mirror of
https://github.com/task-17-lct/backend.git
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398 lines
12 KiB
Python
398 lines
12 KiB
Python
from annoy import AnnoyIndex
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from .mapping.mapping import *
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from .models.models import *
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from passfinder.events.models import Event, Region, Hotel, BasePoint, City
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from passfinder.recomendations.models import UserPreferences, NearestEvent, NearestHotel
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from random import choice
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from collections import Counter
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from passfinder.users.models import User
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from collections.abc import Iterable
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from django.db.models import Q
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from geopy.distance import geodesic as GD
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from datetime import timedelta, time, datetime
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def get_nearest_(instance_model, model_type, mapping, rev_mapping, nearest_n, ml_model):
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how_many = len(Event.objects.filter(type=model_type))
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index = rev_mapping[instance_model.oid]
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nearest = ml_model.get_nns_by_item(index, len(mapping))
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res = []
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for i in range(how_many):
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try:
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res.append(Event.objects.get(oid=mapping[nearest[i]]))
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except Event.DoesNotExist:
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...
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if len(res) == nearest_n:
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break
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return res
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def nearest_attraction(attraction, nearest_n):
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return get_nearest_(
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attraction,
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"attraction",
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attraction_mapping,
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rev_attraction_mapping,
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nearest_n,
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attracion_model,
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)
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def nearest_movie(movie, nearest_n):
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return get_nearest_(
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movie, "movie", cinema_mapping, rev_cinema_mapping, nearest_n, cinema_model
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)
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def nearest_plays(play, nearest_n):
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return get_nearest_(
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play, "plays", plays_mapping, rev_plays_mapping, nearest_n, plays_model
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)
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def nearest_excursion(excursion, nearest_n):
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return get_nearest_(
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excursion,
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"excursion",
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excursion_mapping,
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rev_excursion_mapping,
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nearest_n,
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excursion_model,
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)
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def nearest_concert(concert, nearest_n):
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return get_nearest_(
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concert,
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"concert",
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concert_mapping,
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rev_concert_mapping,
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nearest_n,
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concert_model,
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)
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def get_nearest_event(event, nearest_n):
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if event.type == "plays":
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return nearest_plays(event, nearest_n)
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if event.type == "concert":
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return nearest_concert(event, nearest_n)
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if event.type == "movie":
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return nearest_movie(event, nearest_n)
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def update_preferences_state(user, event, direction):
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pref = UserPreferences.objects.get(user=user)
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if direction == "left":
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if event.type == "plays":
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pref.unpreffered_plays.add(event)
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if event.type == "movie":
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pref.unpreffered_movies.add(event)
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if event.type == "concert":
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pref.unpreferred_concerts.add(event)
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else:
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if event.type == "plays":
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pref.preffered_plays.add(event)
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if event.type == "movie":
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pref.preffered_movies.add(event)
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if event.type == "concert":
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pref.preferred_concerts.add(event)
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pref.save()
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def get_next_tinder(user, prev_event, prev_direction):
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pref = UserPreferences.objects.get(user=user)
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print(prev_event.type, len(pref.preferred_concerts.all()))
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if prev_direction == "left":
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if prev_event.type == "plays" and len(pref.unpreffered_plays.all()) <= 2:
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candidates = nearest_plays(prev_event, 100)
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# print(candidates, type(candidates), len(Event.objects.filter(type='plays')))
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return candidates[-1]
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if prev_event.type == "movie" and len(pref.unpreffered_movies.all()) <= 2:
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candidates = nearest_movie(prev_event, 100)
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return candidates[-1]
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if prev_event.type == "concert" and len(pref.unpreferred_concerts.all()) <= 2:
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candidates = nearest_concert(prev_event, 100)
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return candidates[-1]
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if prev_direction == "right":
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if prev_event.type == "plays" and len(pref.preffered_plays.all()) < 2:
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candidates = nearest_plays(prev_event, 2)
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return candidates[1]
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if prev_event.type == "movie" and len(pref.preffered_movies.all()) < 2:
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candidates = nearest_movie(prev_event, 2)
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return candidates[1]
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if prev_event.type == "concert" and len(pref.preferred_concerts.all()) < 2:
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candidates = nearest_concert(prev_event, 2)
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return candidates[1]
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if prev_event.type == "plays":
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if not len(pref.preffered_movies.all()) and not len(
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pref.unpreffered_movies.all()
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):
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return choice(Event.objects.filter(type="movie"))
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if not len(pref.preferred_concerts.all()) and not len(
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pref.unpreferred_concerts.all()
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):
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return choice(Event.objects.filter(type="concert"))
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if prev_event.type == "movie":
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if not len(pref.preffered_plays.all()) and not len(
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pref.unpreffered_plays.all()
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):
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return choice(Event.objects.filter(type="plays"))
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if not len(pref.preferred_concerts.all()) and not len(
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pref.unpreferred_concerts.all()
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):
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return choice(Event.objects.filter(type="concert"))
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if prev_event.type == "concert":
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if not len(pref.preffered_plays.all()) and not len(
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pref.unpreffered_plays.all()
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):
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return choice(Event.objects.filter(type="plays"))
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if not len(pref.preffered_movies.all()) and not len(
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pref.unpreffered_movies.all()
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):
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return choice(Event.objects.filter(type="movie"))
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return None
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def rank_candidates(candidates_list, negative_candidates_list):
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flatten_c_list = []
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ranks = {}
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flatten_negatives = []
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for negative in negative_candidates_list:
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flatten_negatives.extend(negative)
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for lst in candidates_list:
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flatten_c_list.extend(lst)
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for cand in lst:
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ranks.update({cand: {"rank": 0, "lst": lst}})
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cnt = Counter(flatten_c_list)
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for candidate, how_many in cnt.most_common(len(flatten_c_list)):
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ranks[candidate]["rank"] = how_many * (
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len(ranks[candidate]["lst"]) - ranks[candidate]["lst"].index(candidate)
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)
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res = []
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for cand in ranks.keys():
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res.append((ranks[cand]["rank"], cand))
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return list(
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filter(
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lambda x: x[1] not in flatten_negatives, sorted(res, key=lambda x: -x[0])
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)
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)
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def get_personal_recommendation(prefer, unprefer):
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candidates = []
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negative_candidates = []
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for rec in prefer:
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candidates.append(list(map(lambda x: x.oid, get_nearest_event(rec, 10)[1:])))
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for neg in unprefer:
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negative_candidates.append(
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list(map(lambda x: x.oid, get_nearest_event(neg, 10)[1:]))
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)
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ranked = rank_candidates(candidates, negative_candidates)
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return list(map(lambda x: (x[0], Event.objects.get(oid=x[1])), ranked[0:5]))
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def get_personal_plays_recommendation(user):
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pref = UserPreferences.objects.get(user=user)
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prefer = pref.preffered_plays.all()
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unprefer = pref.unpreffered_plays.all()
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return get_personal_recommendation(prefer, unprefer)
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def get_personal_concerts_recommendation(user):
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pref = UserPreferences.objects.get(user=user)
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prefer = pref.preferred_concerts.all()
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unprefer = pref.unpreferred_concerts.all()
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return get_personal_recommendation(prefer, unprefer)
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def get_personal_movies_recommendation(user):
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pref = UserPreferences.objects.get(user=user)
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prefer = pref.preffered_movies.all()
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unprefer = pref.unpreffered_movies.all()
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return get_personal_recommendation(prefer, unprefer)
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def dist_func(event1: Event, event2: Event):
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# cords1 = [event1.lat, event1.lon]
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# cords2 = [event2.lat, event2.lon]
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# try:
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# dist = GD(cords1, cords2).km
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# return dist
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# except:
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# return 1000000
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return (event1.lon - event2.lon) ** 2 + (event1.lat - event2.lat) ** 2
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def generate_nearest():
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NearestEvent.objects.all().delete()
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all_events = list(Event.objects.all())
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for i, event in enumerate(Event.objects.all()):
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event_all_events = list(
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sorted(all_events.copy(), key=lambda x: dist_func(event, x))
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)
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nearest = NearestEvent.objects.create(event=event)
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nearest.nearest.set(event_all_events[0:100])
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nearest.save()
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if i % 100 == 0:
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print(i)
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def generate_hotel_nearest():
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NearestHotel.objects.all().delete()
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all_events = list(Event.objects.all())
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hotels = list(Hotel.objects.all())
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for i, hotel in enumerate(hotels):
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event_all_events = list(
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sorted(all_events.copy(), key=lambda x: dist_func(hotel, x))
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)
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nearest = NearestHotel.objects.create(hotel=hotel)
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nearest.nearest_events.set(event_all_events[0:100])
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if i % 100 == 0:
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print(i)
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def match_points():
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regions = list(City.objects.all())
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for i, point in enumerate(Event.objects.all()):
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s_regions = list(sorted(regions.copy(), key=lambda x: dist_func(point, x)))
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point.city = s_regions[0]
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point.save()
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if i % 10 == 0:
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print(i)
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def calculate_mean_metric(
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favorite_events: Iterable[Event],
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target_event: Event,
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model: AnnoyIndex,
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rev_mapping,
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):
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if not len(favorite_events):
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return 100000
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dists = []
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target_event_idx = rev_mapping[target_event.oid]
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for fav in favorite_events:
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dists.append(model.get_distance(rev_mapping[fav.oid], target_event_idx))
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return sum(dists) / len(dists)
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def calculate_favorite_metric(event: Event, user: User):
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pref = UserPreferences.objects.get(user=user)
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if event.type == "plays":
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preferred = pref.preffered_plays.all()
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return calculate_mean_metric(preferred, event, plays_model, rev_plays_mapping)
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if event.type == "concert":
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preferred = pref.preferred_concerts.all()
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return calculate_mean_metric(
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preferred, event, concert_model, rev_concert_mapping
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)
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if event.type == "movie":
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preferred = pref.preffered_movies.all()
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return calculate_mean_metric(preferred, event, cinema_model, rev_cinema_mapping)
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return 1000000
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def get_nearest_favorite(
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events: Iterable[Event], user: User, exclude_events: Iterable[Event] = []
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):
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first_event = None
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for candidate in events:
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if candidate not in exclude_events:
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first_event = candidate
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break
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result = first_event
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result_min = calculate_favorite_metric(result, user)
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for event in events:
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if event in exclude_events:
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continue
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local_min_metric = calculate_favorite_metric(event, user)
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if local_min_metric < result_min:
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result_min = local_min_metric
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result = event
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return result
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def filter_hotel(region: Region, user: User, stars: Iterable[int]):
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hotels = Hotel.objects.filter(city=region)
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return choice(hotels)
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def time_func(km_distance: float):
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return timedelta(minutes=(km_distance) / (4.0 / 60))
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def generate_route(point1: BasePoint, point2: BasePoint):
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distance = dist_func(point1, point2)
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time = time_func(distance)
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return {
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"type": "transition",
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"from": point1,
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"to": point2,
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"distance": distance,
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"time": time,
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}
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def generate_point(point: BasePoint):
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return {
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"type": "point",
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"point": point,
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"point_type": "",
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"time": timedelta(minutes=90+choice(range(-10, 90, 10)))
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}
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def generate_path(region: Region, user: User):
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# region_events = Event.objects.filter(region=region)
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hotel = filter_hotel(region, user, [])
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candidates = NearestHotel.objects.get(hotel=hotel).nearest_events.all()
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start_point = get_nearest_favorite(candidates, user, [])
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candidates = NearestEvent.objects.get(event=start_point).nearest.all()
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points = [start_point]
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path = [generate_point(points[-1])]
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start_time = datetime.combine(datetime.now(), time(hour=10))
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while start_time.hour < 22:
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candidates = NearestEvent.objects.get(event=points[-1]).nearest.all()
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points.append(get_nearest_favorite(candidates, user, points))
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transition_route = generate_route(points[-1], points[-2])
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start_time += transition_route["time"]
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point_route = generate_point(points[-1])
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start_time += point_route["time"]
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path.extend([transition_route, point_route])
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return hotel, points, path
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