def handle(self, *args, **options):
"""
Read from file
"""
parser = SafeConfigParser()
parser.read(args[0])
print("Starting import...")
print("Reading config from file {0}".format(args[0]))
header_name = "importdata"
prompt = parser.get(header_name, 'prompt')
essay_file = parser.get(header_name, 'essay_file')
essay_limit = int(parser.get(header_name, 'essay_limit'))
name = parser.get(header_name, "name")
add_score = parser.get(header_name, "add_grader_object") == "True"
max_target_scores = json.loads(parser.get(header_name, "max_target_scores"))
grader_type = parser.get(header_name, "grader_type")
try:
User.objects.create_user('vik', '*****@*****.**', 'vik')
except:
# User already exists, but doesn't matter to us
pass
user = User.objects.get(username='******')
organization, created = Organization.objects.get_or_create(
organization_name="edX"
)
course, created = Course.objects.get_or_create(
course_name="edX101",
)
if created:
course.organizations.add(organization)
user.profile.organization = organization
user.save()
course.users.add(user)
course.save()
problem, created = Problem.objects.get_or_create(
prompt=prompt,
name=name,
)
problem.courses.add(course)
problem.save()
grades, text = [], []
combined_raw = open(settings.REPO_PATH / essay_file).read()
raw_lines = combined_raw.splitlines()
for row in xrange(1, len(raw_lines)):
line_split = raw_lines[row].strip().split("\t")
text.append(line_split[0])
grades.append(line_split[1:])
max_scores = []
for i in xrange(0, len(grades[0])):
scores_at_point = [g[i] for g in grades]
max_scores.append(max(scores_at_point))
problem.max_target_scores = json.dumps(max_scores)
problem.save()
for i in range(0, min(essay_limit, len(text))):
essay = Essay(
problem=problem,
user=user,
essay_type="train",
essay_text=text[i],
)
essay.save()
score = EssayGrade(
target_scores=json.dumps(grades[i]),
feedback="",
grader_type=grader_type,
essay=essay,
success=True,
)
score.save()
print ("Successfully imported {0} essays using configuration in file {1}.".format(
min(essay_limit, len(text)),
args[0],
))
def handle_single_essay(essay):
#Needed to ensure that the DB is not wrapped in a transaction and pulls old data
transaction.commit_unless_managed()
#strip out unicode and other characters in student response
#Needed, or grader may potentially fail
#TODO: Handle unicode in student responses properly
student_response = essay.essay_text.encode('ascii', 'ignore')
#Gets both the max scores for each target and the number of targets
target_max_scores = json.loads(essay.problem.max_target_scores)
target_counts = len(target_max_scores)
target_scores=[]
for m in xrange(0,target_counts):
#Gets latest model for a given problem and target
success, created_model=ml_grading_util.get_latest_created_model(essay.problem,m)
if not success:
error_message = "Could not identify a valid created model!"
log.error(error_message)
results= RESULT_FAILURE_DICT
formatted_feedback="error"
return False, error_message
#Create grader path from location in submission
grader_path = os.path.join(settings.ML_MODEL_PATH,created_model.model_relative_path)
#Indicates whether the model is stored locally or in the cloud
model_stored_in_s3=created_model.model_stored_in_s3
#Try to load the model file
success, grader_data=load_model_file(created_model,use_full_path=False)
if success:
#Send to ML grading algorithm to be graded
results = grade.grade(grader_data, student_response)
else:
results=RESULT_FAILURE_DICT
#If the above fails, try using the full path in the created_model object
if not results['success'] and not created_model.model_stored_in_s3:
#Before, we used the relative path to load. Possible that the full path may work
grader_path=created_model.model_full_path
try:
success, grader_data=load_model_file(created_model,use_full_path=True)
if success:
results = grade.grade(grader_data, student_response)
else:
results=RESULT_FAILURE_DICT
except:
error_message="Could not find a valid model file."
log.exception(error_message)
results=RESULT_FAILURE_DICT
if m==0:
final_results=results
if results['success'] == False:
error_message = "Unsuccessful grading: {0}".format(results)
log.exception(error_message)
return False, error_message
target_scores.append(int(results['score']))
grader_dict = {
'essay' : essay,
'target_scores' : json.dumps(target_scores),
'grader_type' : GraderTypes.machine,
'feedback' : '',
'annotated_text' : '',
'premium_feedback_scores' : json.dumps([]),
'success' :final_results['success'],
'confidence' : final_results['confidence'],
}
# Create grader object in controller by posting back results
essay_grade = EssayGrade(**grader_dict)
essay_grade.save()
#Update the essay so that it doesn't keep trying to re-grade
essay.has_been_ml_graded = True
essay.save()
transaction.commit_unless_managed()
return True, "Successfully scored!"
def handle(self, *args, **options):
"""
Read from file
"""
parser = SafeConfigParser()
parser.read(args[0])
print("Starting import...")
print("Reading config from file {0}".format(args[0]))
header_name = "importdata"
prompt = parser.get(header_name, 'prompt')
essay_file = parser.get(header_name, 'essay_file')
essay_limit = int(parser.get(header_name, 'essay_limit'))
name = parser.get(header_name, "name")
add_score = parser.get(header_name, "add_grader_object") == "True"
max_target_scores = json.loads(parser.get(header_name, "max_target_scores"))
grader_type = parser.get(header_name, "grader_type")
try:
User.objects.create_user('vik', '*****@*****.**', 'vik')
except:
#User already exists, but doesn't matter to us
pass
user = User.objects.get(username='******')
organization, created = Organization.objects.get_or_create(
organization_name = "edX"
)
course, created = Course.objects.get_or_create(
course_name = "edX101",
)
if created:
course.organizations.add(organization)
user.profile.organization = organization
user.save()
course.users.add(user)
course.save()
problem, created = Problem.objects.get_or_create(
prompt = prompt,
name = name,
)
problem.courses.add(course)
problem.save()
grades, text = [], []
combined_raw = open(settings.REPO_PATH / essay_file).read()
raw_lines = combined_raw.splitlines()
for row in xrange(1, len(raw_lines)):
line_split = raw_lines[row].strip().split("\t")
text.append(line_split[0])
grades.append(line_split[1:])
max_scores = []
for i in xrange(0,len(grades[0])):
scores_at_point = [g[i] for g in grades]
max_scores.append(max(scores_at_point))
problem.max_target_scores = json.dumps(max_scores)
problem.save()
for i in range(0, min(essay_limit, len(text))):
essay = Essay(
problem = problem,
user =user,
essay_type = "train",
essay_text = text[i],
)
essay.save()
score = EssayGrade(
target_scores=json.dumps(grades[i]),
feedback="",
grader_type = grader_type,
essay = essay,
success = True,
)
score.save()
print ("Successfully imported {0} essays using configuration in file {1}.".format(
min(essay_limit, len(text)),
args[0],
))
def handle_single_essay(essay):
# Needed to ensure that the DB is not wrapped in a transaction and pulls old data
transaction.commit()
# strip out unicode and other characters in student response
# Needed, or grader may potentially fail
# TODO: Handle unicode in student responses properly
student_response = essay.essay_text.encode('ascii', 'ignore')
# Gets both the max scores for each target and the number of targets
target_max_scores = json.loads(essay.problem.max_target_scores)
target_counts = len(target_max_scores)
target_scores = []
for m in xrange(0, target_counts):
# Gets latest model for a given problem and target
success, created_model = ml_grading_util.get_latest_created_model(
essay.problem, m)
if not success:
results = RESULT_FAILURE_DICT
formatted_feedback = "error"
transaction.commit()
return False, formatted_feedback
# Try to load the model file
success, grader_data = load_model_file(created_model,
use_full_path=False)
if success:
# Send to ML grading algorithm to be graded
results = grade.grade(grader_data, student_response)
else:
results = RESULT_FAILURE_DICT
# If the above fails, try using the full path in the created_model object
if not results['success'] and not created_model.model_stored_in_s3:
try:
success, grader_data = load_model_file(created_model,
use_full_path=True)
if success:
results = grade.grade(grader_data, student_response)
else:
results = RESULT_FAILURE_DICT
except:
error_message = "Could not find a valid model file."
log.exception(error_message)
results = RESULT_FAILURE_DICT
if m == 0:
final_results = results
if results['success'] == False:
error_message = "Unsuccessful grading: {0}".format(results)
log.exception(error_message)
transaction.commit()
return False, error_message
target_scores.append(int(results['score']))
grader_dict = {
'essay': essay,
'target_scores': json.dumps(target_scores),
'grader_type': GraderTypes.machine,
'feedback': '',
'annotated_text': '',
'premium_feedback_scores': json.dumps([]),
'success': final_results['success'],
'confidence': final_results['confidence'],
}
# Create grader object in controller by posting back results
essay_grade = EssayGrade(**grader_dict)
essay_grade.save()
# Update the essay so that it doesn't keep trying to re-grade
essay.has_been_ml_graded = True
essay.save()
# copy permissions from the essay to the essaygrade
helpers.copy_permissions(essay, Essay, essay_grade, EssayGrade)
transaction.commit()
return True, "Successfully scored!"