libuplift.datasets.BMT#

The BMT dataset from Melania Pintilie’s book “Competing Risks, A Practical Perspective”.

Functions#

fetch_BMT([data_home, download_if_missing, ...])

Load the BMT (Bone Marrow Transplant) dataset from Melania

Module Contents#

libuplift.datasets.BMT.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)[source]#

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_homestring, 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_missingboolean, 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_stateint, RandomState instance or None (default)

Determines random number generation for dataset shuffling. Pass an int for reproducible output across multiple function calls.

shufflebool, default=False

Whether to shuffle dataset.

categ_as_stringsbool, default=False

Whether to return categorical variables as strings.

return_X_yboolean, default=False.

If True, returns (data.data, data.target) instead of a Bunch object.

as_frameboolean, 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:
datasetdict-like object with the following attributes:
dataset.datanumpy array

Each row corresponds to the features in the dataset.

dataset.DESCRstring

Description of the dataset.

(data, target_time, target_status)tuple if

return_X_y is True