mofaflex.priors.GaussianProcess

class mofaflex.priors.GaussianProcess(covariates_key=None, covariates_mkey=None, n_inducing=100, kernel='RBF', mefisto_kernel=True, independent_lengthscales=False, group_covar_rank=1, warp=False, warp_interval=20, warp_open_begin=True, warp_open_end=True, warp_reference_group=None)

Gaussian process prior for spatially or temporally smooth factors.

Important

All methods and properties of this class are only accessible through the MofaFlex class.

Parameters:

• covariates_key (str | Mapping[str] | None (default: None)) – The column of .obs/.var that contains covariate values. Cannot be used together with covariates_mkey.

• covariates_mkey (str | Mapping[str] | None (default: None)) – The key in .obsm/.varm that contains covariate values. Cannot be used together with covariates_key.

• n_inducing (int (default: 100)) – Number of inducing points.

• kernel (Literal['RBF', 'Matern'] (default: 'RBF')) – Kernel function to use.

• mefisto_kernel (bool (default: True)) – Whether to use the MEFISTO group covariance kernel or treat groups independently.

• independent_lengthscales (bool (default: False)) – Whether to use a separate lengthscale per covariate dimension.

• group_cvar_rank – Rank of the group correlation matrix. Only relevant if mefisto_kernel=True.

• warp (bool (default: False)) – Whether to use dynamic time warping. Warping is only supported for 1D covariates.

• warp_interval (int (default: 20)) – Apply dynamic time warping every warp_interval epochs.

• warp_open_begin (bool (default: True)) – Perform open-ended alignment.

• warp_open_end (bool (default: True)) – Perform open-ended alignment.

• warp_reference_group (str | None (default: None)) – Reference group to align the others to. Defaults to the first group.

Attributes table

factor_covariates	Covariates for each group.
factor_covariates_names	Covariate names for each group where they could be inferred from the input.
factor_gp_group_correlation	Between-group correlation for each factor.
factor_gp_lengthscale	Inferred lengthscales for each factor.
factor_gp_scale	Inferred variance scales (smoothness) for each factor.
warped_factor_covariates	Time-warped covariates for each group, if dynamic time warping was enabled.
warped_weight_covariates	Time-warped covariates for each group, if dynamic time warping was enabled.
weight_covariates	Covariates for each group.
weight_covariates_names	Covariate names for each group where they could be inferred from the input.
weight_gp_group_correlation	Between-group correlation for each factor.
weight_gp_lengthscale	Inferred lengthscales for each factor.
weight_gp_scale	Inferred variance scales (smoothness) for each factor.

Methods table

get_factor_gps([moment, x, batch_size, ordered])	Get all latent functions.
get_weight_gps([moment, x, batch_size, ordered])	Get all latent functions.

Attributes

property factor_covariates: Mapping[str, ndarray[tuple[int, int], floating]]

Covariates for each group.

property factor_covariates_names: dict[str, ndarray[tuple[int, int], str | str_]]

Covariate names for each group where they could be inferred from the input.

property factor_gp_group_correlation: ndarray[tuple[int, int], floating]

Between-group correlation for each factor.

property factor_gp_lengthscale: ndarray[tuple[int], floating] | ndarray[tuple[int, int], floating]

Inferred lengthscales for each factor.

property factor_gp_scale: ndarray[tuple[int], floating]

Inferred variance scales (smoothness) for each factor.

property warped_factor_covariates: Mapping[str, ndarray[tuple[int, int], floating]] | None

Time-warped covariates for each group, if dynamic time warping was enabled.

property warped_weight_covariates: Mapping[str, ndarray[tuple[int, int], floating]] | None

Time-warped covariates for each group, if dynamic time warping was enabled.

property weight_covariates: Mapping[str, ndarray[tuple[int, int], floating]]

Covariates for each group.

property weight_covariates_names: dict[str, ndarray[tuple[int, int], str | str_]]

Covariate names for each group where they could be inferred from the input.

property weight_gp_group_correlation: ndarray[tuple[int, int], floating]

Between-group correlation for each factor.

property weight_gp_lengthscale: ndarray[tuple[int], floating] | ndarray[tuple[int, int], floating]

Inferred lengthscales for each factor.

property weight_gp_scale: ndarray[tuple[int], floating]

Inferred variance scales (smoothness) for each factor.

Methods

get_factor_gps(moment: Literal['mean', 'std'] = 'mean', x: Mapping[str, ndarray[tuple[int, int], T] | Tensor] | None = None, batch_size: int | None = None, ordered: bool = False) → Mapping[str, DataFrame]

Get all latent functions.

Parameters:

• moment (Literal['mean', 'std'] (default: 'mean')) – Which moment of the posterior distribution to return.

• x (Mapping[str, ndarray[tuple[int, int], TypeVar(T, int, float, complex, bool, bytes, str, memoryview, bool, csingle, cdouble, clongdouble, half, single, double, longdouble, byte, short, intc, int_, longlong, datetime64, timedelta64, object_, bytes_, str_, ubyte, ushort, uintc, uint, ulonglong, void)] | Tensor] | None (default: None)) – Covariate values for each group. If None, will return latent function values at covariate coordinates used for training.

• batch_size (int | None (default: None)) – Minibatch size. Only has an effect if x is not None. Defaults to the minibatch size used for training.

• ordered (bool (default: False)) – Whether to return the factors ordered by explained variance (highest to lowest).

Return type:

Mapping[str, DataFrame]

get_weight_gps(moment: Literal['mean', 'std'] = 'mean', x: Mapping[str, ndarray[tuple[int, int], T] | Tensor] | None = None, batch_size: int | None = None, ordered: bool = False) → Mapping[str, DataFrame]

Get all latent functions.

Parameters:

• moment (Literal['mean', 'std'] (default: 'mean')) – Which moment of the posterior distribution to return.

• x (Mapping[str, ndarray[tuple[int, int], TypeVar(T, int, float, complex, bool, bytes, str, memoryview, bool, csingle, cdouble, clongdouble, half, single, double, longdouble, byte, short, intc, int_, longlong, datetime64, timedelta64, object_, bytes_, str_, ubyte, ushort, uintc, uint, ulonglong, void)] | Tensor] | None (default: None)) – Covariate values for each group. If None, will return latent function values at covariate coordinates used for training.

• batch_size (int | None (default: None)) – Minibatch size. Only has an effect if x is not None. Defaults to the minibatch size used for training.

• ordered (bool (default: False)) – Whether to return the factors ordered by explained variance (highest to lowest).

Return type:

Mapping[str, DataFrame]