gemmr.sample_analysis.addon.loadings_true_pearson

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

Calculates Pearson correlations between estimated and true test loadings.

Requires results:

  • x_test_scores
  • x_test_scores

These are available if addana_scores_true_spearman() was run before.

Requires keyword arguments:

  • kwargs[‘true_loadings’] - which is a dict constructed with ccapwr.sample_analysis.analyzers._calc_true_loadings().
  • Xtest, Ytest

Provides outcome metrics x_loadings_true_pearson, y_loadings_true_pearson, x_crossloadings_true_pearson and y_crossloadings_true_pearson

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)) – if None set to X. 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)) – if None set to Y. 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