i am having problem with StockPricePrediction_v6d_xgboost

[tesyon]

hello sir, when I try to run the algorithm with a different dataset I am having this error [Errno 2] No such file or directory: './out/v6d_val_rmse_bef_tuning_2016-02-09.pickle'

the code line that creates this error is the following

results = defaultdict(list) ests = {} # the predictions** date_list = ['2016-01-04', '2016-02-09', '2016-06-07', '2016-08-22', '2016-11-07', '2017-01-23', '2017-04-10',
'2017-09-07', '2017-11-29', '2018-03-05', '2018-05-07', '2018-09-04']

for date in date_list: results['date'].append(date) results['val_rmse_bef_tuning'].append(pickle.load(open( "./out/v6d_val_rmse_beftuning" + date + ".pickle", "rb"))) results['val_rmse_aft_tuning'].append(pickle.load(open( "./out/v6d_val_rmse_afttuning" + date + ".pickle", "rb"))) results['test_rmse_bef_tuning'].append(pickle.load(open( "./out/v6d_test_rmse_beftuning" + date + ".pickle", "rb"))) results['test_rmse_aft_tuning'].append(pickle.load(open( "./out/v6d_test_rmse_afttuning" + date + ".pickle", "rb"))) results['test_mape_bef_tuning'].append(pickle.load(open( "./out/v6d_test_mape_beftuning" + date + ".pickle", "rb"))) results['test_mape_aft_tuning'].append(pickle.load(open( "./out/v6d_test_mape_afttuning" + date + ".pickle", "rb"))) results['test_mae_bef_tuning'].append(pickle.load(open( "./out/v6d_test_mae_beftuning" + date + ".pickle", "rb"))) results['test_mae_aft_tuning'].append(pickle.load(open( "./out/v6d_test_mae_afttuning" + date + ".pickle", "rb"))) ests[date] = pickle.load(open( "./out/v6d_test_est_afttuning" + date + ".pickle", "rb"))

results = pd.DataFrame(results) results_>

[NGYB]

Did you run this cell first? It will create the pickle files required for the next cell.

# Put results into pickle pickle.dump(rmse_bef_tuning, open("./out/v6d_val_rmse_beftuning" + df.iloc[pred_day]['date'].strftime("%Y-%m-%d") + ".pickle", "wb")) pickle.dump(rmse_aft_tuning, open("./out/v6d_val_rmse_afttuning" + df.iloc[pred_day]['date'].strftime("%Y-%m-%d") + ".pickle", "wb")) pickle.dump(test_rmse_bef_tuning, open("./out/v6d_test_rmse_beftuning" + df.iloc[pred_day]['date'].strftime("%Y-%m-%d") + ".pickle", "wb")) pickle.dump(test_mape_bef_tuning, open("./out/v6d_test_mape_beftuning" + df.iloc[pred_day]['date'].strftime("%Y-%m-%d") + ".pickle", "wb")) pickle.dump(test_mae_bef_tuning, open("./out/v6d_test_mae_beftuning" + df.iloc[pred_day]['date'].strftime("%Y-%m-%d") + ".pickle", "wb")) pickle.dump(test_rmse_aft_tuning, open("./out/v6d_test_rmse_afttuning" + df.iloc[pred_day]['date'].strftime("%Y-%m-%d") + ".pickle", "wb")) pickle.dump(test_mape_aft_tuning, open("./out/v6d_test_mape_afttuning" + df.iloc[pred_day]['date'].strftime("%Y-%m-%d") + ".pickle", "wb")) pickle.dump(test_mae_aft_tuning, open("./out/v6d_test_mae_afttuning" + df.iloc[pred_day]['date'].strftime("%Y-%m-%d") + ".pickle", "wb")) pickle.dump(est, open("./out/v6d_test_est_afttuning" + df.iloc[pred_day]['date'].strftime("%Y-%m-%d") + ".pickle", "wb"))