Utility Functions

IO

mvlearn.utils.check_Xs(Xs, multiview=False, enforce_views=None, copy=False, return_dimensions=False)[source]

Checks Xs and ensures it to be a list of 2D matrices.

Parameters

  • Xs (nd-array, list) -- Input data.

  • multiview (boolean, (default=False)) -- If True, throws error if just 1 data matrix given.

  • enforce_views (int, (default=not checked)) -- If provided, ensures this number of views in Xs. Otherwise not checked.

  • copy (boolean, (default=False)) -- If True, the returned Xs is a copy of the input Xs, and operations on the output will not affect the input. If False, the returned Xs is a view of the input Xs, and operations on the output will change the input.

  • return_dimensions (boolean, (default=False)) -- If True, the function also returns the dimensions of the multiview dataset. The dimensions are n_views, n_samples, n_features where n_samples and n_views are respectively the number of views and the number of samples, and n_features is a list of length n_views containing the number of features of each view.

Returns

  • Xs_converted (object) -- The converted and validated Xs (list of data arrays).

  • n_views (int) -- The number of views in the dataset. Returned only if return_dimensions is True.

  • n_samples (int) -- The number of samples in the dataset. Returned only if return_dimensions is True.

  • n_features (list) -- List of length n_views containing the number of features in each view. Returned only if return_dimensions is True.

mvlearn.utils.check_Xs_y(Xs, y, multiview=False, enforce_views=None, return_dimensions=False)[source]

Checks Xs and y for consistent length. Xs is set to be of dimension 3.

Parameters

  • Xs (nd-array, list) -- Input data.

  • y (nd-array, list) -- Labels.

  • multiview (boolean, (default=False)) -- If True, throws error if just 1 data matrix given.

  • enforce_views (int, (default=not checked)) -- If provided, ensures this number of views in Xs. Otherwise not checked.

  • return_dimensions (boolean, (default=False)) -- If True, the function also returns the dimensions of the multiview dataset. The dimensions are n_views, n_samples, n_features where n_samples and n_views are respectively the number of views and the number of samples, and n_features is a list of length n_views containing the number of features of each view.

Returns

  • Xs_converted (object) -- The converted and validated Xs (list of data arrays).

  • y_converted (object) -- The converted and validated y.

  • n_views (int) -- The number of views in the dataset. Returned only if return_dimensions is True.

  • n_samples (int) -- The number of samples in the dataset. Returned only if return_dimensions is True.

  • n_features (list) -- List of length n_views containing the number of features in each view. Returned only if return_dimensions is True.

mvlearn.utils.check_Xs_y_nan_allowed(Xs, y, multiview=False, enforce_views=None, num_classes=None, max_classes=None, min_classes=None)[source]

Checks Xs and y for consistent length. Xs is set to be of dimension 3. The labels (y) are allowed to be np.nan.

Parameters

  • Xs (nd-array, list) -- Input data.

  • y (nd-array, list) -- Labels.

  • multiview (boolean, default=False) -- If True, throws error if just 1 data matrix given.

  • enforce_views (int, (default=not checked)) -- If provided, ensures this number of views in Xs. Otherwise not checked.

  • num_classes (int, default=None) -- Number of classes that must appear in the labels. If none, then not checked.

  • max_classes (int, default=None) -- Maximum number of classes that must appear in labels. If none, then not checked.

  • min_classes (int, default=None) -- Minimum number of classes that must appear in labels. If none, then not checked.

Returns

  • Xs_converted (object) -- The converted and validated Xs (list of data arrays).

  • y_converted (object) -- The converted and validated y.