def score_both_beta_larger_than_one_aux(self, dst_ds_size, n_units_per_fold, clclf):
fold_generator = FoldGenerator(dst_ds_size, self.k_units, n_units_per_fold)
beta = fold_generator.beta()
print 'n_units_per_fold', n_units_per_fold, 'beta:', beta
# compute clclf score and write down
clclf_score = self.score_clclf(clclf, fold_generator)
self.add_to_scores_file(dst_ds_size, fold_generator.fold_size, n_units_per_fold, beta, self.last_clf_score, clclf_score)
def score_both_beta_smaller_than_one(self, clf, clclf, init_n_units_per_fold, dst_ds_size):
for n_units_per_fold in xrange(init_n_units_per_fold, self.k_units):
fold_generator = FoldGenerator(dst_ds_size, self.k_units, n_units_per_fold)
beta = fold_generator.beta()
print 'n_units_per_fold', n_units_per_fold, 'beta:', beta
# compute scores and write them down
clf_score = self.score_clf(clf, fold_generator)
clclf_score = self.score_clclf(clclf, fold_generator)
self.add_to_scores_file(dst_ds_size, fold_generator.fold_size, n_units_per_fold, beta, clf_score, clclf_score)
# save last clf score - this is the CVscore of the clf for beta=0.9
# namely, the best score we can get for one-language learning..
self.last_clf_score = clf_score
self.last_n_units_per_fold = self.k_units
