f22d673980
Performance improvement, this cache is no longer needed thanks to block_structure caching data. TNL-4874
763 lines
30 KiB
Python
763 lines
30 KiB
Python
# Compute grades using real division, with no integer truncation
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from __future__ import division
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import json
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import logging
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import random
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from collections import defaultdict
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import dogstats_wrapper as dog_stats_api
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from course_blocks.api import get_course_blocks
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from courseware import courses
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from django.conf import settings
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from django.core.cache import cache
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from django.test.client import RequestFactory
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from opaque_keys import InvalidKeyError
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from opaque_keys.edx.keys import CourseKey
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from opaque_keys.edx.locator import BlockUsageLocator
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from openedx.core.djangoapps.content.block_structure.api import get_course_in_cache
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from openedx.core.lib.cache_utils import memoized
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from openedx.core.lib.gating import api as gating_api
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from courseware.model_data import FieldDataCache, ScoresClient
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from openedx.core.djangoapps.signals.signals import GRADES_UPDATED
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from student.models import anonymous_id_for_user
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from util.db import outer_atomic
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from util.module_utils import yield_dynamic_descriptor_descendants
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from xblock.core import XBlock
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from xmodule import graders, block_metadata_utils
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from xmodule.graders import Score
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from xmodule.modulestore.django import modulestore
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from xmodule.modulestore.exceptions import ItemNotFoundError
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from .models import StudentModule
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from .module_render import get_module_for_descriptor
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from .transformers.grades import GradesTransformer
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log = logging.getLogger("edx.courseware")
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class ProgressSummary(object):
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"""
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Wrapper class for the computation of a user's scores across a course.
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Attributes
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chapters: a summary of all sections with problems in the course. It is
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organized as an array of chapters, each containing an array of sections,
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each containing an array of scores. This contains information for graded
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and ungraded problems, and is good for displaying a course summary with
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due dates, etc.
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weighted_scores: a dictionary mapping module locations to weighted Score
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objects.
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locations_to_children: a function mapping locations to their
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direct descendants.
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"""
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def __init__(self, chapters, weighted_scores, locations_to_children):
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self.chapters = chapters
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self.weighted_scores = weighted_scores
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self.locations_to_children = locations_to_children
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def score_for_module(self, location):
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"""
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Calculate the aggregate weighted score for any location in the course.
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This method returns a tuple containing (earned_score, possible_score).
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If the location is of 'problem' type, this method will return the
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possible and earned scores for that problem. If the location refers to a
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composite module (a vertical or section ) the scores will be the sums of
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all scored problems that are children of the chosen location.
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"""
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if location in self.weighted_scores:
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score = self.weighted_scores[location]
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return score.earned, score.possible
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children = self.locations_to_children[location]
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earned = 0.0
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possible = 0.0
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for child in children:
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child_earned, child_possible = self.score_for_module(child)
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earned += child_earned
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possible += child_possible
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return earned, possible
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@memoized
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def block_types_with_scores():
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"""
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Returns the block types that could have a score.
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Something might be a scored item if it is capable of storing a score
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(has_score=True). We also have to include anything that can have children,
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since those children might have scores. We can avoid things like Videos,
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which have state but cannot ever impact someone's grade.
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"""
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return frozenset(
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cat for (cat, xblock_class) in XBlock.load_classes() if (
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getattr(xblock_class, 'has_score', False) or getattr(xblock_class, 'has_children', False)
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)
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)
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def possibly_scored(usage_key):
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"""
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Returns whether the given block could impact grading (i.e. scored, or has children).
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"""
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return usage_key.block_type in block_types_with_scores()
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def grading_context_for_course(course):
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"""
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Same as grading_context, but takes in a course object.
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"""
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course_structure = get_course_in_cache(course.id)
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return grading_context(course_structure)
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def grading_context(course_structure):
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"""
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This returns a dictionary with keys necessary for quickly grading
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a student. They are used by grades.grade()
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The grading context has two keys:
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graded_sections - This contains the sections that are graded, as
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well as all possible children modules that can affect the
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grading. This allows some sections to be skipped if the student
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hasn't seen any part of it.
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The format is a dictionary keyed by section-type. The values are
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arrays of dictionaries containing
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"section_block" : The section block
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"scored_descendant_keys" : An array of usage keys for blocks
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could possibly be in the section, for any student
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all_graded_blocks - This contains a list of all blocks that can
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affect grading a student. This is used to efficiently fetch
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all the xmodule state for a FieldDataCache without walking
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the descriptor tree again.
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"""
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all_graded_blocks = []
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all_graded_sections = defaultdict(list)
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for chapter_key in course_structure.get_children(course_structure.root_block_usage_key):
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for section_key in course_structure.get_children(chapter_key):
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section = course_structure[section_key]
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scored_descendants_of_section = [section]
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if section.graded:
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for descendant_key in course_structure.post_order_traversal(
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filter_func=possibly_scored,
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start_node=section_key,
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):
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scored_descendants_of_section.append(
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course_structure[descendant_key],
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)
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# include only those blocks that have scores, not if they are just a parent
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section_info = {
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'section_block': section,
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'scored_descendants': [
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child for child in scored_descendants_of_section
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if getattr(child, 'has_score', None)
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]
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}
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section_format = getattr(section, 'format', '')
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all_graded_sections[section_format].append(section_info)
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all_graded_blocks.extend(scored_descendants_of_section)
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return {
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'all_graded_sections': all_graded_sections,
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'all_graded_blocks': all_graded_blocks,
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}
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def answer_distributions(course_key):
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"""
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Given a course_key, return answer distributions in the form of a dictionary
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mapping:
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(problem url_name, problem display_name, problem_id) -> {dict: answer -> count}
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Answer distributions are found by iterating through all StudentModule
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entries for a given course with type="problem" and a grade that is not null.
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This means that we only count LoncapaProblems that people have submitted.
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Other types of items like ORA or sequences will not be collected. Empty
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Loncapa problem state that gets created from running the progress page is
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also not counted.
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This method accesses the StudentModule table directly instead of using the
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CapaModule abstraction. The main reason for this is so that we can generate
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the report without any side-effects -- we don't have to worry about answer
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distribution potentially causing re-evaluation of the student answer. This
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also allows us to use the read-replica database, which reduces risk of bad
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locking behavior. And quite frankly, it makes this a lot less confusing.
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Also, we're pulling all available records from the database for this course
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rather than crawling through a student's course-tree -- the latter could
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potentially cause us trouble with A/B testing. The distribution report may
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not be aware of problems that are not visible to the user being used to
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generate the report.
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This method will try to use a read-replica database if one is available.
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"""
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# dict: { module.module_state_key : (url_name, display_name) }
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state_keys_to_problem_info = {} # For caching, used by url_and_display_name
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def url_and_display_name(usage_key):
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"""
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For a given usage_key, return the problem's url and display_name.
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Handle modulestore access and caching. This method ignores permissions.
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Raises:
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InvalidKeyError: if the usage_key does not parse
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ItemNotFoundError: if there is no content that corresponds
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to this usage_key.
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"""
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problem_store = modulestore()
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if usage_key not in state_keys_to_problem_info:
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problem = problem_store.get_item(usage_key)
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problem_info = (problem.url_name, problem.display_name_with_default_escaped)
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state_keys_to_problem_info[usage_key] = problem_info
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return state_keys_to_problem_info[usage_key]
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# Iterate through all problems submitted for this course in no particular
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# order, and build up our answer_counts dict that we will eventually return
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answer_counts = defaultdict(lambda: defaultdict(int))
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for module in StudentModule.all_submitted_problems_read_only(course_key):
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try:
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state_dict = json.loads(module.state) if module.state else {}
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raw_answers = state_dict.get("student_answers", {})
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except ValueError:
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log.error(
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u"Answer Distribution: Could not parse module state for StudentModule id=%s, course=%s",
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module.id,
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course_key,
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)
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continue
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try:
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url, display_name = url_and_display_name(module.module_state_key.map_into_course(course_key))
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# Each problem part has an ID that is derived from the
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# module.module_state_key (with some suffix appended)
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for problem_part_id, raw_answer in raw_answers.items():
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# Convert whatever raw answers we have (numbers, unicode, None, etc.)
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# to be unicode values. Note that if we get a string, it's always
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# unicode and not str -- state comes from the json decoder, and that
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# always returns unicode for strings.
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answer = unicode(raw_answer)
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answer_counts[(url, display_name, problem_part_id)][answer] += 1
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except (ItemNotFoundError, InvalidKeyError):
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msg = (
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"Answer Distribution: Item {} referenced in StudentModule {} " +
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"for user {} in course {} not found; " +
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"This can happen if a student answered a question that " +
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"was later deleted from the course. This answer will be " +
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"omitted from the answer distribution CSV."
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).format(
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module.module_state_key, module.id, module.student_id, course_key
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)
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log.warning(msg)
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continue
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return answer_counts
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def grade(student, course, keep_raw_scores=False, course_structure=None):
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"""
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Returns the grade of the student.
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Also sends a signal to update the minimum grade requirement status.
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"""
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grade_summary = _grade(student, course, keep_raw_scores, course_structure)
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responses = GRADES_UPDATED.send_robust(
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sender=None,
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username=student.username,
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grade_summary=grade_summary,
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course_key=course.id,
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deadline=course.end
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)
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for receiver, response in responses:
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log.info('Signal fired when student grade is calculated. Receiver: %s. Response: %s', receiver, response)
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return grade_summary
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def _grade(student, course, keep_raw_scores, course_structure=None):
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"""
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Unwrapped version of "grade"
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This grades a student as quickly as possible. It returns the
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output from the course grader, augmented with the final letter
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grade. The keys in the output are:
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- course: a CourseDescriptor
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- keep_raw_scores : if True, then value for key 'raw_scores' contains scores
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for every graded module
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More information on the format is in the docstring for CourseGrader.
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"""
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if course_structure is None:
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course_structure = get_course_blocks(student, course.location)
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grading_context_result = grading_context(course_structure)
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scorable_locations = [block.location for block in grading_context_result['all_graded_blocks']]
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with outer_atomic():
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scores_client = ScoresClient.create_for_locations(course.id, student.id, scorable_locations)
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# Dict of item_ids -> (earned, possible) point tuples. This *only* grabs
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# scores that were registered with the submissions API, which for the moment
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# means only openassessment (edx-ora2)
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# We need to import this here to avoid a circular dependency of the form:
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# XBlock --> submissions --> Django Rest Framework error strings -->
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# Django translation --> ... --> courseware --> submissions
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from submissions import api as sub_api # installed from the edx-submissions repository
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with outer_atomic():
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submissions_scores = sub_api.get_scores(
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course.id.to_deprecated_string(),
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anonymous_id_for_user(student, course.id)
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)
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totaled_scores, raw_scores = _calculate_totaled_scores(
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student, grading_context_result, submissions_scores, scores_client, keep_raw_scores
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)
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with outer_atomic():
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# Grading policy might be overriden by a CCX, need to reset it
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course.set_grading_policy(course.grading_policy)
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grade_summary = course.grader.grade(totaled_scores, generate_random_scores=settings.GENERATE_PROFILE_SCORES)
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# We round the grade here, to make sure that the grade is a whole percentage and
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# doesn't get displayed differently than it gets grades
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grade_summary['percent'] = round(grade_summary['percent'] * 100 + 0.05) / 100
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letter_grade = grade_for_percentage(course.grade_cutoffs, grade_summary['percent'])
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grade_summary['grade'] = letter_grade
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grade_summary['totaled_scores'] = totaled_scores # make this available, eg for instructor download & debugging
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if keep_raw_scores:
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# way to get all RAW scores out to instructor
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# so grader can be double-checked
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grade_summary['raw_scores'] = raw_scores
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return grade_summary
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def _calculate_totaled_scores(
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student,
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grading_context_result,
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submissions_scores,
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scores_client,
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keep_raw_scores,
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):
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"""
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Returns the totaled scores, which can be passed to the grader.
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"""
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raw_scores = []
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totaled_scores = {}
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for section_format, sections in grading_context_result['all_graded_sections'].iteritems():
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format_scores = []
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for section_info in sections:
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section = section_info['section_block']
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section_name = block_metadata_utils.display_name_with_default(section)
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with outer_atomic():
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# Check to
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# see if any of our locations are in the scores from the submissions
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# API. If scores exist, we have to calculate grades for this section.
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should_grade_section = any(
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unicode(descendant.location) in submissions_scores
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for descendant in section_info['scored_descendants']
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)
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if not should_grade_section:
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should_grade_section = any(
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descendant.location in scores_client
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for descendant in section_info['scored_descendants']
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)
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# If we haven't seen a single problem in the section, we don't have
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# to grade it at all! We can assume 0%
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if should_grade_section:
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scores = []
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for descendant in section_info['scored_descendants']:
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(correct, total) = get_score(
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student,
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descendant,
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scores_client,
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submissions_scores,
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)
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if correct is None and total is None:
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continue
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if settings.GENERATE_PROFILE_SCORES: # for debugging!
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if total > 1:
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correct = random.randrange(max(total - 2, 1), total + 1)
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else:
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correct = total
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graded = descendant.graded
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if not total > 0:
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# We simply cannot grade a problem that is 12/0, because we might need it as a percentage
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graded = False
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scores.append(
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Score(
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correct,
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total,
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graded,
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block_metadata_utils.display_name_with_default_escaped(descendant),
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descendant.location
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)
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)
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__, graded_total = graders.aggregate_scores(scores, section_name)
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if keep_raw_scores:
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raw_scores += scores
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else:
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graded_total = Score(0.0, 1.0, True, section_name, None)
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# Add the graded total to totaled_scores
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if graded_total.possible > 0:
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format_scores.append(graded_total)
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else:
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log.info(
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"Unable to grade a section with a total possible score of zero. " +
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str(section.location)
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)
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totaled_scores[section_format] = format_scores
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return totaled_scores, raw_scores
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def grade_for_percentage(grade_cutoffs, percentage):
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"""
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Returns a letter grade as defined in grading_policy (e.g. 'A' 'B' 'C' for 6.002x) or None.
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Arguments
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- grade_cutoffs is a dictionary mapping a grade to the lowest
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possible percentage to earn that grade.
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- percentage is the final percent across all problems in a course
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"""
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letter_grade = None
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# Possible grades, sorted in descending order of score
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descending_grades = sorted(grade_cutoffs, key=lambda x: grade_cutoffs[x], reverse=True)
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for possible_grade in descending_grades:
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if percentage >= grade_cutoffs[possible_grade]:
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letter_grade = possible_grade
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break
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return letter_grade
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def progress_summary(student, course, course_structure=None):
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"""
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Returns progress summary for all chapters in the course.
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"""
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progress = _progress_summary(student, course, course_structure)
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if progress:
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return progress.chapters
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else:
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return None
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def get_weighted_scores(student, course):
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"""
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Uses the _progress_summary method to return a ProgressSummary object
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containing details of a students weighted scores for the course.
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"""
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return _progress_summary(student, course)
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def _progress_summary(student, course, course_structure=None):
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"""
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Unwrapped version of "progress_summary".
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This pulls a summary of all problems in the course.
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Returns
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- courseware_summary is a summary of all sections with problems in the course.
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It is organized as an array of chapters, each containing an array of sections,
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each containing an array of scores. This contains information for graded and
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ungraded problems, and is good for displaying a course summary with due dates,
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etc.
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- None if the student does not have access to load the course module.
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Arguments:
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student: A User object for the student to grade
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course: A Descriptor containing the course to grade
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"""
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if course_structure is None:
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course_structure = get_course_blocks(student, course.location)
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if not len(course_structure):
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return None
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scorable_locations = [block_key for block_key in course_structure if possibly_scored(block_key)]
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with outer_atomic():
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scores_client = ScoresClient.create_for_locations(course.id, student.id, scorable_locations)
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# We need to import this here to avoid a circular dependency of the form:
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# XBlock --> submissions --> Django Rest Framework error strings -->
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# Django translation --> ... --> courseware --> submissions
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from submissions import api as sub_api # installed from the edx-submissions repository
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|
with outer_atomic():
|
|
submissions_scores = sub_api.get_scores(
|
|
unicode(course.id), anonymous_id_for_user(student, course.id)
|
|
)
|
|
|
|
# Check for gated content
|
|
gated_content = gating_api.get_gated_content(course, student)
|
|
|
|
chapters = []
|
|
locations_to_weighted_scores = {}
|
|
|
|
for chapter_key in course_structure.get_children(course_structure.root_block_usage_key):
|
|
chapter = course_structure[chapter_key]
|
|
sections = []
|
|
for section_key in course_structure.get_children(chapter_key):
|
|
if unicode(section_key) in gated_content:
|
|
continue
|
|
|
|
section = course_structure[section_key]
|
|
|
|
graded = getattr(section, 'graded', False)
|
|
scores = []
|
|
|
|
for descendant_key in course_structure.post_order_traversal(
|
|
filter_func=possibly_scored,
|
|
start_node=section_key,
|
|
):
|
|
descendant = course_structure[descendant_key]
|
|
|
|
(correct, total) = get_score(
|
|
student,
|
|
descendant,
|
|
scores_client,
|
|
submissions_scores,
|
|
)
|
|
if correct is None and total is None:
|
|
continue
|
|
|
|
weighted_location_score = Score(
|
|
correct,
|
|
total,
|
|
graded,
|
|
block_metadata_utils.display_name_with_default_escaped(descendant),
|
|
descendant.location
|
|
)
|
|
|
|
scores.append(weighted_location_score)
|
|
locations_to_weighted_scores[descendant.location] = weighted_location_score
|
|
|
|
escaped_section_name = block_metadata_utils.display_name_with_default_escaped(section)
|
|
section_total, _ = graders.aggregate_scores(scores, escaped_section_name)
|
|
|
|
sections.append({
|
|
'display_name': escaped_section_name,
|
|
'url_name': block_metadata_utils.url_name_for_block(section),
|
|
'scores': scores,
|
|
'section_total': section_total,
|
|
'format': getattr(section, 'format', ''),
|
|
'due': getattr(section, 'due', None),
|
|
'graded': graded,
|
|
})
|
|
|
|
chapters.append({
|
|
'course': course.display_name_with_default_escaped,
|
|
'display_name': block_metadata_utils.display_name_with_default_escaped(chapter),
|
|
'url_name': block_metadata_utils.url_name_for_block(chapter),
|
|
'sections': sections
|
|
})
|
|
|
|
return ProgressSummary(chapters, locations_to_weighted_scores, course_structure.get_children)
|
|
|
|
|
|
def weighted_score(raw_correct, raw_total, weight):
|
|
"""Return a tuple that represents the weighted (correct, total) score."""
|
|
# If there is no weighting, or weighting can't be applied, return input.
|
|
if weight is None or raw_total == 0:
|
|
return (raw_correct, raw_total)
|
|
return (float(raw_correct) * weight / raw_total, float(weight))
|
|
|
|
|
|
def get_score(user, block, scores_client, submissions_scores_cache):
|
|
"""
|
|
Return the score for a user on a problem, as a tuple (correct, total).
|
|
e.g. (5,7) if you got 5 out of 7 points.
|
|
|
|
If this problem doesn't have a score, or we couldn't load it, returns (None,
|
|
None).
|
|
|
|
user: a Student object
|
|
block: a BlockStructure's BlockData object
|
|
scores_client: an initialized ScoresClient
|
|
submissions_scores_cache: A dict of location names to (earned, possible) point tuples.
|
|
If an entry is found in this cache, it takes precedence.
|
|
"""
|
|
submissions_scores_cache = submissions_scores_cache or {}
|
|
|
|
if not user.is_authenticated():
|
|
return (None, None)
|
|
|
|
location_url = unicode(block.location)
|
|
if location_url in submissions_scores_cache:
|
|
return submissions_scores_cache[location_url]
|
|
|
|
if not getattr(block, 'has_score', False):
|
|
# These are not problems, and do not have a score
|
|
return (None, None)
|
|
|
|
# Check the score that comes from the ScoresClient (out of CSM).
|
|
# If an entry exists and has a total associated with it, we trust that
|
|
# value. This is important for cases where a student might have seen an
|
|
# older version of the problem -- they're still graded on what was possible
|
|
# when they tried the problem, not what it's worth now.
|
|
score = scores_client.get(block.location)
|
|
if score and score.total is not None:
|
|
# We have a valid score, just use it.
|
|
correct = score.correct if score.correct is not None else 0.0
|
|
total = score.total
|
|
else:
|
|
# This means we don't have a valid score entry and we don't have a
|
|
# cached_max_score on hand. We know they've earned 0.0 points on this.
|
|
correct = 0.0
|
|
total = block.transformer_data[GradesTransformer].max_score
|
|
|
|
# Problem may be an error module (if something in the problem builder failed)
|
|
# In which case total might be None
|
|
if total is None:
|
|
return (None, None)
|
|
|
|
return weighted_score(correct, total, block.weight)
|
|
|
|
|
|
def iterate_grades_for(course_or_id, students, keep_raw_scores=False):
|
|
"""Given a course_id and an iterable of students (User), yield a tuple of:
|
|
|
|
(student, gradeset, err_msg) for every student enrolled in the course.
|
|
|
|
If an error occurred, gradeset will be an empty dict and err_msg will be an
|
|
exception message. If there was no error, err_msg is an empty string.
|
|
|
|
The gradeset is a dictionary with the following fields:
|
|
|
|
- grade : A final letter grade.
|
|
- percent : The final percent for the class (rounded up).
|
|
- section_breakdown : A breakdown of each section that makes
|
|
up the grade. (For display)
|
|
- grade_breakdown : A breakdown of the major components that
|
|
make up the final grade. (For display)
|
|
- raw_scores: contains scores for every graded module
|
|
"""
|
|
if isinstance(course_or_id, (basestring, CourseKey)):
|
|
course = courses.get_course_by_id(course_or_id)
|
|
else:
|
|
course = course_or_id
|
|
|
|
for student in students:
|
|
with dog_stats_api.timer('lms.grades.iterate_grades_for', tags=[u'action:{}'.format(course.id)]):
|
|
try:
|
|
gradeset = grade(student, course, keep_raw_scores)
|
|
yield student, gradeset, ""
|
|
except Exception as exc: # pylint: disable=broad-except
|
|
# Keep marching on even if this student couldn't be graded for
|
|
# some reason, but log it for future reference.
|
|
log.exception(
|
|
'Cannot grade student %s (%s) in course %s because of exception: %s',
|
|
student.username,
|
|
student.id,
|
|
course.id,
|
|
exc.message
|
|
)
|
|
yield student, {}, exc.message
|
|
|
|
|
|
def _get_mock_request(student):
|
|
"""
|
|
Make a fake request because grading code expects to be able to look at
|
|
the request. We have to attach the correct user to the request before
|
|
grading that student.
|
|
"""
|
|
request = RequestFactory().get('/')
|
|
request.user = student
|
|
return request
|
|
|
|
|
|
def _calculate_score_for_modules(user_id, course, modules):
|
|
"""
|
|
Calculates the cumulative score (percent) of the given modules
|
|
"""
|
|
|
|
# removing branch and version from exam modules locator
|
|
# otherwise student module would not return scores since module usage keys would not match
|
|
modules = [m for m in modules]
|
|
locations = [
|
|
BlockUsageLocator(
|
|
course_key=course.id,
|
|
block_type=module.location.block_type,
|
|
block_id=module.location.block_id
|
|
)
|
|
if isinstance(module.location, BlockUsageLocator) and module.location.version
|
|
else module.location
|
|
for module in modules
|
|
]
|
|
|
|
scores_client = ScoresClient(course.id, user_id)
|
|
scores_client.fetch_scores(locations)
|
|
|
|
# Iterate over all of the exam modules to get score percentage of user for each of them
|
|
module_percentages = []
|
|
ignore_categories = ['course', 'chapter', 'sequential', 'vertical', 'randomize', 'library_content']
|
|
for index, module in enumerate(modules):
|
|
if module.category not in ignore_categories and (module.graded or module.has_score):
|
|
module_score = scores_client.get(locations[index])
|
|
if module_score:
|
|
correct = module_score.correct or 0
|
|
total = module_score.total or 1
|
|
module_percentages.append(correct / total)
|
|
|
|
return sum(module_percentages) / float(len(module_percentages)) if module_percentages else 0
|
|
|
|
|
|
def get_module_score(user, course, module):
|
|
"""
|
|
Collects all children of the given module and calculates the cumulative
|
|
score for this set of modules for the given user.
|
|
|
|
Arguments:
|
|
user (User): The user
|
|
course (CourseModule): The course
|
|
module (XBlock): The module
|
|
|
|
Returns:
|
|
float: The cumulative score
|
|
"""
|
|
def inner_get_module(descriptor):
|
|
"""
|
|
Delegate to get_module_for_descriptor
|
|
"""
|
|
field_data_cache = FieldDataCache([descriptor], course.id, user)
|
|
return get_module_for_descriptor(
|
|
user,
|
|
_get_mock_request(user),
|
|
descriptor,
|
|
field_data_cache,
|
|
course.id,
|
|
course=course
|
|
)
|
|
|
|
modules = yield_dynamic_descriptor_descendants(
|
|
module,
|
|
user.id,
|
|
inner_get_module
|
|
)
|
|
return _calculate_score_for_modules(user.id, course, modules)
|