def predict(self, data: BaseModel.Dataset):
"""
The returned prediction is a 2D-array, each row corresponds to all the candidates,
and the ground-truth item poses the first.
Example: ground-truth items: [1, 2], 2 negative items for each instance: [[3,4], [5,6]]
predictions like: [[1,3,4], [2,5,6]]
"""
data.model.eval()
predictions = list()
js_div = list()
dis = list()
dl = DataLoader(data,
batch_size=self.eval_batch_size,
shuffle=False,
num_workers=self.num_workers,
collate_fn=data.collate_batch,
pin_memory=self.pin_memory)
for batch in tqdm(dl,
leave=False,
ncols=100,
mininterval=1,
desc='Predict'):
out_dict = data.model(utils.batch_to_gpu(batch, data.model.device))
predictions.extend(out_dict['prediction'].cpu().data.numpy())
if 'js' in out_dict:
js_div.extend(out_dict['js'].cpu().data.numpy())
if 'dis' in out_dict:
dis.extend(out_dict['dis'].cpu().data.numpy())
if len(js_div) > 0:
print('JS DIV:', np.mean(js_div))
np.save('../log/js', np.array(js_div))
if len(dis) > 0:
print('Interest Dis:', np.mean(dis))
return np.array(predictions)
def predict(self, data: BaseModel.Dataset) -> np.ndarray:
"""
The returned prediction is a 2D-array, each row corresponds to all the candidates,
and the ground-truth item poses the first.
Example: ground-truth items: [1, 2], 2 negative items for each instance: [[3,4], [5,6]]
predictions like: [[1,3,4], [2,5,6]]
"""
data.model.eval()
predictions = list()
dl = DataLoader(data,
batch_size=self.eval_batch_size,
shuffle=False,
num_workers=self.num_workers,
collate_fn=data.collate_batch,
pin_memory=self.pin_memory)
for batch in tqdm(dl,
leave=False,
ncols=100,
mininterval=1,
desc='Predict'):
prediction = data.model(
utils.batch_to_gpu(batch, data.model.device))['prediction']
predictions.extend(prediction.cpu().data.numpy())
return np.array(predictions)