def generate_csv(test_path,save_path):
test_csv = pd.read_csv(test_path,sep = "\t")
phrase_id = test_csv['PhraseId'].values
strings = test_csv['Phrase']
# print(strings[0])
cleaned_strings = [clean_string(s) for s in strings]
vocab,word2idx,idx2word = generate_vocab()
input_list = pad_samples(encoder_strings(cleaned_strings,word2idx))
input_tensor = torch.tensor(input_list)
zero_label = torch.zeros(input_tensor.shape[0])
input_set = torch.utils.data.TensorDataset(input_tensor,zero_label)
input_iter = torch.utils.data.DataLoader(input_set,batch_size = opt.batch_size, shuffle = False)
rst = []
net.eval()
with torch.no_grad():
for feature,_ in input_iter:
if opt.evaluate_use_cuda:
feature = feature.cuda()
score = net(feature)
this_rst = torch.argmax(score, dim = 1).cpu().numpy().tolist()
rst += this_rst
rst = np.array(rst,dtype = int)
final_answer = pd.DataFrame({'PhraseId':phrase_id,'Sentiment':rst})
final_answer.to_csv(save_path,index = False)
def analysis_str(string,debug = False):
cleaned_string = clean_string(string)
vocab,word2idx,idx2word = generate_vocab()
clean_strings = [cleaned_string]
input_list = pad_samples(encoder_strings(clean_strings,word2idx))
input_tensor = torch.tensor(input_list)
if opt.evaluate_use_cuda:
input_tensor = input_tensor.cuda()
score = net(input_tensor)
soft_score = F.softmax(score,dim = 1).cpu().detach().numpy().tolist()[0]
# print(soft_score[0])
rst_string = ""
for e in input_list[0]:
rst_string += (idx2word[e] + " ")
if debug:
for e in input_list[0]:
print(idx2word[e],end = " ")
print("\n")
rst = torch.argmax(score,dim = 1).item()
return rst,soft_score,rst_string