from utils.definitions import *
# Datareader
dr = Datareader(mode='online', only_load=True)
#ev = Evaluator(dr)
# Dataframe with interactions
df_train = dr.get_df_train_interactions()
df_test = dr.get_df_test_interactions()
df = pd.concat([df_train, df_test], axis=0, join='outer')
playlists = df['pid'].as_matrix()
tracks = df['tid'].as_matrix()
dictionary = dr.get_track_to_artist_dict()
pids = list(dr.get_train_pids()) + list(dr.get_test_pids())
# URM
urm = dr.get_urm()
urm = urm[pids]
print(urm.shape)
print('artists...')
artists = [dictionary[t] for t in tracks]
print('ucm...')
ucm = sparse.csr_matrix((np.ones(len(playlists)), (playlists, artists)),
shape=(1049361, len(dr.get_artists())))
ucm = ucm.tocsr()
ucm = ucm[pids]
print(ucm.shape)
if mode == "online":
nlp = NLP(dr,
stopwords=[],
norm=norm,
work=work,
split=split,
date=date,
skip_words=skip_words,
porter=porter,
porter2=porter2,
lanca=lanca,
lanca2=lanca2)
pids = list(dr.get_train_pids()) + list(dr.get_test_pids())
test_playlists = dr.get_test_pids()
ucm = nlp.get_UCM(data1=data1)
dr_old = Datareader(mode='online', only_load='True', type='old')
train = ucm[:1000000]
test = ucm[1000000:]
test_indices = []
for cat in range(1, 11):
indices = dr_old.get_test_pids_indices(cat=cat)
test_indices.extend(indices)
new_indices = list(
