#
# File: capa/capa_problem.py
#
# Nomenclature:
#
# A capa Problem is a collection of text and capa Response questions.
# Each Response may have one or more Input entry fields.
# The capa problem may include a solution.
#
"""
Main module which shows problems (of "capa" type).
This is used by capa_module.
"""
from datetime import datetime
import logging
import os.path
import re
from lxml import etree
from xml.sax.saxutils import unescape
from copy import deepcopy
from capa.correctmap import CorrectMap
import capa.inputtypes as inputtypes
import capa.customrender as customrender
import capa.responsetypes as responsetypes
from capa.util import contextualize_text, convert_files_to_filenames
import capa.xqueue_interface as xqueue_interface
from capa.safe_exec import safe_exec
from pytz import UTC
# extra things displayed after "show answers" is pressed
solution_tags = ['solution']
# these get captured as student responses
response_properties = ["codeparam", "responseparam", "answer", "openendedparam"]
# special problem tags which should be turned into innocuous HTML
html_transforms = {
'problem': {'tag': 'div'},
'text': {'tag': 'span'},
'math': {'tag': 'span'},
}
# These should be removed from HTML output, including all subelements
html_problem_semantics = [
"codeparam",
"responseparam",
"answer",
"script",
"hintgroup",
"openendedparam",
"openendedrubric",
]
log = logging.getLogger(__name__)
#-----------------------------------------------------------------------------
# main class for this module
class LoncapaSystem(object):
"""
An encapsulation of resources needed from the outside.
These interfaces are collected here so that a caller of LoncapaProblem
can provide these resources however make sense for their environment, and
this code can remain independent.
Attributes:
i18n: an object implementing the `gettext.Translations` interface so
that we can use `.ugettext` to localize strings.
See :class:`ModuleSystem` for documentation of other attributes.
"""
def __init__( # pylint: disable=invalid-name
self,
ajax_url,
anonymous_student_id,
cache,
can_execute_unsafe_code,
DEBUG, # pylint: disable=invalid-name
filestore,
i18n,
node_path,
render_template,
seed, # Why do we do this if we have self.seed?
STATIC_URL, # pylint: disable=invalid-name
xqueue,
):
self.ajax_url = ajax_url
self.anonymous_student_id = anonymous_student_id
self.cache = cache
self.can_execute_unsafe_code = can_execute_unsafe_code
self.DEBUG = DEBUG # pylint: disable=invalid-name
self.filestore = filestore
self.i18n = i18n
self.node_path = node_path
self.render_template = render_template
self.seed = seed # Why do we do this if we have self.seed?
self.STATIC_URL = STATIC_URL # pylint: disable=invalid-name
self.xqueue = xqueue
class LoncapaProblem(object):
"""
Main class for capa Problems.
"""
def __init__(self, problem_text, id, capa_system, state=None, seed=None):
"""
Initializes capa Problem.
Arguments:
problem_text (string): xml defining the problem.
id (string): identifier for this problem, often a filename (no spaces).
capa_system (LoncapaSystem): LoncapaSystem instance which provides OS,
rendering, user context, and other resources.
state (dict): containing the following keys:
- `seed` (int) random number generator seed
- `student_answers` (dict) maps input id to the stored answer for that input
- `correct_map` (CorrectMap) a map of each input to their 'correctness'
- `done` (bool) indicates whether or not this problem is considered done
- `input_state` (dict) maps input_id to a dictionary that holds the state for that input
seed (int): random number generator seed.
"""
## Initialize class variables from state
self.do_reset()
self.problem_id = id
self.capa_system = capa_system
state = state or {}
# Set seed according to the following priority:
# 1. Contained in problem's state
# 2. Passed into capa_problem via constructor
self.seed = state.get('seed', seed)
assert self.seed is not None, "Seed must be provided for LoncapaProblem."
self.student_answers = state.get('student_answers', {})
if 'correct_map' in state:
self.correct_map.set_dict(state['correct_map'])
self.done = state.get('done', False)
self.input_state = state.get('input_state', {})
# Convert startouttext and endouttext to proper
problem_text = re.sub(r"startouttext\s*/", "text", problem_text)
problem_text = re.sub(r"endouttext\s*/", "/text", problem_text)
self.problem_text = problem_text
# parse problem XML file into an element tree
self.tree = etree.XML(problem_text)
# handle any tags
self._process_includes()
# construct script processor context (eg for customresponse problems)
self.context = self._extract_context(self.tree)
# Pre-parse the XML tree: modifies it to add ID's and perform some in-place
# transformations. This also creates the dict (self.responders) of Response
# instances for each question in the problem. The dict has keys = xml subtree of
# Response, values = Response instance
self._preprocess_problem(self.tree)
if not self.student_answers: # True when student_answers is an empty dict
self.set_initial_display()
# dictionary of InputType objects associated with this problem
# input_id string -> InputType object
self.inputs = {}
# Run response late_transforms last (see MultipleChoiceResponse)
# Sort the responses to be in *_1 *_2 ... order.
responses = self.responders.values()
responses = sorted(responses, key=lambda resp: int(resp.id[resp.id.rindex('_') + 1:]))
for response in responses:
if hasattr(response, 'late_transforms'):
response.late_transforms(self)
self.extracted_tree = self._extract_html(self.tree)
def do_reset(self):
"""
Reset internal state to unfinished, with no answers
"""
self.student_answers = dict()
self.correct_map = CorrectMap()
self.done = False
def set_initial_display(self):
"""
Set the student's answers to the responders' initial displays, if specified.
"""
initial_answers = dict()
for responder in self.responders.values():
if hasattr(responder, 'get_initial_display'):
initial_answers.update(responder.get_initial_display())
self.student_answers = initial_answers
def __unicode__(self):
return u"LoncapaProblem ({0})".format(self.problem_id)
def get_state(self):
"""
Stored per-user session data neeeded to:
1) Recreate the problem
2) Populate any student answers.
"""
return {'seed': self.seed,
'student_answers': self.student_answers,
'correct_map': self.correct_map.get_dict(),
'input_state': self.input_state,
'done': self.done}
def get_max_score(self):
"""
Return the maximum score for this problem.
"""
maxscore = 0
for responder in self.responders.values():
maxscore += responder.get_max_score()
return maxscore
def get_score(self):
"""
Compute score for this problem. The score is the number of points awarded.
Returns a dictionary {'score': integer, from 0 to get_max_score(),
'total': get_max_score()}.
"""
correct = 0
for key in self.correct_map:
try:
correct += self.correct_map.get_npoints(key)
except Exception:
log.error('key=%s, correct_map = %s', key, self.correct_map)
raise
if (not self.student_answers) or len(self.student_answers) == 0:
return {'score': 0,
'total': self.get_max_score()}
else:
return {'score': correct,
'total': self.get_max_score()}
def update_score(self, score_msg, queuekey):
"""
Deliver grading response (e.g. from async code checking) to
the specific ResponseType that requested grading
Returns an updated CorrectMap
"""
cmap = CorrectMap()
cmap.update(self.correct_map)
for responder in self.responders.values():
if hasattr(responder, 'update_score'):
# Each LoncapaResponse will update its specific entries in cmap
# cmap is passed by reference
responder.update_score(score_msg, cmap, queuekey)
self.correct_map.set_dict(cmap.get_dict())
return cmap
def ungraded_response(self, xqueue_msg, queuekey):
"""
Handle any responses from the xqueue that do not contain grades
Will try to pass the queue message to all inputtypes that can handle ungraded responses
Does not return any value
"""
# check against each inputtype
for the_input in self.inputs.values():
# if the input type has an ungraded function, pass in the values
if hasattr(the_input, 'ungraded_response'):
the_input.ungraded_response(xqueue_msg, queuekey)
def is_queued(self):
"""
Returns True if any part of the problem has been submitted to an external queue
(e.g. for grading.)
"""
return any(self.correct_map.is_queued(answer_id) for answer_id in self.correct_map)
def get_recentmost_queuetime(self):
"""
Returns a DateTime object that represents the timestamp of the most recent
queueing request, or None if not queued
"""
if not self.is_queued():
return None
# Get a list of timestamps of all queueing requests, then convert it to a DateTime object
queuetime_strs = [
self.correct_map.get_queuetime_str(answer_id)
for answer_id in self.correct_map
if self.correct_map.is_queued(answer_id)
]
queuetimes = [
datetime.strptime(qt_str, xqueue_interface.dateformat).replace(tzinfo=UTC)
for qt_str in queuetime_strs
]
return max(queuetimes)
def grade_answers(self, answers):
"""
Grade student responses. Called by capa_module.check_problem.
`answers` is a dict of all the entries from request.POST, but with the first part
of each key removed (the string before the first "_").
Thus, for example, input_ID123 -> ID123, and input_fromjs_ID123 -> fromjs_ID123
Calls the Response for each question in this problem, to do the actual grading.
"""
# if answers include File objects, convert them to filenames.
self.student_answers = convert_files_to_filenames(answers)
return self._grade_answers(answers)
def supports_rescoring(self):
"""
Checks that the current problem definition permits rescoring.
More precisely, it checks that there are no response types in
the current problem that are not fully supported (yet) for rescoring.
This includes responsetypes for which the student's answer
is not properly stored in state, i.e. file submissions. At present,
we have no way to know if an existing response was actually a real
answer or merely the filename of a file submitted as an answer.
It turns out that because rescoring is a background task, limiting
it to responsetypes that don't support file submissions also means
that the responsetypes are synchronous. This is convenient as it
permits rescoring to be complete when the rescoring call returns.
"""
return all('filesubmission' not in responder.allowed_inputfields for responder in self.responders.values())
def rescore_existing_answers(self):
"""
Rescore student responses. Called by capa_module.rescore_problem.
"""
return self._grade_answers(None)
def _grade_answers(self, student_answers):
"""
Internal grading call used for checking new 'student_answers' and also
rescoring existing student_answers.
For new student_answers being graded, `student_answers` is a dict of all the
entries from request.POST, but with the first part of each key removed
(the string before the first "_"). Thus, for example,
input_ID123 -> ID123, and input_fromjs_ID123 -> fromjs_ID123.
For rescoring, `student_answers` is None.
Calls the Response for each question in this problem, to do the actual grading.
"""
# old CorrectMap
oldcmap = self.correct_map
# start new with empty CorrectMap
newcmap = CorrectMap()
# Call each responsetype instance to do actual grading
for responder in self.responders.values():
# File objects are passed only if responsetype explicitly allows
# for file submissions. But we have no way of knowing if
# student_answers contains a proper answer or the filename of
# an earlier submission, so for now skip these entirely.
# TODO: figure out where to get file submissions when rescoring.
if 'filesubmission' in responder.allowed_inputfields and student_answers is None:
_ = self.capa_system.i18n.ugettext
raise Exception(_(u"Cannot rescore problems with possible file submissions"))
# use 'student_answers' only if it is provided, and if it might contain a file
# submission that would not exist in the persisted "student_answers".
if 'filesubmission' in responder.allowed_inputfields and student_answers is not None:
results = responder.evaluate_answers(student_answers, oldcmap)
else:
results = responder.evaluate_answers(self.student_answers, oldcmap)
newcmap.update(results)
self.correct_map = newcmap
return newcmap
def get_question_answers(self):
"""
Returns a dict of answer_ids to answer values. If we cannot generate
an answer (this sometimes happens in customresponses), that answer_id is
not included. Called by "show answers" button JSON request
(see capa_module)
"""
# dict of (id, correct_answer)
answer_map = dict()
for response in self.responders.keys():
results = self.responder_answers[response]
answer_map.update(results)
# include solutions from ... stanzas
for entry in self.tree.xpath("//" + "|//".join(solution_tags)):
answer = etree.tostring(entry)
if answer:
answer_map[entry.get('id')] = contextualize_text(answer, self.context)
log.debug('answer_map = %s', answer_map)
return answer_map
def get_answer_ids(self):
"""
Return the IDs of all the responses -- these are the keys used for
the dicts returned by grade_answers and get_question_answers. (Though
get_question_answers may only return a subset of these.
"""
answer_ids = []
for response in self.responders.keys():
results = self.responder_answers[response]
answer_ids.append(results.keys())
return answer_ids
def do_targeted_feedback(self, tree):
"""
Implements the targeted-feedback=N in-place on --
choice-level explanations shown to a student after submission.
Does nothing if there is no targeted-feedback attribute.
"""
for mult_choice_response in tree.xpath('//multiplechoiceresponse[@targeted-feedback]'):
# Note that the modifications has been done, avoiding problems if called twice.
if hasattr(self, 'has_targeted'):
continue
self.has_targeted = True # pylint: disable=W0201
show_explanation = mult_choice_response.get('targeted-feedback') == 'alwaysShowCorrectChoiceExplanation'
# Grab the first choicegroup (there should only be one within each tag)
choicegroup = mult_choice_response.xpath('./choicegroup[@type="MultipleChoice"]')[0]
choices_list = list(choicegroup.iter('choice'))
# Find the student answer key that matches our id
student_answer = self.student_answers.get(choicegroup.get('id'))
expl_id_for_student_answer = None
# Keep track of the explanation-id that corresponds to the student's answer
# Also, keep track of the solution-id
solution_id = None
for choice in choices_list:
if choice.get('name') == student_answer:
expl_id_for_student_answer = choice.get('explanation-id')
if choice.get('correct') == 'true':
solution_id = choice.get('explanation-id')
# Filter out targetedfeedback that doesn't correspond to the answer the student selected
# Note: following-sibling will grab all following siblings, so we just want the first in the list
targetedfeedbackset = mult_choice_response.xpath('./following-sibling::targetedfeedbackset')
if len(targetedfeedbackset) != 0:
targetedfeedbackset = targetedfeedbackset[0]
targetedfeedbacks = targetedfeedbackset.xpath('./targetedfeedback')
for targetedfeedback in targetedfeedbacks:
# Don't show targeted feedback if the student hasn't answer the problem
# or if the target feedback doesn't match the student's (incorrect) answer
if not self.done or targetedfeedback.get('explanation-id') != expl_id_for_student_answer:
targetedfeedbackset.remove(targetedfeedback)
# Do not displace the solution under these circumstances
if not show_explanation or not self.done:
continue
# The next element should either be or
next_element = targetedfeedbackset.getnext()
parent_element = tree
solution_element = None
if next_element is not None and next_element.tag == 'solution':
solution_element = next_element
elif next_element is not None and next_element.tag == 'solutionset':
solutions = next_element.xpath('./solution')
for solution in solutions:
if solution.get('explanation-id') == solution_id:
parent_element = next_element
solution_element = solution
# If could not find the solution element, then skip the remaining steps below
if solution_element is None:
continue
# Change our correct-choice explanation from a "solution explanation" to within
# the set of targeted feedback, which means the explanation will render on the page
# without the student clicking "Show Answer" or seeing a checkmark next to the correct choice
parent_element.remove(solution_element)
# Add our solution instead to the targetedfeedbackset and change its tag name
solution_element.tag = 'targetedfeedback'
targetedfeedbackset.append(solution_element)
def get_html(self):
"""
Main method called externally to get the HTML to be rendered for this capa Problem.
"""
self.do_targeted_feedback(self.tree)
html = contextualize_text(etree.tostring(self._extract_html(self.tree)), self.context)
return html
def handle_input_ajax(self, data):
"""
InputTypes can support specialized AJAX calls. Find the correct input and pass along the correct data
Also, parse out the dispatch from the get so that it can be passed onto the input type nicely
"""
# pull out the id
input_id = data['input_id']
if self.inputs[input_id]:
dispatch = data['dispatch']
return self.inputs[input_id].handle_ajax(dispatch, data)
else:
log.warning("Could not find matching input for id: %s", input_id)
return {}
# ======= Private Methods Below ========
def _process_includes(self):
"""
Handle any tags by reading in the specified file and inserting it
into our XML tree. Fail gracefully if debugging.
"""
includes = self.tree.findall('.//include')
for inc in includes:
filename = inc.get('file')
if filename is not None:
try:
# open using LoncapaSystem OSFS filestore
ifp = self.capa_system.filestore.open(filename)
except Exception as err:
log.warning(
'Error %s in problem xml include: %s',
err,
etree.tostring(inc, pretty_print=True)
)
log.warning(
'Cannot find file %s in %s', filename, self.capa_system.filestore
)
# if debugging, don't fail - just log error
# TODO (vshnayder): need real error handling, display to users
if not self.capa_system.DEBUG:
raise
else:
continue
try:
# read in and convert to XML
incxml = etree.XML(ifp.read())
except Exception as err:
log.warning(
'Error %s in problem xml include: %s',
err,
etree.tostring(inc, pretty_print=True)
)
log.warning('Cannot parse XML in %s', (filename))
# if debugging, don't fail - just log error
# TODO (vshnayder): same as above
if not self.capa_system.DEBUG:
raise
else:
continue
# insert new XML into tree in place of include
parent = inc.getparent()
parent.insert(parent.index(inc), incxml)
parent.remove(inc)
log.debug('Included %s into %s' % (filename, self.problem_id))
def _extract_system_path(self, script):
"""
Extracts and normalizes additional paths for code execution.
For now, there's a default path of data/course/code; this may be removed
at some point.
script : ?? (TODO)
"""
DEFAULT_PATH = ['code']
# Separate paths by :, like the system path.
raw_path = script.get('system_path', '').split(":") + DEFAULT_PATH
# find additional comma-separated modules search path
path = []
for dir in raw_path:
if not dir:
continue
# path is an absolute path or a path relative to the data dir
dir = os.path.join(self.capa_system.filestore.root_path, dir)
# Check that we are within the filestore tree.
reldir = os.path.relpath(dir, self.capa_system.filestore.root_path)
if ".." in reldir:
log.warning("Ignoring Python directory outside of course: %r", dir)
continue
abs_dir = os.path.normpath(dir)
path.append(abs_dir)
return path
def _extract_context(self, tree):
"""
Extract content of from the problem.xml file, and exec it in the
context of this problem. Provides ability to randomize problems, and also set
variables for problem answer checking.
Problem XML goes to Python execution context. Runs everything in script tags.
"""
context = {}
context['seed'] = self.seed
all_code = ''
python_path = []
for script in tree.findall('.//script'):
stype = script.get('type')
if stype:
if 'javascript' in stype:
continue # skip javascript
if 'perl' in stype:
continue # skip perl
# TODO: evaluate only python
for d in self._extract_system_path(script):
if d not in python_path and os.path.exists(d):
python_path.append(d)
XMLESC = {"'": "'", """: '"'}
code = unescape(script.text, XMLESC)
all_code += code
if all_code:
try:
safe_exec(
all_code,
context,
random_seed=self.seed,
python_path=python_path,
cache=self.capa_system.cache,
slug=self.problem_id,
unsafely=self.capa_system.can_execute_unsafe_code(),
)
except Exception as err:
log.exception("Error while execing script code: " + all_code)
msg = "Error while executing script code: %s" % str(err).replace('<', '<')
raise responsetypes.LoncapaProblemError(msg)
# Store code source in context, along with the Python path needed to run it correctly.
context['script_code'] = all_code
context['python_path'] = python_path
return context
def _extract_html(self, problemtree): # private
"""
Main (private) function which converts Problem XML tree to HTML.
Calls itself recursively.
Returns Element tree of XHTML representation of problemtree.
Calls render_html of Response instances to render responses into XHTML.
Used by get_html.
"""
if not isinstance(problemtree.tag, basestring):
# Comment and ProcessingInstruction nodes are not Elements,
# and we're ok leaving those behind.
# BTW: etree gives us no good way to distinguish these things
# other than to examine .tag to see if it's a string. :(
return
if (problemtree.tag == 'script' and problemtree.get('type')
and 'javascript' in problemtree.get('type')):
# leave javascript intact.
return deepcopy(problemtree)
if problemtree.tag in html_problem_semantics:
return
problemid = problemtree.get('id') # my ID
if problemtree.tag in inputtypes.registry.registered_tags():
# If this is an inputtype subtree, let it render itself.
status = "unsubmitted"
msg = ''
hint = ''
hintmode = None
input_id = problemtree.get('id')
if problemid in self.correct_map:
pid = input_id
status = self.correct_map.get_correctness(pid)
msg = self.correct_map.get_msg(pid)
hint = self.correct_map.get_hint(pid)
hintmode = self.correct_map.get_hintmode(pid)
value = ""
if self.student_answers and problemid in self.student_answers:
value = self.student_answers[problemid]
if input_id not in self.input_state:
self.input_state[input_id] = {}
# do the rendering
state = {
'value': value,
'status': status,
'id': input_id,
'input_state': self.input_state[input_id],
'feedback': {
'message': msg,
'hint': hint,
'hintmode': hintmode,
}
}
input_type_cls = inputtypes.registry.get_class_for_tag(problemtree.tag)
# save the input type so that we can make ajax calls on it if we need to
self.inputs[input_id] = input_type_cls(self.capa_system, problemtree, state)
return self.inputs[input_id].get_html()
# let each Response render itself
if problemtree in self.responders:
overall_msg = self.correct_map.get_overall_message()
return self.responders[problemtree].render_html(
self._extract_html, response_msg=overall_msg
)
# let each custom renderer render itself:
if problemtree.tag in customrender.registry.registered_tags():
renderer_class = customrender.registry.get_class_for_tag(problemtree.tag)
renderer = renderer_class(self.capa_system, problemtree)
return renderer.get_html()
# otherwise, render children recursively, and copy over attributes
tree = etree.Element(problemtree.tag)
for item in problemtree:
item_xhtml = self._extract_html(item)
if item_xhtml is not None:
tree.append(item_xhtml)
if tree.tag in html_transforms:
tree.tag = html_transforms[problemtree.tag]['tag']
else:
# copy attributes over if not innocufying
for (key, value) in problemtree.items():
tree.set(key, value)
tree.text = problemtree.text
tree.tail = problemtree.tail
return tree
def _preprocess_problem(self, tree): # private
"""
Assign IDs to all the responses
Assign sub-IDs to all entries (textline, schematic, etc.)
Annoted correctness and value
In-place transformation
Also create capa Response instances for each responsetype and save as self.responders
Obtain all responder answers and save as self.responder_answers dict (key = response)
"""
response_id = 1
self.responders = {}
for response in tree.xpath('//' + "|//".join(responsetypes.registry.registered_tags())):
response_id_str = self.problem_id + "_" + str(response_id)
# create and save ID for this response
response.set('id', response_id_str)
response_id += 1
answer_id = 1
input_tags = inputtypes.registry.registered_tags()
inputfields = tree.xpath(
"|".join(['//' + response.tag + '[@id=$id]//' + x for x in (input_tags + solution_tags)]),
id=response_id_str
)
# assign one answer_id for each input type or solution type
for entry in inputfields:
entry.attrib['response_id'] = str(response_id)
entry.attrib['answer_id'] = str(answer_id)
entry.attrib['id'] = "%s_%i_%i" % (self.problem_id, response_id, answer_id)
answer_id = answer_id + 1
# instantiate capa Response
responsetype_cls = responsetypes.registry.get_class_for_tag(response.tag)
responder = responsetype_cls(response, inputfields, self.context, self.capa_system)
# save in list in self
self.responders[response] = responder
# get responder answers (do this only once, since there may be a performance cost,
# eg with externalresponse)
self.responder_answers = {}
for response in self.responders.keys():
try:
self.responder_answers[response] = self.responders[response].get_answers()
except:
log.debug('responder %s failed to properly return get_answers()',
self.responders[response]) # FIXME
raise
# ... may not be associated with any specific response; give
# IDs for those separately
# TODO: We should make the namespaces consistent and unique (e.g. %s_problem_%i).
solution_id = 1
for solution in tree.findall('.//solution'):
solution.attrib['id'] = "%s_solution_%i" % (self.problem_id, solution_id)
solution_id += 1