file.write(str(Train_CV.cv_results) + "\n \n")
file.write("************************************************************ \n")
file.close()
# *****************************************************************************
# %% Best Hyper-parameters Training:
# Training over whole training dataset, using best hyper-parameters
alg = SVD()
alg.biased = Train_CV.best_params['rmse']['biased']
alg.n_epochs = Train_CV.best_params['rmse']['n_epochs']
alg.n_factors = Train_CV.best_params['rmse']['n_factors']
alg.reg_pu = Train_CV.best_params['rmse']['reg_pu']
alg.reg_qi = Train_CV.best_params['rmse']['reg_qi']
alg.reg_bu = Train_CV.best_params['rmse']['reg_bu']
alg.reg_bi = Train_CV.best_params['rmse']['reg_bi']
alg.lr_pu = Train_CV.best_params['rmse']['lr_all']
alg.lr_qi = Train_CV.best_params['rmse']['lr_all']
alg.verbose = True
alg.random_state = 0
alg.fit(data_train.build_full_trainset())
# *****************************************************************************
# %% Loading Test Data
file_path = "Data/sample_submission.csv"
data_test = utils.load_data_desired(file_path)
# *****************************************************************************
# %% Predicting test data labels
Train_CV.cv_results['mean_test_rmse'], '.k')
plt.xlabel('Number of Factores')
plt.ylabel('RMSE')
plt.grid()
plt.title('3-Fold CV - Number of Factors')
plt.savefig('3_fold_CV_Factors.png')
# %% Best Hyper-parameters Training
alg = SVD()
alg.biased = Grid_Search_Result.best_params['rmse']['biased']
alg.n_epochs = Grid_Search_Result.best_params['rmse']['n_epochs']
alg.n_factors = Grid_Search_Result.best_params['rmse']['n_factors']
alg.reg_pu = Grid_Search_Result.best_params['rmse']['reg_pu']
alg.reg_qi = Grid_Search_Result.best_params['rmse']['reg_qi']
alg.reg_bu = Grid_Search_Result.best_params['rmse']['reg_bu']
alg.reg_bi = Grid_Search_Result.best_params['rmse']['reg_bi']
alg.lr_pu = Grid_Search_Result.best_params['rmse']['lr_all']
alg.lr_qi = Grid_Search_Result.best_params['rmse']['lr_all']
alg.fit(data_train.build_full_trainset())
# %% Loading Test Data
file_path = "Data/sample_submission.csv"
data_test = utils.load_data_desired(file_path)
# %% Prediction
Predict_Test = []
for line in data_test:
Predict_Test.append(alg.predict(str(line[1]), str(line[0])).est)
