hazardous.data.make_synthetic_competing_weibull

hazardous.data.make_synthetic_competing_weibull(n_events=3, n_samples=3000, return_X_y=False, base_scale=1000, feature_rounding=2, target_rounding=1, shape_ranges=((0.4, 0.9), (1.0, 1.0), (1.2, 3)), scale_ranges=((1, 20), (1, 10), (1.5, 5)), censoring_relative_scale=1.5, random_state=None)

Generate a synthetic dataset with competing Weibull-distributed events.

For each individual, we first sample one pair of shape and scale parameters for each event type uniformly from the given ranges. Each event type has a different range of shape and scale parameters.

Then we sample event durations for each event type from the corresponding Weibull distribution parametrized by the sampled shape and scale parameters. The shape and scale parameters are returned as features.

Then, we apply the same procedure to sample the duration for the censoring event (event = 0) if censoring_relative_scale is not None or 0.

For each individual, the event type with the shortest duration is kept as the target event (competing risks setting) and its event identifier and duration are returned as the target dataframe.

Parameters:

n_events: int, default=3

Number of events of interest.

n_samples: int, default=3000

Number of individuals in the population.

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 column duration of the target. If None, no rounding will be applied.

shape_ranges: tuple of shape (n_events, 2)

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 (n_events, 2)

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_stateint, 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 dataframes. The first containing a 2D array of shape (n_samples, n_features) with each row representing one sample and each column representing the features. The second dataframe of shape (n_samples, 2) containing the target samples. The first column contains the event identifier (event = 0 represents the censoring event) and the second column contains the duration of the target event.