def fit(self,
URM,
n_factors=10,
learning_rate=1e-4,
epochs=10,
user_regularization=0.001,
positive_item_regularization=0.001,
negative_item_regularization=0.001,
evaluate_every=1):
self.URM = URM
self.epochs = epochs
self.n_users = self.URM.shape[0]
self.n_items = self.URM.shape[1]
e = MFBPR_Epoch(
URM,
n_factors=n_factors,
learning_rate=learning_rate,
user_regularization=user_regularization,
positive_item_regularization=positive_item_regularization,
negative_item_regularization=negative_item_regularization)
print('Fitting MFBPR...')
for numEpoch in range(self.epochs):
print('Epoch:', numEpoch)
e.epochIteration()
if (numEpoch + 1) % evaluate_every == 0:
self.user_factors, self.item_factors = e.get_user_item_factors(
)
recs = self.recommend_batch(userids=d.get_target_playlists())
self.evaluate(recs, d.get_urm_test_1())
self.user_factors, self.item_factors = e.get_user_item_factors()
# let's see how fine it performs in the test set:
# getting as positive sample a semple in the test set but not in the training
trials = 10000
count_wrong = 0
for _ in range(trials):
test = d.get_urm_test_1()
user_id = np.random.choice(self.n_users)
user_seen_items = d.get_urm()[user_id, :].indices
test_items = test[user_id, :].indices
pos_item_id = np.random.choice(test_items)
neg_item_selected = False
while (not neg_item_selected):
neg_item_id = np.random.randint(0, self.n_items)
if (neg_item_id not in user_seen_items):
neg_item_selected = True
xui = np.dot(self.user_factors[user_id, :],
self.item_factors[pos_item_id, :])
xuj = np.dot(self.user_factors[user_id, :],
self.item_factors[neg_item_id, :])
xuij = xui - xuj
if xuij < 0:
count_wrong += 1
# print('u: {}, i: {}, j: {}. xui - xuj: {}'.format(user_id, pos_item_id, neg_item_id, xuij))
print('percentange of wrong preferences in test set: {}'.format(
count_wrong / trials))
def fit(self, URM_train=d.get_urm_train_1(), epochs=190, URM_test=d.get_urm_test_1(),
user_ids=d.get_target_playlists(),
batch_size=1000, validate_every_N_epochs=2, start_validation_after_N_epochs=191, lambda_i=0.0,
lambda_j=0.0, learning_rate=0.01, topK=1500, sgd_mode='adagrad'):
"""
train the model finding matrix W
:param epochs(int)
:param batch_size(int) after how many items the params should be updated
:param lambda_i(float) first regularization term
:param lambda_j(float) second regularization term
:param learning_rate(float) algorithm learning rate
:param topK(int) how many elements should be taken into account while computing URM*W
:param sgd_mode(string) optimization algorithm
:param URM_train(csr_matrix) the URM used to train the model. Either the full or the validation one
:param URM_test(csr_matrix) needed if we'd like to perform validation
:param user_ids(list) needed if we'd like to perform validation
:param validate_every_N_epochs(int) how often the MAP evaluation should be displayed
:param start_validation_after_N_epochs(int)
"""
self.URM_train = URM_train.T
self.n_users = URM_train.shape[0]
self.n_items = URM_train.shape[1]
self.sgd_mode = sgd_mode
print('before cython')
from cythoncompiled.SLIM_BPR.SLIM_BPR_Cython_Epoch import SLIM_BPR_Cython_Epoch
self.cythonEpoch = SLIM_BPR_Cython_Epoch(self.URM_train,
sparse_weights=False,
topK=topK,
learning_rate=learning_rate,
li_reg=lambda_i,
lj_reg=lambda_j,
batch_size=100,
symmetric=True,
sgd_mode=sgd_mode)
print('after cython')
# Cal super.fit to start training
self._fit_alreadyInitialized(epochs=epochs,
logFile=None,
URM_test=URM_test,
user_ids=user_ids,
filterTopPop=False,
minRatingsPerUser=1,
batch_size=batch_size,
validate_every_N_epochs=validate_every_N_epochs,
start_validation_after_N_epochs=start_validation_after_N_epochs,
lambda_i=lambda_i,
lambda_j=lambda_j,
learning_rate=learning_rate,
topK=topK)
print('after already_initialized')
def run(self, urm_train=None, urm=None, urm_test=None, targetids=None,
factors=100, regularization=0.01, iterations=100, alpha=25, with_scores=False, export=True, verbose=True):
"""
Run the model and export the results to a file
Parameters
----------
num_factors : int, number of latent factors
urm : csr matrix, URM. If None, used: data.get_urm_train(). This should be the
entire URM for which the targetids corresponds to the row indexes.
urm_test : csr matrix, urm where to test the model. If None, use: data.get_urm_test()
targetids : list, target user ids. If None, use: data.get_target_playlists()
Returns
-------
recs: (list) recommendations
map10: (float) MAP10 for the provided recommendations
"""
_urm = data.get_urm_train_1()
_icm = data.get_icm()
_urm_test = data.get_urm_test_1()
_targetids = data.get_target_playlists()
#_targetids = data.get_all_playlists()
start = time.time()
urm_train = _urm if urm_train is None else urm_train
#urm = _urm if urm is None else urm
urm_test = _urm_test if urm_test is None else urm_test
targetids = _targetids if targetids is None else targetids
self.fit(urm=urm_train, factors=factors, regularization=regularization, iterations=iterations, alpha=alpha)
recs = self.recommend_batch(userids=targetids, with_scores=with_scores, verbose=verbose)
map10 = None
if len(recs) > 0:
map10 = self.evaluate(recs, test_urm=urm_test, verbose=verbose)
else:
log.warning('No recommendations available, skip evaluation')
if export:
exportcsv(recs, path='submission', name=self.name, verbose=verbose)
if verbose:
log.info('Run in: {:.2f}s'.format(time.time()-start))
return recs, map10
def run(self, distance, ucm_train=None, urm=None, urm_test=None, targetids=None, k=100, shrink=10, threshold=0,
implicit=True, alpha=None, beta=None, l=None, c=None, with_scores=False, export=True, verbose=True):
"""
Run the model and export the results to a file
Parameters
----------
distance : str, distance metric
targetids : list, target user ids. If None, use: data.get_target_playlists()
k : int, K nearest neighbour to consider
shrink : float, shrink term used in the normalization
threshold : float, all the values under this value are cutted from the final result
implicit : bool, if true, treat the URM as implicit, otherwise consider explicit ratings (real values) in the URM
Returns
-------
recs: (list) recommendations
map10: (float) MAP10 for the provided recommendations
"""
start = time.time()
_ucm_train = data.get_ucm_train()
_urm = data.get_urm_train_1()
_urm_test = data.get_urm_test_1()
_targetids = data.get_target_playlists()
ucm_train = _ucm_train if ucm_train is None else ucm_train
urm = _urm if urm is None else urm
urm_test = _urm_test if urm_test is None else urm_test
targetids = _targetids if targetids is None else targetids
self.fit(ucm_train, k=k, distance=distance, alpha=alpha, beta=beta, c=c, l=l, shrink=shrink, threshold=threshold, implicit=implicit)
recs = self.recommend_batch(targetids, urm=urm, with_scores=with_scores, verbose=verbose)
map10 = None
if len(recs) > 0:
map10 = self.evaluate(recs, test_urm=urm_test, verbose=verbose)
else:
log.warning('No recommendations available, skip evaluation')
if export:
exportcsv(recs, path='submission', name='{}_{}'.format(self.name,distance), verbose=verbose)
if verbose:
log.info('Run in: {:.2f}s'.format(time.time()-start))
return recs, map10
def validate(self,
user_ids=d.get_target_playlists(),
log_path=None,
normalize_similarity=[False],
damp_coeff=[1],
add_zeros_quota=[1],
loss_tolerance=[1e-6],
iteration_limit=[30],
use_incremental=[False]):
if log_path != None:
orig_stdout = sys.stdout
f = open(
log_path + '/' + self.name + ' ' + time.strftime('_%H-%M-%S') +
' ' + time.strftime('%d-%m-%Y') + '.txt', 'w')
sys.stdout = f
for ns in normalize_similarity:
for dc in damp_coeff:
for adq in add_zeros_quota:
for lt in loss_tolerance:
for il in iteration_limit:
for ui in use_incremental:
print(
self._print(normalize_similarity=ns,
add_zeros_quota=dc,
loss_tolerance=lt,
iteration_limit=il,
damp_coeff=dc,
use_incremental=ui))
self.fit(ICM=d.get_icm(),
URM_train=d.get_urm_train_1(),
normalize_similarity=ns,
add_zeros_quota=adq,
loss_tolerance=lt,
iteration_limit=il,
damp_coeff=dc,
use_incremental=ui)
recs = self.recommend_batch(
user_ids, urm=d.get_urm_train_1())
r.evaluate(recs, d.get_urm_test_1())
if log_path != None:
sys.stdout = orig_stdout
f.close()
def run(self, distance, h, k=100, shrink=10, threshold=0,
alpha=None, beta=None, l=None, c=None, export=True, verbose=True):
"""
Run the model and export the results to a file
Parameters
----------
distance : str, distance metric
k : int, K nearest neighbour to consider
shrink : float, shrink term used in the normalization
threshold : float, all the values under this value are cutted from the final result
implicit : bool, if true, treat the URM as implicit, otherwise consider explicit ratings (real values) in the URM
Returns
-------
recs: (list) recommendations
map10: (float) MAP10 for the provided recommendations
"""
start = time.time()
self.fit(k=k, distance=distance, shrink=shrink, alpha=alpha, beta=beta, l=l, c=c, verbose=verbose)
recs = self.recommend_batch(N=10, filter_already_liked=True, verbose=verbose)
map10 = None
if len(recs) > 0:
map10 = self.evaluate(recs, test_urm=data.get_urm_test_1(), verbose=verbose)
else:
log.warning('No recommendations available, skip evaluation')
if export:
exportcsv(recs, path='submission', name='{}_{}'.format(self.name,distance), verbose=verbose)
if verbose:
log.info('Run in: {:.2f}s'.format(time.time()-start))
return recs, map10
def validate(l1_ratio_array,
alpha_array,
max_iter_array,
topK_array,
userids=data.get_target_playlists(),
urm_train=data.get_urm_train_1(),
urm_test=data.get_urm_test_1(),
filter_already_liked=True,
items_to_exclude=[],
N=10,
verbose=True,
write_on_file=True):
"""
-----------
:return: _
"""
#create the initial model
recommender = SLIMElasticNetRecommender()
path = 'validation_results/'
name = 'slim_rmse'
folder = time.strftime('%d-%m-%Y')
filename = '{}/{}/{}{}.csv'.format(path, folder, name,
time.strftime('_%H-%M-%S'))
# create dir if not exists
os.makedirs(os.path.dirname(filename), exist_ok=True)
with open(filename, 'w') as out:
for l in l1_ratio_array:
for a in alpha_array:
for m in max_iter_array:
for k in topK_array:
#train the model with the parameters
if verbose:
print(
'\n\nTraining slim_rmse with\n l1_ratio: {}\n alpha: {}\n'
'Iterations: {}\n topK: {}'.format(l, a, m, k))
print('\n training phase...')
recommender.fit(urm=urm_train,
l1_ratio=l,
alpha=a,
max_iter=m,
topK=k)
#get the recommendations from the trained model
recommendations = recommender.recommend_batch(
userids=userids,
N=N,
filter_already_liked=filter_already_liked,
items_to_exclude=items_to_exclude)
#evaluate the model with map10
map10 = recommender.evaluate(recommendations,
test_urm=urm_test)
if verbose:
print('map@10: {}'.format(map10))
#write on external files on folder models_validation
if write_on_file:
out.write(
'\n\nl1_ratio: {}\n alpha: {}\n Iterations: {}\n '
'topK: {}\n evaluation map@10: {}'.format(
l, a, m, k, map10))
def run(self,
urm_train=None,
urm=None,
urm_test=None,
targetids=None,
factors=100,
regularization=0.01,
iterations=100,
alpha=25,
with_scores=False,
export=True,
verbose=True):
"""
Run the model and export the results to a file
Returns
-------
:return: recs: (list) recommendations
:return: map10: (float) MAP10 for the provided recommendations
"""
_urm_train = data.get_urm_train_1()
_urm = data.get_urm()
_icm = data.get_icm()
_urm_test = data.get_urm_test_1()
_targetids = data.get_target_playlists()
# _targetids = data.get_all_playlists()
start = time.time()
urm_train = _urm_train if urm_train is None else urm_train
urm = _urm if urm is None else urm
urm_test = _urm_test if urm_test is None else urm_test
targetids = _targetids if targetids is None else targetids
self.fit(l1_ratio=0.1,
positive_only=True,
alpha=1e-4,
fit_intercept=False,
copy_X=False,
precompute=False,
selection='random',
max_iter=100,
topK=100,
tol=1e-4,
workers=multiprocessing.cpu_count())
recs = self.recommend_batch(userids=targetids,
with_scores=with_scores,
verbose=verbose)
map10 = None
if len(recs) > 0:
map10 = self.evaluate(recs, test_urm=urm_test, verbose=verbose)
else:
log.warning('No recommendations available, skip evaluation')
if export:
exportcsv(recs, path='submission', name=self.name, verbose=verbose)
if verbose:
log.info('Run in: {:.2f}s'.format(time.time() - start))
return recs, map10
userids=userids,
N=N,
filter_already_liked=filter_already_liked,
items_to_exclude=items_to_exclude)
#evaluate the model with map10
map10 = recommender.evaluate(recommendations,
test_urm=urm_test)
if verbose:
print('map@10: {}'.format(map10))
#write on external files on folder models_validation
if write_on_file:
out.write(
'\n\nl1_ratio: {}\n alpha: {}\n Iterations: {}\n '
'topK: {}\n evaluation map@10: {}'.format(
l, a, m, k, map10))
"""
If this file is executed, test the SPLUS distance metric
"""
if __name__ == '__main__':
rec = SLIMElasticNetRecommender()
rec.fit(urm=data.get_urm_train_1(),
max_iter=1,
topK=400,
alpha=1e-4,
l1_ratio=0.5)
recs = rec.recommend_batch(userids=data.get_target_playlists())
rec.evaluate(recommendations=recs, test_urm=data.get_urm_test_1())
log.warning('(s) Save the similarity matrix')
log.warning('(v) Validate the model')
log.warning('(e) Export the submission')
log.warning('(x) Exit')
arg = input()[0]
print()
if arg == 't':
model = P3alphaRecommender(data.get_urm_train_1())
model.fit(topK=900,
alpha=1.2,
min_rating=0,
implicit=True,
normalize_similarity=False)
recs = model.recommend_batch(data.get_target_playlists())
evaluate(recs, test_urm=data.get_urm_test_1())
elif arg == 'r':
log.info('Wanna save for evaluation (y/n)?')
if input()[0] == 'y':
model = P3alphaRecommender(data.get_urm())
path = 'raw_data/saved_r_hat_evaluation/'
else:
model = P3alphaRecommender(data.get_urm_train_1())
path = 'raw_data/saved_r_hat/'
model.fit(topK=500, alpha=1.7, min_rating=1, normalize_similarity=True)
print('Saving the R^...')
r_hat = sps.csr_matrix(
np.dot(model.URM_train[data.get_target_playlists()],
model.W_sparse))
def validate(self, factors_array, iteration_array, urm_train=data.get_urm_train_1(), urm_test=data.get_urm_test_1(), verbose=True,
write_on_file=True, userids=data.get_target_playlists(), N=10, filter_already_liked=True, items_to_exclude=[]):
#create the initial model
recommender = Pure_SVD()
path = 'validation_results/'
name = 'pure_SVD'
folder = time.strftime('%d-%m-%Y')
filename = '{}/{}/{}{}.csv'.format(path, folder, name, time.strftime('_%H-%M-%S'))
# create dir if not exists
os.makedirs(os.path.dirname(filename), exist_ok=True)
with open(filename, 'w') as out:
for f in factors_array:
for i in iteration_array:
#train the model with the parameters
if verbose:
print('\n\nTraining PURE_SVD with\n Factors: {}\n Iteration: {}\n'.format(f, i))
print('\n training phase...')
recommender.fit(urm_train=urm_train, num_factors=f, iteration=i)
#get the recommendations from the trained model
recommendations = recommender.recommend_batch(userids=userids, N=N, filter_already_liked=filter_already_liked,
items_to_exclude=items_to_exclude)
#evaluate the model with map10
map10 = recommender.evaluate(recommendations, test_urm=urm_test)
if verbose:
print('map@10: {}'.format(map10))
#write on external files on folder models_validation
if write_on_file:
out.write('\n\nFactors: {}\n Iteration: {}\n evaluation map@10: {}'.format(f, i, map10))
def wizard_hybrid():
SIM_MATRIX = ['saved_sim_matrix', 'saved_sim_matrix_evaluation']
R_HAT = ['saved_r_hat', 'saved_r_hat_evaluation']
SAVE = ['saved_sim_matrix', 'saved_r_hat']
EVALUATE = ['saved_sim_matrix_evaluation', 'saved_r_hat_evaluation']
start = time.time()
matrices_array, folder, models = hb.create_matrices_array()
print('matrices loaded in {:.2f} s'.format(time.time() - start))
log.success('You have loaded: {}'.format(models))
NORMALIZATION_MODE = normalization_mode_selection()
if folder in SAVE:
WEIGHTS = weights_selection(models)
if folder in SIM_MATRIX:
name, urm_filter_tracks, rel_path = option_selection_save('SIM')
hybrid_rec = HybridSimilarity(
matrices_array,
normalization_mode=NORMALIZATION_MODE,
urm_filter_tracks=urm_filter_tracks)
sps.save_npz('raw_data/' + rel_path + name,
hybrid_rec.get_r_hat(weights_array=WEIGHTS))
if folder in R_HAT:
name, urm_filter_tracks, rel_path, EXPORT = option_selection_save(
'R_HAT')
hybrid_rec = HybridRHat(matrices_array,
normalization_mode=NORMALIZATION_MODE,
urm_filter_tracks=urm_filter_tracks)
if EXPORT:
N = ask_number_recommendations()
recommendations = hybrid_rec.recommend_batch(
weights_array=WEIGHTS,
target_userids=data.get_target_playlists(),
N=N)
exportcsv(recommendations, path='submission', name=name)
else:
sps.save_npz('raw_data/' + rel_path + name,
hybrid_rec.get_r_hat(weights_array=WEIGHTS))
elif folder in EVALUATE:
log.success('|WHAT YOU WANT TO DO ???|')
log.warning('\'1\' BAYESIAN SEARCH VALIDATION')
log.warning('\'2\' HAND CRAFTED WEIGHTS')
mode = input()[0]
# BAYESIAN SEARCH
if mode == '1':
log.success(
'|SELECT A NUMBER OF |||ITERATIONS||| FOR THE ALGORITHM|')
iterations = float(input())
urm_filter_tracks = data.get_urm_train_1()
if folder in SIM_MATRIX:
hybrid_rec = HybridSimilarity(
matrices_array,
normalization_mode=NORMALIZATION_MODE,
urm_filter_tracks=urm_filter_tracks)
if folder in R_HAT:
hybrid_rec = HybridRHat(matrices_array,
normalization_mode=NORMALIZATION_MODE,
urm_filter_tracks=urm_filter_tracks)
hybrid_rec.validate(iterations=iterations,
urm_test=data.get_urm_test_1(),
userids=data.get_target_playlists())
# MANUAL WEIGHTS
elif mode == '2':
WEIGHTS = weights_selection(models)
urm_filter_tracks = data.get_urm_train_1()
chose = option_selection_evaluation_2() # save, evaluate or csv
if chose == 's':
log.success('|CHOSE A NAME FOR THE MATRIX...|')
name = input()
if folder in SIM_MATRIX:
type = 'SIM'
hybrid_rec = HybridSimilarity(
matrices_array,
normalization_mode=NORMALIZATION_MODE,
urm_filter_tracks=urm_filter_tracks)
elif folder in R_HAT:
type = 'R_HAT'
hybrid_rec = HybridRHat(
matrices_array,
normalization_mode=NORMALIZATION_MODE,
urm_filter_tracks=urm_filter_tracks)
sps.save_npz('raw_data/saved_r_hat_evaluation/' + name,
hybrid_rec.get_r_hat(weights_array=WEIGHTS))
sym_rec = symmetric_recommender_creator(
models,
type,
NORMALIZATION_MODE,
urm_filter_tracks=data.get_urm_train_2())
sps.save_npz('raw_data/saved_r_hat_evaluation_2/' + name,
sym_rec.get_r_hat(weights_array=WEIGHTS))
elif chose == 'e':
if folder in SIM_MATRIX:
type = 'SIM'
hybrid_rec = HybridSimilarity(
matrices_array,
normalization_mode=NORMALIZATION_MODE,
urm_filter_tracks=urm_filter_tracks)
elif folder in R_HAT:
type = 'R_HAT'
hybrid_rec = HybridRHat(
matrices_array,
normalization_mode=NORMALIZATION_MODE,
urm_filter_tracks=urm_filter_tracks)
N = ask_number_recommendations()
print('Recommending...')
recs = hybrid_rec.recommend_batch(
weights_array=WEIGHTS,
target_userids=data.get_target_playlists(),
N=N)
hybrid_rec.evaluate(recommendations=recs,
test_urm=data.get_urm_test_1())
# export the recommendations
log.success(
'Do you want to save the CSV with these recomendations? (y/n)'
)
if input()[0] == 'y':
export_csv_wizard(recs)
sym_rec = symmetric_recommender_creator(
models,
type,
NORMALIZATION_MODE,
urm_filter_tracks=data.get_urm_train_2())
recs2 = sym_rec.recommend_batch(
weights_array=WEIGHTS,
target_userids=data.get_target_playlists())
sym_rec.evaluate(recommendations=recs2,
test_urm=data.get_urm_test_2())
elif chose == 'c':
if folder in R_HAT:
hybrid_rec = HybridRHat(
matrices_array,
normalization_mode=NORMALIZATION_MODE,
urm_filter_tracks=urm_filter_tracks)
N = ask_number_recommendations()
print('Recommending...')
recs = hybrid_rec.recommend_batch(
weights_array=WEIGHTS,
target_userids=data.get_target_playlists(),
N=N)
export_csv_wizard(recs)
else:
log.error('not implemented yet')
else:
log.error('WRONG FOLDER')
#input_data_tensor = Variable(torch.from_numpy(np.asarray(input_data, dtype=np.int64))).to(self.device)
input_data_tensor = Variable(input_data).to(self.device)
label_tensor = Variable(label).to(self.device)
user_coordinates = input_data_tensor[:, 0]
item_coordinates = input_data_tensor[:, 1]
# FORWARD pass
prediction = self.pyTorchModel(user_coordinates, item_coordinates)
# Pass prediction and label removing last empty dimension of prediction
loss = self.lossFunction(prediction.view(-1), label_tensor)
# BACKWARD pass
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
def get_r_hat(self, load_from_file=False, path=''):
pass
def run(self):
pass
m = MF_MSE_PyTorch()
m.fit(d.get_urm_train_1(),
user_ids=d.get_target_playlists(),
URM_test=d.get_urm_test_1())
"""
if not at_k > 0:
log.error('Invalid value of k {}'.format(at_k))
return
aps = 0.0
for r in recommendations:
row = test_urm.getrow(r[0]).indices
m = min(at_k, len(row))
ap = 0.0
n_elems_found = 0.0
for j in range(1, m+1):
if r[j] in row:
n_elems_found += 1
ap = ap + n_elems_found/j
if m > 0:
ap = ap/m
aps = aps + ap
result = aps/len(recommendations)
if verbose:
log.warning('MAP: {}'.format(result))
return result
rec = AsySVD()
rec.fit(data.get_urm_train_1())
recs = rec.recommend_batch(data.get_target_playlists())
rec.evaluate(recs, data.get_urm_test_1())
distance=model.SIM_SPLUS,
k=600,
alpha=0.25,
beta=0.5,
shrink=10,
l=0.25,
c=0.5)
print('Saving the similarity matrix...')
sps.save_npz(
'raw_data/saved_sim_matrix_evaluation_2/{}'.format(model.name),
model.get_sim_matrix())
elif arg == 'v':
# model.validate(iterations=10, urm_train=data.get_urm_train_1(), urm_test=data.get_urm_test_1(), targetids=data.get_target_playlists(),
# distance=model.SIM_SPLUS, k=(100, 600), alpha=(0,2), beta=(0,2),shrink=(0,100),l=(0,1),c=(0,1))
model.validate(iterations=10,
urm_train=data.get_urm_train_1(),
urm_test=data.get_urm_test_1(),
targetids=data.get_target_playlists(),
distance=model.SIM_RP3BETA,
k=(100, 600),
alpha=(0, 2),
beta=(0, 2),
shrink=(0, 100),
l=1,
c=1)
#model.test(distance=CFItemBased.SIM_P3ALPHA, k=300,alpha=(0,2),shrink=(0,100))
elif arg == 'x':
pass
else:
log.error('Wrong option!')
