uqregressors.conformal.conformal_ens

This method employs normalized conformal prediction as described in Tibshirani, 2023. The difficulty measure, $\sigma$, used for normalization is taken to be the standard deviation of the predictions of all models in an ensemble, while the ensemble mean is returned as the mean prediction.

Normalized Conformal Ensemble

This module implements normalized conformal ensemble prediction in a split conformal context for regression on a one dimensional output

Key features are

• Customizable neural network architecture
• Customizable dropout to increase ensemble diversity
• Prediction intervals without distributional assumptions
• Customizable optimizer and loss function
• Optional Input/Output Normalization

ConformalEnsRegressor

Bases: BaseEstimator, RegressorMixin

Conformal Ensemble Regressor for uncertainty estimation in regression tasks.

This class trains an ensemble of MLP models, and applies normalized conformal prediction on a split calibration set to calibrate prediction intervals.

Parameters:

Name	Type	Description	Default
name	str	Name of the model.	'Conformal_Ens_Regressor'
n_estimators	int	Number of models to train.	5
hidden_sizes	list	sizes of the hidden layers for each quantile regressor.	[64, 64]
alpha	float	Miscoverage rate (1 - confidence level).	0.1
requires_grad	bool	Whether inputs should require gradient, determines output type.	False
dropout	float or None	Dropout rate for the neural network layers.	None
pred_with_dropout	bool	Whether dropout should be applied at test time, dropout must be non-Null	False
activation_str	str	String identifier of the activation function.	'ReLU'
cal_size	float	Proportion of training samples to use for calibration, between 0 and 1.	0.2
gamma	float	Stability constant added to difficulty score .	0
learning_rate	float	Learning rate for training.	0.001
epochs	int	Number of training epochs.	200
batch_size	int	Batch size for training.	32
optimizer_cls	type	Optimizer class.	Adam
optimizer_kwargs	dict	Keyword arguments for optimizer.	None
scheduler_cls	type or None	Learning rate scheduler class.	None
scheduler_kwargs	dict	Keyword arguments for scheduler.	None
loss_fn	callable or None	Loss function, defaults to quantile loss.	mse_loss
device	str	Device to use for training and inference.	'cpu'
use_wandb	bool	Whether to log training with Weights & Biases.	False
wandb_project	str or None	wandb project name.	None
wandb_run_name	str or None	wandb run name.	None
n_jobs	float	Number of parallel jobs for training.	1
random_seed	int or None	Random seed for reproducibility.	None
scale_data	bool	Whether to normalize input/output data.	True
input_scaler	TorchStandardScaler	Scaler for input features.	None
output_scaler	TorchStandardScaler	Scaler for target outputs.	None
tuning_loggers	list	Optional list of loggers for tuning.	[]

Attributes:

Name	Type	Description
models	list[QuantNN]	A list of the models in the ensemble.
residuals	Tensor	The combined residuals on the calibration set.
conformal_width	Tensor	The width needed to conformalize the quantile regressor, q.
_loggers	list[Logger]	Training loggers for each ensemble member.

Source code in uqregressors\conformal\conformal_ens.py

class ConformalEnsRegressor(BaseEstimator, RegressorMixin): 
    """
    Conformal Ensemble Regressor for uncertainty estimation in regression tasks. 

    This class trains an ensemble of MLP models, and applies normalized conformal prediction on a split
    calibration set to calibrate prediction intervals. 

    Args: 
        name (str): Name of the model. 
        n_estimators (int): Number of models to train. 
        hidden_sizes (list): sizes of the hidden layers for each quantile regressor. 
        alpha (float): Miscoverage rate (1 - confidence level). 
        requires_grad (bool): Whether inputs should require gradient, determines output type.
        dropout (float or None): Dropout rate for the neural network layers. 
        pred_with_dropout (bool): Whether dropout should be applied at test time, dropout must be non-Null
        activation_str (str): String identifier of the activation function. 
        cal_size (float): Proportion of training samples to use for calibration, between 0 and 1.  
        gamma (float): Stability constant added to difficulty score . 
        learning_rate (float): Learning rate for training.
        epochs (int): Number of training epochs.
        batch_size (int): Batch size for training.
        optimizer_cls (type): Optimizer class.
        optimizer_kwargs (dict): Keyword arguments for optimizer.
        scheduler_cls (type or None): Learning rate scheduler class.
        scheduler_kwargs (dict): Keyword arguments for scheduler.
        loss_fn (callable or None): Loss function, defaults to quantile loss.
        device (str): Device to use for training and inference.
        use_wandb (bool): Whether to log training with Weights & Biases.
        wandb_project (str or None): wandb project name.
        wandb_run_name (str or None): wandb run name.
        n_jobs (float): Number of parallel jobs for training.
        random_seed (int or None): Random seed for reproducibility.
        scale_data (bool): Whether to normalize input/output data.
        input_scaler (TorchStandardScaler): Scaler for input features.
        output_scaler (TorchStandardScaler): Scaler for target outputs.
        tuning_loggers (list): Optional list of loggers for tuning.

    Attributes: 
        models (list[QuantNN]): A list of the models in the ensemble.
        residuals (Tensor): The combined residuals on the calibration set. 
        conformal_width (Tensor): The width needed to conformalize the quantile regressor, q. 
        _loggers (list[Logger]): Training loggers for each ensemble member. 
    """
    def __init__(self, 
                 name="Conformal_Ens_Regressor",
                 n_estimators=5, 
                 hidden_sizes=[64, 64], 
                 alpha=0.1, 
                 requires_grad=False,
                 dropout=None,
                 pred_with_dropout=False,
                 activation_str="ReLU",
                 cal_size = 0.2, 
                 gamma = 0,
                 learning_rate=1e-3,
                 epochs=200,
                 batch_size=32,
                 optimizer_cls=torch.optim.Adam,
                 optimizer_kwargs=None,
                 scheduler_cls=None,
                 scheduler_kwargs=None,
                 loss_fn=nn.functional.mse_loss,
                 device="cpu", 
                 use_wandb=False, 
                 wandb_project=None, 
                 wandb_run_name=None, 
                 n_jobs=1, 
                 random_seed=None, 
                 scale_data=True, 
                 input_scaler=None,
                 output_scaler=None,
                 tuning_loggers = []
    ): 
        self.name = name
        self.n_estimators = n_estimators
        self.hidden_sizes = hidden_sizes
        self.alpha = alpha
        self.requires_grad = requires_grad
        self.dropout = dropout
        self.pred_with_dropout = pred_with_dropout
        self.activation_str = activation_str
        self.cal_size = cal_size
        self.gamma = gamma
        self.learning_rate = learning_rate
        self.epochs = epochs
        self.batch_size = batch_size
        self.optimizer_cls = optimizer_cls
        self.optimizer_kwargs = optimizer_kwargs or {}
        self.scheduler_cls = scheduler_cls
        self.scheduler_kwargs = scheduler_kwargs or {}
        self.loss_fn = loss_fn
        self.device = device

        self.use_wandb = use_wandb
        self.wandb_project = wandb_project
        self.wandb_run_name = wandb_run_name

        self.n_jobs = n_jobs
        self.random_seed = random_seed

        self.scale_data = scale_data 
        self.input_scaler = input_scaler or TorchStandardScaler() 
        self.output_scaler = output_scaler or TorchStandardScaler()

        self.input_dim = None
        self.conformity_scores = None
        self.conformity_score = None
        self.models = []
        self.residuals = []

        self._loggers = []
        self.training_logs = None
        self.tuning_loggers = tuning_loggers
        self.tuning_logs = None

    def _train_single_model(self, X_tensor, y_tensor, input_dim, train_idx, cal_idx, model_idx): 
        if self.random_seed is not None: 
            torch.manual_seed(self.random_seed + model_idx)
            np.random.seed(self.random_seed + model_idx)

        activation = get_activation(self.activation_str)
        model = MLP(input_dim, self.hidden_sizes, self.dropout, activation).to(self.device)

        optimizer = self.optimizer_cls(
            model.parameters(), lr=self.learning_rate, **self.optimizer_kwargs
        )
        scheduler = None 
        if self.scheduler_cls: 
            scheduler = self.scheduler_cls(optimizer, **self.scheduler_kwargs)

        dataset = TensorDataset(X_tensor[train_idx], y_tensor[train_idx])
        dataloader = DataLoader(dataset, batch_size=self.batch_size, shuffle=True)

        logger = Logger(
            use_wandb=self.use_wandb,
            project_name=self.wandb_project,
            run_name=self.wandb_run_name + str(model_idx) if self.wandb_run_name is not None else None,
            config={"n_estimators": self.n_estimators, "learning_rate": self.learning_rate, "epochs": self.epochs},
            name=f"Estimator-{model_idx}"
        )

        for epoch in range(self.epochs): 
            model.train()
            epoch_loss = 0.0 
            for xb, yb in dataloader: 
                optimizer.zero_grad() 
                preds = model(xb)
                loss = self.loss_fn(preds, yb)
                loss.backward() 
                optimizer.step() 
                epoch_loss += loss.item()

            if epoch % (self.epochs / 20) == 0:
                logger.log({"epoch": epoch, "train_loss": epoch_loss})

            if scheduler: 
                scheduler.step()

        if self.pred_with_dropout: 
            model.train()
        else: 
            model.eval()

        test_X = X_tensor[cal_idx]
        cal_preds = model(test_X)

        logger.finish()
        return model, cal_preds, logger

    def fit(self, X, y): 
        """
        Fit the ensemble on training data.

        Args:
            X (array-like or torch.Tensor): Training inputs.
            y (array-like or torch.Tensor): Training targets.

        Returns:
            (ConformalEnsRegressor): Fitted estimator.
        """
        X_tensor, y_tensor = validate_and_prepare_inputs(X, y, device=self.device)
        input_dim = X_tensor.shape[1]
        self.input_dim = input_dim

        if self.scale_data: 
            X_tensor = self.input_scaler.fit_transform(X_tensor)
            y_tensor = self.output_scaler.fit_transform(y_tensor)

        train_idx, cal_idx = self._train_test_split(X_tensor, 0.2, self.random_seed)
        results = Parallel(n_jobs=self.n_jobs)(
            delayed(self._train_single_model)(X_tensor, y_tensor, input_dim, train_idx, cal_idx, i)
            for i in range(self.n_estimators)
        )

        self.models = [result[0] for result in results]
        cal_preds = torch.stack([result[1] for result in results]).squeeze()
        self._loggers = [result[2] for result in results]

        mean_cal_preds = torch.mean(cal_preds, dim=0).squeeze()
        var_cal_preds = torch.var(cal_preds, dim=0).squeeze()
        std_cal_preds = var_cal_preds.sqrt()
        self.residuals = torch.abs(mean_cal_preds - y_tensor[cal_idx].squeeze())

        self.conformity_scores = self.residuals / (std_cal_preds + self.gamma)

        return self 

    def predict(self, X): 
        """
        Predicts the target values with uncertainty estimates.

        Args:
            X (np.ndarray): Feature matrix of shape (n_samples, n_features).

        Returns:
            (Union[Tuple[np.ndarray, np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]): Tuple containing:
                mean predictions,
                lower bound of the prediction interval,
                upper bound of the prediction interval.

        !!! note
            If `requires_grad` is False, all returned arrays are NumPy arrays.
            Otherwise, they are PyTorch tensors with gradients.
        """        
        X_tensor = validate_X_input(X, input_dim=self.input_dim, device=self.device, requires_grad=self.requires_grad)
        if self.scale_data: 
            X_tensor = self.input_scaler.transform(X_tensor)
        n = len(self.residuals)
        q = int((1 - self.alpha) * (n+1)) 
        q = min(q, n-1) 

        res_quantile = n-q
        self.conformity_score = torch.topk(self.conformity_scores, res_quantile).values[-1]

        preds = []

        with torch.no_grad(): 
            for model in self.models: 
                if self.pred_with_dropout: 
                    model.train()
                else: 
                    model.eval()
                pred = model(X_tensor)
                preds.append(pred)

        preds = torch.stack(preds)[:, :, 0]
        mean = torch.mean(preds, dim=0)
        variances = torch.var(preds, dim=0)
        stds = variances.sqrt()
        conformal_widths = self.conformity_score * (stds + self.gamma) 
        lower = mean - conformal_widths 
        upper = mean + conformal_widths 

        if self.scale_data: 
            mean = self.output_scaler.inverse_transform(mean.view(-1, 1)).squeeze()
            lower = self.output_scaler.inverse_transform(lower.view(-1, 1)).squeeze()
            upper = self.output_scaler.inverse_transform(upper.view(-1, 1)).squeeze()

        if not self.requires_grad: 
            return mean.detach().cpu().numpy(), lower.detach().cpu().numpy(), upper.detach().cpu().numpy()

        else: 
            return mean, lower, upper

    def save(self, path):
        """
        Save the trained model and associated configuration to disk.

        Args:
            path (str or Path): Directory to save model files.
        """
        path = Path(path)
        path.mkdir(parents=True, exist_ok=True)

        # Save config (exclude non-serializable or large objects)
        config = {
            k: v for k, v in self.__dict__.items()
            if k not in ["models", "residuals", "conformity_score", "conformity_scores", "optimizer_cls", "optimizer_kwargs", "scheduler_cls", "scheduler_kwargs", 
                         "input_scaler", "output_scaler", "_loggers", "training_logs", "tuning_loggers", "tuning_logs"]
            and not callable(v)
            and not isinstance(v, (torch.nn.Module,))
        }

        config["optimizer"] = self.optimizer_cls.__class__.__name__ if self.optimizer_cls is not None else None
        config["scheduler"] = self.scheduler_cls.__class__.__name__ if self.scheduler_cls is not None else None
        config["input_scaler"] = self.input_scaler.__class__.__name__ if self.input_scaler is not None else None 
        config["output_scaler"] = self.output_scaler.__class__.__name__ if self.output_scaler is not None else None

        with open(path / "config.json", "w") as f:
            json.dump(config, f, indent=4)

        # Save model weights
        for i, model in enumerate(self.models):
            torch.save(model.state_dict(), path / f"model_{i}.pt")

        # Save residuals and conformity score
        torch.save({
            "residuals": self.residuals.cpu(),
            "conformity_score": self.conformity_score, 
            "conformity_scores": self.conformity_scores
        }, path / "extras.pt")

        with open(path / "extras.pkl", 'wb') as f: 
            pickle.dump([self.optimizer_cls, 
                         self.optimizer_kwargs, self.scheduler_cls, self.scheduler_kwargs, self.input_scaler, self.output_scaler], f)

        for i, logger in enumerate(getattr(self, "_loggers", [])):
            logger.save_to_file(path, idx=i, name="estimator")

        for i, logger in enumerate(getattr(self, "tuning_loggers", [])): 
            logger.save_to_file(path, name="tuning", idx=i)


    @classmethod
    def load(cls, path, device="cpu", load_logs=False):
        """
        Load a saved KFoldCQR model from disk.

        Args:
            path (str or Path): Directory containing saved model files.
            device (str): Device to load the model on ("cpu" or "cuda").
            load_logs (bool): Whether to also load training logs.

        Returns:
            (ConformalEnsRegressor): The loaded model instance.
        """
        path = Path(path)

        # Load config
        with open(path / "config.json", "r") as f:
            config = json.load(f)
        config["device"] = device

        config.pop("optimizer", None)
        config.pop("scheduler", None)
        config.pop("input_scaler", None)
        config.pop("output_scaler", None)

        input_dim = config.pop("input_dim", None)
        model = cls(**config)

        with open(path / "extras.pkl", 'rb') as f: 
            optimizer_cls, optimizer_kwargs, scheduler_cls, scheduler_kwargs, input_scaler, output_scaler = pickle.load(f)

        # Recreate models
        model.input_dim = input_dim
        activation = get_activation(config["activation_str"])
        model.models = []
        for i in range(config["n_estimators"]):
            m = MLP(model.input_dim, config["hidden_sizes"], config["dropout"], activation).to(device)
            m.load_state_dict(torch.load(path / f"model_{i}.pt", map_location=device))
            model.models.append(m)

        # Load extras
        extras_path = path / "extras.pt"
        if extras_path.exists():
            extras = torch.load(extras_path, map_location=device, weights_only=False)
            model.residuals = extras.get("residuals", None)
            model.conformity_score = extras.get("conformity_score", None)
            model.conformity_scores = extras.get("conformity_scores", None)
        else:
            model.residuals = None
            model.conformity_score = None
            model.conformity_scores = None

        model.optimizer_cls = optimizer_cls 
        model.optimizer_kwargs = optimizer_kwargs 
        model.scheduler_cls = scheduler_cls 
        model.scheduler_kwargs = scheduler_kwargs
        model.input_scaler = input_scaler 
        model.output_scaler = output_scaler

        if load_logs: 
            logs_path = path / "logs"
            training_logs = [] 
            tuning_logs = []
            if logs_path.exists() and logs_path.is_dir(): 
                estimator_log_files = sorted(logs_path.glob("estimator_*.log"))
                for log_file in estimator_log_files:
                    with open(log_file, "r", encoding="utf-8") as f:
                        training_logs.append(f.read())

                tuning_log_files = sorted(logs_path.glob("tuning_*.log"))
                for log_file in tuning_log_files: 
                    with open(log_file, "r", encoding="utf-8") as f: 
                        tuning_logs.append(f.read())

            model.training_logs = training_logs
            model.tuning_logs = tuning_logs

        return model

    def _train_test_split(self, X, cal_size, seed=None):
        """
        For internal use in calibration splitting only, 
        see uqregressors/utils/torch_sklearn_utils for a global version
        """
        if seed is not None: 
            torch.manual_seed(seed)

        n = len(X)
        n_cal = int(np.ceil(cal_size * n))
        all_idx = np.arange(n)
        cal_idx = np.random.randint(n, size=n_cal)
        mask = np.ones(n, dtype=bool)
        mask[cal_idx] = False 
        train_idx = all_idx[mask] 
        return train_idx, cal_idx

fit(X, y)

Fit the ensemble on training data.

Parameters:

Name	Type	Description	Default
X	array - like or Tensor	Training inputs.	required
y	array - like or Tensor	Training targets.	required

Returns:

Type	Description
ConformalEnsRegressor	Fitted estimator.

Source code in uqregressors\conformal\conformal_ens.py

def fit(self, X, y): 
    """
    Fit the ensemble on training data.

    Args:
        X (array-like or torch.Tensor): Training inputs.
        y (array-like or torch.Tensor): Training targets.

    Returns:
        (ConformalEnsRegressor): Fitted estimator.
    """
    X_tensor, y_tensor = validate_and_prepare_inputs(X, y, device=self.device)
    input_dim = X_tensor.shape[1]
    self.input_dim = input_dim

    if self.scale_data: 
        X_tensor = self.input_scaler.fit_transform(X_tensor)
        y_tensor = self.output_scaler.fit_transform(y_tensor)

    train_idx, cal_idx = self._train_test_split(X_tensor, 0.2, self.random_seed)
    results = Parallel(n_jobs=self.n_jobs)(
        delayed(self._train_single_model)(X_tensor, y_tensor, input_dim, train_idx, cal_idx, i)
        for i in range(self.n_estimators)
    )

    self.models = [result[0] for result in results]
    cal_preds = torch.stack([result[1] for result in results]).squeeze()
    self._loggers = [result[2] for result in results]

    mean_cal_preds = torch.mean(cal_preds, dim=0).squeeze()
    var_cal_preds = torch.var(cal_preds, dim=0).squeeze()
    std_cal_preds = var_cal_preds.sqrt()
    self.residuals = torch.abs(mean_cal_preds - y_tensor[cal_idx].squeeze())

    self.conformity_scores = self.residuals / (std_cal_preds + self.gamma)

    return self 

load(path, device='cpu', load_logs=False) classmethod

Load a saved KFoldCQR model from disk.

Parameters:

Name	Type	Description	Default
path	str or Path	Directory containing saved model files.	required
device	str	Device to load the model on ("cpu" or "cuda").	'cpu'
load_logs	bool	Whether to also load training logs.	False

Returns:

Type	Description
ConformalEnsRegressor	The loaded model instance.

Source code in uqregressors\conformal\conformal_ens.py

@classmethod
def load(cls, path, device="cpu", load_logs=False):
    """
    Load a saved KFoldCQR model from disk.

    Args:
        path (str or Path): Directory containing saved model files.
        device (str): Device to load the model on ("cpu" or "cuda").
        load_logs (bool): Whether to also load training logs.

    Returns:
        (ConformalEnsRegressor): The loaded model instance.
    """
    path = Path(path)

    # Load config
    with open(path / "config.json", "r") as f:
        config = json.load(f)
    config["device"] = device

    config.pop("optimizer", None)
    config.pop("scheduler", None)
    config.pop("input_scaler", None)
    config.pop("output_scaler", None)

    input_dim = config.pop("input_dim", None)
    model = cls(**config)

    with open(path / "extras.pkl", 'rb') as f: 
        optimizer_cls, optimizer_kwargs, scheduler_cls, scheduler_kwargs, input_scaler, output_scaler = pickle.load(f)

    # Recreate models
    model.input_dim = input_dim
    activation = get_activation(config["activation_str"])
    model.models = []
    for i in range(config["n_estimators"]):
        m = MLP(model.input_dim, config["hidden_sizes"], config["dropout"], activation).to(device)
        m.load_state_dict(torch.load(path / f"model_{i}.pt", map_location=device))
        model.models.append(m)

    # Load extras
    extras_path = path / "extras.pt"
    if extras_path.exists():
        extras = torch.load(extras_path, map_location=device, weights_only=False)
        model.residuals = extras.get("residuals", None)
        model.conformity_score = extras.get("conformity_score", None)
        model.conformity_scores = extras.get("conformity_scores", None)
    else:
        model.residuals = None
        model.conformity_score = None
        model.conformity_scores = None

    model.optimizer_cls = optimizer_cls 
    model.optimizer_kwargs = optimizer_kwargs 
    model.scheduler_cls = scheduler_cls 
    model.scheduler_kwargs = scheduler_kwargs
    model.input_scaler = input_scaler 
    model.output_scaler = output_scaler

    if load_logs: 
        logs_path = path / "logs"
        training_logs = [] 
        tuning_logs = []
        if logs_path.exists() and logs_path.is_dir(): 
            estimator_log_files = sorted(logs_path.glob("estimator_*.log"))
            for log_file in estimator_log_files:
                with open(log_file, "r", encoding="utf-8") as f:
                    training_logs.append(f.read())

            tuning_log_files = sorted(logs_path.glob("tuning_*.log"))
            for log_file in tuning_log_files: 
                with open(log_file, "r", encoding="utf-8") as f: 
                    tuning_logs.append(f.read())

        model.training_logs = training_logs
        model.tuning_logs = tuning_logs

    return model

predict(X)

Predicts the target values with uncertainty estimates.

Parameters:

Name	Type	Description	Default
X	ndarray	Feature matrix of shape (n_samples, n_features).	required

Returns:

Type	Description
Union[Tuple[ndarray, ndarray, ndarray], Tuple[Tensor, Tensor, Tensor]]	Tuple containing: mean predictions, lower bound of the prediction interval, upper bound of the prediction interval.

Note

If requires_grad is False, all returned arrays are NumPy arrays. Otherwise, they are PyTorch tensors with gradients.

Source code in uqregressors\conformal\conformal_ens.py

def predict(self, X): 
    """
    Predicts the target values with uncertainty estimates.

    Args:
        X (np.ndarray): Feature matrix of shape (n_samples, n_features).

    Returns:
        (Union[Tuple[np.ndarray, np.ndarray, np.ndarray], Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]): Tuple containing:
            mean predictions,
            lower bound of the prediction interval,
            upper bound of the prediction interval.

    !!! note
        If `requires_grad` is False, all returned arrays are NumPy arrays.
        Otherwise, they are PyTorch tensors with gradients.
    """        
    X_tensor = validate_X_input(X, input_dim=self.input_dim, device=self.device, requires_grad=self.requires_grad)
    if self.scale_data: 
        X_tensor = self.input_scaler.transform(X_tensor)
    n = len(self.residuals)
    q = int((1 - self.alpha) * (n+1)) 
    q = min(q, n-1) 

    res_quantile = n-q
    self.conformity_score = torch.topk(self.conformity_scores, res_quantile).values[-1]

    preds = []

    with torch.no_grad(): 
        for model in self.models: 
            if self.pred_with_dropout: 
                model.train()
            else: 
                model.eval()
            pred = model(X_tensor)
            preds.append(pred)

    preds = torch.stack(preds)[:, :, 0]
    mean = torch.mean(preds, dim=0)
    variances = torch.var(preds, dim=0)
    stds = variances.sqrt()
    conformal_widths = self.conformity_score * (stds + self.gamma) 
    lower = mean - conformal_widths 
    upper = mean + conformal_widths 

    if self.scale_data: 
        mean = self.output_scaler.inverse_transform(mean.view(-1, 1)).squeeze()
        lower = self.output_scaler.inverse_transform(lower.view(-1, 1)).squeeze()
        upper = self.output_scaler.inverse_transform(upper.view(-1, 1)).squeeze()

    if not self.requires_grad: 
        return mean.detach().cpu().numpy(), lower.detach().cpu().numpy(), upper.detach().cpu().numpy()

    else: 
        return mean, lower, upper

save(path)

Save the trained model and associated configuration to disk.

Parameters:

Name	Type	Description	Default
path	str or Path	Directory to save model files.	required

Source code in uqregressors\conformal\conformal_ens.py

def save(self, path):
    """
    Save the trained model and associated configuration to disk.

    Args:
        path (str or Path): Directory to save model files.
    """
    path = Path(path)
    path.mkdir(parents=True, exist_ok=True)

    # Save config (exclude non-serializable or large objects)
    config = {
        k: v for k, v in self.__dict__.items()
        if k not in ["models", "residuals", "conformity_score", "conformity_scores", "optimizer_cls", "optimizer_kwargs", "scheduler_cls", "scheduler_kwargs", 
                     "input_scaler", "output_scaler", "_loggers", "training_logs", "tuning_loggers", "tuning_logs"]
        and not callable(v)
        and not isinstance(v, (torch.nn.Module,))
    }

    config["optimizer"] = self.optimizer_cls.__class__.__name__ if self.optimizer_cls is not None else None
    config["scheduler"] = self.scheduler_cls.__class__.__name__ if self.scheduler_cls is not None else None
    config["input_scaler"] = self.input_scaler.__class__.__name__ if self.input_scaler is not None else None 
    config["output_scaler"] = self.output_scaler.__class__.__name__ if self.output_scaler is not None else None

    with open(path / "config.json", "w") as f:
        json.dump(config, f, indent=4)

    # Save model weights
    for i, model in enumerate(self.models):
        torch.save(model.state_dict(), path / f"model_{i}.pt")

    # Save residuals and conformity score
    torch.save({
        "residuals": self.residuals.cpu(),
        "conformity_score": self.conformity_score, 
        "conformity_scores": self.conformity_scores
    }, path / "extras.pt")

    with open(path / "extras.pkl", 'wb') as f: 
        pickle.dump([self.optimizer_cls, 
                     self.optimizer_kwargs, self.scheduler_cls, self.scheduler_kwargs, self.input_scaler, self.output_scaler], f)

    for i, logger in enumerate(getattr(self, "_loggers", [])):
        logger.save_to_file(path, idx=i, name="estimator")

    for i, logger in enumerate(getattr(self, "tuning_loggers", [])): 
        logger.save_to_file(path, name="tuning", idx=i)

MLP

Bases: Module

A simple feedforward neural network with dropout for regression.

This MLP supports customizable hidden layer sizes, activation functions, and dropout. It outputs a single scalar per input — the predictive mean.

Parameters:

Name	Type	Description	Default
input_dim	int	Number of input features.	required
hidden_sizes	list of int	Sizes of the hidden layers.	required
dropout	float	Dropout rate (applied after each activation).	required
activation	callable	Activation function (e.g., nn.ReLU).	required

Source code in uqregressors\conformal\conformal_ens.py

class MLP(nn.Module): 
    """
    A simple feedforward neural network with dropout for regression.

    This MLP supports customizable hidden layer sizes, activation functions,
    and dropout. It outputs a single scalar per input — the predictive mean.

    Args:
        input_dim (int): Number of input features.
        hidden_sizes (list of int): Sizes of the hidden layers.
        dropout (float): Dropout rate (applied after each activation).
        activation (callable): Activation function (e.g., nn.ReLU).
    """
    def __init__(self, input_dim, hidden_sizes, dropout, activation): 
        super().__init__()
        layers = []
        for h in hidden_sizes: 
            layers.append(nn.Linear(input_dim, h))
            layers.append(activation())
            if dropout is not None: 
                layers.append(nn.Dropout(dropout))
            input_dim=h 
        output_layer = nn.Linear(hidden_sizes[-1], 1)
        layers.append(output_layer)
        self.model = nn.Sequential(*layers)

    def forward(self, x): 
        return self.model(x)