https://www.cril.univ-artois.fr/pyxai/documentation/releases/

API Reference documentation

This website now includes a dedicated API Reference section, generated from the PyXAI source docstrings. It covers the main modules and classes:

• Learning — the learning module (dataset loading, model training, instance selection)
• Explaining — the explainer module (initialisation, explanation computation)
• Builder — hand-crafting tree models
• Class references: Explainer, Learner, DecisionTree, RandomForest, and more.

Docstrings fully rewritten

All public docstrings have been rewritten with a consistent format (Parameters, Returns, Raises, Notes, Examples sections).

Updated API signatures

Several method signatures and module-level names were renamed as part of a broader refactoring to support non-tabular datasets (images, time series, etc.) stored in JSON format via the NonTabularPreprocessor class.

Imports

The Explainer entry point has been renamed Explaining:

Learner.__init__()

Learner.evaluate()

Hyperparameters passed via model_parameters now use the same parameter names as the underlying ML library (scikit-learn, XGBoost, LightGBM). For example, model_parameters={'n_estimators': 100, 'max_depth': 4} maps directly to scikit-learn's RandomForestClassifier(n_estimators=100, max_depth=4). There is no longer any PyXAI-specific naming for model hyperparameters.

Learner.get_instances()

Constants in the Learning module

Save and load

XGBoost compatibility

PyXAI is now compatible with the latest versions of XGBoost (previously limited to version 1.7.3).

Recent XGBoost releases changed how the base_score parameter is stored internally. The base_score is the initial bias added to all predictions before the trees are applied. Its handling differs by task:

• Binary classification — the base score is logit-transformed before being added to the sum of tree outputs.
• Multi-class classification — the base score is added to each class score before the softmax normalisation.
• Regression — the base score is a direct additive offset.

PyXAI now correctly extracts and applies base_score in all three cases, both for prediction and for computing formal explanations via the C++ backend. This fixes incorrect predictions and explanations that occurred with XGBoost versions newer than 1.7.3.

Refactoring and internal improvements

ModelIO utility class — save, load, and import operations have been extracted from the Learner class into a dedicated ModelIO static class. This makes the API cleaner and the import workflow consistent across all supported libraries (Scikit-learn, XGBoost, LightGBM):

from pyxai import Learning
learner, model = Learning.import_models(saved_model)

Preprocessors split into two classes:

• TabularPreprocessor — for standard CSV / DataFrame datasets.
• NonTabularPreprocessor — for non-tabular datasets (e.g. image datasets loaded from directories).

Private method naming — internal methods in data structure classes (BoostedTrees, DecisionTree, RandomForest) have been prefixed with _ to clearly distinguish the public API from implementation details.

Bug fixes:

• Fixed incorrect handling of discrete feature values in LightGBM models.
• Fixed XGBoost prediction errors introduced by the base_score API change.
• Fixed a memory issue in the Builder where learner_information was incorrectly shared between instances.

PyXAI is available on MacOS via pypi: https://pypi.org/project/pyxai/1.0.14/

• Supports ARM architecture on Linux and MacOS
• Some bugs have been resolved

pip install -U pyxai to update or install.

To fix the issue #10.

• To resolve compatibility issues with PyQt6, since V1.0.10, PyXAI’s Graphical Interface is independent and no longer mandatory.
• Remove the PyQt6 dependency and implementation of new methods to display explanations without PyQt6:
  • show_in_notebook()
  • show_on_screen()
  • get_PILImage()
  • save_png()
  • resize_PILimage()
  • More information on a new page of the documentation
• Contrastive for BT classification (binary classes)(documentation in progress)
• Change function name in explainer (unset_specific_features -> unset_excluded_features)
• New procedure installation (github and pypi)
• New visualization for time series
  image
  More information at the end of this page.
• Compilation error resolution

The documentation will be updated tomorrow.

New metrics (documentation page is in progress)

For binary classification:

• accuracy
• precision
• recall
• f1_score
• specificity
• tp, tn, fp, fn

For multiclass classification:

• micro_averaging_accuracy
• micro_averaging_precision
• micro_averaging_recall
• macro_averaging_accuracy
• macro_averaging_precision
• macro_averaging_recall

For regression:

• mean_squared_error
• root_mean_squared_error
• mean_absolute_error

Examples:

labels = [1,1,1,1,1,0,0,0,0,0]
predictions = [1,1,1,1,1,0,0,0,0,0]
metrics = Tools.Metric.compute_metrics_binary_classification(labels, predictions)

learner = Learning.Scikitlearn("tests/dermatology.csv", learner_type=Learning.CLASSIFICATION)
models = learner.evaluate(method=Learning.K_FOLDS, output=Learning.DT, test_size=0.2)
for id, models in enumerate(models):
 metrics = learner.get_details()[id]["metrics"]

Build and Tests with CI

This is the release 1.0.0 of PyXAI with new features:

• Regression for Boosted Trees with XGBoost or LightGBM
• Adding Theories (knowledge about the dataset)
• Easier model Import (automatic detection of model types)
• PyXAI's Graphical User Interface (GUI): displaying, loading and saving explanations.
• Supports Multiple Image Formats for imaging datasets
• Supports Data Pre-Processing (tool for preparing and cleansing a dataset)
• Unit Tests with the Unittest module

This is the first release of PyXAI.