def fit(self, train_set, val_set=None):
"""Fit the model to observations.
Parameters
----------
train_set: :obj:`cornac.data.Dataset`, required
User-Item preference data as well as additional modalities.
val_set: :obj:`cornac.data.Dataset`, optional, default: None
User-Item preference data for model selection purposes (e.g., early stopping).
Returns
-------
self : object
"""
Recommender.fit(self, train_set)
from cornac.models.pmf import pmf
if self.trainable:
# converting data to the triplet format (needed for cython function pmf)
(uid, iid, rat) = train_set.uir_tuple
rat = np.array(rat, dtype='float32')
if self.variant == 'non_linear': # need to map the ratings to [0,1]
if [self.train_set.min_rating, self.train_set.max_rating] != [0, 1]:
rat = scale(rat, 0., 1., self.train_set.min_rating, self.train_set.max_rating)
uid = np.array(uid, dtype='int32')
iid = np.array(iid, dtype='int32')
if self.verbose:
print('Learning...')
if self.variant == 'linear':
res = pmf.pmf_linear(uid, iid, rat, k=self.k, n_users=train_set.num_users, n_items=train_set.num_items,
n_ratings=len(rat), n_epochs=self.max_iter,
lamda=self.lamda, learning_rate=self.learning_rate, gamma=self.gamma,
init_params=self.init_params, verbose=self.verbose, seed=self.seed)
elif self.variant == 'non_linear':
res = pmf.pmf_non_linear(uid, iid, rat, k=self.k, n_users=train_set.num_users,
n_items=train_set.num_items, n_ratings=len(rat), n_epochs=self.max_iter,
lamda=self.lamda, learning_rate=self.learning_rate, gamma=self.gamma,
init_params=self.init_params, verbose=self.verbose, seed=self.seed)
else:
raise ValueError('variant must be one of {"linear","non_linear"}')
self.U = np.asarray(res['U'])
self.V = np.asarray(res['V'])
if self.verbose:
print('Learning completed')
elif self.verbose:
print('%s is trained already (trainable = False)' % (self.name))
return self
def fit(self, train_set):
"""Fit the model to observations.
Parameters
----------
train_set: object of type TrainSet, required
An object contraining the user-item preference in csr scipy sparse format,\
as well as some useful attributes such as mappings to the original user/item ids.\
Please refer to the class TrainSet in the "data" module for details.
"""
from cornac.models.pmf import pmf
Recommender.fit(self, train_set)
if self.trainable:
# converting data to the triplet format (needed for cython function pmf)
(uid, iid, rat) = train_set.uir_tuple
rat = np.array(rat, dtype='float32')
if self.variant == 'non_linear': # need to map the ratings to [0,1]
if [self.train_set.min_rating, self.train_set.max_rating
] != [0, 1]:
rat = scale(rat, 0., 1., self.train_set.min_rating,
self.train_set.max_rating)
uid = np.array(uid, dtype='int32')
iid = np.array(iid, dtype='int32')
if self.verbose:
print('Learning...')
if self.variant == 'linear':
res = pmf.pmf_linear(uid,
iid,
rat,
k=self.k,
n_users=train_set.num_users,
n_items=train_set.num_items,
n_ratings=len(rat),
n_epochs=self.max_iter,
lamda=self.lamda,
learning_rate=self.learning_rate,
gamma=self.gamma,
init_params=self.init_params,
verbose=self.verbose,
seed=self.seed)
elif self.variant == 'non_linear':
res = pmf.pmf_non_linear(uid,
iid,
rat,
k=self.k,
n_users=train_set.num_users,
n_items=train_set.num_items,
n_ratings=len(rat),
n_epochs=self.max_iter,
lamda=self.lamda,
learning_rate=self.learning_rate,
gamma=self.gamma,
init_params=self.init_params,
verbose=self.verbose,
seed=self.seed)
else:
raise ValueError(
'variant must be one of {"linear","non_linear"}')
self.U = np.asarray(res['U'])
self.V = np.asarray(res['V'])
if self.verbose:
print('Learning completed')
elif self.verbose:
print('%s is trained already (trainable = False)' % (self.name))