def run_model(model_name, dataset_name, test_size=0.3, clean=False):
print('*' * 70)
print('\tThis is %s model trained on %s with test_size = %.2f' %
(model_name, dataset_name, test_size))
print('*' * 70 + '\n')
model_manager = utils.ModelManager(dataset_name, test_size)
try:
trainset = model_manager.load_model('trainset')
testset = model_manager.load_model('testset')
except OSError:
ratings = DataSet.load_dataset(name=dataset_name)
trainset, testset = DataSet.train_test_split(ratings,
test_size=test_size)
model_manager.save_model(trainset, 'trainset')
model_manager.save_model(testset, 'testset')
'''Do you want to clean workspace and retrain model again?'''
'''if you want to change test_size or retrain model, please set clean_workspace True'''
model_manager.clean_workspace(clean)
if model_name == 'ItemCF':
model = ItemBasedCF()
elif model_name == 'UserCF':
model = UserBasedCF()
else:
raise ValueError('No model named ' + model_name)
model.fit(trainset)
recommend_test(model, [1, 100, 233, 666, 888])
model.test(testset)
def run_model(model_name, dataset_name, test_size=0.3, clean=False):
print('*' * 70)
print('\tThis is %s model trained on %s with test_size = %.2f' % (model_name, dataset_name, test_size))
print('*' * 70 + '\n')
model_manager = utils.ModelManager(dataset_name, test_size)
try:
trainset = model_manager.load_model('trainset')
testset = model_manager.load_model('testset')
except OSError:
ratings = DataSet.load_dataset(name=dataset_name)
trainset, testset = DataSet.train_test_split(ratings, test_size=test_size)
model_manager.save_model(trainset, 'trainset')
model_manager.save_model(testset, 'testset')
'''Do you want to clean workspace and retrain model again?'''
'''if you want to change test_size or retrain model, please set clean_workspace True'''
model_manager.clean_workspace(clean)
if model_name == 'UserCF':
model = UserBasedCF()
elif model_name == 'ItemCF':
model = ItemBasedCF()
elif model_name == 'Random':
model = RandomPredict()
elif model_name == 'MostPopular':
model = MostPopular()
elif model_name == 'UserCF-IIF':
model = UserBasedCF(use_iif_similarity=True)
elif model_name == 'ItemCF-IUF':
model = ItemBasedCF(use_iuf_similarity=True)
elif model_name == 'LFM':
# K, epochs, alpha, lamb, n_rec_movie
model = LFM(10, 20, 0.1, 0.01, 10)
else:
raise ValueError('No model named ' + model_name)
model.fit(trainset)
recommend_test(model, [1, 100, 233, 666, 888])
model.test(testset)