mirror of
https://github.com/task-17-lct/backend.git
synced 2024-11-24 07:33:43 +03:00
273 lines
9.3 KiB
Python
273 lines
9.3 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
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from passfinder.recomendations.models import UserPreferences, NearestEvent
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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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def get_nearest_(instance_model, model_type, mapping, nearest_n, ml_model):
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how_many = len(Event.objects.filter(type=model_type))
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index = 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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if len(res) == nearest_n: break
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return res
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def nearest_attraction(attraction, nearest_n):
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return get_nearest_(attraction, 'attraction', attraction_mapping, nearest_n, attracion_model)
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def nearest_movie(movie, nearest_n):
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return get_nearest_(movie, 'movie', cinema_mapping, nearest_n, cinema_model)
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def nearest_plays(play, nearest_n):
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return get_nearest_(play, 'plays', plays_mapping, nearest_n, plays_model)
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def nearest_excursion(excursion, nearest_n):
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return get_nearest_(excursion, 'excursion', excursion_mapping, nearest_n, excursion_model)
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def nearest_concert(concert, nearest_n):
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return get_nearest_(concert, 'concert', concert_mapping, nearest_n, concert_model)
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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(pref.unpreffered_movies.all()):
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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(pref.unpreferred_concerts.all()):
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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(pref.unpreffered_plays.all()):
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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(pref.unpreferred_concerts.all()):
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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(pref.unpreffered_plays.all()):
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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(pref.unpreffered_movies.all()):
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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 * (len(ranks[candidate]['lst']) - ranks[candidate]['lst'].index(candidate))
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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(filter(lambda x: x[1] not in flatten_negatives, sorted(res, key=lambda x: -x[0])))
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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(list(map(lambda x: x.oid, get_nearest_event(neg, 10)[1:])))
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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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return (event1.lat - event2.lat) ** 2 + (event2.lon - event2.lon) ** 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(sorted(all_events.copy(), key=lambda x: dist_func(event, x)))
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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 calculate_mean_metric(favorite_events: Iterable[Event], target_event: Event, model: AnnoyIndex, rev_list: Iterable[str]):
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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_list[target_event.oid]
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for fav in favorite_events:
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dists.append(model.get_distance(rev_list[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(
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preferred,
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event,
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plays_model,
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plays_mapping
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)
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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,
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event,
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concert_model,
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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(
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preferred,
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event,
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cinema_model,
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cinema_mapping
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)
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return 1000000
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def get_nearest_favorite(events: Iterable[Event], user: User, exclude_events: Iterable[Event]=[]):
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result = events[0]
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result_min = calculate_favorite_metric(events[0], user)
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for event in events:
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if event in exclude_events: 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 generate_path(region: Region, user: User):
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region_events = Event.objects.filter(region=region)
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start_point = get_nearest_favorite(region_events, 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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while len(points) < 5:
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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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return points
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