b = sps.load_npz(dir + matrix_dict["cbf_item_artist"] + "-cat" + str(cat) +
".npz")
c = sps.load_npz(dir + matrix_dict["nlp"] + "-cat" + str(cat) + ".npz")
d = sps.load_npz(dir + matrix_dict["rp3beta"] + "-cat" + str(cat) + ".npz")
e = sps.load_npz(dir + matrix_dict["cf_user"] + "-cat" + str(cat) + ".npz")
f = sps.load_npz(dir + matrix_dict["slim"] + "-cat" + str(cat) + ".npz")
g = sps.load_npz(dir + matrix_dict["cbf_user_artist"] + "-cat" + str(cat) +
".npz")
h = sps.load_npz(dir + matrix_dict["cbf_user_album"] + "-cat" + str(cat) +
".npz")
matrix.append([a, b, c, d, e, f, g, h])
rprec = []
for i in range(0, 10):
print("[ Ensembling cat", i + 1, "]")
rprec.append(ensembler(matrix[i], w[i], normalization_type="max"))
res = sps.vstack(rprec).tocsr()
import time
name = "ensemble-" + mode + "-data-" + time.strftime(
"%x") + "-" + time.strftime("%X")
name = name.replace("/", "_")
sps.save_npz("results/" + name + ".npz", res)
print("[ Initizalizing Datereader ]")
dr = Datareader(verbose=False, mode=mode, only_load="False")
res = eurm_to_recommendation_list(res, datareader=dr)
if mode == "offline":
print("[ Initizalizing Evaluator ]")
e = sps.load_npz("../offline/offline-cfuser-cat" + str(cat) + ".npz")
f = sps.load_npz("../offline/slim_bpr_completo_test1-cat" + str(cat) + ".npz")
g = sps.load_npz("../offline/eurm_cbfu_artists_offline-cat" + str(cat) +
".npz")
matrix = [a, b, c, d, e, f, g]
a = float(sys.argv[1])
b = float(sys.argv[2])
c = float(sys.argv[3])
d = float(sys.argv[4])
e = float(sys.argv[5])
f = float(sys.argv[6])
g = float(sys.argv[7])
res = ensembler(matrix, [a, b, c, d, e, f, g], normalization_type="max")
ev = Evaluator(dr)
ret = [
-ev.evaluate_single_metric(eurm_to_recommendation_list(res, cat=cat),
cat=cat,
name="ens" + str(cat),
metric='prec',
level='track')
]
if os.path.isfile("best.npy"):
best = np.load("best.npy")
if ret[0] < best[-1].astype(np.float):
b = sys.argv[1:]
b.append(ret[0])
print(arg)
best = list(arg[1:].astype(np.float))
w.append(best)
for i in tqdm(range(1,11)):
if mode == "offline":
CBF_ALBUM = sps.load_npz(mode+"/offline-cbf_item_album-cat"+str(i)+".npz")
CBF_ARTISTA = sps.load_npz(mode+"/offline-cbf_item_artist-cat"+str(i)+".npz")
NLP = norm_max_row(sps.load_npz(mode + "/nlp_eurm_offline_bm25-cat" + str(1) + ".npz"))
RP3BETA = sps.load_npz(mode+"/offline-rp3beta-cat"+str(i)+".npz")
CF_USER = sps.load_npz(mode + "/cfu_eurm-cat"+str(i)+".npz")
SLIM = sps.load_npz(mode +"/slim_bpr_completo_test1-cat"+str(i)+".npz")
CBF_USER_ARTIST = sps.load_npz(mode +"/eurm_cbfu_artists_offline-cat"+str(i)+".npz")
matrix = [CBF_ALBUM, CBF_ARTISTA, NLP, RP3BETA, CF_USER, SLIM, CBF_USER_ARTIST]
we = w[i-1]
res.append(ensembler(matrix, we, normalization_type="lele"))
ret = sps.vstack(res).tocsr()
if mode == "offline":
ev.evaluate(eurm_to_recommendation_list(ret), "best_test", verbose=True)
# sps.save_npz("ensemble_per_cat_"+mode+"_new_data_28_maggio.npz", ret)
if mode == "online":
sb = Submitter(dr)
sb.submit(recommendation_list=eurm_to_recommendation_list_submission(ret), name="best_test", track="main", verify=True, gzipped=False)