def create_essay_set(text, score, prompt_string, generate_additional=True):
"""
Creates an essay set from given data.
Text should be a list of strings corresponding to essay text.
Score should be a list of scores where score[n] corresponds to text[n]
Prompt string is just a string containing the essay prompt.
Generate_additional indicates whether to generate additional essays at the minimum score point or not.
"""
x = EssaySet()
for i in range(0, len(text)):
x.add_essay(text[i], score[i])
if score[i] == min(score) and generate_additional == True:
x.generate_additional_essays(x._clean_text[len(x._clean_text) - 1], score[i])
x.update_prompt(prompt_string)
return x
def create_essay_set(text, score, prompt_string, generate_additional=True):
"""
Creates an essay set from given data.
Text should be a list of strings corresponding to essay text.
Score should be a list of scores where score[n] corresponds to text[n]
Prompt string is just a string containing the essay prompt.
Generate_additional indicates whether to generate additional essays at the minimum score point or not.
"""
x = EssaySet()
for i in xrange(0, len(text)):
x.add_essay(text[i], score[i])
if score[i] == min(score) and generate_additional == True:
x.generate_additional_essays(x._clean_text[len(x._clean_text) - 1], score[i])
x.update_prompt(prompt_string)
return x
def grade(grader_data, submission):
"""
Grades a specified submission using specified models
grader_data - A dictionary:
{
'model' : trained model,
'extractor' : trained feature extractor,
'prompt' : prompt for the question,
'algorithm' : algorithm for the question,
}
submission - The student submission (string)
"""
#Initialize result dictionary
results = {
'errors': [],
'tests': [],
'score': 0,
'feedback': "",
'success': False,
'confidence': 0
}
has_error = False
grader_set = EssaySet(essaytype="test")
feedback = {}
model, extractor = get_classifier_and_ext(grader_data)
#This is to preserve legacy functionality
if 'algorithm' not in grader_data:
grader_data['algorithm'] = util_functions.AlgorithmTypes.classification
try:
#Try to add essay to essay set object
grader_set.add_essay(str(submission), 0)
grader_set.update_prompt(str(grader_data['prompt']))
except Exception:
error_message = "Essay could not be added to essay set:{0}".format(
submission)
log.exception(error_message)
results['errors'].append(error_message)
has_error = True
#Try to extract features from submission and assign score via the model
try:
grader_feats = extractor.gen_feats(grader_set)
feedback = extractor.gen_feedback(grader_set, grader_feats)[0]
results['score'] = int(model.predict(grader_feats)[0])
except Exception:
error_message = "Could not extract features and score essay."
log.exception(error_message)
results['errors'].append(error_message)
has_error = True
#Try to determine confidence level
try:
results['confidence'] = get_confidence_value(grader_data['algorithm'],
model, grader_feats,
results['score'],
grader_data['score'])
except Exception:
#If there is an error getting confidence, it is not a show-stopper, so just log
log.exception("Problem generating confidence value")
if not has_error:
#If the essay is just a copy of the prompt, return a 0 as the score
if ('too_similar_to_prompt' in feedback
and feedback['too_similar_to_prompt']):
results['score'] = 0
results['correct'] = False
results['success'] = True
#Generate short form output--number of problem areas identified in feedback
#Add feedback to results if available
results['feedback'] = {}
if 'topicality' in feedback and 'prompt_overlap' in feedback:
results['feedback'].update({
'topicality':
feedback['topicality'],
'prompt-overlap':
feedback['prompt_overlap'],
})
results['feedback'].update({
'spelling': feedback['spelling'],
'grammar': feedback['grammar'],
'markup-text': feedback['markup_text'],
})
else:
#If error, success is False.
results['success'] = False
return results
def grade(grader_data, submission):
"""
Grades a specified submission using specified models
grader_data - A dictionary:
{
'model' : trained model,
'extractor' : trained feature extractor,
'prompt' : prompt for the question,
'algorithm' : algorithm for the question,
}
submission - The student submission (string)
"""
# Initialize result dictionary
results = {'errors': [], 'tests': [], 'score': 0, 'feedback': "", 'success': False, 'confidence': 0}
has_error = False
grader_set = EssaySet(type="test")
# This is to preserve legacy functionality
if 'algorithm' not in grader_data:
grader_data['algorithm'] = util_functions.AlgorithmTypes.classification
try:
# Try to add essay to essay set object
grader_set.add_essay(str(submission), 0)
grader_set.update_prompt(str(grader_data['prompt']))
except:
results['errors'].append("Essay could not be added to essay set:{0}".format(submission))
has_error = True
# Try to extract features from submission and assign score via the model
try:
grader_feats = grader_data['extractor'].gen_feats(grader_set)
feedback = grader_data['extractor'].gen_feedback(grader_set, grader_feats)[0]
results['score'] = int(grader_data['model'].predict(grader_feats)[0])
except:
results['errors'].append("Could not extract features and score essay.")
has_error = True
# Try to determine confidence level
try:
results['confidence'] = get_confidence_value(grader_data['algorithm'], grader_data['model'], grader_feats, results['score'], grader_data['score'])
except:
# If there is an error getting confidence, it is not a show-stopper, so just log
log.exception("Problem generating confidence value")
if not has_error:
# If the essay is just a copy of the prompt, return a 0 as the score
if(feedback['too_similar_to_prompt']):
results['score'] = 0
results['correct'] = False
results['success'] = True
# Generate short form output--number of problem areas identified in feedback
# Add feedback to results if available
results['feedback'] = {}
if 'topicality' in feedback and 'prompt_overlap' in feedback:
results['feedback'].update({
'topicality': feedback['topicality'],
'prompt-overlap': feedback['prompt_overlap'],
})
results['feedback'].update(
{
'spelling': feedback['spelling'],
'grammar': feedback['grammar'],
'markup-text': feedback['markup_text'],
}
)
else:
# If error, success is False.
results['success'] = False
return results
