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.mcf import mcf
Recommender.fit(self, train_set)
if self.trainable:
# user-item interactions
(rat_uid, rat_iid, rat_val) = train_set.uir_tuple
# item-item affinity network
map_iid = train_set.iid_list
(net_iid, net_jid, net_val) = train_set.item_graph.get_train_triplet(map_iid, map_iid)
if [self.train_set.min_rating, self.train_set.max_rating] != [0, 1]:
if self.train_set.min_rating == self.train_set.max_rating:
rat_val = scale(rat_val, 0., 1., 0., self.train_set.max_rating)
else:
rat_val = scale(rat_val, 0., 1., self.train_set.min_rating, self.train_set.max_rating)
if [min(net_val), max(net_val)] != [0, 1]:
if min(net_val) == max(net_val):
net_val = scale(net_val, 0., 1., 0., max(net_val))
else:
net_val = scale(net_val, 0., 1., min(net_val), max(net_val))
rat_val = np.array(rat_val, dtype='float32')
rat_uid = np.array(rat_uid, dtype='int32')
rat_iid = np.array(rat_iid, dtype='int32')
net_val = np.array(net_val, dtype='float32')
net_iid = np.array(net_iid, dtype='int32')
net_jid = np.array(net_jid, dtype='int32')
if self.verbose:
print('Learning...')
res = mcf.mcf(rat_uid, rat_iid, rat_val, net_iid, net_jid, net_val, k=self.k, n_users=train_set.num_users,
n_items=train_set.num_items, n_ratings=len(rat_val), n_edges=len(net_val),
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)
self.U = np.asarray(res['U'])
self.V = np.asarray(res['V'])
self.Z = np.asarray(res['Z'])
if self.verbose:
print('Learning completed')
elif self.verbose:
print('%s is trained already (trainable = False)' % self.name)
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
"""
from cornac.models.mcf import mcf
Recommender.fit(self, train_set, val_set)
if self.trainable:
# user-item interactions
(rat_uid, rat_iid, rat_val) = train_set.uir_tuple
# item-item affinity network
map_iid = train_set.item_indices
(net_iid, net_jid,
net_val) = train_set.item_graph.get_train_triplet(
map_iid, map_iid)
if [self.train_set.min_rating, self.train_set.max_rating
] != [0, 1]:
if self.train_set.min_rating == self.train_set.max_rating:
rat_val = scale(rat_val, 0., 1., 0.,
self.train_set.max_rating)
else:
rat_val = scale(rat_val, 0., 1., self.train_set.min_rating,
self.train_set.max_rating)
if [min(net_val), max(net_val)] != [0, 1]:
if min(net_val) == max(net_val):
net_val = scale(net_val, 0., 1., 0., max(net_val))
else:
net_val = scale(net_val, 0., 1., min(net_val),
max(net_val))
rat_val = np.array(rat_val, dtype='float32')
rat_uid = np.array(rat_uid, dtype='int32')
rat_iid = np.array(rat_iid, dtype='int32')
net_val = np.array(net_val, dtype='float32')
net_iid = np.array(net_iid, dtype='int32')
net_jid = np.array(net_jid, dtype='int32')
if self.verbose:
print('Learning...')
res = mcf.mcf(rat_uid,
rat_iid,
rat_val,
net_iid,
net_jid,
net_val,
k=self.k,
n_users=train_set.num_users,
n_items=train_set.num_items,
n_ratings=len(rat_val),
n_edges=len(net_val),
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)
self.U = np.asarray(res['U'])
self.V = np.asarray(res['V'])
self.Z = np.asarray(res['Z'])
if self.verbose:
print('Learning completed')
elif self.verbose:
print('%s is trained already (trainable = False)' % self.name)
return self