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.
"""
Recommender.fit(self, train_set)
X = sp.csc_matrix(self.train_set.matrix)
# recover the striplet sparse format from csc sparse matrix X (needed to feed c++)
(rid, cid, val) = sp.find(X)
val = np.array(val, dtype='float32')
rid = np.array(rid, dtype='int32')
cid = np.array(cid, dtype='int32')
tX = np.concatenate((np.concatenate(
([rid], [cid]), axis=0).T, val.reshape((len(val), 1))),
axis=1)
del rid, cid, val
if self.trainable:
if self.hierarchical:
res = hpf.hpf(tX, X.shape[0], X.shape[1], self.k,
self.max_iter, self.init_params)
else:
res = hpf.pf(tX, X.shape[0], X.shape[1], self.k, self.max_iter,
self.init_params)
self.Theta = np.asarray(res['Z'])
self.Beta = np.asarray(res['W'])
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
"""
Recommender.fit(self, train_set, val_set)
if self.trainable:
# use pre-trained params if exists, otherwise from constructor
init_params = {
"G_s": self.Gs,
"G_r": self.Gr,
"L_s": self.Ls,
"L_r": self.Lr,
}
X = sp.csc_matrix(self.train_set.matrix)
# recover the striplet sparse format from csc sparse matrix X (needed to feed c++)
(rid, cid, val) = sp.find(X)
val = np.array(val, dtype="float32")
rid = np.array(rid, dtype="int32")
cid = np.array(cid, dtype="int32")
tX = np.concatenate(
(np.concatenate(
([rid], [cid]), axis=0).T, val.reshape((len(val), 1))),
axis=1,
)
del rid, cid, val
if self.hierarchical:
res = hpf.hpf(tX, X.shape[0], X.shape[1], self.k,
self.max_iter, self.seed, init_params)
else:
res = hpf.pf(tX, X.shape[0], X.shape[1], self.k, self.max_iter,
self.seed, init_params)
self.Theta = np.asarray(res["Z"])
self.Beta = np.asarray(res["W"])
# overwrite init_params for future fine-tuning
self.Gs = np.asarray(res["G_s"])
self.Gr = np.asarray(res["G_r"])
self.Ls = np.asarray(res["L_s"])
self.Lr = np.asarray(res["L_r"])
elif self.verbose:
print("%s is trained already (trainable = False)" % (self.name))
return self
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, val_set)
X = sp.csc_matrix(self.train_set.matrix)
# recover the striplet sparse format from csc sparse matrix X (needed to feed c++)
(rid, cid, val) = sp.find(X)
val = np.array(val, dtype='float32')
rid = np.array(rid, dtype='int32')
cid = np.array(cid, dtype='int32')
tX = np.concatenate((np.concatenate(
([rid], [cid]), axis=0).T, val.reshape((len(val), 1))),
axis=1)
del rid, cid, val
if self.trainable:
if self.hierarchical:
res = hpf.hpf(tX, X.shape[0], X.shape[1], self.k,
self.max_iter, self.init_params)
else:
res = hpf.pf(tX, X.shape[0], X.shape[1], self.k, self.max_iter,
self.init_params)
self.Theta = np.asarray(res['Z'])
self.Beta = np.asarray(res['W'])
elif self.verbose:
print('%s is trained already (trainable = False)' % (self.name))
return self