Redirect logs from native library into Python console

Redirect warnings from native library into Python console

Convert to dictionary

Convert to dictionary

Build an editable installation. We mostly delegate to Hatchling.

Build a source distribution

Build a wheel

Declare the current node as a categorical test node, where the test is of form [feature value] \\in [category list]. Paramet

Specify the number of data points (samples) that are mapped to the current node. Parameters ---------- data_count:

Placeholder to indicate that a section of code is not expected to raise any exception

Export a model as a scikit-learn RandomForest. Note ---- Currently only random forests can be exported as scikit-learn model objects

Load a tree ensemble model from a LightGBM Booster object Parameters ---------- booster : object of type :py:class:`lightgbm.Booster

Load a tree ensemble model from an XGBoost Booster object Parameters ---------- booster : Object of type :py:class:`xgboost.Booster`

Specify the gain (loss reduction) that's resulted from the current split. Parameters ---------- gain: Ga

Get a field Parameters ---------- name: Name of the field. Consult :doc:`the model spec </serialization/

Access the handle to the associated C++ object

Access the handle to the associated C++ object

Access the handle to the associated C++ object

Load a tree ensemble model from a scikit-learn model object Note ---- For :py:class:`~sklearn.ensemble.IsolationForest`, the loaded

This function was removed in Treelite 4.0; please use :py:meth:`~treelite.sklearn.import_model` instead.

This step ccurs immediately before each build.

Input type

\brief Check if a given key is recognized by the handler * \param key Key to look up */

Load a tree ensemble model from a LightGBM model file. Parameters ---------- filename : Path to model file Returns

Load a tree ensemble model from XGBoost model, stored using JSON or UBJSON format. Parameters ---------- filename : Path to