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(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 ) : #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
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)
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 )