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_bef_tuning_" + date + ".pickle", "rb")))
results['val_rmse_aft_tuning'].append(pickle.load(open( "./out/v6d_val_rmse_aft_tuning_" + date + ".pickle", "rb")))
results['test_rmse_bef_tuning'].append(pickle.load(open( "./out/v6d_test_rmse_bef_tuning_" + date + ".pickle", "rb")))
results['test_rmse_aft_tuning'].append(pickle.load(open( "./out/v6d_test_rmse_aft_tuning_" + date + ".pickle", "rb")))
results['test_mape_bef_tuning'].append(pickle.load(open( "./out/v6d_test_mape_bef_tuning_" + date + ".pickle", "rb")))
results['test_mape_aft_tuning'].append(pickle.load(open( "./out/v6d_test_mape_aft_tuning_" + date + ".pickle", "rb")))
results['test_mae_bef_tuning'].append(pickle.load(open( "./out/v6d_test_mae_bef_tuning_" + date + ".pickle", "rb")))
results['test_mae_aft_tuning'].append(pickle.load(open( "./out/v6d_test_mae_aft_tuning_" + date + ".pickle", "rb")))
ests[date] = pickle.load(open( "./out/v6d_test_est_aft_tuning_" + 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_bef_tuning_" + df.iloc[pred_day]['date'].strftime("%Y-%m-%d") + ".pickle", "wb"))
pickle.dump(rmse_aft_tuning, open("./out/v6d_val_rmse_aft_tuning_" + df.iloc[pred_day]['date'].strftime("%Y-%m-%d") + ".pickle", "wb"))
pickle.dump(test_rmse_bef_tuning, open("./out/v6d_test_rmse_bef_tuning_" + df.iloc[pred_day]['date'].strftime("%Y-%m-%d") + ".pickle", "wb"))
pickle.dump(test_mape_bef_tuning, open("./out/v6d_test_mape_bef_tuning_" + df.iloc[pred_day]['date'].strftime("%Y-%m-%d") + ".pickle", "wb"))
pickle.dump(test_mae_bef_tuning, open("./out/v6d_test_mae_bef_tuning_" + df.iloc[pred_day]['date'].strftime("%Y-%m-%d") + ".pickle", "wb"))
pickle.dump(test_rmse_aft_tuning, open("./out/v6d_test_rmse_aft_tuning_" + df.iloc[pred_day]['date'].strftime("%Y-%m-%d") + ".pickle", "wb"))
pickle.dump(test_mape_aft_tuning, open("./out/v6d_test_mape_aft_tuning_" + df.iloc[pred_day]['date'].strftime("%Y-%m-%d") + ".pickle", "wb"))
pickle.dump(test_mae_aft_tuning, open("./out/v6d_test_mae_aft_tuning_" + df.iloc[pred_day]['date'].strftime("%Y-%m-%d") + ".pickle", "wb"))
pickle.dump(est, open("./out/v6d_test_est_aft_tuning_" + df.iloc[pred_day]['date'].strftime("%Y-%m-%d") + ".pickle", "wb"))