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DOC Add parameters description for make_synthetic_competing_weibull() #67

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DOC Add parameters description for make_synthetic_competing_weibull() #67

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ae216ce
add parameter description
jovan-stojanovic Jul 3, 2024
05e08ca
improve explanations
jovan-stojanovic Jul 3, 2024
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add parameter description
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@jovan-stojanovic
jovan-stojanovic committed Jul 3, 2024
commit ae216ce3ac3f20d46b723fe0083b4c32c30337af
68 changes: 61 additions & 7 deletions hazardous/data/_competing_weibull.py
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Expand Up @@ -20,6 +20,25 @@


def _censor(y, relative_scale, random_state=None):
"""Censoring a population based on a relative scale.

Individuals are censored by sampling a censoring time from
a Weibull distribution with shape 1 and scale equal to
the mean duration of the target event times the
``censoring_relative_scale``.

Parameters
----------
y: ndarray
The target population.
relative_scale: float
Relative scale of the censoring. Setting it to 0 or None
disables censoring, setting it to a small value (e.g. 0.5
instead of 1.5) will result in a larger fraction of
censored individuals.

"""

if relative_scale == 0 or relative_scale is None:
return y

Expand Down Expand Up @@ -59,13 +78,48 @@ def make_synthetic_competing_weibull(
event (competing risks setting) and its event identifier and duration are
returned as the target dataframe.

A fraction of the individuals are censored by sampling a censoring time
from a Weibull distribution with shape 1 and scale equal to the mean
duration of the target event times the ``censoring_relative_scale``.
A fraction of the individuals are censored if ``censoring_relative_scale``
is not None or 0.

Parameters
----------
n_events: int, default=3
Number of events.
n_samples: int, default=3000
Number of samples by event type.
return_X_y: bool, default=False
If True, returns ``(data, target)`` instead of a Bunch object.
feature_rounding: int or None, default=2
Round the feature values. If None, no rounding will be applied.
target_rounding: int or None, default=1
Round the target values. If None, no rounding will be applied.
shape_ranges: tuple of shape (2, n_events)
The lower and upper boundary of the shape, `n_samples` shape
values for `n_events` will be drawn from a uniform distribution.
scale_ranges: tuple of shape (2, n_events)
The lower and upper boundary of the scale, `n_samples` scale
values for `n_events` will be drawn from a uniform distribution.
base_scale: int, default=1000
Scaling parameter of the ``scale_range``.
censoring_relative_scale: float, default=1.5
Relative scale of the censoring level. Individuals are censored by
sampling a censoring time from a Weibull distribution with shape 1
and scale equal to the mean duration of the target event times
the ``censoring_relative_scale``.
Setting ``censoring_relative_scale`` to 0 or None disables censoring.
Setting it to a small value (e.g. 0.5 instead of 1.5) will result in a
larger fraction of censored individuals.
random_state : int, RandomState instance or None, default=None
Controls the randomness of the uniform time sampler.

Returns
-------
(data, target): tuple if ``return_X_y`` is True
A tuple of two ndarray. The first containing a 2D array of shape
(n_samples, n_features) with each row representing one sample
and each column representing the events. The second ndarray
of shape (n_samples,) containing the target samples.

Setting ``censoring_relative_scale`` to 0 or None disables censoring.
Setting it to a small value (e.g. 0.5 instead of 1.5) will result in a
larger fraction of censored individuals.
"""
rng = check_random_state(random_state)
all_features = []
Expand Down Expand Up @@ -101,4 +155,4 @@ def make_synthetic_competing_weibull(
return X, y

frame = pd.concat([X, y], axis=1)
return Bunch(data=frame[X.columns], target=X[y.columns], frame=frame)
return Bunch(data=frame[X.columns], target=frame[y.columns], frame=frame)