def train(train_corpus: str,
dev_corpus: str,
c1: float = 0.0,
c2: float = 0.0,
algorithm: str = 'lbfgs',
max_iterations: int = 100,
all_possible_transitions: bool = False,
window_size: int = 1,
model_filename: str = None,
_run: Run = None,
_log: logger = None):
"""
running crf experiment
"""
_run.add_resource(train_corpus)
_run.add_resource(dev_corpus)
train_sents, _ = get_tagged_sents_and_words(train_corpus)
dev_sents, _ = get_tagged_sents_and_words(dev_corpus)
X_train = [sent2features(s, window_size) for s in train_sents]
y_train = [sent2labels(s) for s in train_sents]
X_dev = [sent2features(s, window_size) for s in dev_sents]
y_dev = [sent2labels(s) for s in dev_sents]
crf = sklearn_crfsuite.CRF(
algorithm=algorithm,
c1=c1,
c2=c2,
max_iterations=max_iterations,
all_possible_transitions=all_possible_transitions,
model_filename=model_filename,
)
crf.fit(X_train, y_train)
y_pred = crf.predict(X_dev)
overall, by_type = evaluate(y_dev, y_pred)
_run.info[f'overall_f1'] = overall.f1_score
_run.log_scalar('overall_f1', overall.f1_score)
_run.info[f'overall_precision'] = overall.precision
_run.log_scalar('overall_precision', overall.precision)
_run.info[f'overall_recall'] = overall.recall
_run.log_scalar('overall_recall', overall.recall)
_log.info(f'Overall F1 score: {overall.f1_score}')
for _, key in enumerate(sorted(by_type.keys())):
for metric_key in by_type[key]._fields:
metric_val = getattr(by_type[key], metric_key)
_run.info[f'{key}-{metric_key}'] = metric_val
_run.log_scalar(f'{key}-{metric_key}', metric_val)
_log.info(f'{key}-{metric_key}: {metric_val}')
if model_filename is not None:
_log.info(f'saving to: {model_filename}.pkl')
joblib.dump(crf, f'{model_filename}.pkl')
_run.add_artifact(f'{model_filename}.pkl')
def test(model_filename: str,
test_corpus: str,
window_size: int = 5,
_run: Run = None,
_log: logger = None):
_run.add_resource(test_corpus)
_run.add_resource(f'{model_filename}.pkl')
test_sents, _ = get_tagged_sents_and_words(test_corpus)
X_test = [sent2features(s, window_size) for s in test_sents]
y_test = [sent2labels(s) for s in test_sents]
_log.info(f'load from: {model_filename}.pkl')
crf = sklearn_crfsuite.CRF(model_filename=model_filename)
y_pred = crf.predict(X_test)
overall, by_type = evaluate(y_test, y_pred)
_run.info[f'overall_f1'] = overall.f1_score
_run.log_scalar('overall_f1', overall.f1_score)
_run.info[f'overall_precision'] = overall.precision
_run.log_scalar('overall_precision', overall.precision)
_run.info[f'overall_recall'] = overall.recall
_run.log_scalar('overall_recall', overall.recall)
_log.info(f'Overall F1 score: {overall.f1_score}')
for _, key in enumerate(sorted(by_type.keys())):
for metric_key in by_type[key]._fields:
metric_val = getattr(by_type[key], metric_key)
_run.info[f'{key}-{metric_key}'] = metric_val
_run.log_scalar(f'{key}-{metric_key}', metric_val)
_log.info(f'{key}-{metric_key}: {metric_val}')