import numpy as np
from lightfm import LightFM
from fetch_lastfm import fetch_lastfm
data = fetch_lastfm()
model = LightFM(loss='warp')
model.fit(data['matrix'], epochs=30, num_thread=2)
# Get recommendations function
def get_recommendations(model, coo_mtrx, users_ids):
n_items = coo_mtrx.shape[1]
for user in users_ids:
# TODO create known positives
# Artists the model predicts they will like
scores = model.predict(user, np.arange(n_items))
top_scores = np.argsort(-score)[:3]
print('Recommendations for user %s' % user)
for x in top_scores.tolist():
for artist, values in data['artists'].iteritems():
if int(x) == values['id']:
print(' - %s' % values['name'])
print('\n') # Get it pretty
import numpy as np
from fetch_lastfm import fetch_lastfm
from lightfm import LightFM
min_plays = 200
data, dic, res = fetch_lastfm(min_plays)
model = LightFM(loss='warp')
model.fit(data['matrix'], epochs=30, num_threads=2)
def get_recommendations(model, coo_mtrx, users_ids):
n_items = coo_mtrx.shape[1]
for user in users_ids:
#known positives
user_sha_id = res[int(user)]
i = 1
top_3_artists = []
list_of_liked_artists = dic[user_sha_id]
while i < 4:
top_3_artists.append(list_of_liked_artists[i][0])
i += 1
print 'Artists liked by user %s:' % user
for x in top_3_artists:
print ' - %s' % x
# Artists the model predicts they will like
scores = model.predict(user, np.arange(n_items))