def recalculate_concepts(self, concepts, lang=None):
"""
Recalculated given concepts for given users
Args:
concepts (dict): user id (int -> set of concepts to recalculate)
lang(Optional[str]): language used to get items in all concepts (cached).
Defaults to None, in that case are get items only in used concepts
"""
if len(concepts) == 0:
return
if lang is None:
items = Concept.objects.get_concept_item_mapping(concepts=Concept.objects.filter(pk__in=set(flatten(concepts.values()))))
else:
items = Concept.objects.get_concept_item_mapping(lang=lang)
environment = get_environment()
mastery_threshold = get_mastery_trashold()
for user, concepts in concepts.items():
all_items = list(set(flatten([items[c] for c in concepts])))
answer_counts = dict(list(zip(all_items, environment.number_of_answers_more_items(all_items, user))))
correct_answer_counts = dict(list(zip(all_items,
environment.number_of_correct_answers_more_items(all_items, user))))
predictions = dict(list(zip(all_items, get_predictive_model().
predict_more_items(environment, user, all_items, time=None))))
new_user_stats = []
stats_to_delete_condition = Q()
for concept in concepts:
answer_aggregates = Answer.objects.filter(user=user, item__in=items[concept]).aggregate(
time_spent=Sum("response_time"),
sessions=Count("session", True),
time_first=Min("time"),
time_last=Max("time"),
)
stats = {
"answer_count": sum(answer_counts[i] for i in items[concept]),
"correct_answer_count": sum(correct_answer_counts[i] for i in items[concept]),
"item_count": len(items[concept]),
"practiced_items_count": sum([answer_counts[i] > 0 for i in items[concept]]),
"mastered_items_count": sum([predictions[i] >= mastery_threshold for i in items[concept]]),
"prediction": sum([predictions[i] for i in items[concept]]) / len(items[concept]),
"time_spent": answer_aggregates["time_spent"] / 1000,
"session_count": answer_aggregates["sessions"],
"time_first": answer_aggregates["time_first"].timestamp(),
"time_last": answer_aggregates["time_last"].timestamp(),
}
stats_to_delete_condition |= Q(user=user, concept=concept)
for stat_name, value in stats.items():
new_user_stats.append(UserStat(user_id=user, concept_id=concept, stat=stat_name, value=value))
self.filter(stats_to_delete_condition).delete()
self.bulk_create(new_user_stats)
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 = models.get_time_for_knowledge_overview(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 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, language=get_language(request))
all_leaves = list(set(flatten(leaves.values())))
user = get_user_id(request)
time = models.get_time_for_knowledge_overview(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 recalculate_concepts(self, concepts, lang=None):
"""
Recalculated given concepts for given users
Args:
concepts (dict): user id (int -> set of concepts to recalculate)
lang(Optional[str]): language used to get items in all concepts (cached).
Defaults to None, in that case are get items only in used concepts
"""
if len(concepts) == 0:
return
if lang is None:
items = Concept.objects.get_concept_item_mapping(
concepts=Concept.objects.filter(
pk__in=set(flatten(concepts.values()))))
else:
items = Concept.objects.get_concept_item_mapping(lang=lang)
environment = get_environment()
mastery_threshold = get_mastery_trashold()
for user, concepts in concepts.items():
all_items = list(set(flatten([items[c] for c in concepts])))
answer_counts = dict(
list(
zip(
all_items,
environment.number_of_answers_more_items(
all_items, user))))
correct_answer_counts = dict(
list(
zip(
all_items,
environment.number_of_correct_answers_more_items(
all_items, user))))
predictions = dict(
list(
zip(
all_items,
get_predictive_model().predict_more_items(environment,
user,
all_items,
time=None))))
new_user_stats = []
stats_to_delete_condition = Q()
for concept in concepts:
answer_aggregates = Answer.objects.filter(
user=user, item__in=items[concept]).aggregate(
time_spent=Sum("response_time"),
sessions=Count("session", True),
time_first=Min("time"),
time_last=Max("time"),
)
stats = {
"answer_count":
sum(answer_counts[i] for i in items[concept]),
"correct_answer_count":
sum(correct_answer_counts[i] for i in items[concept]),
"item_count":
len(items[concept]),
"practiced_items_count":
sum([answer_counts[i] > 0 for i in items[concept]]),
"mastered_items_count":
sum([
predictions[i] >= mastery_threshold
for i in items[concept]
]),
"prediction":
sum([predictions[i]
for i in items[concept]]) / len(items[concept]),
"time_spent":
answer_aggregates["time_spent"] / 1000,
"session_count":
answer_aggregates["sessions"],
"time_first":
answer_aggregates["time_first"].timestamp(),
"time_last":
answer_aggregates["time_last"].timestamp(),
}
stats_to_delete_condition |= Q(user=user, concept=concept)
for stat_name, value in stats.items():
new_user_stats.append(
UserStat(user_id=user,
concept_id=concept,
stat=stat_name,
value=value))
self.filter(stats_to_delete_condition).delete()
self.bulk_create(new_user_stats)