libuplift.datasets.BMT
======================

.. py:module:: libuplift.datasets.BMT

.. autoapi-nested-parse::

   The BMT dataset from Melania Pintilie's book "Competing Risks, A
   Practical Perspective".

   ..
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Functions
---------

.. autoapisummary::

   libuplift.datasets.BMT.fetch_BMT


Module Contents
---------------

.. py:function:: fetch_BMT(data_home=None, download_if_missing=True, random_state=None, shuffle=False, categ_as_strings=False, return_X_y=False, as_frame=False)

   
   Load the BMT (Bone Marrow Transplant) dataset from Melania
   Pintilie's book "Competing Risks, A Practical Perspective.

   Use a local copy of the data.

   The agvhdgd variable (Grade of acute GVHD) is treated as another
   target.

   **Targets**

   - target_surv_time: survival time
   - target_surv_status: survival censoring status  1=death
   - target_relapse_time: time to relapse
   - target_relapse_status: 1=relapse
   - target_agvh_time: time to AGVH
   - target_agvh: 1=AGVH
   - target_agvhdgd: AGVH grade 0 (absent) - 4, ordinal scale
   - target_cgvh_time: time to CGVH
   - target_cgvh: 1=CGVH

   :Parameters:

       **data_home** : string, optional
           Specify another download and cache folder for the datasets. By default
           all scikit-learn data is stored in '~/scikit_learn_data' subfolders.

       **download_if_missing** : boolean, default=True
           If False, raise a IOError if the data is not locally available
           instead of trying to download the data from the source site.

       **random_state** : int, RandomState instance or None (default)
           Determines random number generation for dataset shuffling. Pass an int
           for reproducible output across multiple function calls.

       **shuffle** : bool, default=False
           Whether to shuffle dataset.

       **categ_as_strings** : bool, default=False
           Whether to return categorical variables as strings.

       **return_X_y** : boolean, default=False.
           If True, returns ``(data.data, data.target)`` instead of a Bunch
           object.

       **as_frame** : boolean, default=False
           If True features are returned as pandas DataFrame.  If False
           features are returned as object or float array.  Float array
           is returned if all features are floats.



   :Returns:

       **dataset** : dict-like object with the following attributes:
           ..

       **dataset.data** : numpy array
           Each row corresponds to the features in the dataset.

       **dataset.DESCR** : string
           Description of the dataset.

       **(data, target_time, target_status)** : tuple if
           ``return_X_y`` is True











   ..
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