def check_email():
# set script to run at intervals (time.sleep?) - morn, mid-day, afternoon
current_time = datetime.now()
previous_check = cpm.unpickle(os.path.join(pkl_dir, 'last_check_time.pkl'))
print previous_check
emails = cpm.get_emails('INBOX', previous_check)
if not emails: # avoid unpickling if empty inbox
return
feature_model = cpm.unpickle(os.path.join(pkl_dir, 'final_vec.pkl'))
classifier_model = cpm.unpickle(os.path.join(pkl_dir, 'final_model.pkl'))
print 'classifier model', classifier_model
for email in emails:
if email.sent_at > previous_check:
clean_b = cpm.clean_raw_txt(email.body)
clean_s = cpm.clean_raw_txt(email.subject)
print 'clean_email', clean_s, clean_b
email_features = feature_model.transform([clean_b+clean_s])
print 'email_bag', email_features.shape
classifier_result = classifier_model.predict(email_features)
eval_email(classifier_result, email)
cpm.pickle(current_time, os.path.join(pkl_dir,'last_check_time.pkl'))
def setup_model(model_name, model, X_train, y_train, feature_names, save=False):
model = build_model(model, X_train, y_train)
cross_validate(model_name, model, X_train, y_train)
print
request_feature_rank(model_name, model)
if save:
model_fn='final_'+ model_name + '_.pkl'
cpm.pickle(model, os.path.join(pkl_dir, model_fn))
print "New classifier saved."
return model
