File size: 2,034 Bytes
ebdb5af | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | 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)
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