Muse-recommendations

muse-5.py

# API: get recommended tracks data based on seed and target values
recommendations_api_endpoint = SPOTIFY_API_URL  + "/recommendations?seed_artists=" + ",".join(seed_artists) + "&target_energy=" + target_energy + "&target_liveness=" + target_liveness + "&target_tempo=" + target_tempo + "&target_speechiness=" + target_speechiness + "&target_acousticness=" + target_acousticness + "&target_instrumentalness=" + target_instrumentalness + "&target_danceability=" + target_danceability + "&target_loudness=" + target_loudness + "&limit=20"
recommendations_response = requests.get(recommendations_api_endpoint, headers=GLOBAL['authorization_header'])
recommendation_data['data'] = json.loads(recommendations_response.text)['tracks']

# set recommended track title and ids
for track in recommendation_data['data']:
    # Dont add duplicate recommendations or songs already in your playlist
    if track['id'] not in recommendation_data['ids'] and track['id'] not in seed_data['song_ids']:
        recommendation_data['titles'].append(track['name'])
        recommendation_data['artists'].append(track['artists'][0]['name'])
        recommendation_data['ids'].append(track['id'])
        recommendation_data['images'].append(track['album']['images'][0]['url'])

# API: get collected tracks attributes
attributes_api_endpoint = SPOTIFY_API_URL  + "/audio-features?ids=" + ",".join(recommendation_data['ids'])
attributes_response = requests.get(attributes_api_endpoint, headers=GLOBAL['authorization_header'])
recommendation_data['attributes'] = json.loads(attributes_response.text)['audio_features']

# create tracks with just the float variables
recommendation_track_attributes = []
for track in recommendation_data['attributes']:
    track_float_values = {}
    for attribute in target_attributes:
        track_float_values[attribute] = track[attribute]
    recommendation_track_attributes.append(track_float_values.values())

# get recommendation_distances from phantom_average_track
recommendation_distances = [phantom_average_track.values()] + recommendation_track_attributes
dist = DistanceMetric.get_metric('euclidean')
distances = dist.pairwise(recommendation_distances)[0]
recommendation_data['distances'] = distances[1:len(distances)]

# sort recommendation_data object based on recommendation_data distances
sorted_recommendation_indexes = recommendation_data['distances'].argsort()[:len(recommendation_data['distances'])]
for key in recommendation_data.keys():
    if recommendation_data[key] != []:
        recommendation_data[key] = [recommendation_data[key][i] for i in sorted_recommendation_indexes]