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()
Provides outcome metrics
between_covs_cv
,between_corrs_cv
,x_weights_cv
andy_weights_cv
.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)) – ifNone
set toX
. Allows to provide an alternative set of X features for calculating loadings. I.e. an implicit assumption is that the rows inX
andXorig
correspond to the same samples (subjects). - Yorig (
None
or np.ndarray (n_samples, n_orig_features)) – ifNone
set toY
. Allows to provide an alternative set of Y features for calculating loadings. I.e. an implicit assumption is that the rows inY
andYorig
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