def hyperparameter_tuning(): train_x_data = getTrainXData('train_scored.csv') train_y_data = getTrainYData('train_scored_y.csv') test_x_data = getTestXData('test_scored.csv') params = { 'max_features': [8, 16, 32, 'sqrt', 'auto'], 'n_estimators': [2500, 3000, 3500, 4000, 4500, 5000] } est = ExtraTreesRegressor() print est.get_params().keys() gs_cv = GridSearchCV(est, params) gs_cv.fit(train_x_data, train_y_data) print gs_cv.best_params_
print("after shuffling: {}".format(combined_filenames)) # shuffling works ok. i = 0 score_rows_list = [] scores_dict = {} mse_dict = {} mae_dict = {} mape_dict = {} scores_dict_f3 = {} mse_dict_f3 = {} mae_dict_f3 = {} mape_dict_f3 = {} test_start_time = time.clock() getparams_dict = aux_reg_regressor.get_params(deep=True) print("getparams_dict: ", getparams_dict) getparams_df = pd.DataFrame.from_dict(data=getparams_dict,orient='index') getparams_df.to_csv(analysis_path + model_id + str(lstm_model)[:-4] + "getparams.csv") model_as_pkl_filename = analysis_path + model_id + str(lstm_model)[:-4] + ".pkl" joblib.dump(aux_reg_regressor,filename=model_as_pkl_filename) #np.savetxt(analysis_path + "rf5getparams.txt",fmt='%s',X=str(aux_reg_regressor.get_params(deep=True))) #np.savetxt(analysis_path + "rf5estimatorparams.txt",fmt='%s',X=aux_reg_regressor.estimator_params) USELESS #np.savetxt(analysis_path + "rf5classes.txt",fmt='%s',X=aux_reg_regressor.classes_) #np.savetxt(analysis_path + "rf5baseestim.txt",fmt='%s',X=aux_reg_regressor.base_estimator_) #TODO: CHANGE THIS BACK IF CUT SHORT!! for files in combined_filenames: print("filename", files) i += 1 data_load_path = test_path + '/data/' + files[0]