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Copy pathresult_eval.py
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executable file
·87 lines (73 loc) · 3.26 KB
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import numpy as np
import argparse,pdb, sys
parser = argparse.ArgumentParser(description='Eval model outputs')
parser.add_argument('-model', dest = "model", required=True, help='Dataset to use')
parser.add_argument('-eval_mode', dest = "eval_mode", required=True, help='To evaluate test or validation')
parser.add_argument('-test_freq', dest = "freq", required=True, type =int, help='what is to be predicted')
#parser.add_argument('-entity2id' , dest="entity2id", required=True, help='Entity 2 id')
#parser.add_argument('-relation2id', dest="relation2id", required=True, help=' relation to id')
args = parser.parse_args()
best_rank = sys.maxsize
print(args.model)
for k in range(args.freq,30000,args.freq):
valid_output = open('results/'+args.model+'/'+args.eval_mode+'.txt')
model_output_head = open('results/'+args.model+'/'+args.eval_mode+'_head_pred_{}.txt'.format(k))
model_output_tail = open('results/'+args.model+'/'+args.eval_mode+'_tail_pred_{}.txt'.format(k))
model_out_head = []
model_out_tail = []
count = 0
for line in model_output_head:
count = 0
temp_out = []
for ele in line.split():
tup = (float(ele),count)
temp_out.append(tup)
count = count+1
model_out_head.append(temp_out)
for line in model_output_tail:
count = 0
temp_out = []
for ele in line.split():
tup = (float(ele),count)
temp_out.append(tup)
count = count+1
model_out_tail.append(temp_out)
for row in model_out_head:
row.sort(key=lambda x:x[0])
for row in model_out_tail:
row.sort(key=lambda x:x[0])
final_out_head , final_out_tail= [], []
for row in model_out_head:
temp_dict =dict()
count = 0
for ele in row:
temp_dict[ele[1]] = count
count += 1
final_out_head.append(temp_dict)
for row in model_out_tail:
temp_dict =dict()
count = 0
for ele in row:
temp_dict[ele[1]] = count
count += 1
final_out_tail.append(temp_dict)
ranks_head = []
ranks_tail = []
for i,row in enumerate(valid_output):
ranks_head.append(final_out_head[i][int(row.split()[0])])
ranks_tail.append(final_out_tail[i][int(row.split()[2])])
print('Epoch {} : {}_tail rank {}\t {}_head rank {}'.format(k, args.eval_mode, np.mean(np.array(ranks_tail))+1, args.eval_mode, np.mean(np.array(ranks_head))+1))
tail_array = np.array(ranks_tail)
head_array = np.array(ranks_head)
hit_at_10_tail = tail_array[np.where(tail_array < 10)]
hit_at_10_head = head_array[np.where(head_array < 10)]
print('Epoch {} : {}_tail HIT@10 {}\t {}_head HIT@!) {}'.format(k, args.eval_mode, len(hit_at_10_tail)/float(len(tail_array))*100, args.eval_mode, len(hit_at_10_head)/float(len(head_array))*100))
if args.eval_mode == 'valid':
if (np.mean(np.array(ranks_tail))+1 + np.mean(np.array(ranks_head))+1)/2 < best_rank:
best_rank = (np.mean(np.array(ranks_tail))+1 + np.mean(np.array(ranks_head))+1)/2
best_epoch = k
best_tail_rank = np.mean(np.array(ranks_tail))+1
best_head_rank = np.mean(np.array(ranks_head))+1
print('------------------------------------------')
print('Best Validation Epoch till now Epoch {}, tail rank: {}, head rank: {}'. format(best_epoch, best_tail_rank, best_head_rank))
print('------------------------------------------')