alg_NMF.fit(data_train.build_full_trainset())
end = time.time()
print("***********************************************")
print("Exe time:")
print(end - start)
# %% Best Hyper-parameters Training - SVD
alg_SVD = SVD()
alg_SVD.biased = True
alg_SVD.n_epochs = 50
alg_SVD.n_factors = 35
alg_SVD.reg_pu = 0.1
alg_SVD.reg_qi = 0.1
alg_SVD.verbose = True
start = time.time()
alg_SVD.fit(data_train.build_full_trainset())
end = time.time()
print("***********************************************")
print("Exe time:")
print(end - start)
# %% Best Hyper-parameters Training - Slope One
alg_SL1 = SlopeOne()
start = time.time()
# *****************************************************************************
# %% 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
Predict_Test = []
for line in data_test:
Predict_Test.append(alg.predict(str(line[1]), str(line[0])).est)
testset_reordered.to_csv("testset_reordered.csv", index=False)
# # Train Algorithms
# Based on each gridsearch, we apply the same parameters for each algorithms on
# sample test set to get individual predictions.
# ## SVD
# In[ ]:
#SVD with baselines
algo = SVD()
algo.n_factors = 400
algo.verbose = False
algo.biased = True
algo.reg_all = 0.1
algo.lr_all = 0.01
algo.n_epochs = 500
algo.random_state = seed
print("Training SVD...")
algo.fit(trainset)
print("Computing predictions for SVD... \n")
test_predictions_svd = algo.test(
testset) #Get real predictions to append to big final matrix
# In[ ]:
