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test_funcs.py
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test_funcs.py
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import tensorflow as tf
import numpy as np
import os
import csv
from nltk.translate.bleu_score import sentence_bleu
from nltk.util import ngrams
import data_utils
import args
FLAGS = args.FLAGS
KG = data_utils.KG
edim = len(data_utils.edge_types)
def create_out_results(prefix):
if not os.path.exists(FLAGS.results_dir):
os.makedirs(FLAGS.results_dir)
outs_dir = FLAGS.results_dir + '/' + prefix + '_outs.txt'
open(outs_dir,'w').close()
return outs_dir
def create_path_results(prefix):
paths_dir = FLAGS.results_dir + '/' + prefix + '_paths.txt'
kws_dir = FLAGS.results_dir + '/' + prefix + '_kws.txt'
open(paths_dir,'w').close()
open(kws_dir,'w').close()
return paths_dir, kws_dir
def relax_global_path(Tproj, neA, S, target, loop_id, pre=None, maxlen=6):
S_indices = np.nonzero(S)[0]
nt = data_utils.str_nodes.index(target)
ne_arr = np.take(neA, indices=nt, axis=2)
ne_indices = np.nonzero(ne_arr)
paths_lists = []
if nt in S_indices:
self_prob = Tproj[nt][0][edim]/np.sum(Tproj[nt])
paths_lists.append(([target], self_prob))
for ne in zip(ne_indices[0], ne_indices[1]):
ns_idx = ne[0]
et = ne[1]
if pre == ns_idx:
continue
if ns_idx == nt:
continue
ne_prob = Tproj[ns_idx][0][et] / np.sum(Tproj[ns_idx])
ns_name = data_utils.str_nodes[ns_idx]
if et == edim:
e_type = 'ToSelf'
else:
e_type = data_utils.edge_types[et]
if loop_id < maxlen:
past_path_lists = relax_global_path(Tproj, neA, S, ns_name, loop_id+1, nt, maxlen)
for past_path in past_path_lists:
paths_lists.append(([target, e_type]+past_path[0], ne_prob*past_path[1]))
return paths_lists
def get_global_path(Tproj, neA, S, target):
paths_lists = relax_global_path(Tproj, neA, S, target, 0, maxlen=6)
maxpath = []
maxprob = 0.
for path in paths_lists:
if path[1] > maxprob:
maxprob = path[1]
maxpath = path[0]
return maxpath, maxprob
def write_global_path(maxpath, fpath):
for j in range(round((len(maxpath)-1)/2)):
fpath.write('{} {} {}'.format(maxpath[2*j+2], maxpath[2*j+1], maxpath[2*j]))
if j+2 < len(maxpath)-1:
fpath.write(' < ')
def check_Rdebugs(Rdebugs):
if len(Rdebugs) == 0:
return False
elif len(Rdebugs[0]) != 4:
return False
else:
return True
def compute_acc(sess, results_dirs, model, vocabs, data, batch_size=1000, feed_prev=True):
if feed_prev != False and feed_prev != True:
raise ValueError("feed_prev must be either True or False.")
vocab, rev_vocab = vocabs
encoder_inputs, decoder_inputs, targets, weights, \
masks, seq_lens, neAs, Ss, facts, kg_indices = data
if feed_prev == False:#eval_pred_acc
decoder_inputs = decoder_inputs
elif feed_prev == True:#pred_acc
decoder_inputs = [[data_utils.GO_ID for _ in range(batch_size)]]
if feed_prev == False:
eval_loss, outputs, soft_outs, a1s, kdists, Ndists, Rdebugs \
= model.train_step(sess, encoder_inputs, \
decoder_inputs, targets, \
weights, masks, \
-1, seq_lens, neAs, Ss, \
facts, kg_indices, forward=True)
elif feed_prev == True:
outputs, enc_state, a1s, kdists, Ndists, logits, Rdebugs \
= model.dynamic_decode(sess, \
encoder_inputs, seq_lens, \
decoder_inputs, neAs, Ss, facts, kg_indices, -1)
if not os.path.exists(FLAGS.results_dir):
os.makedirs(FLAGS.results_dir)
fout = open(results_dirs[0],'a')
if len(results_dirs) == 3:
fpath = open(results_dirs[1],'a')
fkw = open(results_dirs[2],'a')
# ACC
if feed_prev == False:
acc_nu = 0.
TP_num, FN_num, TN_num, FP_num = 0., 0., 0., 0.
ppx = 0.
else:
acc, prec, count, pcount = 0.0, 0.0, 0, 0
f1 = 0.0
sen_bleu, corpus_bleu = 0.0, 0.0
corpus_gts = [[target_ids[i] \
for target_ids in targets] \
for i in range(batch_size)]
kb_counts = {}
sssps_lens = {}
distinct_1, distinct_2, \
distinct_3, distinct_4 = [], [], [], []
total_word_num = 0
for i in range(batch_size):
# ground-truth response
gts = [target_ids[i] for target_ids in targets]
if data_utils.EOS_ID in gts:
gts = gts[:gts.index(data_utils.EOS_ID)]
# generated response
subouts = [output_ids[i] for output_ids in outputs]
if feed_prev == True:
if data_utils.EOS_ID in subouts:
subouts = subouts[:subouts.index(data_utils.EOS_ID)]
fout.write(" ".join([tf.compat.as_str(rev_vocab[out]) for out in subouts]))
fout.write('\n')
# for TAware, KAware, Qadpt
if feed_prev == False:
a1_list = [a1[i] for a1 in a1s]
kdist_list = [kdist[i] for kdist in kdists]
Ndist_list = [Ndist[i] for Ndist in Ndists]
if len(kdist_list) > 0:
# for TAware, KAware, Qadpt
kg_word, softmax = [], []
for j, token in enumerate(gts):
kg_word.append(np.argmax(kdist_list[j]))
if token < len(data_utils.str_nodes):
softmax.append(a1_list[j]*kdist_list[j][token])
else:
softmax.append(Ndist_list[j][token-len(data_utils.str_nodes)])
else:
# for Seq2Seq, MemNet
kg_word = subouts
softmax = [soft_outs[j][i][token] for j, token in enumerate(gts)]
if feed_prev == False:
sen_ppx = 0.
for j, token in enumerate(gts):
if token < len(data_utils.str_nodes):
if kg_word[j] == token:
acc_nu += 1
if subouts[j] < len(data_utils.str_nodes):
TP_num += 1
else:
FN_num += 1
else:
if subouts[j] < len(data_utils.str_nodes):
FP_num += 1
else:
TN_num += 1
sen_ppx += np.log2(softmax[j] + 1e-12)
sen_ppx /= len(gts)
ppx += sen_ppx
else:
for ng in list(ngrams(subouts, 1)):
if ng not in distinct_1:
distinct_1.append(ng)
for ng in list(ngrams(subouts, 2)):
if ng not in distinct_2:
distinct_2.append(ng)
for ng in list(ngrams(subouts, 3)):
if ng not in distinct_3:
distinct_3.append(ng)
for ng in list(ngrams(subouts, 4)):
if ng not in distinct_4:
distinct_4.append(ng)
total_word_num += len(subouts)
gt_kws = []
out_kws = []
sen_acc = 0.0
sen_prec = 0.0
sen_bleu += sentence_bleu([gts], subouts, weights=[0.5,0.5])
for j, token in enumerate(gts):
if token < len(data_utils.str_nodes):
if token not in gt_kws:
gt_kws.append(token)
for j, token in enumerate(subouts):
if token < len(data_utils.str_nodes):
if data_utils.str_nodes[token] not in kb_counts:
kb_counts[data_utils.str_nodes[token]] = 0
kb_counts[data_utils.str_nodes[token]] += 1
#TODO
if check_Rdebugs(Rdebugs):
path, path_prob = get_global_path(Rdebugs[j][1][i], neAs[i], Ss[i], data_utils.str_nodes[token])
hops = (len(path)-1)/2
if hops not in sssps_lens:
sssps_lens[hops] = 0
sssps_lens[hops] += 1
write_global_path(path, fpath)
fpath.write(' | ')
if token not in out_kws:
out_kws.append(token)
if token in gt_kws:
sen_acc += 1.
sen_prec += 1.
if len(gt_kws) > 0:
sen_acc /= len(gt_kws)
acc += sen_acc
count += 1
if len(out_kws) > 0:
sen_prec /= len(out_kws)
prec += sen_prec
pcount += 1
for kw in out_kws:
fkw.write(data_utils.str_nodes[kw])
fkw.write(' ')
fkw.write('\n')
fpath.write('\n')
fout.close()
if len(results_dirs) == 3:
fpath.close()
fkw.close()
if feed_prev == False:
return [acc_nu, TP_num, FN_num, TN_num, FP_num, ppx]
else:
return [acc, prec, count, pcount, sen_bleu, kb_counts, sssps_lens, \
(distinct_1, distinct_2, distinct_3, distinct_4), total_word_num]