batch_size=4, sequence_length=1000):
    """
    Verify that gradients remain isolated in parallel training.
    """
    device = trainer.device

    # Create test data
    coords = torch.linspace(0, 1, sequence_length).unsqueeze(-1)  # [N, 1]
    coords = coords.unsqueeze(0).repeat(batch_size, 1, 1)  # [B, N, 1]

    # Create synthetic signals
    targets = torch.stack([
        torch.sin(2 * torch.pi * (i + 1) * coords.squeeze(-1))
        for i in range(batch_size)
    ]).to(device)

    # Create batch of INRs
    inrs = trainer.create_batch_inrs()

    # Store initial parameters
    initial_params = [{name: param.clone().detach()
                       for name, param in inr.named_parameters()}
                      for inr in inrs]

    # Create mock batch
    batch = {
        'audio': {
            'coords': coords.to(device),
            'fft_mag': targets.clone(),
            'array': targets.clone()
        }
    }

    # Run one training step
    trainer.train_batch(batch, 0, device)

    # Verify parameter changes
    isolation_verified = True
    for i, inr in enumerate(inrs):
        params_changed = False
        for name, param in inr.named_parameters():
            if not torch.allclose(param, initial_params[i][name]):
                params_changed = True
                # Verify that the changes are unique to this INR
                for j, other_inr in enumerate(inrs):
                    if i != j:
                        other_param = dict(other_inr.named_parameters())[name]
                        if not torch.allclose(other_param, initial_params[j][name]):
                            isolation_verified = False
                            print(f"Warning: Parameter {name} of INR {j} changed when only INR {i} should have changed")

    return isolation_verified

class XGBoostTrainer():
    def __init__(self, model_args, train_ds, val_ds, test_ds):
        self.train_ds = train_ds
        self.test_ds = test_ds
        print("creating train dataframe...")
        self.x_train, self.y_train = self.create_dataframe(train_ds, save_name='train')
        print("creating validation dataframe...")
        self.x_val, self.y_val = self.create_dataframe(val_ds, save_name='val')
        print("creating test dataframe...")
        self.x_test, self.y_test = self.create_dataframe(test_ds, save_name='test')

        # Convert the data to DMatrix format
        self.dtrain = xgb.DMatrix(self.x_train, label=self.y_train)
        self.dval = xgb.DMatrix(self.x_val, label=self.y_val)
        self.dtest = xgb.DMatrix(self.x_test, label=self.y_test)

        # Model initialization
        self.model_args = model_args
        self.model = xgb.XGBClassifier(**model_args)

    def create_dataframe(self, ds, save_name='train'):
        try:
            df = pd.read_csv(f"tasks/utils/dfs/{save_name}.csv")
        except FileNotFoundError:
            data = []

            # Iterate over the dataset
            pbar = tqdm(enumerate(ds))
            for i, batch in pbar:
                label = batch['label']
                features = batch['audio']['features']

                # Flatten the nested dictionary structure
                feature_dict = {'label': label}
                for k, v in features.items():
                    if isinstance(v, dict):
                        for sub_k, sub_v in v.items():
                            feature_dict[f"{k}_{sub_k}"] = sub_v[0].item()  # Aggregate (e.g., mean)
                data.append(feature_dict)
            # Convert to DataFrame
            df = pd.DataFrame(data)
            print(os.getcwd())
            df.to_csv(f"tasks/utils/dfs/{save_name}.csv", index=False)
        X = df.drop(columns=['label'])
        y = df['label']
        return X, y

    def fit(self):
        # Train using the `train` method with early stopping
        params = {
            'objective': 'binary:logistic',
            'eval_metric': 'logloss',
            **self.model_args
        }

        evals_result = {}
        num_boost_round = 1000  # Set a large number of boosting rounds

        # Watchlist to monitor performance on train and validation data
        watchlist = [(self.dtrain, 'train'), (self.dval, 'eval')]

        # Train the model
        self.model = xgb.train(
            params,
            self.dtrain,
            num_boost_round=num_boost_round,
            evals=watchlist,
            early_stopping_rounds=10,  # Early stopping after 10 rounds with no improvement
            evals_result=evals_result,
            verbose_eval=True  # Show evaluation results for each iteration
        )

        return evals_result

    def train_xgboost_in_batches(self, dataloader, eval_metric="logloss"):
        evals_result = {}
        for i, batch in enumerate(dataloader):
            # Convert batch data to NumPy arrays
            X_batch = torch.cat([batch[key].view(batch[key].size(0), -1) for key in batch if key != "label"],
                                dim=1).numpy()
            y_batch = batch["label"].numpy()

            # Create DMatrix for XGBoost
            dtrain = xgb.DMatrix(X_batch, label=y_batch)

            # Use `train` with each batch
            self.model = xgb.train(
                params,
                dtrain,
                num_boost_round=1000,  # Use a large number of rounds
                evals=[(self.dval, 'eval')],
                eval_metric=eval_metric,
                early_stopping_rounds=10,
                evals_result=evals_result,
                verbose_eval=False  # Avoid printing every iteration
            )

            # Optionally print progress
            if i % 10 == 0:
                print(f"Batch {i + 1}/{len(dataloader)} processed.")

        return evals_result

    def predict(self):
        # Predict probabilities for class 1
        y_prob = self.model.predict(self.dtest, output_margin=False)

        # Convert probabilities to binary labels (0 or 1) using a threshold (e.g., 0.5)
        y_pred = (y_prob >= 0.5).astype(int)

        # Evaluate performance
        accuracy = accuracy_score(self.y_test, y_pred)
        roc_auc = roc_auc_score(self.y_test, y_prob)

        print(f'Accuracy: {accuracy:.4f}')
        print(f'ROC AUC Score: {roc_auc:.4f}')
        print(classification_report(self.y_test, y_pred))

    def plot_results(self, evals_result):
        train_logloss = evals_result["train"]["logloss"]
        val_logloss = evals_result["eval"]["logloss"]
        iterations = range(1, len(train_logloss) + 1)

        # Plot
        plt.figure(figsize=(8, 5))
        plt.plot(iterations, train_logloss, label="Train LogLoss", color="blue")
        plt.plot(iterations, val_logloss, label="Validation LogLoss", color="red")
        plt.xlabel("Boosting Round (Iteration)")
        plt.ylabel("Log Loss")
        plt.title("Training and Validation Log Loss over Iterations")
        plt.legend()
        plt.grid()
        plt.show()