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)
urm = dr.get_urm()
pid = dr.get_test_pids()
#Fitting data
rec.fit(urm, pid)
#Computing similarity/model
rec.compute_model(top_k=knn,
sm_type=sm.TVERSKY,
shrink=200,
alpha=0.1,
beta=1,
binary=True,
verbose=True)
#Computing ratings
rec.compute_rating(top_k=topk, verbose=True, small=True)
#submission and saving
sps.save_npz(complete_name + ".npz", rec.eurm)
sb = Submitter(dr)
sb.submit(recommendation_list=eurm_to_recommendation_list_submission(
rec.eurm),
name=complete_name,
track="main",
verify=True,
gzipped=True)
else:
print("invalid mode.")
user_profile_batch = slim.URM_train[pids_converted]
eurm = dot_product(user_profile_batch, slim.W_sparse, k=500).tocsr()
recommendation_list = eurm_to_recommendation_list(eurm)
# calculating eurm, evaluation, save
user_profile_batch = slim.URM_train[pids_converted]
eurm = dot_product(user_profile_batch, slim.W_sparse, k=500).tocsr()
recommendation_list = eurm_to_recommendation_list(eurm)
sps.save_npz(ROOT_DIR + "/results/" + complete_name + ".npz",
eurm,
compressed=False)
sb = Submitter(dr)
sb.submit(
recommendation_list=eurm_to_recommendation_list_submission(eurm),
name=name,
track="main",
verify=True,
gzipped=False)
else:
print("invalid mode.")
# ev.evaluate(recommendation_list=recommendation_list,
# name="slim ")
# except Exception as e:
# bot.error("Exception "+str(e))
#
# bot.end()