def gendescr_pathast_2(model, data, comstok, comlen, batchsize, config, w=1):
tdats, sdats, coms, wsmlpaths = zip(*data.values())
tdats = np.array(tdats)
coms = np.array(coms)
sdats = np.array(sdats)
wsmlpaths = np.array(wsmlpaths)
for i in range(1, comlen):
if (config['use_sdats']):
results = model.predict([tdats, sdats, coms, wsmlpaths],
batch_size=batchsize)
else:
results = model.predict([tdats, coms, wsmlpaths],
batch_size=batchsize)
for c, s in enumerate(results):
for k in range(w):
ps = np.argmax(s)
s[ps] = 0
new_ps = np.argmax(s)
coms[c][i] = new_ps
final_data = {}
for fid, com in zip(data.keys(), coms):
final_data[fid] = seq2sent(com, comstok)
return final_data
def gendescr_pathast_threed(model, data, comstok, comlen, batchsize, config,
strat, bw):
if strat == 'beam':
return gendescr_pathast_beam(model, data, comstok, comlen, batchsize,
config, bw)
tdats, sdats, coms, wsmlpaths = zip(*data.values())
tdats = np.array(tdats)
coms = np.array(coms)
sdats = np.array(sdats)
wsmlpaths = np.array(wsmlpaths)
for i in range(1, comlen):
if (config['use_sdats']):
results = model.predict([tdats, sdats, coms, wsmlpaths],
batch_size=batchsize)
else:
results = model.predict([tdats, coms, wsmlpaths],
batch_size=batchsize)
for c, s in enumerate(results):
coms[c][i] = np.argmax(s)
final_data = {}
for fid, com in zip(data.keys(), coms):
final_data[fid] = seq2sent(com, comstok)
return final_data
def predictorLime(codeList):
print(len(codeList))
retList = []
inpts = np.zeros((len(codeList), 100))
coms = np.zeros((len(codeList), comlen))
coms[:, 0] = st
for i, c in enumerate(codeList):
for j, w in enumerate(c.split(' ')):
if j >= 100:
break
if w not in datstok.w2i.keys():
inpts[i][j] = 0
else:
inpts[i][j] = datstok.w2i[w]
l = 0
r = batchSize
while l < len(codeList):
tmpInpts = inpts[l:r]
tmpComs = coms[l:r]
for i in range(1, comlen):
results = model.predict([tmpInpts, tmpComs], batch_size=r - l)
for c, s in enumerate(results):
coms[l + c][i] = np.argmax(s)
r += batchSize
l += batchSize
if r >= len(codeList):
r = len(codeList) - 1
for c in coms:
if key in seq2sent(c, comstok).split(' '):
retList.append([0, 1])
else:
retList.append([1, 0])
return np.array(retList)
def gendescr_4inp(model,
data,
comstok,
comlen,
batchsize,
config,
strat='greedy'):
# right now, only greedy search is supported...
tdats, sdats, coms, smls = zip(*data.values())
tdats = np.array(tdats)
sdats = np.array(sdats)
coms = np.array(coms)
smls = np.array(smls)
#print(sdats)
for i in range(1, comlen):
results = model.predict([tdats, sdats, coms, smls],
batch_size=batchsize)
for c, s in enumerate(results):
coms[c][i] = np.argmax(s)
final_data = {}
for fid, com in zip(data.keys(), coms):
final_data[fid] = seq2sent(com, comstok)
return final_data
def gendescr_pathast(model, data, comstok, comlen, batchsize, config, refcoms, count, strat='greedy'):
# right now, only greedy search is supported...
tdats, sdats, coms, wsmlpaths = zip(*data.values())
tdats = np.array(tdats)
coms = np.array(coms)
sdats = np.array(sdats)
wsmlpaths = np.array(wsmlpaths)
print('refcoms len:', len(refcoms))
print('coms len:', coms.shape)
#coms[:,1] = np.array(list(refcoms.values())[count*batchsize:((count+1)*batchsize)])[:,1]
coms[:,1] = np.array(list(refcoms.values()))[:,1]
for i in range(2, comlen):
if(config['use_sdats']):
results = model.predict([tdats, sdats, coms, wsmlpaths], batch_size=batchsize)
else:
results = model.predict([tdats, coms, wsmlpaths], batch_size=batchsize)
for c, s in enumerate(results):
coms[c][i] = np.argmax(s)
final_data = {}
for fid, com in zip(data.keys(), coms):
final_data[fid] = seq2sent(com, comstok)
return final_data
def gendescr_2inp(model, data, comstok, comlen, batchsize, config, strat,
beamwidth, outfile, stopword):
# right now, only greedy search is supported...
fid = [*data.keys()]
tdats, coms = list(zip(*data.values()))
tdats = np.array(tdats)
coms = np.array(coms)
for i in range(1, stopword):
results = model.predict([tdats, coms], batch_size=batchsize)
for c, s in enumerate(results):
coms[c][i] = np.argmax(s)
act1 = get_activations(model, [tdats, coms], layer_name='activation_1')
act1_softmax_val_path = '/nfs/projects/attn-to-fc/data/outdir/viz/{}-{}-act1-stopword-{}.txt'.format(
fid[0],
outfile.split('.')[0], stopword)
act1_prob_file = open(act1_softmax_val_path, 'w')
for j in act1:
display_activations(j, 'tdats_activation', fid, act1_prob_file,
outfile, stopword)
act1_prob_file.close()
final_data = {}
for fid, com in zip(data.keys(), coms):
final_data[fid] = seq2sent(com, comstok)
return final_data
def gendescr_astattendfcpluspc(model, modelpc, data, data1, comstok, comlen, batchsize, strat='greedy'):
# right now, only greedy search is supported...
tdats, pdats,smls,coms, = list(zip(*data.values()))
_,sdats,_,_ = list(zip(*data1.values()))
tdats = np.array(tdats)
coms = np.array(coms)
smls = np.array(smls)
sdats = np.array(sdats)
pdats = np.array(pdats)
#dats = np.zeros_like(dats)
for i in range(1, comlen):
results = model.predict([tdats,sdats, coms, smls], batch_size=batchsize)
pcresults = modelpc.predict([tdats, pdats, smls, coms], batch_size=batchsize)
for c, (t, a) in enumerate(zip(results, pcresults)):
m = np.argmax(np.mean([t, a], axis=0))
coms[c][i] = m
final_data = {}
for fid, com in zip(data.keys(), coms):
final_data[fid] = seq2sent(com, comstok)
return final_data
def gendescr_code2seqplusfc(model, modelpc, data, comstok, comlen, batchsize, strat='greedy'):
# right now, only greedy search is supported...
tdats,sdats, coms, pathast = zip(*data.values())
tdats = np.array(tdats)
coms = np.array(coms)
sdats = np.array(sdats)
pathast = np.array(pathast)
#dats = np.zeros_like(dats)
for i in range(1, comlen):
results = model.predict([tdats, coms, pathast], batch_size=batchsize)
pcresults = modelpc.predict([tdats, sdats, coms, pathast], batch_size=batchsize)
for c, (t, a) in enumerate(zip(results, pcresults)):
#tm = t[np.argmax(t)]
#am = a[np.argmax(a)]
#m = np.argmax(t)
#if(am > tm):
# m = np.argmax(a)
m = np.argmax(np.mean([t, a], axis=0))
coms[c][i] = m
final_data = {}
for fid, com in zip(data.keys(), coms):
final_data[fid] = seq2sent(com, comstok)
return final_data
def gendescr_pathast_beam(model, data, comstok, comlen, batchsize, config, w):
tdats, sdats, coms, wsmlpaths = zip(*data.values())
tdats = np.array(tdats)
coms = np.array(coms)
sdats = np.array(sdats)
wsmlpaths = np.array(wsmlpaths)
bms = coms.shape[0]
beamcoms = np.tile(coms, [w, 1])
beamcoms = np.reshape(beamcoms, [w, bms, comlen])
beamprobs = np.zeros((w, bms))
psmat = np.zeros((bms, w * w, comlen))
prmat = np.zeros((bms, w * w))
if (config['use_sdats']):
results = model.predict([tdats, sdats, coms, wsmlpaths],
batch_size=batchsize)
else:
results = model.predict([tdats, coms, wsmlpaths], batch_size=batchsize)
for c, s in enumerate(results):
for j in range(w):
ps = np.argmax(s)
pr = np.max(s)
pr = -np.log(pr)
s[ps] = 0
beamprobs[j][c] = pr
beamcoms[j][c][1] = ps
for i in range(2, comlen):
for j in range(w):
if (config['use_sdats']):
results = model.predict([tdats, sdats, beamcoms[j], wsmlpaths],
batch_size=batchsize)
else:
results = model.predict([tdats, beamcoms[j], wsmlpaths],
batch_size=batchsize)
for c, s in enumerate(results):
for k in range(w):
ps = np.argmax(s)
pr = np.max(s)
pr = -np.log(pr)
s[ps] = 0
prmat[c][(j * w) + k] = beamprobs[j][c] + pr
psmat[c][(j * w) + k] = beamcoms[j][c]
psmat[c][(j * w) + k][i] = ps
for c, s in enumerate(prmat):
for j in range(w):
ps = np.argmin(s)
pr = np.min(s)
s[ps] = np.inf
beamprobs[j][c] = pr
beamcoms[j][c] = psmat[c][ps]
coms = beamcoms[0]
final_data = {}
for fid, com in zip(data.keys(), coms):
final_data[fid] = seq2sent(com, comstok)
return final_data
def gendescr_2inp(model, data, comstok, comlen, batchsize, config, refcoms, count, strat='greedy'):
# right now, only greedy search is supported...
tdats, coms = list(zip(*data.values()))
tdats = np.array(tdats)
coms = np.array(coms)
coms[:,1] = np.array(list(refcoms.values())[count*batchsize:((count+1)*batchsize)])[:,1]
for i in range(2, comlen):
results = model.predict([tdats, coms], batch_size=batchsize)
for c, s in enumerate(results):
coms[c][i] = np.argmax(s)
final_data = {}
for fid, com in zip(data.keys(), coms):
final_data[fid] = seq2sent(com, comstok)
return final_data
def gendescr_projcon(model, data, comstok, comlen, batchsize, config, strat,
bw):
tdats, pdats, coms = zip(*data.values())
tdats = np.array(tdats)
coms = np.array(coms)
pdats = np.array(pdats)
for i in range(1, comlen):
results = model.predict([tdats, pdats, coms], batch_size=batchsize)
for c, s in enumerate(results):
coms[c][i] = np.argmax(s)
final_data = {}
for fid, com in zip(data.keys(), coms):
final_data[fid] = seq2sent(com, comstok)
return final_data
def gendescr_3inp(model, data, comstok, comlen, batchsize, config, strat, bw):
if strat == 'beam':
return gendescr_3inp_beam(model, data, comstok, comlen, batchsize,
config, bw)
tdats, coms, smls = list(zip(*data.values()))
tdats = np.array(tdats)
coms = np.array(coms)
smls = np.array(smls)
for i in range(1, comlen):
results = model.predict([tdats, coms, smls], batch_size=batchsize)
for c, s in enumerate(results):
coms[c][i] = np.argmax(s)
final_data = {}
for fid, com in zip(data.keys(), coms):
final_data[fid] = seq2sent(com, comstok)
return final_data
def gendescr_3inp(model, data, anstok, anslen, batchsize, config, strat,
beamwidth, outfile, stopword):
# right now, only greedy search is supported...
fid = [*data.keys()]
context, ans, ques = list(zip(*data.values()))
context = np.array(context)
ans = np.array(ans)
ques = np.array(ques)
for i in range(1, stopword):
results = model.predict([context, ans, ques], batch_size=batchsize)
for c, s in enumerate(results):
ans[c][i] = np.argmax(s)
act1 = get_activations(model, [context, ans, ques],
layer_name='activation_1')
act2 = get_activations(model, [context, ans, ques],
layer_name='activation_2')
act1_softmax_val_path = '../qadatasetKstudy/outdir/viz/{}-{}-act1-stopword-{}.txt'.format(
fid[0],
outfile.split('.')[0], stopword)
act2_softmax_val_path = '../qadatasetKstudy/outdir/viz/{}-{}-act2-stopword-{}.txt'.format(
fid[0],
outfile.split('.')[0], stopword)
act1_prob_file = open(act1_softmax_val_path, 'w')
act2_prob_file = open(act2_softmax_val_path, 'w')
for j in act1:
display_activations(j, 'context_activation', fid, act1_prob_file,
outfile, stopword)
for j in act2:
display_activations(j, 'context2_activation', fid, act2_prob_file,
outfile, stopword)
act1_prob_file.close()
act2_prob_file.close()
final_data = {}
for fid, an in zip(data.keys(), ans):
final_data[fid] = seq2sent(an, anstok)
def translate(code, sbt=False, sml=None):
words = code.split(' ')
inpt = [np.zeros(100)]
for i, w in enumerate(words):
if i >= 100:
break
if w not in datstok.w2i.keys():
inpt[0][i] = 0
else:
inpt[0][i] = datstok.w2i[w]
coms = np.zeros(comlen)
coms[0] = st
coms = [coms]
for i in range(1, comlen):
if not sbt:
results = model.predict([inpt, coms], batch_size=1)
else:
results = model.predict([inpt, coms, sml], batch_size=1)
for c, s in enumerate(results):
coms[c][i] = np.argmax(s)
return seq2sent(coms[0], comstok).split(' ')
def predictor(codeList):
retList = []
inpts = np.zeros((len(codeList), 100))
coms = np.zeros((len(codeList), comlen))
coms[:, 0] = st
for i, c in enumerate(codeList):
for j, w in enumerate(c.split(' ')):
if w not in datstok.w2i.keys():
inpts[i][j] = 0
else:
inpts[i][j] = datstok.w2i[w]
for i in range(1, comlen):
results = model.predict([inpts, coms], batch_size=len(codeList))
for c, s in enumerate(results):
coms[c][i] = np.argmax(s)
for c in coms:
if key in seq2sent(c, comstok).split(' '):
retList.append(1)
else:
retList.append(0)
return np.array(retList)
def translate(self, code, sbt, sml):
words = code.split(' ')
inp = [np.zeros(100)]
for i, w in enumerate(words):
if i >= 100:
break
if w not in self.datstok.w2i.keys():
inp[0][i] = 0
else:
inp[0][i] = self.datstok.w2i[w]
coms = np.zeros(comlen)
coms[0] = self.st
coms = [coms]
for i in range(1, comlen):
if not sbt:
results = self.model.predict([inp, coms], batch_size=1)
else:
# print(sml)
results = self.model.predict([inp, coms, [sml]], batch_size=1)
for c, s in enumerate(results):
coms[c][i] = np.argmax(s)
return seq2sent(coms[0], self.comstok).split(' ')
def gendescr_3inp(model,
data,
anstok,
anslen,
batchsize,
config,
strat='greedy'):
# right now, only greedy search is supported...
context, ans, ques = list(zip(*data.values()))
context = np.array(context)
ans = np.array(ans)
ques = np.array(ques)
for i in range(1, anslen):
results = model.predict([context, ans, ques], batch_size=batchsize)
for c, s in enumerate(results):
ans[c][i] = np.argmax(s)
final_data = {}
for fid, an in zip(data.keys(), ans):
final_data[fid] = seq2sent(an, anstok)
return final_data
def gendescr_graphastpc(model, data, comstok, comlen, batchsize, config, strat,
bw):
if strat == 'beam':
return gendescr_graphast_beam(model, data, comstok, comlen, batchsize,
config, bw)
tdats, coms, wsmlnodes, wsmledges, pdats = zip(*data.values())
tdats = np.array(tdats)
coms = np.array(coms)
pdats = np.array(pdats)
wsmlnodes = np.array(wsmlnodes)
wsmledges = np.array(wsmledges)
for i in range(1, comlen):
results = model.predict([tdats, coms, wsmlnodes, wsmledges, pdats],
batch_size=batchsize)
for c, s in enumerate(results):
coms[c][i] = np.argmax(s)
final_data = {}
for fid, com in zip(data.keys(), coms):
final_data[fid] = seq2sent(com, comstok)
return final_data
