modeva.models.MoGLMTreeClassifier#

class modeva.models.MoGLMTreeClassifier(name: str = None, max_depth=3, min_samples_leaf=50, min_impurity_decrease=0, split_custom=None, n_screen_grid=1, n_feature_search=10, n_split_grid=20, clip_predict=False, reg_lambda=0.1, random_state=0)#

A tree-based model that fits logistic regression models in the leaves for binary classification.

This model recursively partitions the feature space and fits logistic regression models in each leaf node. It combines the interpretability of decision trees with the flexibility of logistic regression.

Parameters:

name (str, default=None) – Identifier name for the model instance.
max_depth (int, default=3) – Maximum depth of the tree. Controls model complexity.
min_samples_leaf (int, default=50) – Minimum number of samples required in a leaf node.
min_impurity_decrease (float, default=0) – Minimum required decrease in impurity to split a node.
split_custom (dict, default=None) – Dictionary mapping feature indices to custom split points.
n_screen_grid (int, default=1) – Number of grid points used in initial feature screening.
n_feature_search (int, default=10) – Number of top features to consider after screening.
n_split_grid (int, default=20) – Number of grid points to evaluate for splitting.
reg_lambda (float, default=0.1) – L1 regularization strength for leaf models.
clip_predict (bool, default=False) – Whether to clip predictions to training data range.
random_state (int, default=0) – Random seed for reproducibility.

tree_#

The fitted tree structure containing nodes and their parameters.

Type:: dict

leaf_estimators_#

Dictionary mapping leaf node IDs to their fitted logistic regression models.

Type:: dict

calibrate_interval(X, y, alpha=0.1)#

Fit a conformal prediction model to the given data.

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

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.

Raises:

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

calibrate_proba(X, y, sample_weight=None, method='sigmoid')#

Fit the calibration method on the model’s predictions.

Parameters:

X (np.ndarray of shape (n_samples, n_features)) – Feature matrix for prediction.
y (np.ndarray of shape (n_samples, )) – Ground truth labels.
sample_weight (array-like, shape (n_samples,), default=None) – Sample weights.
method ({'sigmoid', 'isotonic'}, default='sigmoid') –
The calibration method.
- ’sigmoid’: Platt’s method, i.e., fit a logistic regression on predicted probabilities and y
- ’isotonic’: Fit an isotonic regression on predicted probabilities and y.

Returns:

self

Return type:

Calibrated estimator

decision_function(X, calibration: bool = True)#

Computes the decision function for the given input data.

Parameters:

X (np.ndarray of shape (n_samples, n_features)) – Feature matrix for prediction.
calibration (bool, default=True) – If True, will use calibrated probability if calibration is done. Otherwise, will use raw probability.

Returns:

logit_prediction – Array of (calibrated) logit predictions.

Return type:

array, shape (n_samples,) or (n_samples, n_classes)

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, calibration: bool = True)#

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

Parameters:

X (np.ndarray of shape (n_samples, n_features)) – Feature matrix for prediction.
calibration (bool, default=True) – If True, will use calibrated probability if calibration is done. Otherwise, will use raw probability.

Returns:

np.ndarray

Return type:

The (calibrated) final prediction

predict_interval(X)#

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

This method computes the model prediction interval (regression) or prediction sets (classification) using conformal prediction.

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.

predict_proba(X, calibration: bool = True)#

Predict (calibrated) probabilities for X.

Parameters:

X (np.ndarray of shape (n_samples, n_features)) – Feature matrix for prediction.
calibration (bool, default=True) – If True, will return calibrated probability if calibration is done. Otherwise, will return raw probability.

Returns:

np.ndarray

Return type:

The (calibrated) predicted probabilities

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