def read(request, key):
if 'user' in request.GET:
user = get_user_id(request)
else:
user = None
item = int(request.GET['item']) if 'item' in request.GET else None
item_secondary = int(request.GET['item_secondary']) if 'item_secondary' in request.GET else None
time = get_time(request)
environment = get_environment()
if is_time_overridden(request):
environment.shift_time(time)
value = environment.read(key, user=user, item=item, item_secondary=item_secondary)
if value is None:
return render_json(
request,
{'error': 'value with key "%s" not found' % key},
template='models_json.html', status=404)
else:
return render_json(
request,
{
'object_type': 'value',
'key': key,
'item_primary_id': item,
'item_secondary_id': item_secondary,
'user_id': user,
'value': value
},
template='models_json.html'
)
def read(request, key):
if 'user' in request.GET:
user = get_user_id(request)
else:
user = None
item = int(request.GET['item']) if 'item' in request.GET else None
item_secondary = int(request.GET['item_secondary']
) if 'item_secondary' in request.GET else None
time = get_time(request)
environment = get_environment()
if is_time_overridden(request):
environment.shift_time(time)
value = environment.read(key,
user=user,
item=item,
item_secondary=item_secondary)
if value is None:
return render_json(request,
{'error': 'value with key "%s" not found' % key},
template='models_json.html',
status=404)
else:
return render_json(request, {
'object_type': 'value',
'key': key,
'item_primary_id': item,
'item_secondary_id': item_secondary,
'user_id': user,
'value': value
},
template='models_json.html')
def audit(request, key):
if 'user' in request.GET:
user = get_user_id(request)
else:
user = None
limit = 100
if request.user.is_staff:
limit = request.GET.get('limit', limit)
item = int(request.GET['item']) if 'item' in request.GET else None
item_secondary = int(request.GET['item_secondary']) if 'item_secondary' in request.GET else None
time = get_time(request)
environment = get_environment()
if is_time_overridden(request):
environment.shift_time(time)
values = environment.audit(
key, user=user, item=item, item_secondary=item_secondary, limit=limit)
def _to_json_audit(audit):
(time, value) = audit
return {
'object_type': 'value',
'key': key,
'item_primary_id': item,
'item_secondary_id': item_secondary,
'user_id': user,
'value': value,
'time': time.strftime('%Y-%m-%d %H:%M:%S')
}
return render_json(request, list(map(_to_json_audit, values)), template='models_json.html')
def options(request, json_list, nested):
environment = get_environment()
user_id = get_user_id(request)
time = get_time(request)
if is_time_overridden(request):
environment.shift_time(time)
item_selector = get_item_selector()
option_selector = get_option_selector(item_selector)
option_sets = get_option_set().get_option_for_flashcards([
(question['payload'], question['question_type'])
for question in json_list
if question['payload']['object_type'] == 'fc_flashcard'
])
metas = [question.get('meta', {}) for question in json_list]
test_position = _test_index(metas)
selected_items = [question['payload']['item_id'] for question in json_list
if question['payload']['object_type'] == 'fc_flashcard']
allow_zero_option = {}
for question in json_list:
if question['payload']['object_type'] != 'fc_flashcard':
continue
if len(option_sets[question['payload']['item_id']]) == 0 and 'term_secondary' not in question['payload']:
# If we do not have enough options, we have to force direction
question['question_type'] = FlashcardAnswer.FROM_TERM
disable_open_questions = False
if question['payload']['disable_open_questions']:
disable_open_questions = True
elif question['payload']['restrict_open_questions']:
disable_open_questions = question['question_type'] in {FlashcardAnswer.FROM_DESCRIPTION, FlashcardAnswer.FROM_TERM_TO_TERM_SECONDARY}
allow_zero_option[question['payload']['item_id']] = question['question_type'] in {FlashcardAnswer.FROM_TERM, FlashcardAnswer.FROM_TERM_SECONDARY_TO_TERM} and not disable_open_questions
all_options = {i: options for i, options in zip(selected_items, option_selector.select_options_more_items(
environment, user_id, selected_items, time, option_sets,
allow_zero_options=allow_zero_option
))}
options_json_list = []
# HACK: Here, we have to take into account reference questions with zero
# options. In case of zero options we have to force a question type if the
# restriction for zero options is enabled.
config_zero_options_restriction = get_config('proso_models', 'options_count.parameters.allow_zero_options_restriction', default=False)
for i, question in enumerate(json_list):
if question['payload']['object_type'] != 'fc_flashcard':
continue
if test_position is not None and test_position == i:
if 'term_secondary' not in question['payload'] and config_zero_options_restriction:
question['question_type'] = FlashcardAnswer.FROM_TERM
question['payload']['options'] = []
continue
options = all_options[question['payload']['item_id']]
question['payload']['options'] = [Item.objects.item_id_to_json(o) for o in options]
options_json_list += question['payload']['options']
item2object(request, options_json_list, nested=True)
for question in json_list:
if question['payload']['object_type'] != 'fc_flashcard':
continue
sort_key = 'term_secondary' if question['question_type'] == FlashcardAnswer.FROM_TERM_TO_TERM_SECONDARY else 'term'
question['payload']['options'] = sorted(question['payload']['options'], key=lambda o: o[sort_key]['name'])
def status(request):
user_id = get_user_id(request)
time = get_time(request)
environment = get_environment()
if is_time_overridden(request):
environment.shift_time(time)
return render_json(request, {
'object_type': 'status',
'number_of_answers': environment.number_of_answers(user=user_id),
'number_of_correct_answers': environment.number_of_correct_answers(user=user_id),
'environment_info': get_active_environment_info(),
}, template='models_json.html')
def options(request, json_list, nested):
environment = get_environment()
user_id = get_user_id(request)
time = get_time(request)
if is_time_overridden(request):
environment.shift_time(time)
item_selector = get_item_selector()
option_selector = get_option_selector(item_selector)
option_sets = get_option_set().get_option_for_flashcards([
question['payload'] for question in json_list
if question['payload']['object_type'] == 'fc_flashcard'
])
metas = [question.get('meta', {}) for question in json_list]
test_position = _test_index(metas)
selected_items = [question['payload']['item_id'] for question in json_list]
allow_zero_option = {}
for question in json_list:
if question['payload']['object_type'] != 'fc_flashcard':
continue
if len(option_sets[question['payload']['item_id']]) == 0:
# If we do not have enough options, we have to force direction
question['question_type'] = FlashcardAnswer.FROM_TERM
allow_zero_option[question['payload']['item_id']] = question[
'question_type'] == FlashcardAnswer.FROM_TERM
is_flashcard_question = [
question['payload']['object_type'] == 'fc_flashcard'
for question in json_list
]
if not all(is_flashcard_question):
# TODO: We should support mixed questions in the future
raise Exception('All questions must be for flashcards!')
all_options = option_selector.select_options_more_items(
environment,
user_id,
selected_items,
time,
option_sets,
allow_zero_options=allow_zero_option)
options_json_list = []
for i, (question, options) in enumerate(zip(json_list, all_options)):
if test_position is not None and test_position == i:
question['question_type'] = FlashcardAnswer.FROM_TERM
question['payload']['options'] = []
continue
question['payload']['options'] = [
Item.objects.item_id_to_json(o) for o in options
]
options_json_list += question['payload']['options']
item2object(request, options_json_list, nested=False)
def prediction(request, json_list, nested):
if 'stats' not in request.GET:
return
object_item_ids = [x['item_id'] for x in json_list]
user = get_user_id(request)
time = get_time(request)
predictions = _predictive_model().predict_more_items(_environment(request), user, object_item_ids, time)
mastery_threshold = get_mastery_trashold()
for object_json, prediction in zip(json_list, predictions):
object_json['prediction'] = float("{0:.2f}".format(prediction))
object_json['mastered'] = prediction >= mastery_threshold
if "new_user_predictions" in request.GET:
user = -1
predictions = _predictive_model().predict_more_items(_environment(request), user, object_item_ids, time)
for object_json, prediction in zip(json_list, predictions):
object_json['new_user_prediction'] = float("{0:.2f}".format(prediction))
return json_list
def status(request):
user_id = get_user_id(request)
time = get_time(request)
environment = get_environment()
if is_time_overridden(request):
environment.shift_time(time)
return render_json(request, {
'object_type':
'status',
'number_of_answers':
environment.number_of_answers(user=user_id),
'number_of_correct_answers':
environment.number_of_correct_answers(user=user_id),
'environment_info':
get_active_environment_info(),
},
template='models_json.html')
def avg_prediction(request, json_list, nested):
if 'stats' not in request.GET:
return
object_item_ids = [x['item_id'] for x in json_list]
leaves = models.Item.objects.get_leaves(object_item_ids)
all_leaves = list(set(flatten(leaves.values())))
user = get_user_id(request)
time = get_time(request)
predictions = dict(list(zip(all_leaves, _predictive_model().predict_more_items(
_environment(request),
user,
all_leaves,
time
))))
mastery_threshold = get_mastery_trashold()
for object_json in json_list:
leaf_predictions = [predictions[leave] for leave in leaves[object_json['item_id']]]
object_json['avg_predicton'] = numpy.mean(leaf_predictions)
object_json['mastered'] = sum([p > mastery_threshold for p in leaf_predictions])
def audit(request, key):
if 'user' in request.GET:
user = get_user_id(request)
else:
user = None
limit = 100
if request.user.is_staff:
limit = request.GET.get('limit', limit)
item_identifier = request.GET['item'] if 'item' in request.GET else None
item_secondary_identifier = request.GET[
'item_secondary'] if 'item_secondary' in request.GET else None
translated = Item.objects.translate_identifiers([
i
for i in [item_identifier, item_secondary_identifier] if i is not None
], get_language(request))
item = translated.get(item_identifier)
item_secondary = translated.get(item_secondary_identifier)
time = get_time(request)
environment = get_environment()
if is_time_overridden(request):
environment.shift_time(time)
values = environment.audit(key,
user=user,
item=item,
item_secondary=item_secondary,
limit=limit)
def _to_json_audit(audit):
(time, value) = audit
return {
'object_type': 'value',
'key': key,
'item_primary_id': item,
'item_secondary_id': item_secondary,
'user_id': user,
'value': value,
'time': time.strftime('%Y-%m-%d %H:%M:%S')
}
return render_json(request,
list(map(_to_json_audit, values)),
template='models_json.html')
def options(request, json_list, nested):
environment = get_environment()
user_id = get_user_id(request)
time = get_time(request)
if is_time_overridden(request):
environment.shift_time(time)
item_selector = get_item_selector()
option_selector = get_option_selector(item_selector)
option_sets = get_option_set().get_option_for_flashcards([
(question['payload'], question['question_type'])
for question in json_list
if question['payload']['object_type'] == 'fc_flashcard'
])
metas = [question.get('meta', {}) for question in json_list]
test_position = _test_index(metas)
selected_items = [
question['payload']['item_id'] for question in json_list
if question['payload']['object_type'] == 'fc_flashcard'
]
allow_zero_option = {}
for question in json_list:
if question['payload']['object_type'] != 'fc_flashcard':
continue
if len(option_sets[question['payload']['item_id']]
) == 0 and 'term_secondary' not in question['payload']:
# If we do not have enough options, we have to force direction
question['question_type'] = FlashcardAnswer.FROM_TERM
disable_open_questions = False
if question['payload']['disable_open_questions']:
disable_open_questions = True
elif question['payload']['restrict_open_questions']:
disable_open_questions = question['question_type'] in {
FlashcardAnswer.FROM_DESCRIPTION,
FlashcardAnswer.FROM_TERM_TO_TERM_SECONDARY
}
allow_zero_option[question['payload']
['item_id']] = question['question_type'] in {
FlashcardAnswer.FROM_TERM,
FlashcardAnswer.FROM_TERM_SECONDARY_TO_TERM
} and not disable_open_questions
all_options = {
i: options
for i, options in zip(
selected_items,
option_selector.select_options_more_items(
environment,
user_id,
selected_items,
time,
option_sets,
allow_zero_options=allow_zero_option))
}
options_json_list = []
# HACK: Here, we have to take into account reference questions with zero
# options. In case of zero options we have to force a question type if the
# restriction for zero options is enabled.
config_zero_options_restriction = get_config(
'proso_models',
'options_count.parameters.allow_zero_options_restriction',
default=False)
for i, question in enumerate(json_list):
if question['payload']['object_type'] != 'fc_flashcard':
continue
if test_position is not None and test_position == i:
if 'term_secondary' not in question[
'payload'] and config_zero_options_restriction:
question['question_type'] = FlashcardAnswer.FROM_TERM
question['payload']['options'] = []
continue
options = all_options[question['payload']['item_id']]
question['payload']['options'] = [
Item.objects.item_id_to_json(o) for o in options
]
options_json_list += question['payload']['options']
item2object(request, options_json_list, nested=True)
for question in json_list:
if question['payload']['object_type'] != 'fc_flashcard':
continue
sort_key = 'term_secondary' if question[
'question_type'] == FlashcardAnswer.FROM_TERM_TO_TERM_SECONDARY else 'term'
question['payload']['options'] = sorted(
question['payload']['options'], key=lambda o: o[sort_key]['name'])
def _environment(request):
environment = models.get_environment()
if is_time_overridden(request):
time = get_time(request)
environment.shift_time(time)
return environment
def practice(request):
"""
Return the given number of questions to practice adaptively. In case of
POST request, try to save the answer(s).
GET parameters:
filter:
list of lists of identifiers (may be prefixed by minus sign to
mark complement)
language:
language (str) of items
avoid:
list of item ids to avoid
limit:
number of returned questions (default 10, maximum 100)
time:
time in format '%Y-%m-%d_%H:%M:%S' used for practicing
user:
identifier for the practicing user (only for stuff users)
stats:
turn on the enrichment of the objects by some statistics
html:
turn on the HTML version of the API
BODY:
see answer resource
"""
if request.user.id is None: # Google Bot
return render_json(
request, {
'error': _('There is no user available for the practice.'),
'error_type': 'user_undefined'
},
status=400,
template='models_json.html')
limit = min(int(request.GET.get('limit', 10)), 100)
# prepare
user = get_user_id(request)
time = get_time(request)
avoid = load_query_json(request.GET, "avoid", "[]")
practice_filter = get_filter(request)
practice_context = PracticeContext.objects.from_content(practice_filter)
environment = get_environment()
item_selector = get_item_selector()
if is_time_overridden(request):
environment.shift_time(time)
# save answers
if request.method == 'POST':
_save_answers(request, practice_context, False)
elif request.method == 'GET':
PracticeSet.objects.filter(answer__user_id=request.user.id).update(
finished=True)
if limit > 0:
item_ids = Item.objects.filter_all_reachable_leaves(
practice_filter, get_language(request))
item_ids = list(set(item_ids) - set(avoid))
limit_size = get_config('proso_models',
'practice.limit_item_set_size_to_select_from',
default=None)
if limit_size is not None and limit_size < len(item_ids):
item_ids = sample(item_ids, limit_size)
if len(item_ids) == 0:
return render_json(request, {
'error':
_('There is no item for the given filter to practice.'),
'error_type':
'empty_practice'
},
status=404,
template='models_json.html')
selected_items, meta = item_selector.select(environment,
user,
item_ids,
time,
practice_context.id,
limit,
items_in_queue=len(avoid))
result = []
for item, item_meta in zip(selected_items, meta):
question = {
'object_type': 'question',
'payload': Item.objects.item_id_to_json(item),
}
if item_meta is not None:
question['meta'] = item_meta
result.append(question)
else:
result = []
return render_json(request,
result,
template='models_json.html',
help_text=practice.__doc__)
def user_stats(request):
"""
Get user statistics for selected groups of items
time:
time in format '%Y-%m-%d_%H:%M:%S' used for practicing
user:
identifier of the user (only for stuff users)
username:
username of user (only for users with public profile)
filters: -- use this or body
json as in BODY
mastered:
use model to compute number of mastered items - can be slowed
language:
language of the items
BODY
json in following format:
{
"#identifier": [] -- custom identifier (str) and filter
...
}
"""
timer('user_stats')
response = {}
data = None
if request.method == "POST":
data = json.loads(request.body.decode("utf-8"))["filters"]
if "filters" in request.GET:
data = load_query_json(request.GET, "filters")
if data is None:
return render_json(request, {},
template='models_user_stats.html',
help_text=user_stats.__doc__)
environment = get_environment()
if is_time_overridden(request):
environment.shift_time(get_time(request))
user_id = get_user_id(request)
language = get_language(request)
filter_names, filter_filters = list(zip(*sorted(data.items())))
reachable_leaves = Item.objects.filter_all_reachable_leaves_many(
filter_filters, language)
all_leaves = sorted(list(set(flatten(reachable_leaves))))
answers = environment.number_of_answers_more_items(all_leaves, user_id)
correct_answers = environment.number_of_correct_answers_more_items(
all_leaves, user_id)
if request.GET.get("mastered"):
timer('user_stats_mastered')
mastery_threshold = get_mastery_trashold()
predictions = Item.objects.predict_for_overview(
environment, user_id, all_leaves)
mastered = dict(
list(zip(all_leaves,
[p >= mastery_threshold for p in predictions])))
LOGGER.debug(
"user_stats - getting predictions for items took %s seconds",
(timer('user_stats_mastered')))
for identifier, items in zip(filter_names, reachable_leaves):
if len(items) == 0:
response[identifier] = {
"filter": data[identifier],
"number_of_items": 0,
}
else:
response[identifier] = {
"filter":
data[identifier],
"number_of_items":
len(items),
"number_of_practiced_items":
sum(answers[i] > 0 for i in items),
"number_of_answers":
sum(answers[i] for i in items),
"number_of_correct_answers":
sum(correct_answers[i] for i in items),
}
if request.GET.get("mastered"):
response[identifier]["number_of_mastered_items"] = sum(
mastered[i] for i in items)
return render_json(request,
response,
template='models_user_stats.html',
help_text=user_stats.__doc__)
def practice(request):
"""
Return the given number of questions to practice adaptively. In case of
POST request, try to save the answer(s).
GET parameters:
filter:
list of lists of identifiers (may be prefixed by minus sign to
mark complement)
language:
language (str) of flashcards
avoid:
list of item ids to avoid
limit:
number of returned questions (default 10, maximum 100)
time:
time in format '%Y-%m-%d_%H:%M:%S' used for practicing
user:
identifier for the practicing user (only for stuff users)
stats:
turn on the enrichment of the objects by some statistics
html:
turn on the HTML version of the API
BODY:
see answer resource
"""
if request.user.id is None: # Google Bot
return render_json(request, {
'error': _('There is no user available for the practice.'),
'error_type': 'user_undefined'
}, status=400, template='models_json.html')
limit = min(int(request.GET.get('limit', 10)), 100)
# prepare
user = get_user_id(request)
time = get_time(request)
avoid = load_query_json(request.GET, "avoid", "[]")
practice_filter = get_filter(request)
practice_context = PracticeContext.objects.from_content(practice_filter)
environment = get_environment()
item_selector = get_item_selector()
if is_time_overridden(request):
environment.shift_time(time)
# save answers
if request.method == 'POST':
_save_answers(request, practice_context)
if len(practice_filter) > 0:
item_ids = Item.objects.filter_all_reachable_leaves(practice_filter, get_language(request))
else:
item_ids = Item.objects.get_all_available_leaves()
item_ids = list(set(item_ids) - set(avoid))
if len(item_ids) == 0:
return render_json(request, {
'error': _('There is no item for the given filter to practice.'),
'error_type': 'empty_practice'
}, status=404, template='models_json.html')
selected_items, meta = item_selector.select(environment, user, item_ids, time, practice_context.id, limit, items_in_queue=len(avoid))
result = []
for item, item_meta in zip(selected_items, meta):
question = {
'object_type': 'question',
'payload': Item.objects.item_id_to_json(item),
}
if item_meta is not None:
question['meta'] = item_meta
result.append(question)
return render_json(request, result, template='models_json.html', help_text=practice.__doc__)
def user_stats(request):
"""
Get user statistics for selected groups of items
time:
time in format '%Y-%m-%d_%H:%M:%S' used for practicing
user:
identifier of the user (only for stuff users)
username:
username of user (only for users with public profile)
filters: -- use this or body
json as in BODY
mastered:
use model to compute number of mastered items - can be slowed
language:
language of the items
BODY
json in following format:
{
"#identifier": [] -- custom identifier (str) and filter
...
}
"""
timer('user_stats')
response = {}
data = None
if request.method == "POST":
data = json.loads(request.body.decode("utf-8"))["filters"]
if "filters" in request.GET:
data = load_query_json(request.GET, "filters")
if data is None:
return render_json(request, {}, template='models_user_stats.html', help_text=user_stats.__doc__)
environment = get_environment()
if is_time_overridden(request):
environment.shift_time(get_time(request))
user_id = get_user_id(request)
language = get_language(request)
filter_names, filter_filters = list(zip(*sorted(data.items())))
reachable_leaves = Item.objects.filter_all_reachable_leaves_many(filter_filters, language)
all_leaves = flatten(reachable_leaves)
answers = dict(list(zip(all_leaves, environment.number_of_answers_more_items(all_leaves, user_id))))
correct_answers = dict(list(zip(all_leaves, environment.number_of_correct_answers_more_items(all_leaves, user_id))))
if request.GET.get("mastered"):
timer('user_stats_mastered')
mastery_threshold = get_mastery_trashold()
predictions = get_predictive_model().predict_more_items(environment, user_id, all_leaves, get_time(request))
mastered = dict(list(zip(all_leaves, [p >= mastery_threshold for p in predictions])))
LOGGER.debug("user_stats - getting predictions for flashcards took %s seconds", (timer('user_stats_mastered')))
for identifier, items in zip(filter_names, reachable_leaves):
if len(items) == 0:
response[identifier] = {
"filter": data[identifier],
"number_of_flashcards": 0,
}
else:
response[identifier] = {
"filter": data[identifier],
"number_of_flashcards": len(items),
"number_of_practiced_flashcards": sum(answers[i] > 0 for i in items),
"number_of_answers": sum(answers[i] for i in items),
"number_of_correct_answers": sum(correct_answers[i] for i in items),
}
if request.GET.get("mastered"):
response[identifier]["number_of_mastered_flashcards"]= sum(mastered[i] for i in items)
return render_json(request, response, template='models_user_stats.html', help_text=user_stats.__doc__)