mape = abs(pred[i] - target_train[i]) / target_train[i]
cleaned.append((feature_train[i], target_train[i], pred[i], mape))
cleaned.sort(key = lambda x: x[3])
cleaned = cleaned[: int(len(pred) * 0.80)]
feature_train, target_train, pred, mape = zip(* cleaned)
start_time = time()
model.fit(feature_train, target_train)
print("re-training time after cleaning outliers", round(time() - start_time, 3), "s")
# Start prediction using cross validation set
start_time = time()
pred = model.predict(feature_test)
print("predition time", round(time() - start_time, 3), "s")
# Show MAPE score of trained model with cross validation set
score = mape_score(pred, target_test)
print('MAPE score ', score)
# Save model for later use
save_model(model, './model', MODEL_NAME)
from result_helper import save_result
data_file = '../../processed_data/prediction_data/to_predict_features'
test_metadata_list, test_features_list, test_target_list = load_and_split_data(data_file)
test_features_list = feature_selection.transform(test_features_list)
if __name__ == "__main__":
# Load and split training data into metadata, features, target
data_file = '../../processed_data/features_data/training_data/features_ready'
metadata_list, features_list, target_list = load_and_split_data(data_file)
# Split data into training set and cross validation set
feature_train, feature_test, target_train, target_test = train_test_split(
features_list,
target_list,
test_size=TEST_SIZE,
random_state=RANDOM_STATE)
# Start training
start_time = time()
model = create_model()
model.fit(feature_train, target_train)
print("training time", round(time() - start_time, 3), "s")
# Start prediction using cross validation set
start_time = time()
pred = model.predict(feature_test)
print("predition time", round(time() - start_time, 3), "s")
# Show MAPE score of trained model with cross validation set
score = mape_score(pred, target_test)
print('MAPE score ', score)
# Save model for later use
save_model(model, './model', MODEL_NAME)