modeva.models.MoNeuralTreeRegressor#

class modeva.models.MoNeuralTreeRegressor(name: str = None, estimator: MoGLMTreeBoostRegressor = None, val_ratio=0.2, verbose=False, device=None, random_state=0, feature_names=None, nn_temperature=0.0001, nn_lr=0.0001, nn_max_epochs=200, nn_n_epoch_no_change=10, nn_batch_size=200, reg_mono=0.1, mono_sample_size=1000, mono_increasing_list=(), mono_decreasing_list=(), **kwargs)#

A neural network-based regression model that combines GLM trees with monotonicity constraints.

This model first fits a depth-1 boosted GLMTree, converts it to an equivalent neural network, and then fine-tunes the network parameters. It supports monotonicity constraints and provides interpretability through feature effects analysis.

Parameters:

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

  • estimator (MoGLMTreeBoostRegressor, default=None) – Pre-fitted or unfitted GLMTree regressor for initialization. If None, creates a new instance.

  • feature_names (list of str, default=None) – Names of input features for interpretability.

  • val_ratio (float, default=0.2) – Proportion of data used for validation during training (0 to 1).

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

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

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

  • nn_temperature (float, default=0.0001) – Smoothing parameter for neural network activation.

  • nn_lr (float, default=0.001) – Learning rate for neural network optimization.

  • nn_max_epochs (int, default=200) – Maximum number of training epochs.

  • nn_batch_size (int, default=200) – Number of samples per training batch.

  • nn_n_epoch_no_change (int, default=10) – Early stopping patience - number of epochs without improvement.

  • reg_mono (float, default=0.1) – Strength of monotonicity regularization.

  • mono_sample_size (int, default=1000) – Number of random samples for monotonicity regularization.

  • mono_increasing_list (tuple of str, default=()) – Features that should have monotonically increasing relationships.

  • mono_decreasing_list (tuple of str, default=()) – Features that should have monotonically decreasing relationships.

  • **kwargs – Additional parameters passed to MoGLMTreeBoostRegressor.

net_#

The internal Pytorch network object.

Type:

object

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.:

get_params(deep=True)#

Get parameters for this estimator. :param deep: 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:

mapping of string to any

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