modeva.models.MoReLUDNNRegressor#

class modeva.models.MoReLUDNNRegressor(name: str = None, hidden_layer_sizes=(40, 40), max_epochs=1000, learning_rate=0.001, batch_size=500, l1_reg=1e-05, val_ratio=0.2, n_epoch_no_change=20, device=None, n_jobs=10, verbose=False, random_state=0)#

A deep neural network regressor using ReLU activation functions.

This model implements a multi-layer neural network for regression tasks, using ReLU activation functions and supporting early stopping, L1 regularization, and batch training.

Parameters:

  • name (str, default=None) – Optional identifier for the model instance.

  • hidden_layer_sizes (tuple of int, default=(40, 40)) – Architecture of the neural network specified as a tuple of integers, where each integer represents the number of neurons in a hidden layer.

  • max_epochs (int, default=1000) – Maximum number of complete passes through the training dataset.

  • learning_rate (float, default=0.001) – Step size used for gradient updates during optimization.

  • batch_size (int, default=500) – Number of training samples used in each gradient update.

  • l1_reg (float, default=1e-5) – Strength of L1 regularization applied to model weights.

  • val_ratio (float, default=0.2) – Proportion of training data to use for validation in early stopping.

  • n_epoch_no_change (int, default=20) – Number of epochs with no improvement after which training will be stopped.

  • device (string, default=None) – Computing device to use for training (‘cpu’, ‘cuda’, etc.).

  • n_jobs (int, default=10) – Number of parallel processes for computation (-1 for using all available cores).

  • verbose (bool, default=False) – If True, prints training progress and statistics.

  • random_state (int, default=0) – Seed for reproducible random number generation.

net_#

Trained neural network model.

Type:

torch.nn.Module

train_epoch_loss_#

History of training loss values for each epoch.

Type:

list of float

validation_epoch_loss_#

History of validation loss values for each epoch.

Type:

list of float

calibrate_interval(X, y, alpha=0.1, max_depth: int = 5)#

Fit a conformal prediction model to the given data.

This method computes the model’s prediction interval calibrated to the given data.

If the model is a regressor, splits the data with 50% for fitting lower (alpha / 5) and upper (1 - alpha / 2) gradient boosting trees-based quantile regression to the model’s residual; and 50% for calibration.

If the model is a binary classifiers, it computes the calibration quantile based on predicted probabilities for the positive class.

Parameters:

  • X (X : np.ndarray of shape (n_samples, n_features)) – Feature matrix for prediction.

  • y (array-like of shape (n_samples, )) – Target values.

  • alpha (float, default=0.1) – Expected miscoverage for the conformal prediction.

  • max_depth (int, default=5) – Maximum depth of the gradient boosting trees for regression tasks. Only used when task_type is REGRESSION.

Raises:

ValueError – If the model is neither a regressor nor a classifier.:

fit(X, y, sample_weight=None)#

Fit ReLuDNN model.

Parameters:

  • X (np.ndarray of shape (n_samples, n_features)) – Data features.

  • y (np.ndarray of shape (n_samples, )) – Target response.

  • sample_weight (np.ndarray of shape (n_samples, ), default=None) – Sample weight.

Returns:

self – Fitted Estimator.

Return type:

object

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

load(file_name: str)#

Load the model into memory from file system.

Parameters:

file_name (str) – The path and name of the file.

Return type:

estimator object

predict(X)#

Model predictions, calling the child class’s ‘_predict’ method.

Parameters:

X (np.ndarray of shape (n_samples, n_features)) – Feature matrix for prediction.

Returns:

np.ndarray

Return type:

The (calibrated) final prediction

predict_interval(X)#

Predict the prediction interval for the given data based on the conformal prediction model.

It splits the data with 50% for fitting lower (alpha / 5) and upper (1 - alpha / 2) gradient boosting trees-based quantile regression to the model’s residual; and 50% for calibration.

Parameters:

X (np.ndarray of shape (n_samples, n_features)) – Feature matrix for prediction.

Returns:

np.ndarray – in the format [n_samples, 2] for regressors or a flattened array for classifiers.

Return type:

The lower and upper bounds of the prediction intervals for each sample

Raises:

ValueError – If fit_conformal has not been called to fit the conformal prediction model: before calling this method.

save(file_name: str)#

Save the model into file system.

Parameters:

file_name (str) – The path and name of the file.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance