89cb97fe8802687fb67a3a636f5c50a764b41e34,tgen/rank.py,PerceptronRanker,train,#PerceptronRanker#Any#Any#,197

Before Change

            ttree, feats = ttrees[ttree_no], X[ttree_no]
            log_debug("------\nDATA %d:" % ttree_no)
            log_debug("DA: %s" % unicode(da))
            log_debug("SENT: %s" % sentences[ttree_no])
            log_debug("TTREE: %s" % unicode(ttree))
            log_debug("FEATS:", self._feat_val_str(feats, "\t", nonzero=True))

        // initialize weights

After Change

                // find the top-scoring generated tree, evaluate F-score against gold t-tree
                // (disregarding whether it was selected as the best one)
                evaler.append(TreeNode(gold_ttree),
                              TreeNode(rival_ttrees[scores[1:].index(max(scores[1:]))]))

                log_debug("TTREE-NO: %04d, SEL_CAND: %04d, LEN: %02d" % (ttree_no, top_cand_idx, len(cands)))
                log_debug("ALL CAND TTREES:")

Instances