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edx-platform/xmodule/graders.py
2026-01-07 13:30:53 +05:00

472 lines
19 KiB
Python

"""
Code used to calculate learner grades.
"""
import abc
import inspect
import logging
import random
import sys
from collections import OrderedDict
from django.utils.translation import gettext_lazy as _
from xmodule.util.misc import get_short_labeler
log = logging.getLogger("edx.courseware")
class ScoreBase(metaclass=abc.ABCMeta):
"""
Abstract base class for encapsulating fields of values scores.
"""
def __init__(self, graded, first_attempted):
"""
Fields common to all scores include:
:param graded: Whether or not this block is graded
:type graded: bool
:param first_attempted: When the block was first attempted, or None
:type first_attempted: datetime|None
"""
self.graded = graded
self.first_attempted = first_attempted
def __eq__(self, other):
if type(other) is type(self):
return self.__dict__ == other.__dict__
return False
def __ne__(self, other):
return not self.__eq__(other)
def __repr__(self):
return f"{self.__class__.__name__}({self.__dict__})"
class ProblemScore(ScoreBase):
"""
Encapsulates the fields of a Problem's score.
"""
def __init__(self, raw_earned, raw_possible, weighted_earned, weighted_possible, weight, *args, **kwargs):
"""
In addition to the fields in ScoreBase, arguments include:
:param raw_earned: Raw points earned on this problem
:type raw_earned: int|float|None
:param raw_possible: Raw points possible to earn on this problem
:type raw_possible: int|float|None
:param weighted_earned: Weighted value of the points earned
:type weighted_earned: int|float|None
:param weighted_possible: Weighted possible points on this problem
:type weighted_possible: int|float|None
:param weight: Weight of this problem
:type weight: int|float|None
"""
super().__init__(*args, **kwargs)
self.raw_earned = float(raw_earned) if raw_earned is not None else None
self.raw_possible = float(raw_possible) if raw_possible is not None else None
self.earned = float(weighted_earned) if weighted_earned is not None else None
self.possible = float(weighted_possible) if weighted_possible is not None else None
self.weight = weight
class AggregatedScore(ScoreBase):
"""
Encapsulates the fields of a Subsection's score.
"""
def __init__(self, tw_earned, tw_possible, *args, **kwargs):
"""
In addition to the fields in ScoreBase, also includes:
:param tw_earned: Total aggregated sum of all weighted earned values
:type tw_earned: int|float|None
:param tw_possible: Total aggregated sum of all weighted possible values
:type tw_possible: int|float|None
"""
super().__init__(*args, **kwargs)
self.earned = float(tw_earned) if tw_earned is not None else None
self.possible = float(tw_possible) if tw_possible is not None else None
def float_sum(iterable):
"""
Sum the elements of the iterable, and return the result as a float.
"""
return float(sum(iterable))
def aggregate_scores(scores):
"""
scores: A list of ProblemScore objects
returns: A tuple (all_total, graded_total).
all_total: An AggregatedScore representing the total score summed over all input scores
graded_total: An AggregatedScore representing the score summed over all graded input scores
"""
total_correct_graded = float_sum(score.earned for score in _iter_graded(scores))
total_possible_graded = float_sum(score.possible for score in _iter_graded(scores))
first_attempted_graded = _min_or_none(
score.first_attempted for score in _iter_graded(scores) if score.first_attempted
)
total_correct = float_sum(score.earned for score in scores)
total_possible = float_sum(score.possible for score in scores)
first_attempted = _min_or_none(
score.first_attempted for score in scores if score.first_attempted
)
# regardless of whether it is graded
all_total = AggregatedScore(total_correct, total_possible, False, first_attempted=first_attempted)
# selecting only graded things
graded_total = AggregatedScore(
total_correct_graded,
total_possible_graded,
True,
first_attempted=first_attempted_graded
)
return all_total, graded_total
def invalid_args(func, argdict):
"""
Given a function and a dictionary of arguments, returns a set of arguments
from argdict that aren't accepted by func
"""
args, _, keywords, _, _, _, _ = inspect.getfullargspec(func)
if keywords:
return set() # All accepted
return set(argdict) - set(args)
def grader_from_conf(conf):
"""
This creates a CourseGrader from a configuration (such as in course_settings.py).
The conf can simply be an instance of CourseGrader, in which case no work is done.
More commonly, the conf is a list of dictionaries. A WeightedSubsectionsGrader
with AssignmentFormatGraders will be generated. Every dictionary should contain
the parameters for making an AssignmentFormatGrader, in addition to a 'weight' key.
"""
if isinstance(conf, CourseGrader):
return conf
subgraders = []
for subgraderconf in conf:
subgraderconf = subgraderconf.copy()
weight = subgraderconf.pop("weight", 0)
try:
if 'min_count' in subgraderconf:
subgrader_class = AssignmentFormatGrader
else:
raise ValueError("Configuration has no appropriate grader class.")
bad_args = invalid_args(subgrader_class.__init__, subgraderconf)
if bad_args:
log.warning("Invalid arguments for a subgrader: %s", bad_args)
for key in bad_args:
del subgraderconf[key]
subgrader = subgrader_class(**subgraderconf)
subgraders.append((subgrader, subgrader.category, weight))
except (TypeError, ValueError) as error:
# Add info and re-raise
msg = ("Unable to parse grader configuration:\n " +
str(subgraderconf) +
"\n Error was:\n " + str(error))
raise ValueError(msg).with_traceback(sys.exc_info()[2])
return WeightedSubsectionsGrader(subgraders)
class CourseGrader(metaclass=abc.ABCMeta):
"""
A course grader takes the totaled scores for each graded section (that a student has
started) in the course. From these scores, the grader calculates an overall percentage
grade. The grader should also generate information about how that score was calculated,
to be displayed in graphs or charts.
A grader has one required method, grade(), which is passed a grade_sheet. The grade_sheet
contains scores for all graded section that the student has started. If a student has
a score of 0 for that section, it may be missing from the grade_sheet. The grade_sheet
is keyed by section format. Each value is a list of Score namedtuples for each section
that has the matching section format.
The grader outputs a dictionary with the following keys:
- percent: Contains a float value, which is the final percentage score for the student.
- section_breakdown: This is a list of dictionaries which provide details on sections
that were graded. These are used for display in a graph or chart. The format for a
section_breakdown dictionary is explained below.
- grade_breakdown: This is a dict of dictionaries, keyed by category, which provide details on
the contributions of the final percentage grade. This is a higher level breakdown, for when the
grade is constructed of a few very large sections (such as Homeworks, Labs, a Midterm, and a Final).
The format for a grade_breakdown is explained below. This section is optional.
A dictionary in the section_breakdown list has the following keys:
percent: A float percentage for the section.
label: A short string identifying the section. Preferably fixed-length. E.g. "HW 3".
detail: A string explanation of the score. E.g. "Homework 1 - Ohms Law - 83% (5/6)"
category: A string identifying the category. Items with the same category are grouped together
in the display (for example, by color).
prominent: A boolean value indicating that this section should be displayed as more prominent
than other items.
A dictionary in the grade_breakdown dict has the following keys:
percent: A float percentage in the breakdown. All percents should add up to the final percentage.
detail: A string explanation of this breakdown. E.g. "Homework - 10% of a possible 15%"
category: A string identifying the category. Items with the same category are grouped together
in the display (for example, by color).
"""
@abc.abstractmethod
def grade(self, grade_sheet, generate_random_scores=False):
'''Given a grade sheet, return a dict containing grading information'''
raise NotImplementedError
class WeightedSubsectionsGrader(CourseGrader):
"""
This grader takes a list of tuples containing (grader, category_name, weight) and computes
a final grade by totalling the contribution of each sub grader and multiplying it by the
given weight. For example, the sections may be
[ (homeworkGrader, "Homework", 0.15), (labGrader, "Labs", 0.15), (midtermGrader, "Midterm", 0.30),
(finalGrader, "Final", 0.40) ]
All items in section_breakdown for each subgrader will be combined. A grade_breakdown will be
composed using the score from each grader.
Note that the sum of the weights is not taken into consideration. If the weights add up to
a value > 1, the student may end up with a percent > 100%. This allows for sections that
are extra credit.
"""
def __init__(self, subgraders): # pylint: disable=super-init-not-called
self.subgraders = subgraders
@property
def sum_of_weights(self): # lint-amnesty, pylint: disable=missing-function-docstring
result = 0
for _, _, weight in self.subgraders:
result += weight
return result
def grade(self, grade_sheet, generate_random_scores=False):
total_percent = 0.0
section_breakdown = []
grade_breakdown = OrderedDict()
for subgrader, assignment_type, weight in self.subgraders:
subgrade_result = subgrader.grade(grade_sheet, generate_random_scores)
weighted_percent = subgrade_result['percent'] * weight
section_detail = _("{assignment_type} = {weighted_percent:.2%} of a possible {weight:.2%}").format(
assignment_type=assignment_type,
weighted_percent=weighted_percent,
weight=weight)
total_percent += weighted_percent
section_breakdown += subgrade_result['section_breakdown']
grade_breakdown[assignment_type] = {
'percent': weighted_percent,
'detail': section_detail,
'category': assignment_type,
}
return {
'percent': total_percent,
'section_breakdown': section_breakdown,
'grade_breakdown': grade_breakdown
}
class AssignmentFormatGrader(CourseGrader):
"""
Grades all sections matching the format 'type' with an equal weight. A specified
number of lowest scores can be dropped from the calculation. The minimum number of
sections in this format must be specified (even if those sections haven't been
written yet).
min_count defines how many assignments are expected throughout the course. Placeholder
scores (of 0) will be inserted if the number of matching sections in the course is < min_count.
If there number of matching sections in the course is > min_count, min_count will be ignored.
show_only_average is to suppress the display of each assignment in this grader and instead
only show the total score of this grader in the breakdown.
hide_average is to suppress the display of the total score in this grader and instead
only show each assignment in this grader in the breakdown.
If there is only a single assignment in this grader, then it returns only one entry for the
grader. Since the assignment and the total are the same, the total is returned but is not
labeled as an average.
category should be presentable to the user, but may not appear. When the grade breakdown is
displayed, scores from the same category will be similar (for example, by color).
section_type is a string that is the type of a singular section. For example, for Labs it
would be "Lab". This defaults to be the same as category.
short_label is similar to section_type, but shorter. For example, for Homework it would be
"HW".
starting_index is the first number that will appear. For example, starting_index=3 and
min_count = 2 would produce the labels "Assignment 3", "Assignment 4"
"""
def __init__( # lint-amnesty, pylint: disable=super-init-not-called
self,
type, # pylint: disable=redefined-builtin
min_count,
drop_count,
category=None,
section_type=None,
short_label=None,
show_only_average=False,
hide_average=False,
starting_index=1
):
self.type = type
self.min_count = min_count
self.drop_count = drop_count
self.category = category or self.type
self.section_type = section_type or self.type
self.short_label = short_label or self.type
self.show_only_average = show_only_average
self.starting_index = starting_index
self.hide_average = hide_average
def total_with_drops(self, breakdown):
"""
Calculates total score for a section while dropping lowest scores
"""
# Create an array of tuples with (index, mark), sorted by mark['percent'] descending
sorted_breakdown = sorted(enumerate(breakdown), key=lambda x: -x[1]['percent'])
# A list of the indices of the dropped scores
dropped_indices = []
if self.drop_count > 0:
dropped_indices = [x[0] for x in sorted_breakdown[-self.drop_count:]]
aggregate_score = 0
for index, mark in enumerate(breakdown):
if index not in dropped_indices:
aggregate_score += mark['percent']
if len(breakdown) - self.drop_count > 0:
aggregate_score /= len(breakdown) - self.drop_count
return aggregate_score, dropped_indices
def grade(self, grade_sheet, generate_random_scores=False):
scores = list(grade_sheet.get(self.type, {}).values())
breakdown = []
labeler = get_short_labeler(self.short_label)
for i in range(max(int(float(self.min_count)), len(scores))):
if i < len(scores) or generate_random_scores:
if generate_random_scores: # for debugging!
earned = random.randint(2, 15)
possible = random.randint(earned, 15)
section_name = _("Generated")
sequential_id = None
else:
earned = scores[i].graded_total.earned
possible = scores[i].graded_total.possible
section_name = scores[i].display_name
sequential_id = str(scores[i].location)
percentage = scores[i].percent_graded
summary_format = "{section_type} {index} - {name} - {percent:.2%} ({earned:.3n}/{possible:.3n})"
summary = summary_format.format(
index=i + self.starting_index,
section_type=self.section_type,
name=section_name,
percent=percentage,
earned=float(earned),
possible=float(possible)
)
else:
percentage = 0.0
# Translators: "Homework 1 - Unreleased - 0% (?/?)" The section has not been released for viewing.
summary = _("{section_type} {index} Unreleased - 0% (?/?)").format(
index=i + self.starting_index,
section_type=self.section_type
)
sequential_id = None
short_label = labeler(i + self.starting_index)
breakdown.append({'percent': percentage, 'label': short_label,
'detail': summary, 'category': self.category, 'sequential_id': sequential_id})
total_percent, dropped_indices = self.total_with_drops(breakdown)
for dropped_index in dropped_indices:
breakdown[dropped_index]['mark'] = {
'detail': _("The lowest {drop_count} {section_type} scores are dropped.").format(
drop_count=self.drop_count,
section_type=self.section_type
)
}
if len(breakdown) == 1:
# if there is only one entry in a section, suppress the existing individual entry and the average,
# and just display a single entry for the section.
total_detail = "{section_type} = {percent:.2%}".format(
percent=total_percent,
section_type=self.section_type,
)
total_label = f"{self.short_label}"
breakdown = [{'percent': total_percent, 'label': total_label,
'detail': total_detail, 'category': self.category, 'prominent': True,
'sequential_id': str(scores[0].location) if scores else None}, ]
else:
# Translators: "Homework Average = 0%"
total_detail = _("{section_type} Average = {percent:.2%}").format(
percent=total_percent,
section_type=self.section_type
)
# Translators: Avg is short for Average
total_label = _("{short_label} Avg").format(short_label=self.short_label)
if self.show_only_average:
breakdown = []
if not self.hide_average:
breakdown.append({'percent': total_percent, 'label': total_label,
'detail': total_detail, 'category': self.category, 'prominent': True})
return {
'percent': total_percent,
'section_breakdown': breakdown,
# No grade_breakdown here
}
def _iter_graded(scores):
"""
Yield the scores that belong to explicitly graded blocks
"""
return (score for score in scores if score.graded)
def _min_or_none(itr):
"""
Return the lowest value in itr, or None if itr is empty.
In python 3, this is just min(itr, default=None)
"""
try:
return min(itr)
except ValueError:
return None