| | from pt_variety_identifier.src.results import Results as BaseResults |
| | import logging |
| |
|
| | class Results(BaseResults): |
| | def __init__(self, filepath, DOMAINS) -> None: |
| | super().__init__(filepath, DOMAINS) |
| |
|
| | def process(self, cross_domain_f1, train_domain, test_results, train_results, balance, pos_prob, ner_prob): |
| | if cross_domain_f1 > self.best_f1_scores[train_domain]["cross_domain_f1"]: |
| | logging.info(f"New best f1 score for {train_domain}") |
| |
|
| | self.best_f1_scores[train_domain]["cross_domain_f1"] = cross_domain_f1 |
| | self.best_f1_scores[train_domain]["test_results"] = test_results |
| | self.best_f1_scores[train_domain]["balance"] = balance |
| | self.best_f1_scores[train_domain]["pos_prob"] = pos_prob |
| | self.best_f1_scores[train_domain]["ner_prob"] = ner_prob |
| |
|
| | logging.info( |
| | f"Saving best cross_domain_f1 scores to file") |
| | |
| | self.best_final_results() |
| |
|
| | |
| |
|
| | self.best_intermediate_results({ |
| | "domain": train_domain, |
| | "balance": balance, |
| | "pos_prob": pos_prob, |
| | "ner_prob": ner_prob, |
| | "train": train_results, |
| | "test": { |
| | 'all': test_results, |
| | 'cross_domain_f1': cross_domain_f1 |
| | } |
| | }) |
