"""The BMT dataset from Melania Pintilie's book "Competing Risks, A
Practical Perspective".
"""
import numpy as np
from .base import _fetch_remote_csv
from .base import RemoteFileMetadata
ARCHIVE = RemoteFileMetadata(
filename=None, url=('local:BMT_data'), checksum=None)
[docs]
def 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
"""
# dictionaries
treatment_values = ['BM', 'PB']
dx_values = ['CML', 'AML']
extent_values = ['L', 'E']
agvhdgd_values = ['0', '1', '2', '3', '4']
# attribute descriptions
treatment_descr = [("treatment", treatment_values, "tx")]
target_descr = [("target_surv_time", float, "survtime"),
("target_surv_status", np.int32, "stat"),
("target_relapse_time", float, "reltime"),
("target_relapse_status", np.int32, "rcens"),
("target_agvh_time", float, "agvhtime"),
("target_agvh", np.int32, "agvh"),
("target_agvhdgd", agvhdgd_values, "agvhdgd"),
("target_cgvh_time", float, "cgvhtime"),
("target_cgvh", np.int32, "cgvh"),
]
feature_descr = [("dx", dx_values),
("extent", extent_values),
("age", float),
]
ret = _fetch_remote_csv(ARCHIVE, "BMT",
feature_attrs=feature_descr,
treatment_attrs=treatment_descr,
target_attrs=target_descr,
categ_as_strings=categ_as_strings,
return_X_y=return_X_y, as_frame=as_frame,
download_if_missing=download_if_missing,
random_state=random_state, shuffle=shuffle,
total_attrs=13
)
if not return_X_y:
ret.descr = __doc__
return ret