def predict_score(sub, model):
save_price_path = '../data/tags_' + sub + '_results'
data, target = TrainData.load_model(save_price_path + '_tag',
'../model/' + sub + '_train_data.pkl')
train_data = data[:-1]
predict_data = data[-1]
if model == 'KNN':
model = knn_classifier(train_data, target)
elif model == 'LR':
model = logistic_regression_classifier(train_data, target)
elif model == 'RF':
model = random_forest_classifier(train_data, target)
elif model == 'DT':
model = decision_tree_classifier(train_data, target)
else:
model = gradient_boosting_classifier(train_data, target)
predicted = model.predict(predict_data)
return predicted[0] + 1
def predict_score(sub, m):
save_price_path = last_path + '/data/tags_' + sub + '_results'
data, target = TrainData.load_model(
save_price_path + '_tag',
last_path + '/model/' + sub + '_train_data.pkl')
train_data = data[:-1]
predict_data = data[-1]
try:
model = joblib.load(last_path + "/model_classifiers/model" + sub +
"_LR.pkl")
predicted = model.predict(predict_data)
except:
if m == 'KNN':
model = knn_classifier(train_data, target)
elif m == 'LR':
model = logistic_regression_classifier(train_data, target)
elif m == 'RF':
model = random_forest_classifier(train_data, target)
elif m == 'DT':
model = decision_tree_classifier(train_data, target)
else:
model = gradient_boosting_classifier(train_data, target)
predicted = model.predict(predict_data)
return predicted[0] + 1