def grade_session_with_automatic_model(request, session_code):
session = get_object_or_404(Session, code=session_code)
task_name = session.task_def.name
ag_model_fn = os.path.join(AUTOGRADING_MODEL_ROOT, task_name, "ag_grading.bin")
if not os.path.exists(ag_model_fn):
return HttpResponse("No model." + ag_model_fn)
temp_root = os.path.join("/var/tmp/autograding", session_code)
if not os.path.exists(temp_root):
os.makedirs(temp_root)
create_flat_file.create_grading_flat_file(session.code, temp_root, [0, 0, 1])
build_te_model.make_predictions_with_model(temp_root, ag_model_fn)
(predictions, gt_url) = round_predictions.round_predictions(temp_root)
(wrk, is_created) = Worker.objects.get_or_create(worker="AUTO-ADDITIVE-GROVES")
resp = HttpResponse("Done.")
for pred, ref in map(None, predictions, gt_url):
submission_id = int(ref.split("\t")[0])
resp.write("%d\t%d\n" % (submission_id, pred))
subm = get_object_or_404(SubmittedTask, id=submission_id)
(rcd, created) = ManualGradeRecord.objects.get_or_create(submission=subm, worker=wrk)
rcd.feedback = "Computer-generated grade"
rcd.quality = pred
rcd.save()
return resp
def grade_session_with_automatic_model(request,session_code):
session = get_object_or_404(Session,code=session_code)
task_name = session.task_def.name;
ag_model_fn = os.path.join(AUTOGRADING_MODEL_ROOT, task_name, 'ag_grading.bin');
if not os.path.exists(ag_model_fn):
return HttpResponse("No model."+ag_model_fn)
temp_root=os.path.join('/var/tmp/autograding',session_code)
if not os.path.exists(temp_root):
os.makedirs(temp_root)
create_flat_file.create_grading_flat_file(session.code,temp_root, [0,0,1])
build_te_model.make_predictions_with_model(temp_root,ag_model_fn)
(predictions,gt_url) = round_predictions.round_predictions(temp_root)
(wrk,is_created)=Worker.objects.get_or_create(worker="AUTO-ADDITIVE-GROVES");
resp=HttpResponse("Done.")
for pred, ref in map(None,predictions,gt_url):
submission_id = int(ref.split('\t')[0])
resp.write("%d\t%d\n" % (submission_id,pred))
subm = get_object_or_404(SubmittedTask,id=submission_id)
(rcd,created)=ManualGradeRecord.objects.get_or_create(submission=subm,
worker=wrk);
rcd.feedback="Computer-generated grade"
rcd.quality=pred;
rcd.save();
return resp
def build_model_from_session(request,session_code):
session = get_object_or_404(Session,code=session_code)
task_name = session.task_def.name;
ag_model_fn = os.path.join(AUTOGRADING_MODEL_ROOT, task_name, 'ag_grading.bin');
ag_training_root = os.path.join(AUTOGRADING_MODEL_ROOT, task_name, 'training');
if not os.path.exists(ag_training_root):
#os.rmdir(ag_training_root);
os.makedirs(ag_training_root);
temp_root=os.path.join('/var/tmp/autograding',session_code)
if not os.path.exists(temp_root):
os.makedirs(temp_root)
resp=HttpResponse("Building grading model:\n",mimetype="text/plain")
resp.write("Creating data file\n")
resp.flush()
create_flat_file.create_grading_flat_file(session.code, ag_training_root, [0.5,0.25,0.25])
resp.write(" done\n")
resp.write("Learning the model on train+val\n")
resp.flush()
build_te_model.build_model(ag_training_root);
resp.write(" done\n")
ag_trained_model_fn=os.path.join(ag_training_root,'model.bin')
build_te_model.make_predictions_with_model(ag_training_root,ag_trained_model_fn)
resp.write("Predicting on the testset\n")
resp.flush()
round_predictions.round_predictions(ag_training_root)
resp.write(" done\n")
resp.write("----------------\n")
resp.write("Testset report\n")
rpt_fn=os.path.join(ag_training_root,'preds.txt.report.txt')
for s in open(rpt_fn,'r').readlines():
resp.write(s)
shutil.copyfile(ag_trained_model_fn,ag_model_fn)
return resp
def build_model_from_session(request, session_code):
session = get_object_or_404(Session, code=session_code)
task_name = session.task_def.name
ag_model_fn = os.path.join(AUTOGRADING_MODEL_ROOT, task_name, "ag_grading.bin")
ag_training_root = os.path.join(AUTOGRADING_MODEL_ROOT, task_name, "training")
if not os.path.exists(ag_training_root):
# os.rmdir(ag_training_root);
os.makedirs(ag_training_root)
temp_root = os.path.join("/var/tmp/autograding", session_code)
if not os.path.exists(temp_root):
os.makedirs(temp_root)
resp = HttpResponse("Building grading model:\n", mimetype="text/plain")
resp.write("Creating data file\n")
resp.flush()
create_flat_file.create_grading_flat_file(session.code, ag_training_root, [0.5, 0.25, 0.25])
resp.write(" done\n")
resp.write("Learning the model on train+val\n")
resp.flush()
build_te_model.build_model(ag_training_root)
resp.write(" done\n")
ag_trained_model_fn = os.path.join(ag_training_root, "model.bin")
build_te_model.make_predictions_with_model(ag_training_root, ag_trained_model_fn)
resp.write("Predicting on the testset\n")
resp.flush()
round_predictions.round_predictions(ag_training_root)
resp.write(" done\n")
resp.write("----------------\n")
resp.write("Testset report\n")
rpt_fn = os.path.join(ag_training_root, "preds.txt.report.txt")
for s in open(rpt_fn, "r").readlines():
resp.write(s)
shutil.copyfile(ag_trained_model_fn, ag_model_fn)
return resp