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