gemmr.sample_analysis.addon.cv

gemmr.sample_analysis.addon.cv(estr, X, Y, Xorg, Yorig, x_align_ref, y_align_ref, results, **kwargs)

Calculates cross-validated outcome metrics.

Required keyword-arguments:

  • kwargs[‘cvs’]: list of (label, cross-validator)

  • kwargs[‘scorers’]: can be created by mk_scorers_for_cv()

Optional keyword-arguments:

  • kwargs[‘fit_params’]: dict

    passed to cross_validate

Provides outcome metrics between_covs_cv, between_corrs_cv, x_weights_cv, y_weights_cv, x_loadings_cv and y_loadingscv.

Parameters:

  • estr (sklearn-style estimator) – fitted estimator

  • X (np.ndarray (n_samples, n_features)) – dataset X

  • Y (np.ndarray (n_samples, n_features)) – dataset Y

  • Xorig (None or np.ndarray (n_samples, n_orig_features)) – can be None. Allows to provide an alternative set of X features for calculating loadings. I.e. an implicit assumption is that the rows in X and Xorig correspond to the same samples (subjects).

  • Yorig (None or np.ndarray (n_samples, n_orig_features)) – can be None. Allows to provide an alternative set of Y features for calculating loadings. I.e. an implicit assumption is that the rows in Y and Yorig correspond to the same samples (subjects).

  • x_align_ref ((n_features,)) – the sign of X weights is chosen such that the cosine-distance between fitted X weights and x_align_ref is positive

  • y_align_ref ((n_features,)) – the sign of Y weights is chosen such that the cosine-distance between fitted Y weights and y_align_ref is positive

  • results (xr.Dataset) – containing outcome features computed so far, and is modified with outcomes of this function

  • kwargs (dict) – keyword arguments