Python NMF.reg_qi Beispiele

Beispiel #1

plt.plot(Train_CV.cv_results['param_n_factors'],
         Train_CV.cv_results['mean_test_rmse'], '.k')
plt.xlabel('Number of Factors')
plt.ylabel('RMSE')
plt.grid()
plt.title('3-Fold CV - Number of Factors')
plt.savefig('3_fold_CV_Reg_Param_NMF_n_factors.png')

# %% Best Hyper-parameters Training
alg = NMF()

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']

start = time.time()

alg.fit(data_train.build_full_trainset())

end = time.time()
print("***********************************************")
print("Exe time:")
print(end - start)

# %% Loading Test Data
file_path = "Data/sample_submission.csv"
data_test = utils.load_data_desired(file_path)

# %% Prediction

Beispiel #2

Datei: NMF_Model_Analysis.py Projekt: Bolisongman/ML_Second_Project

file.write("+ CV Summary: \n \n")
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 = NMF()

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.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 = []

Beispiel #3

Datei: Seventh_Attempt_Combining_Methods.py Projekt: Bolisongman/ML_Second_Project

reader = Reader(line_format='item user rating',
                sep=',',
                rating_scale=(1, 5),
                skip_lines=1)

data_train = Dataset.load_from_file(file_path, reader=reader)

# %% Best Hyper-parameters Training - NMF
alg_NMF = NMF()

alg_NMF.biased = False
alg_NMF.n_epochs = 50
alg_NMF.n_factors = 35
alg_NMF.reg_pu = 0.1
alg_NMF.reg_qi = 0.1
alg_NMF.verbose = True

start = time.time()

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

Beispiel #4

Datei: Sixth_Attempt_NMF_and_Prior_on_Rating.py Projekt: Bolisongman/ML_Second_Project

reader = Reader(line_format='item user rating',
                sep=',',
                rating_scale=(1, 5),
                skip_lines=1)

data_train = Dataset.load_from_file(file_path, reader=reader)

# %% Best Hyper-parameters Training
alg = NMF()

alg.biased = False
alg.n_epochs = 50
alg.n_factors = 35
alg.reg_pu = 0.1
alg.reg_qi = 0.1
alg.verbose = True

start = time.time()

alg.fit(data_train.build_full_trainset())

end = time.time()
print("***********************************************")
print("Exe time:")
print(end - start)

# %% Loading train data
file_path = "Data/data_train.csv"
data_train = utils.load_data_desired(file_path)