Skip to content

adding support for training just with MLP #6

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
msargordi wants to merge 2 commits into
base: master
Choose a base branch
from
Open

adding support for training just with MLP #6

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
a839e0e
Update dataGen.py
msargordi Nov 11, 2023
c5cd09d
a commented example for training with just labels added.
msargordi Nov 11, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 7 additions & 3 deletions examples/shortest_path/dataGen.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -74,17 +74,21 @@ def generateDataset(inPath, outPath):
dataset = generateDataset('data/shortestPath.data', 'data/shorteatPath_')

# for training
training_just_with_labels = False
obsList = []
with open('data/shorteatPath_train.txt', 'r') as f:
obsList = f.read().strip().strip('#evidence').split('#evidence')
dataList = []
for data in dataset['train']:
dataList.append({'g': Variable(torch.from_numpy(data).float(), requires_grad=False)})
for data, label in zip(dataset['train'], dataset['train_label']):
if training_just_with_labels:
dataList.append({'g': (Variable(torch.from_numpy(data).float(), requires_grad=False), {'sp': Variable(torch.from_numpy(label).float().view(-1, 1), requires_grad=False)})})
else:
dataList.append({'g': Variable(torch.from_numpy(data).float(), requires_grad=False)})

# for testing
obsListTest = []
with open('data/shorteatPath_test.txt', 'r') as f:
obsListTest = f.read().strip().strip('#evidence').split('#evidence')
dataListTest = []
for data in dataset['test']:
dataListTest.append({'g': Variable(torch.from_numpy(data).float(), requires_grad=False)})
dataListTest.append({'g': Variable(torch.from_numpy(data).float(), requires_grad=False)})
4 changes: 3 additions & 1 deletion examples/shortest_path/train.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -115,11 +115,13 @@
print('Continuously training for 10 epochs round {}...'.format(i+1))
time1 = time.time()
NeurASPobj.learn(dataList=dataList, obsList=obsList, epoch=10, opt=True, smPickle='data/stableModels.pickle', bar=True)
# If training MLP with just labels, use this line instead of above. Also change training_just_with_labels to True in dataGen.py
# NeurASPobj.learn(dataList=dataList, obsList=obsList, epoch=10, opt=True, smPickle='data/stableModels.pickle', bar=True, alpha=1, lossFunc=torch.nn.BCELoss())
time2 = time.time()
NeurASPobj.testConstraint(dataList=dataListTest, obsList=obsListTest, mvppList=combinations)
print("--- train time: %s seconds ---" % (time2 - time1))
print("--- test time: %s seconds ---" % (time.time() - time2))
print('--- total time from beginning: %s minutes ---' % int((time.time() - startTime)/60) )

print('Storing the trained model into {}'.format(saveModelPath))
torch.save(m.state_dict(), saveModelPath)
torch.save(m.state_dict(), saveModelPath)