def update_attributes(self, candidate_batch_size=None):
"""
Update the attributes for this user/emotion mapping to the average attributes of the most recent songs
the user has upvoted as making them feel this emotion. If the user doesn't have any upvotes for this
emotion, the attributes will be set to `None` and reset to the emotion defaults in the save() call.
:param candidate_batch_size: (int) Number of songs to include in batch for calculating new attribute values
"""
if not candidate_batch_size:
candidate_batch_size = settings.CANDIDATE_BATCH_SIZE_FOR_USER_EMOTION_ATTRIBUTES_UPDATE
# First, get the distinct upvotes by the user for the emotion.
# Avoid factoring in a song more than once if there are multiple upvotes for the song
distinct_votes = self.user.usersongvote_set.filter(
emotion=self.emotion,
vote=True).distinct('song__pk', ).values_list('pk', flat=True)
# Next, get the `candidate_batch_size` most recently distinct songs upvoted for the emotion
song_pks = UserSongVote.objects.filter(
pk__in=distinct_votes).order_by('-created').values_list(
'song__pk', flat=True)[:candidate_batch_size]
# Finally, calculate the average emotion attributes for the songs the user has most
# recently upvoted for the emotion and set the UserEmotion attributes to the average values
songs = Song.objects.filter(pk__in=song_pks)
attributes = average(songs, 'valence', 'energy', 'danceability')
self.valence = attributes['valence__avg']
self.energy = attributes['energy__avg']
self.danceability = attributes['danceability__avg']
self.save()
def test_filter_playlist_by_genre(self):
new_song = MoodyUtil.create_song(genre='super-dope')
MoodyUtil.create_user_song_vote(user=self.user,
song=self.song,
emotion=self.emotion,
vote=True)
MoodyUtil.create_user_song_vote(user=self.user,
song=new_song,
emotion=self.emotion,
vote=True)
data = {'emotion': self.emotion.name, 'genre': new_song.genre}
resp = self.client.get(self.url, data=data)
resp_data = resp.json()
queryset = UserSongVote.objects.filter(user=self.user,
emotion=self.emotion,
vote=True,
song__genre=new_song.genre)
votes_for_emotion_data = average(queryset, 'song__valence',
'song__energy', 'song__danceability')
valence = votes_for_emotion_data['song__valence__avg'] * 100
energy = votes_for_emotion_data['song__energy__avg'] * 100
danceability = votes_for_emotion_data['song__danceability__avg'] * 100
self.assertEqual(resp.status_code, status.HTTP_200_OK)
self.assertEqual(len(resp_data['results']), 1)
self.assertEqual(resp_data['results'][0]['song']['code'],
new_song.code)
self.assertEqual(resp_data['valence'], valence)
self.assertEqual(resp_data['energy'], energy)
self.assertEqual(resp_data['danceability'], danceability)
def test_upvoting_on_song_updates_user_emotion_attributes(self):
user_emotion = self.user.useremotion_set.get(
emotion__name=Emotion.HAPPY)
new_song = MoodyUtil.create_song(energy=.75, valence=.65)
data = {
'emotion': Emotion.HAPPY,
'song_code': self.song.code,
'vote': True
}
self.client.post(self.url, data=data, format='json')
data = {
'emotion': Emotion.HAPPY,
'song_code': new_song.code,
'vote': True
}
self.client.post(self.url, data=data, format='json')
votes = UserSongVote.objects.filter(user=self.user, vote=True)
expected_attributes = average(votes, 'song__valence', 'song__energy',
'song__danceability')
expected_valence = expected_attributes['song__valence__avg']
expected_energy = expected_attributes['song__energy__avg']
expected_danceability = expected_attributes['song__danceability__avg']
user_emotion.refresh_from_db()
self.assertEqual(user_emotion.energy, expected_energy)
self.assertEqual(user_emotion.valence, expected_valence)
self.assertEqual(user_emotion.danceability, expected_danceability)
def test_empty_queryset_returns_null_values(self):
collection = Song.objects.none()
calculated_attrs = average(collection, 'valence', 'energy',
'danceability')
self.assertIsNone(calculated_attrs['valence__avg'])
self.assertIsNone(calculated_attrs['energy__avg'])
self.assertIsNone(calculated_attrs['danceability__avg'])
def test_average_computes_proper_average_for_criteria(self):
collection = Song.objects.all()
expected_valence = .6
expected_energy = .57
expected_danceability = .62
calculated_attrs = average(collection, 'valence', 'energy',
'danceability')
self.assertEqual(calculated_attrs['valence__avg'], expected_valence)
self.assertEqual(calculated_attrs['energy__avg'], expected_energy)
self.assertEqual(calculated_attrs['danceability__avg'],
expected_danceability)
def list(self, request, *args, **kwargs):
logger.info(
'Generating {} emotion playlist for user {}'.format(
self.cleaned_data['emotion'],
self.request.user.username,
),
extra={
'fingerprint': auto_fingerprint('generate_emotion_playlist', **kwargs),
'user_id': self.request.user.pk,
'emotion': Emotion.get_full_name_from_keyword(self.cleaned_data['emotion']),
'genre': self.cleaned_data.get('genre'),
'context': self.cleaned_data.get('context'),
'artist': self.cleaned_data.get('artist'),
'page': self.request.GET.get('page'),
'trace_id': request.trace_id,
}
)
resp = super().list(request, *args, **kwargs)
first_page = last_page = None
if resp.data['previous']:
first_page = re.sub(r'&page=[0-9]*', '', resp.data['previous'])
if resp.data['next']:
last_page = re.sub(r'page=[0-9]*', 'page=last', resp.data['next'])
queryset = self.filter_queryset(self.get_queryset())
# Update response data with analytics for emotion
votes_for_emotion_data = average(queryset, 'song__valence', 'song__energy', 'song__danceability')
valence = votes_for_emotion_data['song__valence__avg'] or 0
energy = votes_for_emotion_data['song__energy__avg'] or 0
danceability = votes_for_emotion_data['song__danceability__avg'] or 0
# Normalize emotion data as whole numbers
normalized_valence = valence * 100
normalized_energy = energy * 100
normalized_danceability = danceability * 100
resp.data.update({
'valence': normalized_valence,
'energy': normalized_energy,
'danceability': normalized_danceability,
'emotion_name': Emotion.get_full_name_from_keyword(self.cleaned_data['emotion']),
'first_page': first_page,
'last_page': last_page,
})
return resp
def test_playlist_for_context_is_generated_with_upvoted_song_attributes_for_context(
self, mock_generate_playlist):
context = 'WORK'
emotion = Emotion.objects.get(name=Emotion.HAPPY)
song = MoodyUtil.create_song(energy=.25, valence=.50, danceability=.75)
song2 = MoodyUtil.create_song(energy=.75,
valence=.50,
danceability=.25)
song3 = MoodyUtil.create_song(energy=.50,
valence=.75,
danceability=.25)
MoodyUtil.create_user_song_vote(self.user,
song,
emotion,
True,
context=context)
MoodyUtil.create_user_song_vote(self.user,
song2,
emotion,
True,
context=context)
MoodyUtil.create_user_song_vote(
self.user, song3, emotion,
True) # Attributes should not be factored in
params = {'emotion': emotion.name, 'jitter': 0, 'context': context}
self.client.get(self.url, data=params)
votes = UserSongVote.objects.filter(user=self.user,
emotion=emotion,
context=context,
vote=True)
attributes_for_votes = average(votes, 'song__valence', 'song__energy',
'song__danceability')
expected_valence = attributes_for_votes['song__valence__avg']
expected_energy = attributes_for_votes['song__energy__avg']
expected_danceability = attributes_for_votes['song__danceability__avg']
call_args = mock_generate_playlist.mock_calls[0][1]
called_energy = call_args[0]
called_valence = call_args[1]
called_danceability = call_args[2]
self.assertEqual(called_valence, expected_valence)
self.assertEqual(called_energy, expected_energy)
self.assertEqual(called_danceability, expected_danceability)
def test_set_precision_returns_average_to_desired_precision(self):
collection = Song.objects.all()
expected_valence = .6
expected_energy = .575
expected_danceability = .625
calculated_attrs = average(collection,
'valence',
'energy',
'danceability',
precision=3)
self.assertEqual(calculated_attrs['valence__avg'], expected_valence)
self.assertEqual(calculated_attrs['energy__avg'], expected_energy)
self.assertEqual(calculated_attrs['danceability__avg'],
expected_danceability)
def test_filter_by_context(self):
expected_song = MoodyUtil.create_song(name='song-with-context')
context = 'WORK'
MoodyUtil.create_user_song_vote(user=self.user,
song=expected_song,
emotion=self.emotion,
vote=True,
context=context)
MoodyUtil.create_user_song_vote(
user=self.user,
song=self.song,
emotion=self.emotion,
vote=True,
)
data = {'emotion': self.emotion.name, 'context': context}
resp = self.client.get(self.url, data=data)
resp_data = resp.json()
queryset = UserSongVote.objects.filter(user=self.user,
emotion=self.emotion,
vote=True,
context=context)
votes_for_emotion_data = average(queryset, 'song__valence',
'song__energy', 'song__danceability')
valence = votes_for_emotion_data['song__valence__avg'] * 100
energy = votes_for_emotion_data['song__energy__avg'] * 100
danceability = votes_for_emotion_data['song__danceability__avg'] * 100
self.assertEqual(resp.status_code, status.HTTP_200_OK)
self.assertEqual(len(resp_data['results']), 1)
self.assertEqual(resp_data['results'][0]['song']['code'],
expected_song.code)
self.assertEqual(resp_data['valence'], valence)
self.assertEqual(resp_data['energy'], energy)
self.assertEqual(resp_data['danceability'], danceability)
def test_happy_path(self):
dispatch_uid = settings.UPDATE_USER_EMOTION_ATTRIBUTES_SIGNAL_UID
with SignalDisconnect(post_save, update_user_emotion_attributes,
UserSongVote, dispatch_uid):
MoodyUtil.create_user_song_vote(self.user, self.song1,
self.emotion, True)
MoodyUtil.create_user_song_vote(self.user, self.song2,
self.emotion, True)
UpdateUserEmotionRecordAttributeTask().run(self.user.pk,
self.emotion.pk)
user_emotion = self.user.get_user_emotion_record(self.emotion.name)
user_votes = self.user.usersongvote_set.all()
expected_attributes = average(user_votes, 'song__valence',
'song__energy', 'song__danceability')
expected_valence = expected_attributes['song__valence__avg']
expected_energy = expected_attributes['song__energy__avg']
expected_danceability = expected_attributes['song__danceability__avg']
self.assertEqual(user_emotion.valence, expected_valence)
self.assertEqual(user_emotion.energy, expected_energy)
self.assertEqual(user_emotion.danceability, expected_danceability)
def filter_queryset(self, queryset, **kwargs):
cached_playlist_manager = CachedPlaylistManager(self.request.user)
jitter = self.cleaned_data.get('jitter')
limit = self.cleaned_data.get('limit') or self.default_limit
artist = self.cleaned_data.get('artist')
energy = None
valence = None
danceability = None
strategy = random.choice(settings.BROWSE_PLAYLIST_STRATEGIES)
# Should be able to supply 0 for jitter, so we'll check explicitly for None
if jitter is None:
jitter = self.default_jitter
# Try to use upvotes for this emotion and context to generate attributes for songs to return
if self.cleaned_data.get('context'):
votes = self.request.user.usersongvote_set.filter(
emotion__name=self.cleaned_data['emotion'],
context=self.cleaned_data['context'],
vote=True
)
if votes.exists():
attributes_for_votes = average(votes, 'song__valence', 'song__energy', 'song__danceability')
valence = attributes_for_votes['song__valence__avg']
energy = attributes_for_votes['song__energy__avg']
danceability = attributes_for_votes['song__danceability__avg']
# If context not provided or the previous query on upvotes for context did return any votes,
# determine attributes from the attributes for the user and emotion
if energy is None or valence is None or valence is None:
user_emotion = self.request.user.get_user_emotion_record(self.cleaned_data['emotion'])
# If the user doesn't have a UserEmotion record for the emotion, fall back to the
# default attributes for the emotion
if not user_emotion:
emotion = Emotion.objects.get(name=self.cleaned_data['emotion'])
energy = emotion.energy
valence = emotion.valence
danceability = emotion.danceability
else:
energy = user_emotion.energy
valence = user_emotion.valence
danceability = user_emotion.danceability
# Try to fetch top artists for user from Spotify
top_artists = None
try:
spotify_data = SpotifyUserData.objects.get(spotify_auth__user=self.request.user)
top_artists = spotify_data.top_artists
except SpotifyUserData.DoesNotExist:
pass
logger.info(
'Generating {} browse playlist for user {}'.format(
self.cleaned_data['emotion'],
self.request.user.username,
),
extra={
'fingerprint': auto_fingerprint('generate_browse_playlist', **kwargs),
'user_id': self.request.user.pk,
'emotion': Emotion.get_full_name_from_keyword(self.cleaned_data['emotion']),
'genre': self.cleaned_data.get('genre'),
'context': self.cleaned_data.get('context'),
'strategy': strategy,
'energy': energy,
'valence': valence,
'danceability': danceability,
'artist': artist,
'jitter': jitter,
'top_artists': top_artists,
'trace_id': self.request.trace_id,
}
)
playlist = generate_browse_playlist(
energy,
valence,
danceability,
strategy=strategy,
limit=limit,
jitter=jitter,
artist=artist,
top_artists=top_artists,
songs=queryset
)
cached_playlist_manager.cache_browse_playlist(
playlist,
self.cleaned_data['emotion'],
self.cleaned_data.get('context'),
self.cleaned_data.get('description')
)
return playlist
def test_invalid_attribute_raises_exception(self):
collection = Song.objects.all()
with self.assertRaises(ValueError):
average(collection, 'foo')