skbel package

Subpackages

Module contents

class skbel.BEL(mode: str = 'tm', copy: bool = True, *, X_pre_processing=None, Y_pre_processing=None, X_post_processing=None, Y_post_processing=None, regression_model=None, n_comp_cca=None, x_dim=None, y_dim=None, random_state=None)[source]

Bases: TransformerMixin, MultiOutputMixin, BaseEstimator

Heart of the framework. Inherits from scikit-learn base classes. BEL stands for Bayesian Evidential Learning.

property X_shape

Predictor original shape.

property Y_shape

Target original shape.

__init__(mode: str = 'tm', copy: bool = True, *, X_pre_processing=None, Y_pre_processing=None, X_post_processing=None, Y_post_processing=None, regression_model=None, n_comp_cca=None, x_dim=None, y_dim=None, random_state=None)[source]

Initialize the BEL class.

Parameters:

  • mode – How to infer the posterior distribution (if CCA is used). “kde”, “mvn” or “tm”.

  • copy – Whether to copy arrays or not (default is True).

  • X_pre_processing – sklearn pipeline for pre-processing the predictor.

  • Y_pre_processing – sklearn pipeline for pre-processing the target.

  • X_post_processing – sklearn pipeline for post-processing the predictor.

  • X_post_processing – sklearn pipeline for post-processing the target.

  • regression_model – The regression model to use. Default is Canonical Correlation Analysis.

  • n_comp_cca – Number of components to keep in CCA (only if CCA is used).

  • x_dim – Predictor original dimensions.

  • y_dim – Target original dimensions.

  • random_state – Seed to reproduce the same samples.

_sklearn_auto_wrap_output_keys = {'transform'}
cca_pc_transform(X=None, Y=None) -> (<built-in function array>, <built-in function array>)[source]

Transform PCs to CVs.

Parameters:

  • X – Predictor array.

  • Y – Target array.

Returns:

CVs

fit(X: array = None, Y: array = None)[source]

Fit all pipelines.

Parameters:

  • X – Predictor array.

  • Y – Target array.

Returns:

self

fit_transform(X, y=None, **fit_params)[source]

Fit-Transform across all pipelines.

Parameters:

  • X – Predictor array.

  • y – Target array.

Returns:

If mode == “mvn” - returns the posterior mean and covariance. If mode == “kde” - returns a dictionary of functions.

inverse_transform(Y_pred: array, dtype: str = 'float64', get_PC=False) → array[source]

Back-transforms the posterior samples Y_pred to their physical space.

Parameters:

  • Y_pred – The posterior samples (shape = (n_obs, n_components, n_samples))

  • get_PC – If True, returns the canonical variates

  • dtype – The dtype of the output array

Returns:

The back-transformed samples

kde_init(X_obs_f: array, obs_n: int = None)[source]

Initialize the KDEs, i.e. the functions that will be used to sample from the posterior distribution.

Parameters:

  • X_obs_f – Observed data points

  • obs_n – Observation number

Returns:

The initialized KDEs

property n_posts

Number of sample to extract from the posterior multivariate distribution after post-processing.

predict(X_obs: array = None, n_posts: int = None, mode: str = None, noise: float = None, return_samples: bool = True, inverse_transform: bool = True, precomputed_kde: array = None, dtype: str = 'float64') → array[source]

Predict the posterior distribution of the target variable.

Parameters:

  • X_obs – The observed data.

  • n_posts – The number of posterior samples to draw.

  • mode – The mode of inference to use. Default is “tm”.

  • noise – The noise level of the model (only if mode == ‘mvn’).

  • return_samples – Option to return samples or not. Default=True.

  • inverse_transform – Option to return the samples in the original space. If the dimensionality of the original space is very high, this can be memory-consuming. It can be set to False to return the samples in the reduced space, which is much faster, so that the samples can be back-transformed later. Default=True.

  • precomputed_kde – (if mode=”kde) Precomputed KDE functions. Computing the KDEs can be time-consuming. If the KDEs are precomputed, they can be passed as an argument.

  • dtype – The data type of the samples. Default=float64.

Returns:

The posterior samples in the original space or in the transformed space.

random_sample(X_obs_f: None, obs_n: int = None, n_posts: int = None, mode: str = None, init_kde: array = None) → array[source]

Random sample the inferred posterior distribution. It can be used to generate samples from the posterior.

Parameters:

  • X_obs_f – Observed data points in the feature space. Shape = (n_obs, n_comp_CCA)

  • obs_n – If we want to generate samples from the posterior of a specific observation point, obs_n is the index of the observation point.

  • n_posts – Number of posterior samples

  • mode – How to sample the posterior distribution

  • init_kde – Initial KDE function. If None, the KDE function is computed from the observed data.

Returns:

Samples from the posterior distribution (n_obs, n_posts, n_comp_CCA)

property random_state

Seed a.k.a.

random state to reproduce the same samples

property seed

Seed a.k.a.

random state to reproduce the same samples

set_inverse_transform_request(*, Y_pred: bool | None | str = '$UNCHANGED$', dtype: bool | None | str = '$UNCHANGED$', get_PC: bool | None | str = '$UNCHANGED$') → BEL

Configure whether metadata should be requested to be passed to the inverse_transform method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to inverse_transform if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to inverse_transform.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters

Y_predstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for Y_pred parameter in inverse_transform.

dtypestr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for dtype parameter in inverse_transform.

get_PCstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for get_PC parameter in inverse_transform.

Returns

selfobject

The updated object.

set_predict_request(*, X_obs: bool | None | str = '$UNCHANGED$', dtype: bool | None | str = '$UNCHANGED$', inverse_transform: bool | None | str = '$UNCHANGED$', mode: bool | None | str = '$UNCHANGED$', n_posts: bool | None | str = '$UNCHANGED$', noise: bool | None | str = '$UNCHANGED$', precomputed_kde: bool | None | str = '$UNCHANGED$', return_samples: bool | None | str = '$UNCHANGED$') → BEL

Configure whether metadata should be requested to be passed to the predict method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters

X_obsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for X_obs parameter in predict.

dtypestr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for dtype parameter in predict.

inverse_transformstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for inverse_transform parameter in predict.

modestr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for mode parameter in predict.

n_postsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for n_posts parameter in predict.

noisestr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for noise parameter in predict.

precomputed_kdestr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for precomputed_kde parameter in predict.

return_samplesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for return_samples parameter in predict.

Returns

selfobject

The updated object.

transform(X=None, Y=None) -> (<built-in function array>, <built-in function array>)[source]

Transform data across all pipelines.

Parameters:

  • X – Predictor array.

  • Y – Target array.

Returns:

Post-processed variables

property x_observation

Pre-processed observation.

property x_pre_processed

Pre-processed predictor.

property y_pre_processed

Pre-processed target.