Call shutil.copytree() with logging

Obtain accessor for fields in a tree. See :ref:`field_accessors` for more details. Parameters ---------- tre

Returns a strategy to generate regression problem input datasets. Note: This function uses the sklearn.datasets.make_regression function

Obtain ctypes type corresponding to a given Type str

! * \brief Tests whether the leaf node has a non-empty leaf vector * \param nid ID of node being queried */

! * \brief Get the type of a node * \param nid ID of node being queried */

Setters **/ ! * \brief Identify two child nodes of the node * \param nid ID of node being modified * \param left_child ID of the left child n

Fully expand a path and convert it to an absolute path

Concatenate multiple model objects into a single model object by copying all member trees into the destination model object.

Obtain accessor for fields in the header. See :ref:`field_accessors` for more details.

Serialize (persist) the model to a checkpoint file in the disk, using a fast binary representation. To recover the model from the che

Obtain ctypes type corresponding to a given Type str

! * \brief Get list of all categories belonging to the left/right child node. * See the category_list_right_child_ field of each test node to de

! * \brief Test whether the list given by CategoryList(nid) is associated with the right * child node or the left child node * \param

! * \brief Get comparison operator * \param nid ID of node being queried */

Get correct postprocessor for prediction, depending on objective function

Load Treelite Library.

Temporarily change working directory. TODO(hcho3): Remove this once we adopt Python 3.11, which implements contextlib.chdir.

Deserialize (recover) the model from a checkpoint file in the disk. It is expected that the file was generated by a call to the :py:f

Download all binary wheels. url_prefix is the URL for remote directory storing the release wheels

Calculates the expected isolation depth for a remainder of uniform points

Deprecated; please use :py:meth:`~treelite.frontend.from_lightgbm` instead.

Load a tree ensemble model from a string containing XGBoost JSON Parameters ---------- model_json_str : A string specifying

Generate data containing outliers.

Get 1D array from text file

Locate libtreelite from the local project directory's lib/ subdirectory.

Serialize (persist) the model to a byte sequence, using a fast binary representation. To recover the model from the byte sequence, us

Deserialize model from PyBuffer frames