lore_sa.bbox.AbstractBBox

class lore_sa.bbox.AbstractBBox(classifier)

Abstract base class for wrapping black box models.

This class provides a unified interface for different types of black box models (e.g., scikit-learn, Keras, PyTorch) to be explained by LORE. Any black box model must be wrapped in a subclass of AbstractBBox that implements the predict() and predict_proba() methods.

The wrapper allows LORE to interact with any machine learning model in a consistent way, regardless of the underlying framework or implementation.

classifier

The underlying black box model

predict()

Predict class labels for samples

predict_proba()

Predict class probabilities for samples

model()

Return the underlying model

Example

To create a wrapper for a custom model:

>>> from lore_sa.bbox import AbstractBBox
>>>
>>> class MyCustomBBox(AbstractBBox):
...     def __init__(self, model):
...         self.classifier = model
...
...     def predict(self, X):
...         return self.classifier.predict(X)
...
...     def predict_proba(self, X):
...         return self.classifier.predict_proba(X)

See also

sklearn_classifier_bbox.sklearnBBox: Pre-built wrapper for scikit-learn models keras_classifier_wrapper: Pre-built wrapper for Keras models

__init__(classifier)

Initialize the black box wrapper.

Parameters:

classifier – The machine learning model to wrap. The model should have methods compatible with the predict() and predict_proba() interface.

Methods

__init__(classifier)	Initialize the black box wrapper.
model()	Return the underlying black box model.
predict(sample_matrix)	Predict class labels for samples (sklearn-like interface).
predict_proba(sample_matrix)	Predict class probabilities for samples (sklearn-like interface).

model()

Return the underlying black box model.

Returns:

The wrapped classifier/model object.

abstract predict(sample_matrix: list)

Predict class labels for samples (sklearn-like interface).

This method wraps the underlying model’s predict method to provide a consistent interface for LORE to get class predictions.

Parameters:

sample_matrix (array-like) – Array of shape (n_samples, n_features) containing the samples to predict. Can be a list, numpy array, or sparse matrix depending on the model’s requirements.

Returns:

Array of shape (n_samples,) containing the predicted

class labels for each sample.

Return type:

np.ndarray

Example

>>> predictions = bbox.predict([[1, 2, 3], [4, 5, 6]])
>>> print(predictions)  # e.g., array([0, 1])

abstract predict_proba(sample_matrix: list)

Predict class probabilities for samples (sklearn-like interface).

This method wraps the underlying model’s predict_proba method to provide probability estimates for each class for the given samples.

Parameters:

sample_matrix (array-like) – Array of shape (n_samples, n_features) containing the samples to predict. Can be a list, numpy array, or sparse matrix depending on the model’s requirements.

Returns:

Array of shape (n_samples, n_classes) containing the

class probabilities for each sample. Each row sums to 1.0.

Return type:

np.ndarray

Example

>>> probas = bbox.predict_proba([[1, 2, 3], [4, 5, 6]])
>>> print(probas)  # e.g., array([[0.8, 0.2], [0.3, 0.7]])

Note

For multi-class classification, the array has shape (n_samples, n_classes). For binary classification, it typically has shape (n_samples, 2).