def get_params(): dataFeeder = TrafficFeeder() out = Parallel(n_jobs=-1)(delayed(put_queue)(n_input, dataFeeder) for n_input in range(4, 21)) return out
ax.legend() plt.show() if __name__ == '__main__': best_estimator = None best_score = np.Inf for loop in np.arange(1, 20): n_input = 4 n_periodic = 1 n_hidden = 15 neural_shape = [n_input + n_periodic, n_hidden, 1] Q = 0.09 epsilon = 0.55 dataFeeder = TrafficFeeder() X_train, y_train = dataFeeder.fetch_traffic_training( n_input, 1, (40, 46)) X_test, y_test = dataFeeder.fetch_traffic_test(n_input, 1, (46, 48)) # retrieve = [n_input+1,(X_train,y_train,X_test,y_test)] acoNet = ACOEstimator(Q=Q, epsilon=epsilon) fit_param = {"neural_shape": neural_shape} acoNet.fit(X_train, y_train, **fit_param) fit_param["weights_matrix"] = acoNet.best_archive neuralNet = NeuralFlowRegressor() neuralNet.fit(X_train, y_train, **fit_param) y_pred = dataFeeder.convert(neuralNet.predict(X_test)) score = np.sqrt(mean_squared_error(y_pred, y_test)) if (score < best_score): best_estimator = acoNet print score