def estimate(self, raw_test_dataset, measures):
with tl.Timer() as t:
error = self._estimate(raw_test_dataset, measures)
print("{} algorithm predict process cost {:.3f} sec".format(
self.__class__.__name__, t.interval))
return error
def estimate(test, measures, train_sparse, bu, bi, y, c, w, q, p, global_mean):
with tl.Timer() as t:
error = _estimate(test, measures, train_sparse, bu, bi, y, c, w, q, p,
global_mean)
error = np.sqrt(np.mean(np.power(error, 2)))
return error
def train(self, train_dataset):
self.train_dataset = train_dataset
with tl.Timer() as t:
self._train()
print("{} algorithm train process cost {:.3f} sec".format(
self.__class__.__name__, t.interval))
def train(self, train_dataset, user_dataset=None):
self.train_dataset = train_dataset
self.user_dataset = user_dataset
with tl.Timer() as t:
self._train()
logger.info("{} algorithm train process cost {:.3f} sec".format(
self.__class__.__name__, t.interval))