gameanalysis.bootstrap module¶
Module for using bootstrap in analysis
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gameanalysis.bootstrap.
game_function
(game, function, num_resamples, num_returned, *, percentiles=None, processes=None)[source]¶ Bootstrap the value of a function over a sample game
Parameters: - game (SampleGame) – The sample game to bootstrap the function value over.
- function (f(Game) -> float or f(Game) -> [float]) – The function of the game to compute. It must be pickleable unless processes is 1, and it must return either a float or an iterable of floats. If an iterable of floats, this bootstrap all indices of the return value independently.
- num_resamples (int) – The number of bootstrap samples. Higher will take longer but also give better accuracy.
- num_returned (int) – The number of float values your function returns.
- percentiles (int or [int]) – The percentiles to compute on the resulting data in [0, 100]. Standard percentiles are 95, or [2.5, 97.5]. By default, return all samples.
- processes (int (optional)) – The number of processes to use for computation. By default this is the number of cores.
Returns: bootstrap_percentiles – An ndarray of the percentiles from bootstrapping. The shape will depend on the number of percentiles and the number of values returned from your function.
Return type: ndarray
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gameanalysis.bootstrap.
mixture_regret
(game, mixtures, num_resamples, *, percentiles=None, processes=None)[source]¶ Compute percentile bounds on mixture regret
Parameters: - game (SampleGame) – The sample game to bootstrap the function value over.
- mixtures (ndararay) – The profiles to compute mixture regret bounds for.
- num_resamples (int) – The number of bootstrap samples. Higher will take longer but also give better accuracy.
- percentiles (int or [int]) – The percentiles to compute on the resulting data in [0, 100]. Standard percentiles are 95, or [2.5, 97.5]. By default, return all samples.
- processes (int (optional)) – The number of processes to use for computation. By default this is the number of cores.
Returns: regret_percentiles – An ndarray of the percentiles for bootstrap regret for each profile.
Return type: ndarray
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gameanalysis.bootstrap.
mixture_welfare
(game, mixtures, num_resamples, *, percentiles=None, processes=None)[source]¶ Compute percentile bounds on mixture welfare
Parameters: - game (SampleGame) – The sample game to bootstrap the function value over.
- mixtures (ndarray) – The profiles to compute mixture welfare bounds for.
- num_resamples (int) – The number of bootstrap samples. Higher will take longer but also give better accuracy.
- percentiles (int or [int]) – The percentiles to compute on the resulting data in [0, 100]. Standard percentiles are 95, or [2.5, 97.5]. By default, return all samples.
- processes (int (optional)) – The number of processes to use for computation. By default this is the number of cores.
Returns: bootstrap_percentiles – An ndarray of the percentiles for bootstrap welfare for each profile.
Return type: ndarray
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gameanalysis.bootstrap.
profile_function
(game, function, profiles, num_resamples, *, percentiles=None, processes=None)[source]¶ Compute a function over profiles
Parameters: - game (SampleGame) – The sample game to bootstrap the function value over.
- function (Game, profile -> float) – The function of the game profile pair to compute. It must be pickleable, and it must return a float (e.g. regret.mixture_regret).
- profiles (ndarray) – The profiles to compute bootstrap bounds over for function.
- num_resamples (int) – The number of bootstrap samples. Higher will take longer but also give better accuracy.
- percentiles (int or [int]) – The percentiles to compute on the resulting data in [0, 100]. Standard percentiles are 95, or [2.5, 97.5]. By default, return all samples.
- processes (int (optional)) – The number of processes to use for computation. By default this is the number of cores.
Returns: bootstrap_percentiles – An ndarray of the percentiles from bootstrapping for each profile. The shape will depend on the number of percentiles and the number of profiles.
Return type: ndarray