def test_ml_creation(self):
"""
Test to see if an ml model can be created and then if essays can be graded
"""
# Create 10 training essays that are scored
problem_resource_uri = create_ml_problem_and_essays("train", 10)
# Get the problem so that we can pass it to ml model generation engine
problem = lookup_object(problem_resource_uri)
problem_id = problem['id']
problem_model = Problem.objects.get(id=problem_id)
# Create the ml model
creator_success, message = ml_model_creation.handle_single_problem(
problem_model)
# Create some test essays and see if the model can score them
essay_list = create_ml_essays_only("test", 10, problem_resource_uri)
# Lookup the first essay and try to score it
essay = lookup_object(essay_list[0])
essay_id = essay['id']
essay_model = Essay.objects.get(id=essay_id)
# Try to score the essay
grader_success, message = ml_grader.handle_single_essay(essay_model)
self.assertEqual(creator_success, settings.FOUND_ML)
self.assertEqual(grader_success, settings.FOUND_ML)
def test_ml_creation(self):
"""
Test to see if an ml model can be created and then if essays can be graded
"""
#Create 10 training essays that are scored
problem_resource_uri = create_ml_problem_and_essays("train",10)
#Get the problem so that we can pass it to ml model generation engine
problem = lookup_object(problem_resource_uri)
problem_id = problem['id']
problem_model = Problem.objects.get(id=problem_id)
#Create the ml model
creator_success, message = ml_model_creation.handle_single_problem(problem_model)
#Create some test essays and see if the model can score them
essay_list = create_ml_essays_only("test",10, problem_resource_uri)
#Lookup the first essay and try to score it
essay = lookup_object(essay_list[0])
essay_id = essay['id']
essay_model = Essay.objects.get(id=essay_id)
#Try to score the essay
grader_success, message = ml_grader.handle_single_essay(essay_model)
self.assertEqual(creator_success, settings.FOUND_ML)
self.assertEqual(grader_success, settings.FOUND_ML)
def grade_ml_essays(problem):
"""
Called by grade_ml. Handles a single grading task for a single essay.
"""
transaction.commit_unless_managed()
lock_id = "celery-essay-grading-{0}".format(problem.id)
acquire_lock = lambda: cache.add(lock_id, "true", settings.GRADING_CACHE_LOCK_TIME)
release_lock = lambda: cache.delete(lock_id)
if acquire_lock():
try:
essays = Essay.objects.filter(problem=problem, has_been_ml_graded=False)
#TODO: Grade essays in batches so ml model doesn't have to be loaded every single time (or cache the model files)
for essay in essays:
handle_single_essay(essay)
finally:
release_lock()
def grade_ml_single_essay(essay):
"""
Called by grade_ml. Handles a single grading task for a single essay.
"""
transaction.commit_unless_managed()
handle_single_essay(essay)
