| from pt_variety_identifier.src.results import Results as BaseResults |
| import logging |
| import os |
| from joblib import dump |
| import pandas as pd |
|
|
|
|
| class Results(BaseResults): |
| def __init__(self, filepath, DOMAINS) -> None: |
| super().__init__(filepath, DOMAINS) |
|
|
| def process(self, cross_domain_f1, train_domain, test_results, grid_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]["params"] = grid_results.best_params_ |
| 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() |
|
|
| with open(os.path.join(self.filepath, "models", f"{train_domain}.joblib"), "wb") as f: |
| dump(grid_results.best_estimator_, f) |
|
|
| self.best_intermediate_results({ |
| "domain": train_domain, |
| "balance": balance, |
| "pos_prob": pos_prob, |
| "ner_prob": ner_prob, |
| "train": { |
| "best_score": grid_results.best_score_, |
| }, |
| "test": { |
| 'all': test_results, |
| 'cross_domain_f1': cross_domain_f1 |
| }, |
| "best_params": grid_results.best_params_ |
| }) |
| |
| self.other_results({ |
| "domain": train_domain, |
| "balance": balance, |
| "pos_prob": pos_prob, |
| "ner_prob": ner_prob, |
| "train": { |
| "cv_results": pd.DataFrame(grid_results.cv_results_).to_json() |
| }, |
| "test": test_results, |
| }) |
