def deserialize_mlp_regressor(model_dict): model = MLPRegressor(**model_dict['params']) model.coefs_ = model_dict['coefs_'] model.loss_ = model_dict['loss_'] model.intercepts_ = model_dict['intercepts_'] model.n_iter_ = model_dict['n_iter_'] model.n_layers_ = model_dict['n_layers_'] model.n_outputs_ = model_dict['n_outputs_'] model.out_activation_ = model_dict['out_activation_'] return model
print('mean squared error = {}'.format(mse)) y_pred = est.predict(X_test) r_squared = est.score(X_test, y_test) print('r squared = {}'.format(r_squared)) else: mse = mean_squared_error(y_test, est.predict(X_test_std)) print('mean squared error = {}'.format(mse)) y_pred = est.predict(X_test_std) r_squared = est.score(X_test_std, y_test) print('r squared = {}'.format(r_squared)) # test_score = np.zeros((n,), dtype=np.float64) for i, y_pred in enumerate(est.staged_predict(X_test)): test_score[i] = est.loss_(y_test, y_pred) plt.figure(figsize=(12, 6)) plt.subplot(1, 2, 1) plt.title('Deviance') plt.plot(np.arange(n) + 1, est.train_score_, 'b-', label='Training Set Deviance') plt.plot(np.arange(n) + 1, test_score, 'r-', label='Test Set Deviance') plt.legend(loc='upper right') plt.xlabel('Boosting Iterations') plt.ylabel('Deviance') # ############################################################################# # Plot feature importance feature_importance = est.feature_importances_ # make importances relative to max importance