| import numpy as np |
| import logging |
| import os |
|
|
|
|
| class Tunning: |
| def __init__(self, data, domains, Results, Trainer, Tester, sample_size, CURRENT_PATH, CURRENT_TIME, params=None) -> None: |
| self.data = data |
| self.Trainer = Trainer |
| self.Tester = Tester |
| self._DOMAINS = domains |
| self.sample_size = sample_size |
| self.CURRENT_PATH = CURRENT_PATH |
| self.CURRENT_TIME = CURRENT_TIME |
| |
| self.results = Results(os.path.join( |
| self.CURRENT_PATH, "out", str(CURRENT_TIME)), self._DOMAINS) |
| |
| self.params = params |
|
|
| def run(self, start_pos_prob=0.0, stop_pos_prob=1.0, start_ner_prob=0.0, stop_ner_prob=1.0): |
|
|
| logging.info(f"Start pos_prob={start_pos_prob}, stop_pos_prob={stop_pos_prob}") |
|
|
| test_dataset = self.data.load_test_set() |
|
|
| for pos_prob in np.arange(start_pos_prob, stop_pos_prob + 0.1, 0.1): |
| for ner_prob in np.arange(start_ner_prob, stop_ner_prob + 0.1, 0.1): |
| for domain in self._DOMAINS: |
| logging.info( |
| f"Running {domain} pos_prob={pos_prob}, ner_prob={ner_prob}") |
|
|
| dataset = self.data.load_domain( |
| domain, balance=True, pos_prob=pos_prob, ner_prob=ner_prob, sample_size=self.sample_size) |
| |
| trainer = self.Trainer(dataset, self.params) |
| |
| results, best_model = trainer.train() |
| |
| validation_results = self.Tester( |
| test_dataset, best_model, train_domain=domain).validate() |
|
|
| logging.info( |
| f"Cross domain f1 score: {validation_results['f1']} | test_results: {validation_results}") |
| |
| self.results.process(validation_results['f1'], domain, validation_results, |
| results, balance=True, pos_prob=pos_prob, ner_prob=ner_prob) |
|
|
