def retrain(session):
'''
Retrains a model using validated samples and original training data if retrain_check() evaluates
to True.
'''
retrain = retrain_check(session)
if retrain:
logger.info("Smartie is retraining a model!")
attachments = fetch_validated_attachments(session)
X, y = train.prepare_samples(attachments)
results, score, best_estimator, params = train.train(
X,
y,
weight_classes=True,
n_iter_search=150,
score='roc_auc',
random_state=123)
logger.info("Smartie is done retraining a model!")
last_score = fetch_last_score(session)
better_model = True if last_score < score else False
if better_model:
train.pickle_model(best_estimator)
logger.info("Smartie has pickled the new model!")
else:
pass
insert_model(session, results, params, score)
else:
logger.info("Smartie decided not to retrain a new model!")
def test_fetch_last_score(self):
results = {'c': 'd'}
params = {'a': 'b'}
score = .99
with session_scope(self.dal) as session:
insert_model(session, results=results, params=params, score=score)
with session_scope(self.dal) as session:
score = fetch_last_score(session)
result = score
expected = .99
self.assertEqual(result, expected)
def test_insert_model(self):
results = {'c': 'd'}
params = {'a': 'b'}
score = .99
with session_scope(self.dal) as session:
insert_model(session, results=results, params=params, score=score)
result = []
with session_scope(self.dal) as session:
models = session.query(Model).all()
for m in models:
model = object_as_dict(m)
model.pop('create_date')
result.append(model)
expected = [{
'id': 1,
'results': results,
'params': params,
'score': score
}]
self.assertCountEqual(result, expected)