aepsych.factory¶
Submodules¶
aepsych.factory.factory module¶
- aepsych.factory.factory.default_mean_covar_factory(config)[source]¶
Default factory for generic GP models
- Parameters
config (Config) – Object containing bounds (and potentially other config details).
- Returns
- Instantiated
ConstantMean and ScaleKernel with priors based on bounds.
- Return type
Tuple[gpytorch.means.Mean, gpytorch.kernels.Kernel]
- aepsych.factory.factory.monotonic_mean_covar_factory(config)[source]¶
Default factory for monotonic GP models based on derivative observations.
- Parameters
config (Config) – Config containing (at least) bounds, and optionally LSE target.
- Returns
- Instantiated mean and
scaled RBF kernels with partial derivative observations.
- Return type
Tuple[ConstantMeanPartialObsGrad, gpytorch.kernels.ScaleKernel]
- aepsych.factory.factory.song_mean_covar_factory(config)[source]¶
Factory that makes kernels like Song et al. 2018: Linear in intensity dimension (assumed to be the last dimension), RBF in context dimensions, summed.
- Parameters
config (Config) – Config object containing (at least) bounds and optionally LSE target.
- Returns
- Instantiated
constant mean object and additive kernel object.
- Return type
Tuple[gpytorch.means.ConstantMean, gpytorch.kernels.AdditiveKernel]
Module contents¶
- aepsych.factory.default_mean_covar_factory(config)[source]¶
Default factory for generic GP models
- Parameters
config (Config) – Object containing bounds (and potentially other config details).
- Returns
- Instantiated
ConstantMean and ScaleKernel with priors based on bounds.
- Return type
Tuple[gpytorch.means.Mean, gpytorch.kernels.Kernel]
- aepsych.factory.monotonic_mean_covar_factory(config)[source]¶
Default factory for monotonic GP models based on derivative observations.
- Parameters
config (Config) – Config containing (at least) bounds, and optionally LSE target.
- Returns
- Instantiated mean and
scaled RBF kernels with partial derivative observations.
- Return type
Tuple[ConstantMeanPartialObsGrad, gpytorch.kernels.ScaleKernel]
- aepsych.factory.song_mean_covar_factory(config)[source]¶
Factory that makes kernels like Song et al. 2018: Linear in intensity dimension (assumed to be the last dimension), RBF in context dimensions, summed.
- Parameters
config (Config) – Config object containing (at least) bounds and optionally LSE target.
- Returns
- Instantiated
constant mean object and additive kernel object.
- Return type
Tuple[gpytorch.means.ConstantMean, gpytorch.kernels.AdditiveKernel]