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test_bert_g_onestep.py
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import os
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from src.utils import get_evaluation
from src.dataset_bert_4 import MyDataset
import argparse
import shutil
import csv
import numpy as np
from src.bert_average import Bert_cls_av
from src.bert_cos import Bert_cos
from src.bert_att import Bert_att
from src.bert_random import Bert_random
from src.bert_han_g import HierAttNet
from src.bert_coss import Bert_coss
from src.bert_han_sg_g import HierGraphAttNet
models_class = {'Bert_avg':Bert_cls_av,
'Bert_han_g':HierAttNet,
'Bert_han_sg_g':HierGraphAttNet,
'Bert_cos':Bert_cos,
'Bert_coss':Bert_coss,
'Bert_random':Bert_random,
'Bert_att':Bert_att}
os.environ["CUDA_VISIBLE_DEVICES"]="0"
def get_args():
parser = argparse.ArgumentParser(
"""Implementation of the model described in the paper: Hierarchical Attention Networks for Document Classification""")
parser.add_argument("--batch_size", type=int, default=256)
parser.add_argument("--data_path", type=str, default="data/cite_acl/test_cite.csv")
parser.add_argument("--pre_trained_model", type=str, default="acl_bert_model/Bert_han_sg_g.pth")
parser.add_argument("--word2vec_path", type=str, default="data/word_embedding/glove.6B.50d.txt")
parser.add_argument("--output", type=str, default="predictions")
parser.add_argument("--word_hidden_size", type=int, default=50)
parser.add_argument("--sent_hidden_size", type=int, default=50)
parser.add_argument("--log_path", type=str, default="tensorboard/han_voc")
parser.add_argument("--saved_path", type=str, default="trained_models")
parser.add_argument("--max_sent_words", type=str, default="5,5")
parser.add_argument("--model_type", type=str, default='Bert_han_sg_g')
args = parser.parse_args()
return args
def masking(pred_pos, mask):
pos_list = []
if mask==[]:
return pred_pos
pred_pos = pred_pos.transpose(0,1)
pred_pos = pred_pos.numpy()
for i, j in zip(pred_pos, mask):
pos = np.multiply(i,j)
pos = torch.from_numpy(pos)
pos = pos.unsqueeze(dim=1)
pos_list.append(pos)
pos_list = torch.cat(pos_list, dim=1)
return pos_list
def pos_cal_mpr(pred_pos, mask, true_pos, label_list, pred_label):
count = 0
mpr = 0
pred_pos = torch.cat(pred_pos, dim=1)
pred_pos = masking(pred_pos, mask)
_, pred_doc_pos = pred_pos.sort(dim=0,descending=True)
pred_doc_pos = pred_doc_pos.transpose(0,1)
pred_doc_pos = pred_doc_pos.numpy()
pred_doc_pos = list(pred_doc_pos)
for pred, true, label, pre_label in zip(pred_doc_pos, true_pos, label_list, pred_label):
if label!=0 and pre_label!=0 and true!=[]:
rank = []
for i in true:
#print(list(pred))
try:
rank.append(list(pred).index(i)+1)
except:
rank.append(21)
rank_f = min(rank)
mpr += 1/rank_f
count +=1
elif pre_label==label:
mpr += 1
count += 1
else:
mpr += 1/21
count += 1
return mpr/count
def pos_accuracy(pred_pos, mask, true_pos, label_list,pred_label, top_num):
true_count = 0
count = 0
print(label_list.shape)
pred_pos = torch.cat(pred_pos, dim=1)
pred_pos = masking(pred_pos, mask)
print(pred_pos.shape)
print(pred_pos.transpose(0,1)[6:10])
_, pred_doc_pos = (pred_pos).topk(top_num,dim=0)
pred_doc_pos = pred_doc_pos.transpose(0,1)
pred_doc_pos = pred_doc_pos.numpy()
pred_doc_pos = list(pred_doc_pos)
print(true_pos[6:20])
print(pred_doc_pos[6:20])
print(label_list[6:20])
for pred, true, label, pre_label in zip(pred_doc_pos, true_pos, label_list, pred_label):
if label!=0 and pre_label!=0:
pred = set(pred)
true = set(true)
if pred.intersection(true) != set():
true_count += 1
count += 1
else:
count +=1
elif label==pre_label:
true_count += 1
count += 1
else:
count += 1
return true_count/count
def test(opt):
test_params = {"batch_size": opt.batch_size,
"shuffle": False,
"drop_last": False}
if os.path.isdir(opt.output):
shutil.rmtree(opt.output)
os.makedirs(opt.output)
if torch.cuda.is_available():
freeze=True
model = models_class[opt.model_type](vector_size=1024)
model.load_state_dict(torch.load(opt.pre_trained_model))
else:
model = torch.load(opt.pre_trained_model, map_location=lambda storage, loc: storage)
test_set = MyDataset(opt.data_path)
pos = test_set.get_pos()
test_generator = DataLoader(test_set, **test_params)
if torch.cuda.is_available():
model.cuda()
model.eval()
te_label_ls = []
te_pred_ls = []
te_pos_ls = []
te_true_post_ls = []
for te_feature1, te_feature2, te_label, _ in test_generator:
num_sample = len(te_label)
if torch.cuda.is_available():
te_feature1 = te_feature1.cuda()
te_feature2 = te_feature2.cuda()
te_label = te_label.cuda()
with torch.no_grad():
model._init_hidden_state(num_sample)
te_predictions = model(te_feature1, te_feature2)
doc_te_predictions = te_predictions[-1]
pos_predictions = te_predictions[:-1]
#te_predictions = F.softmax(te_predictions) #do not know what it is doing?
te_label_ls.extend(te_label.clone().cpu())
te_pred_ls.append(doc_te_predictions.clone().cpu())
te_pos_ls.append(pos_predictions.clone().cpu())
#break
te_pred = torch.cat(te_pred_ls, 0).numpy()
te_label = np.array(te_label_ls)
te_pred = np.where(te_pred > 0.5, 1, 0)
mask = test_set.get_mask()
pos_acc_10 = pos_accuracy(te_pos_ls,mask, pos, te_label, te_pred, 10)
pos_acc_5 = pos_accuracy(te_pos_ls, mask, pos, te_label, te_pred, 5)
pos_mpr = pos_cal_mpr(te_pos_ls, mask, pos, te_label, te_pred)
fieldnames = ['True label', 'Predicted label', 'Content1', 'Content2']
with open(opt.output + os.sep + "predictions.csv", 'w') as csv_file:
writer = csv.DictWriter(csv_file, fieldnames=fieldnames, quoting=csv.QUOTE_NONNUMERIC)
writer.writeheader()
for i, j, k in zip(te_label, te_pred, test_set.texts):
writer.writerow(
{'True label': i, 'Predicted label':j , 'Content1': k[0], 'Content2':k[1]})
test_metrics = get_evaluation(te_label, te_pred,
list_metrics=["accuracy", "loss", "confusion_matrix", "f1"])
print("Prediction:\nLoss: {} Accuracy: {} F1: {} Pos Acc 10: {} Pos Acc 5:{} mpr: {}\nConfusion matrix: \n{}".format(test_metrics["loss"],
test_metrics["accuracy"],
test_metrics["f1"],
pos_acc_10,pos_acc_5,
pos_mpr,
test_metrics["confusion_matrix"]))
if __name__ == "__main__":
opt = get_args()
test(opt)