print('# Test Set Obs: ', len(obs))
#######################START: INSERT CODE HERE#################################
# Produce Scores for a Set of Matching and Set of Non-Matching Pairs
x0 = [] # list of datapoints to pass to model
p0 = []
x1 = [] # list of datapoints to pass to model
p1 = []
for i in range(len(obs)):
for j in range(len(obs)):
if i != j:
d = [obs[i][:],
obs[j][:]] # datapoint consisting of pair of observations
datapoint = trnMod.augmentAndVectorize(d)
if tags[i] != tags[j]:
x0.append(datapoint)
else:
x1.append(datapoint)
print('i', i)
if len(x0) > 0:
y0 = model(torch.FloatTensor(x0, device=device))
x0 = []
p0tmp = [list(np.exp(q.detach().cpu().numpy()))[0] for q in y0]
p0 += p0tmp
if len(x1) > 0:
y1 = model(torch.FloatTensor(x1, device=device))
x1 = []
p1tmp = [list(np.exp(q.detach().cpu().numpy()))[0] for q in y1]
pairs = []
labels = []
# Grab Given Number of Examples of Non Match
while len(pairs) < 3:
i = int(random.uniform(0, len(obs)))
j = int(random.uniform(0, len(obs)))
if i != j:
if tags[i] != tags[j]:
pairs.append((i, j))
labels.append([1])
# Grab Given Number of Examples of Match
while len(pairs) < 5:
i = int(random.uniform(0, len(obs)))
j = int(random.uniform(0, len(obs)))
if i != j:
if tags[i] == tags[j]:
pairs.append((i, j))
labels.append([0])
inputs = []
for i, j in pairs:
datapoint = trnMod.augmentAndVectorize([obs[i][:], obs[j][:]])
inputs.append(datapoint)
inputs = np.array(inputs)
labels = np.array(labels)
print('inputs', inputs.shape)
show_saliency_maps(inputs, labels)
obs, tags = trnMod.loadDataTags(test_data, test_tags)
print('# Test Set Obs: ', len(obs))
print('# Unique RSOs: ', len(set(tags)))
# Produce Scores for a Set of Matching and Set of Non-Matching Pairs
c = []
rsos = []
for i in range(len(obs)):
#for i in range(119,122):
p = {} #Dictionary of Scores
temp = []
k = []
for j in range(len(obs)):
if i != j:
datapoint = trnMod.augmentAndVectorize([obs[i][:], obs[j][:]])
else:
datapoint = trnMod.augmentAndVectorize([obs[0][:], obs[-1][:]]) #dummy
temp.append(datapoint)
y = model(torch.FloatTensor(temp, device=device))
y = [list(np.exp(q.detach().cpu().numpy()))[0] for q in y]
y[i] = 0
for q in range(len(y)):
if 1-y[q] < costMax:
k.append(q)
#print('k', len(k))
if len(k) >= numObs:
#Add to Dictionary