twinlab.EstimatorParams

class twinlab.EstimatorParams(detrend=False, covar_module=None, estimator_type='single_task_gp')

Parameter configuration for the Gaussian Process emulator (estimator).

Variables:

• detrend (bool, optional) – Should the linear trend in the data be removed (detrended) before training the emulator? The defaults is False.

• covar_module (Union[str, None], optional) –

  Specifies the functions that build up the kernel (covariance matrix) of the Gaussian Process. The default is None, which means the library will use a default kernel, which is a scaled Matern 5/2. This can be chosen from a list of possible kernels:

  • "LIN": Linear.

  • "M12": Matern 1/2. A standard kernel for modelling data with a smooth trend

  • "M32": Matern 3/2. A standard kernel for modelling data with a smooth trend.

  • "M52": Matern 5/2. A standard kernel for modelling data with a smooth trend.

  • "PER": Periodic. Good for modelling data that has a periodic structure.

  • "RBF": Radial Basis Function. A standard kernel for modelling data with a smooth trend. A good default choice that can model smooth functions.

  • "RQF": Rational Quadratic Function.

  Kernels can also be composed by using combinations of the "+" (addative) and "*" (multiplicative) operators. For example, covar_module = "(M52*PER)+RQF" is valid.

• estimator_type (str, optional) –

  Specifies the type of Gaussian process to use for the emulator. The default is "single_task_gp", but the value can be chosen from the following list:

  • "single_task_gp": The standard Gaussian Process, which learns a mean, covariance, and noise level.

  • "fixed_noise_gp": A Gaussian Process with fixed noise, which is specified by the user. Particularly useful for modelling noise-free simulated data where the noise can be set to zero manually.

  • "heteroskedastic_gp": A Gaussian Process with fixed noise that is allowed to vary with the input. The noise is specified by the user, and is also learned by the Process.

  • "variational_gp": An approximate Gaussian Process that is more efficient to train with large datasets.

  • "mixed_single_task_gp": A Gaussian Process that works with a mix of continuous and categorical or discrete input data.

  • "multi_fidelity_gp": A Gaussian Process that works with input data that has multiple levels of fidelity. For example, combined data from both a high- and low-resolution simulation.

  • "fixed_noise_multi_fidelity_gp": A Gaussian Process that works with input data that has multiple levels of fidelity and fixed noise.

__init__(detrend=False, covar_module=None, estimator_type='single_task_gp')

Methods

__init__([detrend, covar_module, estimator_type])
unpack_parameters()