Merge branch 'remove-bugs'

passfinder/city.txt

@@ -0,0 +1,270 @@
+Москва
+ 
+ 
+Санкт-Петербург
+ 
+ 
+Новосибирск
+ 
+ 
+Екатеринбург
+ 
+ 
+Нижний Новгород
+ 
+ 
+Самара
+ 
+ 
+Омск
+ 
+ 
+Казань
+ 
+ 
+Челябинск
+ 
+ 
+Ростов-на-Дону
+ 
+ 
+Уфа
+ 
+ 
+Волгоград
+ 
+ 
+Пермь
+ 
+ 
+Красноярск
+ 
+Воронеж
+ 
+Саратов
+ 
+Краснодар
+ 
+Тольятти
+ 
+Ижевск
+ 
+Ульяновск
+ 
+Барнаул
+ 
+Владивосток
+ 
+Ярославль
+ 
+Иркутск
+ 
+Тюмень
+ 
+Махачкала
+ 
+Хабаровск
+ 
+Оренбург
+ 
+Новокузнецк
+ 
+Кемерово
+ 
+Рязань
+ 
+Томск
+ 
+Астрахань
+ 
+Пенза
+ 
+Набережные Челны
+ 
+Липецк
+ 
+Тула
+ 
+Киров
+ 
+Чебоксары
+ 
+Калининград
+ 
+Брянск
+ 
+Курск
+ 
+Иваново
+ 
+Магнитогорск
+ 
+Улан-Удэ
+ 
+Тверь
+ 
+Ставрополь
+ 
+Нижний Тагил
+ 
+Белгород
+ 
+Архангельск
+ 
+Владимир
+ 
+Сочи
+ 
+Курган
+ 
+Смоленск
+ 
+Калуга
+ 
+Чита
+ 
+Ор
+л
+ 
+Волжский
+ 
+Череповец
+ 
+Владикавказ
+ 
+Мурманск
+ 
+Сургут
+ 
+Вологда
+ 
+Саранск
+ 
+Тамбов
+ 
+Стерлитамак
+ 
+Грозный
+ 
+Якутск
+ 
+Кострома
+ 
+Комсомольск-на-Амуре
+ 
+Петрозаводск
+ 
+Таганрог
+ 
+Нижневартовск
+ 
+Йошкар-Ола
+ 
+Братск
+ 
+Новороссийск
+ 
+Дзержинск
+ 
+Шахты
+ 
+Нальчик
+ 
+Орск
+ 
+Сыктывкар
+ 
+Нижнекамск
+ 
+Ангарск
+ 
+Старый Оскол
+ 
+Великий Новгород
+ 
+Балашиха
+ 
+Благовещенск
+ 
+Прокопьевск
+ 
+Бийск
+ 
+Химки
+ 
+Псков
+ 
+Энгельс
+ 
+Рыбинск
+ 
+Балаково
+ 
+Северодвинск
+ 
+Армавир
+ 
+Подольск
+ 
+Корол
+в
+ 
+Южно-Сахалинск
+ 
+Петропавловск-Камчатский
+ 
+Сызрань
+ 
+Норильск
+ 
+Златоуст
+ 
+Каменск-Уральский
+ 
+Мытищи
+ 
+Люберцы
+ 
+Волгодонск
+ 
+Новочеркасск
+ 
+Абакан
+ 
+Находка
+ 
+Уссурийск
+ 
+Березники
+ 
+Салават
+ 
+Электросталь
+ 
+Миасс
+ 
+Первоуральск
+ 
+Рубцовск
+ 
+Альметьевск
+ 
+Ковров
+ 
+Коломна
+ 
+Майкоп
+ 
+Пятигорск
+ 
+Одинцово
+ 
+Колпино
+ 
+Копейск
+ 
+Хасавюрт
+Новомосковск
+Кисловодск
+Серпухов
+Новочебоксарск

passfinder/events/api/consts.py

@@ -1,226 +1,47 @@
-city_in_hotels = ['Абзаково',
+city_in_hotels = {'Астрахань',
- 'Абрамовка',
- 'Абрау-Дюрсо',
- 'Адлер',
- 'Азов',
- 'Аксай',
- 'Альметьевск',
- 'Анапа',
- 'Андрианово',
- 'Арамиль',
- 'Арзамас',
- 'Арнеево',
- 'Архипо-Осиповка',
- 'Бабкино',
- 'Базы отдыха ВТО',
  'Балашиха',
- 'Батайск',
+ 'Березники',
- 'Беличье',
+ 'Благовещенск',
- 'Белореченск',
+ 'Владикавказ',
- 'Бердск',
+ 'Волгоград',
- 'Бердяш',
+ 'Волгодонск',
- 'Березовка',
+ 'Волжский',
- 'Бжид',
+ 'Воронеж',
- 'Битца',
+ 'Грозный',
- 'Благовещенская',
- 'Болтино',
- 'Большой Сочи',
- 'Бор, Нижегородская область',
- 'Борисово',
- 'Борносово',
- 'Будённовск',
- 'Вардане',
- 'Васильево, Ленинградская область',
- 'Васкелово',
- 'Вербилки',
- 'Веселовка, Краснодарский край',
- 'Видное',
- 'Витязево',
- 'Владимировка',
- 'Внуково',
- 'Воскресенск',
- 'Вотря',
- 'Всеволожск',
- 'Всходы',
- 'Выборг',
- 'Вырубово',
- 'Гатчина',
- 'Гвардейское',
- 'Геленджик',
- 'Голиково',
- 'Головинка Краснодарский край',
- 'Голубицкая',
- 'Горки',
- 'Городец',
- 'Горячий ключ',
- 'Григорчиково',
- 'Гуамка',
- 'Д/О Авангард',
- 'Дагомыс',
- 'Джемете',
- 'Джубга',
  'Дзержинск',
- 'Дивеево',
- 'Дивногорье, Краснодарский край',
- 'Дивноморское',
- 'Дмитров',
- 'Домодедово',
- 'Дракино',
- 'Дранишники',
- 'Дубечино',
- 'Егорьевск',
- 'Ейск',
  'Екатеринбург',
- 'Ершово',
+ 'Златоуст',
- 'Ессентуки',
+ 'Ижевск',
- 'Железноводск',
+ 'Каменск-Уральский',
- 'Жуковский',
+ 'Киров',
- 'За Родину',
- 'Звенигород',
- 'Зеленая поляна',
- 'Ивантеевка',
- 'Ильичево',
- 'Иннолово ',
- 'Иноземцево',
- 'Исаково',
- 'Истра',
- 'Кабардинка',
- 'Казань',
- 'Каменск-Шахтинский',
- 'Каневская',
- 'Кингисепп',
  'Кисловодск',
- 'Клин',
+ 'Колпино',
- 'Коломна',
+ 'Комсомольск-на-Амуре',
- 'Коробицыно',
+ 'Копейск',
- 'Королев',
+ 'Кострома',
- 'Косулино',
+ 'Красноярск',
- 'Котельники',
+ 'Курган',
- 'Красная Горка',
- 'Красная Поляна',
- 'Красногорск',
- 'Краснодар',
- 'Красный Колос',
- 'Кудряшовский',
- 'Курово',
- 'Кусимовского Рудника',
- 'Кучугуры',
- 'Лабинск',
- 'Лазаревское',
- 'Лермонтово',
- 'Лесной городок',
- 'Лодейное Поле',
- 'Лоо',
- 'Лосево',
  'Люберцы',
  'Магнитогорск',
- 'Малые Решники',
+ 'Махачкала',
- 'Марьино, Ленинградская область',
- 'Маяковского',
- 'Мезмай',
- 'Мещерино',
- 'Миасс',
- 'Минеральные Воды',
- 'Мистолово',
- 'Мишуткино',
- 'Можайск',
  'Москва',
- 'Мостовской',
  'Мытищи',
- 'Набережные Челны',
+ 'Нальчик',
- 'Наро-Фоминск',
+ 'Нижнекамск',
- 'Нарынка',
- 'Небуг',
- 'Нестерово',
- 'Нижний Новгород',
  'Нижний Тагил',
- 'Новая',
+ 'Новочебоксарск',
- 'Новоабзаково',
- 'Нововолково',
- 'Новомихайловский',
- 'Новороссийск',
- 'Новосибирск',
- 'Новочеркасск',
- 'Новый путь',
- 'Ногинск',
- 'Нурлат',
- 'Овсяники',
  'Одинцово',
- 'Озеры',
+ 'Омск',
- 'Оксино',
+ 'Оренбург',
- 'Октябрьский, Московская область',
+ 'Орск',
- 'Ольгинка',
+ 'Пермь',
- 'Остров, Московская область',
- 'Павловск',
- 'Падиково',
- 'Пересвет',
- 'Платформа 69-й километр, Сосновское сельское поселение',
  'Подольск',
- 'Подпорожье',
- 'Полтавская',
- 'Приморско-Ахтарск',
- 'Приозерск',
- 'Прохорово',
- 'Пушкино',
  'Пятигорск',
- 'Раменское',
+ 'Салават',
- 'Реутов',
- 'Рождествено',
- 'Роза Хутор',
- 'Ростов-на-Дону',
- 'Рощино',
- 'Руза',
  'Санкт-Петербург',
- 'Светлое',
+ 'Томск',
- 'Светлый',
- 'Свирица',
- 'Сергиев Посад',
- 'Серпухов',
- 'Симагино',
- 'Сириус',
- 'Скоково',
- 'Снегири',
- 'Солнечногорск',
- 'Солохаул',
- 'Сосново',
- 'Сосновый Бор, Ленинградская область',
- 'Сосновый Бор, Московская область',
- 'Софрино',
- 'Сочи',
- 'Ставрополь',
- 'Станица Динская',
- 'Станица Должанская',
- 'Старая Руза',
- 'Степаньково',
- 'Суйда',
- 'Сукко',
- 'Супсех',
- 'Таганрог',
- 'Тарасово',
- 'Тимашевск',
- 'Тихвин',
- 'Тихорецк',
- 'Тобольск',
- 'Туапсе',
- 'Тучково',
  'Тюмень',
- 'Увильды ',
- 'Углегорский',
- 'Удельная',
- 'Усть-Койсуг',
- 'Усть-Лабинск',
  'Уфа',
- 'Ушаки',
+ 'Хасавюрт',
- 'Фрязино',
- 'Хадыженск',
  'Химки',
- 'Хоста',
+ 'Энгельс'}
- 'Чебаркуль',
- 'Челябинск',
- 'Чехов, Сахалинская область',
- 'Чудская',
- 'Шахты',
- 'Широкая балка',
- 'Щёлково',
- 'Эсто-Садок',
- 'Якорная щель']

passfinder/events/api/serializers.py

@@ -65,6 +65,49 @@ class RouteInputSerializer(serializers.Serializer):
         min_length=24, max_length=24, required=False, allow_blank=True, allow_null=True
     )
     movement = serializers.ChoiceField(['walk', 'bike', 'scooter', 'auto'], required=False, allow_blank=True)
+    stars = serializers.ListField(
+        child=serializers.ChoiceField([1, 2, 3, 4, 5]),
+        required=False,
+        allow_empty=True,
+        allow_null=True
+    )
+    what_to_see = serializers.ListField(
+        child=serializers.ChoiceField(        
+            [
+                'attractions', 
+                'museum', 
+                'movie', 
+                'concert', 
+                'artwork', 
+                'plays', 
+                'shop', 
+                'gallery', 
+                'theme_park', 
+                'viewpoint', 
+                'zoo'
+            ]
+        ),
+        required=False,
+        allow_empty=True,
+        allow_null=True
+    )
+    where_stay = serializers.ListField(
+        child=serializers.ChoiceField([
+            'hotel', 'apartment', 'hostel'
+        ]),
+        required=False,
+        allow_empty=True,
+        allow_null=True
+    )
+    where_eat = serializers.ListField(
+        child=serializers.ChoiceField(['restaurant', 'bar', 'cafe']),
+        required=False,
+        allow_empty=True,
+        allow_null=True
+    )
+    with_kids = serializers.BooleanField(required=False, allow_null=True)
+    with_animals = serializers.BooleanField(required=False, allow_null=True)
+
 
 class CitySerializer(serializers.ModelSerializer):
     class Meta:

passfinder/events/api/views.py

@@ -75,6 +75,40 @@ def post(self, request):
             movement = data['movement']
         except KeyError:
             movement = 'walk'
+        
+        hotel_stars = data['stars']
+        if hotel_stars is None:
+            hotel_stars = []
+
+        
+        hotel_type = data['where_stay']
+        if hotel_type is None:
+            hotel_type = ['hotel']
+            
+        where_eat = data['where_eat']
+        if where_eat is None:
+            where_eat = ['restaurant', 'bar', 'cafe']
+        
+        what_to_see = data['what_to_see']
+        if what_to_see is None:
+            what_to_see = [
+                'attractions', 
+                'museum', 
+                'movie', 
+                'concert', 
+                'artwork', 
+                'plays', 
+                'shop', 
+                'gallery', 
+                'theme_park', 
+                'viewpoint', 
+                'zoo'
+            ]
+
+        
+        if 'hotel' not in hotel_type:
+            hotel_stars = []
+
         region = None
         
         if city_id:
@@ -95,7 +129,17 @@ def post(self, request):
 
         print(request.user, region, start_date, end_date)
 
-        tour = generate_tour(request.user, region, start_date, end_date, movement_mapping[movement])
+        tour = generate_tour(
+            request.user, 
+            region, 
+            start_date, 
+            end_date, 
+            avg_velocity=movement_mapping[movement],
+            stars=hotel_stars,
+            hotel_type=hotel_type,
+            where_eat=where_eat,
+            what_to_see=what_to_see
+        )
         print(len(tour[1]))
 
         return Response(data=tour[0])
@@ -109,9 +153,7 @@ class ListRegionApiView(ListAPIView):
 class ListCityApiView(ListAPIView):
     serializer_class = CitySerializer
     queryset = (
-        City.objects.annotate(points_num=Count("points"))
+        City.objects.annotate(points_count=Count('points')).filter(title__in=city_in_hotels).filter(points_count__gt=200).order_by('title')
-        .filter(points_num__gte=100)
-        .order_by("title")
     )
 
 

passfinder/recomendations/api/serializers.py

@@ -1,5 +1,5 @@
 from rest_framework import serializers
-from passfinder.events.api.serializers import EventSerializer, HotelSerializer
+from passfinder.events.api.serializers import EventSerializer, HotelSerializer, ObjectRouteSerializer
 
 
 class TinderProceedSerializer(serializers.Serializer):
@@ -49,3 +49,23 @@ class DailySelectionSerializer(serializers.Serializer):
         )
     )
     event = EventSerializer()
+
+
+class StarSelectionSerializer(serializers.Serializer):
+    stars = serializers.ListField(child=serializers.IntegerField(), write_only=True)
+
+
+class CategorySelectionSerializer(serializers.Serializer):
+    categories = serializers.ListField(child=serializers.ChoiceField(
+        ['attractions', 'museum', 'movie', 'concert', 'artwork', 'plays', 'shop', 'gallery', 'theme_park', 'viewpoint', 'zoo']
+    ))
+
+
+class RecomendationNode(serializers.Serializer):
+    category = serializers.CharField()
+    events = serializers.ListField(child=ObjectRouteSerializer())
+
+
+
+class SelfRecomendationSerializer(serializers.Serializer):
+    recomendations = serializers.ListField(child=RecomendationNode(), write_only=True)

passfinder/recomendations/api/views.py

@@ -49,32 +49,13 @@ class PersonalRecommendation(viewsets.GenericViewSet):
     model = Event
     queryset = Event.objects.all()
 
-    @action(methods=['GET'], detail=False)
-    def plays(self, request, *args, **kwargs):
-        recs = get_personal_plays_recommendation(request.user)
-        ans = []
-        for rec in recs:
-            ans.append(EventSerializer(rec[1]).data)
-        return Response(ans, 200)
-    
-
-    @action(methods=['GET'], detail=False)
-    def concerts(self, request, *args, **kwargs):
-        recs = get_personal_concerts_recommendation(request.user)
-        ans = []
-        for rec in recs:
-            ans.append(EventSerializer(rec[1]).data)
-        return Response(ans, 200)
-
-
-    @action(methods=['GET'], detail=False)
-    def movies(self, request, *args, **kwargs):
-        recs = get_personal_movies_recommendation(request.user)
-        ans = []
-        for rec in recs:
-            ans.append(EventSerializer(rec[1]).data)
-        return Response(ans, 200)
 
+    @action(methods=['GET'], detail=False, serializer_class=SelfRecomendationSerializer)
+    def recommendations(self, request,  *args, **kwargs):
+        return Response(
+            data=get_personal_recomendations(request.user),
+            status=200
+        )
     
     @action(methods=['GET'], detail=True)
     def get_nearest_user_distance(self, request, pk, *args, **kwargs):
@@ -166,3 +147,17 @@ def add_to_favorites(self, request, pk, *args, **kwargs):
         pref.save()
         
         return Response(status=200)
+    
+    @action(methods=['POST'], detail=False, serializer_class=StarSelectionSerializer)
+    def set_hotel_stars(self, request, *args, **kwargs):
+        up, _ = UserPreferences.objects.get_or_create(user=request.user)
+        up.preferred_stars = request.data['stars']
+        up.save()
+        return Response(status=200)
+    
+    @action(methods=['POST'], detail=False, serializer_class=CategorySelectionSerializer)
+    def set_categories(self, request, *args, **kwargs):
+        up, _ = UserPreferences.objects.get_or_create(user=request.user)
+        up.preferred_categories = request.data['categories']
+        up.save()
+        return Response(status=200)

passfinder/recomendations/migrations/0008_userpreferences_preferred_categories_and_more.py

@@ -0,0 +1,31 @@
+# Generated by Django 4.2.1 on 2023-05-27 10:21
+
+import django.contrib.postgres.fields
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ("recomendations", "0007_nearesteventtorestaurant"),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name="userpreferences",
+            name="preferred_categories",
+            field=django.contrib.postgres.fields.ArrayField(
+                base_field=models.CharField(max_length=100),
+                blank=True,
+                null=True,
+                size=None,
+            ),
+        ),
+        migrations.AddField(
+            model_name="userpreferences",
+            name="preferred_stars",
+            field=django.contrib.postgres.fields.ArrayField(
+                base_field=models.IntegerField(), blank=True, null=True, size=None
+            ),
+        ),
+    ]

passfinder/recomendations/models.py

@@ -1,6 +1,7 @@
 from django.db import models
 from passfinder.users.models import User
 from passfinder.events.models import Event, Hotel, Restaurant
+from django.contrib.postgres.fields import ArrayField
 
 
 class UserPreferences(models.Model):
@@ -21,6 +22,9 @@ class UserPreferences(models.Model):
     prefferred_museums = models.ManyToManyField(Event, related_name='preffered_users_museums')
     unprefferred_museums = models.ManyToManyField(Event, related_name='unpreffered_users_museums')
 
+    preferred_categories = ArrayField(base_field=models.CharField(max_length=100), null=True, blank=True)
+    preferred_stars = ArrayField(base_field=models.IntegerField(), null=True, blank=True)
+
 
 
 class NearestEvent(models.Model):

passfinder/recomendations/service/service.py

@@ -266,8 +266,7 @@ def dist_func(event1: Event, event2: Event):
         return dist
     except:
         return 1000000
-    #return (event1.lon - event2.lon) ** 2 + (event1.lat - event2.lat) ** 2
+    return (event1.lon - event2.lon) ** 2 + (event1.lat - event2.lat) ** 2
-    # return (event1.lon - event2.lon) ** 2 + (event1.lat - event2.lat) ** 2
 
 
 def generate_nearest():
@@ -349,6 +348,19 @@ def match_points():
             print(i)
 
 
+def match_restaurants():
+    regions = list(City.objects.all())
+
+    for i, point in enumerate(Restaurant.objects.all()):
+        s_regions = list(sorted(regions.copy(), key=lambda x: dist_func(point, x)))
+        point.city = s_regions[0]
+        
+        point.save()
+        if i % 10 == 0:
+            print(i)
+    
+
+
 def calculate_mean_metric(
     favorite_events: Iterable[Event],
     target_event: Event,
@@ -393,21 +405,50 @@ def calculate_favorite_metric(event: Event, user: User):
 def get_exponential_koef(time: timedelta):
     time = time.seconds
     if time < 60 * 10:
-        return 1
+        return 2
     if time < 60 * 20:
-        return 10
+        return 5
     if time < 60 * 30:
-        return 1000
+        return 10
     if time < 60 * 40:
-        return 100000
+        return 20
     return int(1e10)
 
 
+def get_category_similarity_coef(event, user):
+    up, _ = UserPreferences.objects.get_or_create(user=user)
+    cat = up.preferred_categories
+    if event.type in cat:
+        return 0.7
+    else:
+        return 1.2
+
+
 def get_nearest_favorite(
-    events: Iterable[Event], user: User, base_event: Event, exclude_events: Iterable[Event] = [], velocity=3.0, top_k=1
+    events: Iterable[Event], 
+    user: User, 
+    base_event: Event, 
+    exclude_events: Iterable[Event] = [], 
+    velocity=3.0, 
+    top_k=1
 ):
 
-    sorted_events = list(sorted(events, key=lambda event: calculate_favorite_metric(event, user) * get_exponential_koef(time_func(dist_func(event, base_event), velocity))))
+    sorted_events = list(
+        sorted(
+            filter(lambda event: event not in exclude_events, events), 
+            key=lambda event: 
+                calculate_favorite_metric(event, user) * 
+                get_exponential_koef(
+                    time_func(
+                        dist_func(
+                            event, base_event
+                        ), 
+                        velocity
+                    )
+                ) * 
+                get_category_similarity_coef(event, user)
+            )
+        )
 
     if top_k == 1:
         return sorted_events[0]
@@ -424,14 +465,6 @@ def time_func(km_distance: float, velocity: float):
     return timedelta(minutes=(km_distance) / (velocity / 60))
 
 
-def generate_route(point1: BasePoint, point2: BasePoint, velocity: float):
-    distance = dist_func(point1, point2)
-    time = time_func(distance, velocity)
-
-
-def time_func(km_distance: float, velocity):
-    return timedelta(minutes=(km_distance) / (velocity / 60))
-
 
 def generate_route(point1: BasePoint, point2: BasePoint, velocity):
     distance = dist_func(point1, point2)
@@ -470,14 +503,40 @@ def generate_multiple_tours(user: User, city: City, start_date: datetime.date, e
     return pool.map(generate_tour, [(user, start_date, end_date, hotel) for hotel in hotels])
 
 
-def generate_tour(user: User, city: City, start_date: datetime.date, end_date: datetime.date, avg_velocity=3.0):
+def generate_tour(
+        user: User, 
+        city: City, 
+        start_date: datetime.date, 
+        end_date: datetime.date, 
+        avg_velocity=3.0,
+        stars=[],
+        hotel_type=['hotel', 'hostel', 'apartment'],
+        where_eat=['restaurant', 'bar', 'cafe'],
+        what_to_see=['attractions', 'museum', 'movie', 'concert', 'artwork', 'plays', 'shop', 'gallery', 'theme_park', 'viewpoint', 'zoo']
+    ):
     UserPreferences.objects.get_or_create(user=user)
-    hotel = choice(list(Hotel.objects.filter(city=city)))
+    
+    hotels_candidates = Hotel.objects.filter(city=city)
+    if len(hotels_candidates.filter(stars__in=stars)):
+        hotels_candidates = hotels_candidates.filter(stars__in=stars)
+    
+    try:
+        hotel = choice(list(hotels_candidates))
+    except:
+        hotel = city
     current_date = start_date
     paths, points, disallowed_rest = [], [], []
 
     while current_date < end_date:
-        local_points, local_paths, local_disallowed_rest = generate_path(user, points, hotel, disallowed_rest, avg_velocity)
+        local_points, local_paths, local_disallowed_rest = generate_path(
+            user, 
+            points, 
+            hotel, 
+            disallowed_rest, 
+            avg_velocity,
+            where_eat=where_eat,
+            what_to_see=what_to_see
+        )
         points.extend(local_points)
         paths.append(
             {
@@ -514,22 +573,56 @@ def nearest_distance_points(point: BasePoint, user: User, velocity: float=3.0):
         
 
 
-def generate_path(user: User, disallowed_points: Iterable[BasePoint], hotel: Hotel, disallowed_rests: Iterable[Restaurant], avg_velocity: float):
+def generate_path(
-    # region_events = Event.objects.filter(region=region)
+        user: User, 
+        disallowed_points: Iterable[BasePoint], 
+        hotel: Hotel, 
+        disallowed_rests: Iterable[Restaurant], 
+        avg_velocity: float,
+        where_eat=['restaurant', 'bar', 'cafe'],
+        what_to_see=['attractions', 'museum', 'movie', 'concert', 'artwork', 'plays', 'shop', 'gallery', 'theme_park', 'viewpoint', 'zoo']
+    ):
+    allowed_types = [
+        'museum', 
+        'attraction', 
+        'artwork', 
+        'shop', 
+        'gallery', 
+        'theme_park', 
+        'zoo', 
+        'other',
+        'viewpoint'
+    ]
+    if len(set(allowed_types) & set(what_to_see)) == 0:
+        allowed_types = what_to_see
+    else:
+        allowed_types = list(set(allowed_types) & set(what_to_see))
+    print(allowed_types, hotel)
+    if isinstance(hotel, City):
+        start_points_candidate = Restaurant.objects.filter(city=hotel).filter(~Q(oid__in=disallowed_rests))
+    else:
+        start_points_candidate = NearestRestaurantToHotel.objects.filter(hotel=hotel).first().restaurants.filter(~Q(oid__in=disallowed_rests))
     
-    #candidates = NearestHotel.objects.get(hotel=hotel).nearest_events.all()
+    if len(start_points_candidate.filter(type__in=where_eat)):
-    allowed_types = ['museum', 'attraction']
+        start_points_candidate = start_points_candidate.filter(type__in=where_eat)
 
-    start_point = NearestRestaurantToHotel.objects.filter(hotel=hotel).first().restaurants.filter(~Q(oid__in=disallowed_rests)).first()
+    start_point = start_points_candidate[0]
     disallowed_rests.append(start_point.oid)
-    candidates =  list(filter(lambda x: x not in disallowed_points, hotel.nearest_hotel_rel.all().first().nearest_events.filter(type__in=allowed_types)))
+    
-    #candidates = list(filter(lambda x: x.type in allowed_types, map(lambda x: x.event, start_point.nearestrestauranttoevent_set.all()[0:100])))
+    candidates =  NearestEventToRestaurant.objects.get(restaurant=start_point).events.all().filter(type__in=allowed_types)
+    
     points = [start_point]
+
+    if isinstance(hotel, Hotel):
         path = [
             generate_hotel(hotel),
             generate_route(start_point, hotel, avg_velocity), 
             generate_restaurant(start_point)
         ]
+    else:
+        path = [
+            generate_restaurant(start_point)
+        ]
 
     start_time = datetime.combine(datetime.now(), time(hour=10))
 
@@ -537,32 +630,53 @@ def generate_path(user: User, disallowed_points: Iterable[BasePoint], hotel: Hot
 
     while start_time.hour < 22 and start_time.day == datetime.now().day:
         if (start_time.hour > 14 and how_many_eat == 1) or (start_time.hour > 20 and how_many_eat == 2):
-            point = NearestRestaurantToEvent.objects.filter(event=points[-1]).first().restaurants.filter(~Q(oid__in=disallowed_rests))[0]
+            print(points, start_time)
+            try:
+                point_candidates = NearestRestaurantToEvent.objects.filter(event=points[-1]).first().restaurants.filter(~Q(oid__in=disallowed_rests))
+                if len(point_candidates.filter(type__in=where_eat)):
+                    point_candidates = point_candidates.filter(type__in=where_eat)
+                point = point_candidates[0]
+                
                 disallowed_rests.append(point.oid)
                 points.append(point)
-            # Переделать - сделать еще один прекалк на рестораны с точками
-            candidates = NearestEventToRestaurant.objects.get(restaurant=point).events.all().filter(type__in=allowed_types)
-            if len(candidates) < 10:
-                candidates = NearestEventToRestaurant.objects.get(restaurant=point).events.all()
                 
+                candidates = NearestEventToRestaurant.objects.get(restaurant=point).events.all().filter(type__in=allowed_types)
+                if len(candidates) < 2:
+                    candidates = NearestEventToRestaurant.objects.get(restaurant=point).events.all()
                 
                 path.append(generate_restaurant(points[-1]))
                 start_time += timedelta(seconds=path[-1]['time'])
                 how_many_eat += 1
                 continue
+            except:
+                return points, path, disallowed_rests
+ 
 
         if start_time.hour > 17:
-            allowed_types = ['play', 'concert', 'movie']
+            allowed_types = [
+                'play', 
+                'concert', 
+                'movie', 
+                'shop', 
+                'gallery', 
+                'theme_park', 
+                'viewpoint'
+            ]
+            if len(set(allowed_types) & set(what_to_see)) == 0:
+                allowed_types = what_to_see
+            else:
+                allowed_types = list(set(allowed_types) & set(what_to_see))
+
 
         if candidates is None:
             candidates = NearestEvent.objects.get(event=points[-1]).nearest.filter(type__in=allowed_types)
-            if len(candidates) < 10:
+            if len(candidates) < 2:
                 candidates = NearestEvent.objects.get(event=points[-1]).nearest.all()
 
         try:
             points.append(get_nearest_favorite(candidates, user, points[-1], points + disallowed_points))
             
-        except AttributeError:
+        except:
             points.append(get_nearest_favorite(candidates, user, points[-1], points))
 
         transition_route = generate_route(points[-1], points[-2], avg_velocity)
@@ -576,15 +690,6 @@ def generate_path(user: User, disallowed_points: Iterable[BasePoint], hotel: Hot
     return points, path, disallowed_rests
 
 
-def calculate_distance(sample1: Event, samples: Iterable[Event], model: AnnoyIndex, rev_mapping):
-    metrics = []
-
-    for sample in samples:
-        metrics.append(model.get_distance(rev_mapping[sample1.oid], rev_mapping[sample.oid]))
-    
-    return sum(metrics) / len(metrics)
-
-
 def calculate_distance(
     sample1: Event, samples: Iterable[Event], model: AnnoyIndex, rev_mapping
 ):
@@ -627,11 +732,6 @@ def get_onboarding_hotels(stars=Iterable[int]):
 
 
 def generate_points_path(user: User, points: Iterable[Event], velocity=3.0):
-    """
-    Дописать
-    1) генерить маршруты от многих точек (не только по 2) (salesman problem)
-    2) Если в маршруте до 7 точек - добавлять похожие пока не станет 7 точек
-    """
     if len(points) < 7:
         candidates = NearestEvent.objects.get(event=points[0]).nearest.all()
         points.extend(list(get_nearest_favorite(candidates, user, points[0], [], velocity, 7-len(points))))
@@ -663,3 +763,114 @@ def generate_points_path(user: User, points: Iterable[Event], velocity=3.0):
         visited_points.append(pt)
 
     return res
+
+
+def flat_list(lst):
+    res = []
+    for i in lst:
+        res.extend(i)
+    return res
+
+
+def range_candidates(candidates, user, favorite_events):
+    model_mappings = {
+        'attraction': [attracion_model, rev_attraction_mapping],
+        'museum': [mus_model, rev_mus_mapping],
+        'movie': [cinema_model, rev_cinema_mapping],
+        'concert': [concert_model, rev_concert_mapping],
+        'plays': [plays_model, rev_plays_mapping]
+    }
+    
+    if candidates[0].type in ['attraction', 'museum', 'movie', 'concert', 'plays']:
+        candidates = sorted(
+            candidates,
+            key=lambda cand: calculate_mean_metric(
+                favorite_events, 
+                cand,
+                *model_mappings[cand.type]
+            )
+        )
+        return candidates[0:10]
+    return sample(candidates, 10)
+
+
+def get_personal_recomendations(user):
+    up, _ = UserPreferences.objects.get_or_create(user=user)
+    candidates_generate_strategy = {
+        'plays': [lambda pref: flat_list(
+            list(
+                map(
+                    lambda cand: nearest_plays(
+                        cand, 30
+                    ),
+                    pref.preffered_plays.all()
+                )
+            ), 
+        ), lambda pref: pref.preffered_plays.all()],
+        'movie': [lambda pref: flat_list(
+            list(
+                map(
+                    lambda cand: nearest_movie(
+                        cand, 30
+                    ),
+                    pref.preffered_movies.all()
+                )
+            ), 
+        ), lambda pref: pref.preffered_movies.all()],
+        'concert': [lambda pref: flat_list(
+            list(
+                map(
+                    lambda cand: nearest_concert(
+                        cand, 30
+                    ),
+                    pref.preferred_concerts.all()
+                )
+            ), 
+        ), lambda pref: pref.preferred_concerts.all()],
+        'attractions': [lambda pref: flat_list(
+            list(
+                map(
+                    lambda cand: nearest_attraction(
+                        cand, 30
+                    ),
+                    pref.prefferred_attractions.all()
+                )
+            ),
+        ), lambda pref: pref.prefferred_attractions.all()],
+        'museum': [lambda pref: flat_list(
+            list(
+                map(
+                    lambda cand: nearest_mus(
+                        cand, 30
+                    ),
+                    pref.prefferred_museums.all()
+                )
+            ),
+        ), lambda pref: pref.prefferred_museums.all()],
+        'shop': [lambda pref: sample(list(Event.objects.filter(type='shop')), 10), lambda x: []],
+        'gallery': [lambda pref: sample(list(Event.objects.filter(type='gallery')), 10), lambda x: []],
+        'theme_park': [lambda pref: sample(list(Event.objects.filter(type='theme_park')), 10), lambda x: []],
+        'viewpoint': [lambda pref: sample(list(Event.objects.filter(type='viewpoint')), 10), lambda x: []],
+        'zoo': [lambda pref: sample(list(Event.objects.filter(type='zoo')), 10), lambda x: []],
+    }
+
+    res = []
+    for category_candidate in up.preferred_categories:
+        candidates = candidates_generate_strategy[category_candidate][0](up)
+        ranged = range_candidates(
+            candidates, 
+            user, 
+            candidates_generate_strategy[category_candidate][1](up)
+        )
+        res.append(
+            {
+                'category': category_candidate,
+                'events': list(
+                    map(
+                        lambda x: ObjectRouteSerializer(x).data,
+                        ranged
+                    )
+                )
+            }
+        )
+    return res