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Copy pathresult_eval_time.py
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executable file
·55 lines (48 loc) · 1.78 KB
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import numpy as np
import argparse,pdb
parser = argparse.ArgumentParser(description='Eval model outputs')
parser.add_argument('-model', dest = "model", required=True, help='Dataset to use')
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()
print(args.model)
for k in range(args.freq,30000,args.freq):
valid_output = open('results/temp_scope/'+args.model+'/valid.txt')
model_time = open('results/temp_scope/'+args.model+'/valid_time_pred_{}.txt'.format(k))
model_out_time = []
count = 0
for line in model_time:
count = 0
temp_out = []
for ele in line.split():
tup = (float(ele),count)
temp_out.append(tup)
count = count+1
model_out_time.append(temp_out)
for row in model_out_time:
row.sort(key=lambda x:x[0])
final_out_time = []
for row in model_out_time:
temp_dict =dict()
count = 0
for ele in row:
temp_dict[ele[1]] = count
# temp_dict[count] = ele[1]
count += 1
final_out_time.append(temp_dict)
ranks_time = []
for i,row in enumerate(valid_output):
avg_rank = []
top_time = final_out_time[i][0]
start_time = int(row.split()[0])
end_time = int(row.split()[1])
for e in range(start_time,end_time+1,1):
avg_rank.append(final_out_time[i][e])
ranks_time.append(np.min(np.array(avg_rank)))
# if top_time <= end_time and top_time >= start_time:
# ranks_time.append(1)
# else:
# ranks_time.append(0)
# pdb.set_trace()
print('Epoch {} : time_rank {}'.format(k ,np.mean(np.array(ranks_time))))