Upload SOC mapping model weights and inference files

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import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from dataloader.dataloader import MultiRasterDataset 
from dataloader.dataloaderMapping import MultiRasterDatasetMapping
from dataloader.dataframe_loader import filter_dataframe, separate_and_add_data
import pandas as pd
from tqdm import tqdm
from pathlib import Path
import wandb
from accelerate import Accelerator
from config import (TIME_BEGINNING, TIME_END, INFERENCE_TIME, MAX_OC,
                   seasons, years_padded, num_epochs,
                   SamplesCoordinates_Yearly, MatrixCoordinates_1mil_Yearly, 
                   DataYearly, SamplesCoordinates_Seasonally, 
                   MatrixCoordinates_1mil_Seasonally, DataSeasonally,
                   file_path_LUCAS_LFU_Lfl_00to23_Bavaria_OC)
from torch.utils.data import Dataset, DataLoader
from modelCNN import SmallCNN
import argparse

def composite_l1_chi2_loss(outputs, targets, sigma=3.0, alpha=0.5):
    errors = targets - outputs
    l1_loss = torch.mean(torch.abs(errors))
    squared_errors = errors ** 2
    chi2_unscaled = (1/4) * squared_errors * torch.exp(-squared_errors / (2 * sigma))
    chi2_unscaled_mean = torch.mean(chi2_unscaled)
    chi2_unscaled_mean = torch.clamp(chi2_unscaled_mean, min=1e-8)
    scale_factor = l1_loss / chi2_unscaled_mean
    chi2_scaled = scale_factor * chi2_unscaled_mean
    return alpha * l1_loss + (1 - alpha) * chi2_scaled

def composite_l2_chi2_loss(outputs, targets, sigma=3.0, alpha=0.5):
    errors = targets - outputs
    l2_loss = torch.mean(errors ** 2)
    chi2_loss = torch.mean((errors ** 2) / (sigma ** 2))
    chi2_loss = torch.clamp(chi2_loss, min=1e-8)
    scale_factor = l2_loss / chi2_loss
    chi2_scaled = scale_factor * chi2_loss
    return alpha * l2_loss + (1 - alpha) * chi2_scaled

def parse_args():
    parser = argparse.ArgumentParser(description='Train SimpleCNN model with customizable parameters')
    parser.add_argument('--lr', type=float, default=0.001, help='Learning rate')
    parser.add_argument('--loss_type', type=str, default='mse', choices=['composite_l1', 'l1', 'mse','composite_l2'], help='Type of loss function')
    parser.add_argument('--loss_alpha', type=float, default=0.5, help='Weight for L1 loss in composite loss (if used)')
    parser.add_argument('--target_transform', type=str, default='log', choices=['none', 'log', 'normalize'], help='Transformation to apply to targets')
    parser.add_argument('--use_validation', action='store_true', default=True, help='Whether to use validation set')
    return parser.parse_args()

def create_balanced_dataset(df, use_validation=True, n_bins=128, min_ratio=3/4):
    bins = pd.qcut(df['OC'], q=n_bins, labels=False, duplicates='drop')
    df['bin'] = bins
    bin_counts = df['bin'].value_counts()
    max_samples = bin_counts.max()
    min_samples = max(int(max_samples * min_ratio), 5)
    training_dfs = []
    if use_validation:
        validation_indices = []
        for bin_idx in range(len(bin_counts)):
            bin_data = df[df['bin'] == bin_idx]
            if len(bin_data) >= 4:
                val_samples = bin_data.sample(n=min(13, len(bin_data)))
                validation_indices.extend(val_samples.index)
                train_samples = bin_data.drop(val_samples.index)
                if len(train_samples) > 0:
                    if len(train_samples) < min_samples:
                        resampled = train_samples.sample(n=min_samples, replace=True)
                        training_dfs.append(resampled)
                    else:
                        training_dfs.append(train_samples)
        if not training_dfs or not validation_indices:
            raise ValueError("No training or validation data available after binning")
        training_df = pd.concat(training_dfs).drop('bin', axis=1)
        validation_df = df.loc[validation_indices].drop('bin', axis=1)
        print('Size of the training set:   ', len(training_df))
        print('Size of the validation set:   ', len(validation_df))
        return training_df, validation_df
    else:
        for bin_idx in range(len(bin_counts)):
            bin_data = df[df['bin'] == bin_idx]
            if len(bin_data) > 0:
                if len(bin_data) < min_samples:
                    resampled = bin_data.sample(n=min_samples, replace=True)
                    training_dfs.append(resampled)
                else:
                    training_dfs.append(bin_data)
        if not training_dfs:
            raise ValueError("No training data available after binning")
        training_df = pd.concat(training_dfs).drop('bin', axis=1)
        return training_df, None  # Return None for validation_df when no validation

def train_model(args, model, train_loader, val_loader, num_epochs, accelerator, loss_type='L1', target_transform='none'):
    if loss_type == 'composite_l1':
        criterion = lambda outputs, targets: composite_l1_chi2_loss(outputs, targets, sigma=3.0, alpha=args.loss_alpha)
    elif loss_type == 'composite_l2':
        criterion = lambda outputs, targets: composite_l2_chi2_loss(outputs, targets, sigma=3.0, alpha=args.loss_alpha)
    elif loss_type == 'l1':
        criterion = nn.L1Loss()
    elif loss_type == 'mse':
        criterion = nn.MSELoss()
    else:
        raise ValueError(f"Unknown loss type: {loss_type}")
    
    optimizer = optim.Adam(model.parameters(), lr=args.lr)
    train_loader, model, optimizer = accelerator.prepare(
        train_loader, model, optimizer
    )
    if val_loader is not None:
        val_loader = accelerator.prepare(val_loader)

    if target_transform == 'normalize':
        all_targets = []
        for _, _, _, targets in train_loader:
            all_targets.append(targets)
        all_targets = torch.cat(all_targets)
        target_mean = all_targets.mean().item()
        target_std = all_targets.std().item()
        if accelerator.is_main_process:
            print(f"Target mean: {target_mean}, Target std: {target_std}")
    else:
        target_mean, target_std = 0.0, 1.0

    best_r_squared = -float('inf') if args.use_validation else 1.0
    best_model_state = None
    
    for epoch in range(num_epochs):
        model.train()
        running_loss = 0.0
        
        for batch_idx, (longitudes, latitudes, features, targets) in enumerate(tqdm(train_loader)):
            features = features.to(accelerator.device)
            targets = targets.to(accelerator.device).float()
            if target_transform == 'log':
                targets = torch.log(targets + 1e-10)
            elif target_transform == 'normalize':
                targets = (targets - target_mean) / (target_std + 1e-10)
            optimizer.zero_grad()
            outputs = model(features)
            loss = criterion(outputs, targets)
            accelerator.backward(loss)
            optimizer.step()
            running_loss += loss.item()
            
            if accelerator.is_main_process:
                wandb.log({
                    'train_loss': loss.item(),
                    'batch': batch_idx + 1 + epoch * len(train_loader),
                    'epoch': epoch + 1
                })

        train_loss = running_loss / len(train_loader)
        
        if args.use_validation and val_loader is not None:
            model.eval()
            val_loss = 0.0
            val_outputs_list = []
            val_targets_list = []
            
            with torch.no_grad():
                for longitudes, latitudes, features, targets in val_loader:
                    features = features.to(accelerator.device)
                    targets = targets.to(accelerator.device).float()
                    if target_transform == 'log':
                        targets = torch.log(targets + 1e-10)
                    elif target_transform == 'normalize':
                        targets = (targets - target_mean) / (target_std + 1e-10)
                    outputs = model(features)
                    loss = criterion(outputs, targets)
                    val_loss += loss.item()
                    val_outputs_list.append(outputs.cpu())
                    val_targets_list.append(targets.cpu())
            
            val_loss = val_loss / len(val_loader)
            
            # Concatenate outputs and targets from all batches
            val_outputs = torch.cat(val_outputs_list, dim=0).numpy()
            val_targets = torch.cat(val_targets_list, dim=0).numpy()
            
            # Gather data from all processes
            val_outputs_all = torch.from_numpy(val_outputs).to(accelerator.device)
            val_targets_all = torch.from_numpy(val_targets).to(accelerator.device)
            val_outputs_all = accelerator.gather(val_outputs_all).cpu().numpy()
            val_targets_all = accelerator.gather(val_targets_all).cpu().numpy()
            
            if accelerator.is_main_process:
                # Inverse transform to original scale
                if target_transform == 'log':
                    original_val_outputs = np.exp(val_outputs_all)
                    original_val_targets = np.exp(val_targets_all)
                elif target_transform == 'normalize':
                    original_val_outputs = val_outputs_all * target_std + target_mean
                    original_val_targets = val_targets_all * target_std + target_mean
                else:
                    original_val_outputs = val_outputs_all
                    original_val_targets = val_targets_all
                
                # Compute metrics on original scale
                if len(original_val_outputs) > 1 and np.std(original_val_outputs) > 1e-6 and np.std(original_val_targets) > 1e-6:
                    correlation = np.corrcoef(original_val_outputs, original_val_targets)[0, 1]
                    r_squared = correlation ** 2
                    mse = np.mean((original_val_outputs - original_val_targets) ** 2)
                    rmse = np.sqrt(mse)
                    mae = np.mean(np.abs(original_val_outputs - original_val_targets))
                    iqr = np.percentile(original_val_targets, 75) - np.percentile(original_val_targets, 25)
                    rpiq = iqr / rmse if rmse > 0 else float('inf')
                else:
                    correlation = 0.0
                    r_squared = 0.0
                    mse = float('nan')
                    rmse = float('nan')
                    mae = float('nan')
                    rpiq = float('nan')
                
                # Update best model based on R²
                if r_squared > best_r_squared:
                    best_r_squared = r_squared
                    best_model_state = {k: v.cpu() for k, v in model.state_dict().items()}
                    wandb.run.summary['best_r_squared'] = best_r_squared

                wandb.log({
                    'epoch': epoch + 1,
                    'train_loss_avg': train_loss,
                    'val_loss': val_loss,
                    'correlation': correlation,
                    'r_squared': r_squared,
                    'mse': mse,
                    'rmse': rmse,
                    'mae': mae,
                    'rpiq': rpiq
                })
                
            accelerator.print(f'Epoch {epoch+1}:')
            accelerator.print(f'Training Loss: {train_loss:.4f}')
            accelerator.print(f'Validation Loss: {val_loss:.4f}')
            if accelerator.is_main_process:
                accelerator.print(f'RPIQ: {rpiq:.4f}\n')
        else:
            # No validation, update model state and set R² to 1.0
            best_r_squared = 1.0
            best_model_state = {k: v.cpu() for k, v in model.state_dict().items()}
            wandb.run.summary['best_r_squared'] = best_r_squared
            
            if accelerator.is_main_process:
                wandb.log({
                    'epoch': epoch + 1,
                    'train_loss_avg': train_loss,
                })
                
            accelerator.print(f'Epoch {epoch+1}:')
            accelerator.print(f'Training Loss: {train_loss:.4f}\n')

    return model, None, None, best_model_state, best_r_squared

if __name__ == "__main__":
    args = parse_args()
    accelerator = Accelerator()
    
    wandb.init(
        project="socmapping-SimpleTimeCNN",
        config={
            "max_oc": MAX_OC,
            "time_beginning": TIME_BEGINNING,
            "time_end": TIME_END,
            "epochs": num_epochs,
            "batch_size": 256,
            "learning_rate": 0.001,
            "input_channels": 6,
            "loss_type": args.loss_type,
            "target_transform": args.target_transform,
            "use_validation": args.use_validation
        }
    )

    df = filter_dataframe(TIME_BEGINNING, TIME_END, MAX_OC)
    samples_coordinates_array_path, data_array_path = separate_and_add_data()

    def flatten_paths(path_list):
        flattened = []
        for item in path_list:
            if isinstance(item, list):
                flattened.extend(flatten_paths(item))
            else:
                flattened.append(item)
        return flattened

    samples_coordinates_array_path = list(dict.fromkeys(flatten_paths(samples_coordinates_array_path)))
    data_array_path = list(dict.fromkeys(flatten_paths(data_array_path)))

    if args.use_validation:
        train_df, val_df = create_balanced_dataset(df, use_validation=args.use_validation)
    else:
        train_df, val_df = create_balanced_dataset(df, use_validation=args.use_validation)

    train_dataset = MultiRasterDataset(samples_coordinates_array_path, data_array_path, train_df)
    val_dataset = MultiRasterDataset(samples_coordinates_array_path, data_array_path, val_df) if val_df is not None else None

    train_loader = DataLoader(train_dataset, batch_size=256, shuffle=True)
    val_loader = DataLoader(val_dataset, batch_size=256, shuffle=False) if val_dataset is not None else None

    model = SmallCNN(input_channels=6)
    
    if accelerator.is_main_process:
        wandb.run.summary["model_parameters"] = model.count_parameters()
        wandb.run.summary["train_size"] = len(train_df)
        wandb.run.summary["val_size"] = len(val_df) if val_df is not None else 0
        print(f"Model parameters: {model.count_parameters()}")
        print(f"Training set size: {len(train_df)}")
        print(f"Validation set size: {len(val_df) if val_df is not None else 0}")

    model, val_outputs, val_targets, best_model_state, best_r_squared = train_model(
        args, model, train_loader, val_loader, 
        num_epochs=num_epochs, 
        accelerator=accelerator,
        loss_type=args.loss_type,
        target_transform=args.target_transform
    )

    if accelerator.is_main_process and best_model_state is not None:
        final_model_path = (f'simpletimecnn_model_MAX_OC_{MAX_OC}_TIME_BEGINNING_{TIME_BEGINNING}_'
                           f'TIME_END_{TIME_END}_LOSS_{args.loss_type}_TRANSFORM_{args.target_transform}_'
                           f'BEST_R2_{best_r_squared:.4f}.pth')
        torch.save(best_model_state, final_model_path)
        wandb.save(final_model_path)
        print(f"Best model saved with R²: {best_r_squared:.4f}")

    wandb.finish()
    # bst moel with none/l2

	import numpy as np
	import torch
	import torch.nn as nn
	import torch.optim as optim
	from dataloader.dataloader import MultiRasterDataset
	from dataloader.dataloaderMapping import MultiRasterDatasetMapping
	from dataloader.dataframe_loader import filter_dataframe, separate_and_add_data
	import pandas as pd
	from tqdm import tqdm
	from pathlib import Path
	import wandb
	from accelerate import Accelerator
	from config import (TIME_BEGINNING, TIME_END, INFERENCE_TIME, MAX_OC,
	seasons, years_padded, num_epochs,
	SamplesCoordinates_Yearly, MatrixCoordinates_1mil_Yearly,
	DataYearly, SamplesCoordinates_Seasonally,
	MatrixCoordinates_1mil_Seasonally, DataSeasonally,
	file_path_LUCAS_LFU_Lfl_00to23_Bavaria_OC)
	from torch.utils.data import Dataset, DataLoader
	from modelCNN import SmallCNN
	import argparse

	def composite_l1_chi2_loss(outputs, targets, sigma=3.0, alpha=0.5):
	errors = targets - outputs
	l1_loss = torch.mean(torch.abs(errors))
	squared_errors = errors ** 2
	chi2_unscaled = (1/4) * squared_errors * torch.exp(-squared_errors / (2 * sigma))
	chi2_unscaled_mean = torch.mean(chi2_unscaled)
	chi2_unscaled_mean = torch.clamp(chi2_unscaled_mean, min=1e-8)
	scale_factor = l1_loss / chi2_unscaled_mean
	chi2_scaled = scale_factor * chi2_unscaled_mean
	return alpha * l1_loss + (1 - alpha) * chi2_scaled

	def composite_l2_chi2_loss(outputs, targets, sigma=3.0, alpha=0.5):
	errors = targets - outputs
	l2_loss = torch.mean(errors ** 2)
	chi2_loss = torch.mean((errors 2) / (sigma 2))
	chi2_loss = torch.clamp(chi2_loss, min=1e-8)
	scale_factor = l2_loss / chi2_loss
	chi2_scaled = scale_factor * chi2_loss
	return alpha * l2_loss + (1 - alpha) * chi2_scaled

	def parse_args():
	parser = argparse.ArgumentParser(description='Train SimpleCNN model with customizable parameters')
	parser.add_argument('--lr', type=float, default=0.001, help='Learning rate')
	parser.add_argument('--loss_type', type=str, default='mse', choices=['composite_l1', 'l1', 'mse','composite_l2'], help='Type of loss function')
	parser.add_argument('--loss_alpha', type=float, default=0.5, help='Weight for L1 loss in composite loss (if used)')
	parser.add_argument('--target_transform', type=str, default='log', choices=['none', 'log', 'normalize'], help='Transformation to apply to targets')
	parser.add_argument('--use_validation', action='store_true', default=True, help='Whether to use validation set')
	return parser.parse_args()

	def create_balanced_dataset(df, use_validation=True, n_bins=128, min_ratio=3/4):
	bins = pd.qcut(df['OC'], q=n_bins, labels=False, duplicates='drop')
	df['bin'] = bins
	bin_counts = df['bin'].value_counts()
	max_samples = bin_counts.max()
	min_samples = max(int(max_samples * min_ratio), 5)
	training_dfs = []
	if use_validation:
	validation_indices = []
	for bin_idx in range(len(bin_counts)):
	bin_data = df[df['bin'] == bin_idx]
	if len(bin_data) >= 4:
	val_samples = bin_data.sample(n=min(13, len(bin_data)))
	validation_indices.extend(val_samples.index)
	train_samples = bin_data.drop(val_samples.index)
	if len(train_samples) > 0:
	if len(train_samples) < min_samples:
	resampled = train_samples.sample(n=min_samples, replace=True)
	training_dfs.append(resampled)
	else:
	training_dfs.append(train_samples)
	if not training_dfs or not validation_indices:
	raise ValueError("No training or validation data available after binning")
	training_df = pd.concat(training_dfs).drop('bin', axis=1)
	validation_df = df.loc[validation_indices].drop('bin', axis=1)
	print('Size of the training set: ', len(training_df))
	print('Size of the validation set: ', len(validation_df))
	return training_df, validation_df
	else:
	for bin_idx in range(len(bin_counts)):
	bin_data = df[df['bin'] == bin_idx]
	if len(bin_data) > 0:
	if len(bin_data) < min_samples:
	resampled = bin_data.sample(n=min_samples, replace=True)
	training_dfs.append(resampled)
	else:
	training_dfs.append(bin_data)
	if not training_dfs:
	raise ValueError("No training data available after binning")
	training_df = pd.concat(training_dfs).drop('bin', axis=1)
	return training_df, None # Return None for validation_df when no validation

	def train_model(args, model, train_loader, val_loader, num_epochs, accelerator, loss_type='L1', target_transform='none'):
	if loss_type == 'composite_l1':
	criterion = lambda outputs, targets: composite_l1_chi2_loss(outputs, targets, sigma=3.0, alpha=args.loss_alpha)
	elif loss_type == 'composite_l2':
	criterion = lambda outputs, targets: composite_l2_chi2_loss(outputs, targets, sigma=3.0, alpha=args.loss_alpha)
	elif loss_type == 'l1':
	criterion = nn.L1Loss()
	elif loss_type == 'mse':
	criterion = nn.MSELoss()
	else:
	raise ValueError(f"Unknown loss type: {loss_type}")

	optimizer = optim.Adam(model.parameters(), lr=args.lr)
	train_loader, model, optimizer = accelerator.prepare(
	train_loader, model, optimizer
	)
	if val_loader is not None:
	val_loader = accelerator.prepare(val_loader)

	if target_transform == 'normalize':
	all_targets = []
	for _, _, _, targets in train_loader:
	all_targets.append(targets)
	all_targets = torch.cat(all_targets)
	target_mean = all_targets.mean().item()
	target_std = all_targets.std().item()
	if accelerator.is_main_process:
	print(f"Target mean: {target_mean}, Target std: {target_std}")
	else:
	target_mean, target_std = 0.0, 1.0

	best_r_squared = -float('inf') if args.use_validation else 1.0
	best_model_state = None

	for epoch in range(num_epochs):
	model.train()
	running_loss = 0.0

	for batch_idx, (longitudes, latitudes, features, targets) in enumerate(tqdm(train_loader)):
	features = features.to(accelerator.device)
	targets = targets.to(accelerator.device).float()
	if target_transform == 'log':
	targets = torch.log(targets + 1e-10)
	elif target_transform == 'normalize':
	targets = (targets - target_mean) / (target_std + 1e-10)
	optimizer.zero_grad()
	outputs = model(features)
	loss = criterion(outputs, targets)
	accelerator.backward(loss)
	optimizer.step()
	running_loss += loss.item()

	if accelerator.is_main_process:
	wandb.log({
	'train_loss': loss.item(),
	'batch': batch_idx + 1 + epoch * len(train_loader),
	'epoch': epoch + 1
	})

	train_loss = running_loss / len(train_loader)

	if args.use_validation and val_loader is not None:
	model.eval()
	val_loss = 0.0
	val_outputs_list = []
	val_targets_list = []

	with torch.no_grad():
	for longitudes, latitudes, features, targets in val_loader:
	features = features.to(accelerator.device)
	targets = targets.to(accelerator.device).float()
	if target_transform == 'log':
	targets = torch.log(targets + 1e-10)
	elif target_transform == 'normalize':
	targets = (targets - target_mean) / (target_std + 1e-10)
	outputs = model(features)
	loss = criterion(outputs, targets)
	val_loss += loss.item()
	val_outputs_list.append(outputs.cpu())
	val_targets_list.append(targets.cpu())

	val_loss = val_loss / len(val_loader)

	# Concatenate outputs and targets from all batches
	val_outputs = torch.cat(val_outputs_list, dim=0).numpy()
	val_targets = torch.cat(val_targets_list, dim=0).numpy()

	# Gather data from all processes
	val_outputs_all = torch.from_numpy(val_outputs).to(accelerator.device)
	val_targets_all = torch.from_numpy(val_targets).to(accelerator.device)
	val_outputs_all = accelerator.gather(val_outputs_all).cpu().numpy()
	val_targets_all = accelerator.gather(val_targets_all).cpu().numpy()

	if accelerator.is_main_process:
	# Inverse transform to original scale
	if target_transform == 'log':
	original_val_outputs = np.exp(val_outputs_all)
	original_val_targets = np.exp(val_targets_all)
	elif target_transform == 'normalize':
	original_val_outputs = val_outputs_all * target_std + target_mean
	original_val_targets = val_targets_all * target_std + target_mean
	else:
	original_val_outputs = val_outputs_all
	original_val_targets = val_targets_all

	# Compute metrics on original scale
	if len(original_val_outputs) > 1 and np.std(original_val_outputs) > 1e-6 and np.std(original_val_targets) > 1e-6:
	correlation = np.corrcoef(original_val_outputs, original_val_targets)[0, 1]
	r_squared = correlation ** 2
	mse = np.mean((original_val_outputs - original_val_targets) ** 2)
	rmse = np.sqrt(mse)
	mae = np.mean(np.abs(original_val_outputs - original_val_targets))
	iqr = np.percentile(original_val_targets, 75) - np.percentile(original_val_targets, 25)
	rpiq = iqr / rmse if rmse > 0 else float('inf')
	else:
	correlation = 0.0
	r_squared = 0.0
	mse = float('nan')
	rmse = float('nan')
	mae = float('nan')
	rpiq = float('nan')

	# Update best model based on R²
	if r_squared > best_r_squared:
	best_r_squared = r_squared
	best_model_state = {k: v.cpu() for k, v in model.state_dict().items()}
	wandb.run.summary['best_r_squared'] = best_r_squared

	wandb.log({
	'epoch': epoch + 1,
	'train_loss_avg': train_loss,
	'val_loss': val_loss,
	'correlation': correlation,
	'r_squared': r_squared,
	'mse': mse,
	'rmse': rmse,
	'mae': mae,
	'rpiq': rpiq
	})

	accelerator.print(f'Epoch {epoch+1}:')
	accelerator.print(f'Training Loss: {train_loss:.4f}')
	accelerator.print(f'Validation Loss: {val_loss:.4f}')
	if accelerator.is_main_process:
	accelerator.print(f'RPIQ: {rpiq:.4f}\n')
	else:
	# No validation, update model state and set R² to 1.0
	best_r_squared = 1.0
	best_model_state = {k: v.cpu() for k, v in model.state_dict().items()}
	wandb.run.summary['best_r_squared'] = best_r_squared

	if accelerator.is_main_process:
	wandb.log({
	'epoch': epoch + 1,
	'train_loss_avg': train_loss,
	})

	accelerator.print(f'Epoch {epoch+1}:')
	accelerator.print(f'Training Loss: {train_loss:.4f}\n')

	return model, None, None, best_model_state, best_r_squared

	if __name__ == "__main__":
	args = parse_args()
	accelerator = Accelerator()

	wandb.init(
	project="socmapping-SimpleTimeCNN",
	config={
	"max_oc": MAX_OC,
	"time_beginning": TIME_BEGINNING,
	"time_end": TIME_END,
	"epochs": num_epochs,
	"batch_size": 256,
	"learning_rate": 0.001,
	"input_channels": 6,
	"loss_type": args.loss_type,
	"target_transform": args.target_transform,
	"use_validation": args.use_validation
	}
	)

	df = filter_dataframe(TIME_BEGINNING, TIME_END, MAX_OC)
	samples_coordinates_array_path, data_array_path = separate_and_add_data()

	def flatten_paths(path_list):
	flattened = []
	for item in path_list:
	if isinstance(item, list):
	flattened.extend(flatten_paths(item))
	else:
	flattened.append(item)
	return flattened

	samples_coordinates_array_path = list(dict.fromkeys(flatten_paths(samples_coordinates_array_path)))
	data_array_path = list(dict.fromkeys(flatten_paths(data_array_path)))

	if args.use_validation:
	train_df, val_df = create_balanced_dataset(df, use_validation=args.use_validation)
	else:
	train_df, val_df = create_balanced_dataset(df, use_validation=args.use_validation)

	train_dataset = MultiRasterDataset(samples_coordinates_array_path, data_array_path, train_df)
	val_dataset = MultiRasterDataset(samples_coordinates_array_path, data_array_path, val_df) if val_df is not None else None

	train_loader = DataLoader(train_dataset, batch_size=256, shuffle=True)
	val_loader = DataLoader(val_dataset, batch_size=256, shuffle=False) if val_dataset is not None else None

	model = SmallCNN(input_channels=6)

	if accelerator.is_main_process:
	wandb.run.summary["model_parameters"] = model.count_parameters()
	wandb.run.summary["train_size"] = len(train_df)
	wandb.run.summary["val_size"] = len(val_df) if val_df is not None else 0
	print(f"Model parameters: {model.count_parameters()}")
	print(f"Training set size: {len(train_df)}")
	print(f"Validation set size: {len(val_df) if val_df is not None else 0}")

	model, val_outputs, val_targets, best_model_state, best_r_squared = train_model(
	args, model, train_loader, val_loader,
	num_epochs=num_epochs,
	accelerator=accelerator,
	loss_type=args.loss_type,
	target_transform=args.target_transform
	)

	if accelerator.is_main_process and best_model_state is not None:
	final_model_path = (f'simpletimecnn_model_MAX_OC_{MAX_OC}_TIME_BEGINNING_{TIME_BEGINNING}_'
	f'TIME_END_{TIME_END}_LOSS_{args.loss_type}_TRANSFORM_{args.target_transform}_'
	f'BEST_R2_{best_r_squared:.4f}.pth')
	torch.save(best_model_state, final_model_path)
	wandb.save(final_model_path)
	print(f"Best model saved with R²: {best_r_squared:.4f}")

	wandb.finish()
	# bst moel with none/l2