def offline():
# INIT
dr = Datareader(mode='offline', only_load=True, verbose=False)
ev = Evaluator(dr)
# LOAD AND COMBINE
eurm_lele = sparse.load_npz(
ROOT_DIR +
'/data/lele/ensembled_CLUSTERARTISTScat4-5-6-8-10_offline.npz')
eurm_std = sparse.load_npz(ROOT_DIR +
'/data/lele/ensembled_SUBCREATIVA_offline.npz')
eurm_ens = combine_two_eurms(eurm_lele,
eurm_std,
cat_first=[4, 5, 6, 8, 10])
# LOAD
# eurm_ens = sparse.load_npz(ROOT_DIR + '/data/ensembled_creativeFIRE_offline.npz')
sim = sparse.load_npz(ROOT_DIR + '/data/sim_offline.npz')
# TOPBOOST
# topb = TopBoost(dr, eurm_ens, sim)
# eurm_ens = topb.boost_eurm(categories=[9], top_k=100, gamma=0.01)
# HOLEBOOST
hb = HoleBoost(similarity=sim,
eurm=eurm_ens,
datareader=dr,
norm=norm_l1_row)
eurm_ens = hb.boost_eurm(categories=[8], k=300, gamma=1)
hb = HoleBoost(similarity=sim,
eurm=eurm_ens,
datareader=dr,
norm=norm_l1_row)
eurm_ens = hb.boost_eurm(categories=[10], k=150, gamma=1)
# TAILBOOST
tb = TailBoost(similarity=sim,
eurm=eurm_ens,
datareader=dr,
norm=norm_l2_row)
eurm_ens = tb.boost_eurm(categories=[9, 7, 6, 5],
last_tracks=[10, 3, 3, 3],
k=[100, 80, 100, 100],
gamma=[0.01, 0.01, 0.01, 0.01])
# ALBUMBOOST
ab = AlbumBoost(dr, eurm_ens)
eurm_ens = ab.boost_eurm(categories=[3, 4, 7, 9],
gamma=2,
top_k=[3, 3, 10, 40])
# MATCHBOOST
# mb = MatchBoost(datareader=dr, eurm=eurm_ens, top_k_alb=5000, top_k_art=10000)
# eurm_ens, pids = mb.boost_eurm(categories='all', k_art=300, k_alb=300, gamma_art=0.1, gamma_alb=0.1)
# EVALUATION
rec_list = eurm_to_recommendation_list(eurm_ens, datareader=dr)
sparse.save_npz('FINAL.npz', eurm_ens)
ev.evaluate(rec_list, name='LELE_boosts.csv')
def online():
# INIT
dr = Datareader(mode='online', only_load=True, verbose=False)
sb = Submitter(dr)
# LOAD AND COMBINE
eurm_lele = sparse.load_npz(
ROOT_DIR +
'/data/jess/ensembled_CLUSTERARTISTS_CREATIVA_cat3-4-5-8-10_online.npz'
)
eurm_std = sparse.load_npz(ROOT_DIR +
'/data/jess/ensembled_creativeFIRE_online.npz')
eurm_ens = combine_two_eurms(eurm_lele,
eurm_std,
cat_first=[3, 4, 5, 8, 10])
# LOAD MATRICES
# eurm_ens = sparse.load_npz(ROOT_DIR + '/data/ensembled_creativeFIRE_online.npz')
sim = sparse.load_npz(ROOT_DIR + '/data/sim_online.npz')
# HOLEBOOST
hb = HoleBoost(similarity=sim,
eurm=eurm_ens,
datareader=dr,
norm=norm_l1_row)
eurm_ens = hb.boost_eurm(categories=[8], k=300, gamma=1)
hb = HoleBoost(similarity=sim,
eurm=eurm_ens,
datareader=dr,
norm=norm_l1_row)
eurm_ens = hb.boost_eurm(categories=[10], k=150, gamma=1)
# TAILBOOST
tb = TailBoost(similarity=sim,
eurm=eurm_ens,
datareader=dr,
norm=norm_l2_row)
eurm_ens = tb.boost_eurm(categories=[9, 7, 6, 5],
last_tracks=[10, 3, 3, 3],
k=[100, 80, 100, 100],
gamma=[0.01, 0.01, 0.01, 0.01])
# ALBUMBOOST
ab = AlbumBoost(dr, eurm_ens)
eurm_ens = ab.boost_eurm(categories=[3, 4, 7, 9],
gamma=2,
top_k=[3, 3, 10, 40])
# MATCHBOOST
# mb = MatchBoost(datareader=dr, eurm=eurm_ens, top_k_alb=5000, top_k_art=10000)
# eurm_ens, pids = mb.boost_eurm(categories='all', k_art=20, k_alb=20, gamma_art=1.0, gamma_alb=1.0)
# SUBMISSION
rec_list = eurm_to_recommendation_list_submission(eurm_ens, datareader=dr)
sb.submit(rec_list, name='ens_30_june_jess+lele_boosts', track='creative')
def submission(boost, eurm_ens, sim, name):
"""
Function to create a submission from a eurm with or without boosts.
:param boost: apply boosts
:param eurm_ens: eurm from ensemble (10k x 2.2M)
:param sim: similarity matrix (tracks x tracks)
:param name: name of the submission
"""
# INIT
dr = Datareader(mode='online', only_load=True, verbose=False)
sb = Submitter(dr)
if boost:
# HOLEBOOST
hb = HoleBoost(similarity=sim,
eurm=eurm_ens,
datareader=dr,
norm=norm_l1_row)
eurm_ens = hb.boost_eurm(categories=[8, 10], k=300, gamma=5)
# TAILBOOST
tb = TailBoost(similarity=sim,
eurm=eurm_ens,
datareader=dr,
norm=norm_l2_row)
eurm_ens = tb.boost_eurm(categories=[9, 7, 6, 5],
last_tracks=[10, 3, 3, 3],
k=[100, 80, 100, 100],
gamma=[0.01, 0.01, 0.01, 0.01])
# ALBUMBOOST
ab = AlbumBoost(dr, eurm_ens)
eurm_ens = ab.boost_eurm(categories=[3, 4, 7, 9],
gamma=2,
top_k=[3, 3, 10, 40])
# SUBMISSION
rec_list = eurm_to_recommendation_list_submission(eurm_ens, datareader=dr)
sb.submit(rec_list, name=name)
def grid_tailboost():
datareader = Datareader(mode='offline', only_load=True, verbose=False)
ev = Evaluator(datareader)
# LOAD AND COMBINE
eurm_lele = sparse.load_npz(
ROOT_DIR +
'/data/lele/ensembled_CLUSTERARTISTScat4-5-6-8-10_offline.npz')
eurm_std = sparse.load_npz(ROOT_DIR +
'/data/lele/ensembled_SUBCREATIVA_offline.npz')
eurm_ens = combine_two_eurms(eurm_lele,
eurm_std,
cat_first=[4, 5, 6, 8, 10])
sim = sparse.load_npz(ROOT_DIR + '/data/sim_offline.npz')
# TAILBOOST
for lt in [2, 3, 5, 6, 10]:
for k in [20, 50, 80, 100, 150]:
for g in [0.005, 0.01, 0.02, 0.05]:
tb = TailBoost(similarity=sim,
eurm=eurm_ens,
datareader=datareader,
norm=norm_l2_row)
eurm_ens = tb.boost_eurm(categories=[9, 7, 6, 5],
last_tracks=[lt, lt, lt, lt],
k=[k, k, k, k],
gamma=[g, g, g, g])
rec_list = eurm_to_recommendation_list(eurm_ens,
datareader=datareader)
print(
'--------------------------------------------------------------------------'
)
print('LT =', lt)
print('K =', k)
print('G =', g)
ev.evaluate(rec_list, name='tb', save=False)
# rp3b = sps.load_npz(ROOT_DIR + "/data/sub/EURM-rp3beta-online.npz")
# knn_c_i_al = sps.load_npz(ROOT_DIR + "/data/sub/KNN CONTENT ITEM-album-top_k=850-sm_type=cosine-shrink=100.npz")
# knn_c_i_ar = sps.load_npz(ROOT_DIR + "/data/sub/KNN CONTENT ITEM-artist-top_k=850-sm_type=cosine-shrink=100.npz")
nlp = sps.load_npz(ROOT_DIR + "/data/eurm_nlp_offline.npz")
# cf_u = sps.load_npz(ROOT_DIR + "/data/sub/eurm_cfu_online.npz")
eurm_ens = sps.load_npz(ROOT_DIR + "/data/ENSEMBLED.npz")
#matrix = [rp3b, knn_c_i_ar, knn_c_i_al, nlp, cf_u]
#eurm_ens = ensembler(matrix, [0.720, 0.113, 0.177, 0.194, 1.0], normalization_type="max")
# HOLEBOOST
hb = HoleBoost(similarity=sim, eurm=eurm_ens, datareader=dr, norm=norm_l1_row)
eurm_ens = hb.boost_eurm(categories=[8, 10], k=300, gamma=5)
# NINEBOOST
nb = TailBoost(similarity=sim, eurm=eurm_ens, datareader=dr, norm=norm_l2_row)
eurm_ens = nb.boost_eurm(last_tracks=10, k=100, gamma=0.01)
rec_list = eurm_to_recommendation_list(eurm_ens)
rec_list_nlp = eurm_to_recommendation_list(nlp)
indices = dr.get_test_pids_indices(cat=1)
for i in indices:
rec_list[i] = rec_list_nlp[i]
# EVALUATION
ev.evaluate(rec_list, name='ens_with_cfu_nineboosted', show_plot=False)
####### POSTPROCESSING #################################################################
# COMBINE
eurm_ens = combine_two_eurms(clustered_approach_online,
ensembled,
cat_first=[3, 4, 5, 8, 10])
sim = generate_similarity('online')
# HOLEBOOST
hb = HoleBoost(similarity=sim, eurm=eurm_ens, datareader=dr, norm=norm_l1_row)
eurm_ens = hb.boost_eurm(categories=[8], k=300, gamma=1)
hb = HoleBoost(similarity=sim, eurm=eurm_ens, datareader=dr, norm=norm_l1_row)
eurm_ens = hb.boost_eurm(categories=[10], k=150, gamma=1)
# TAILBOOST
tb = TailBoost(similarity=sim, eurm=eurm_ens, datareader=dr, norm=norm_l2_row)
eurm_ens = tb.boost_eurm(categories=[9, 7, 6, 5],
last_tracks=[10, 3, 3, 3],
k=[100, 80, 100, 100],
gamma=[0.01, 0.01, 0.01, 0.01])
# ALBUMBOOST
ab = AlbumBoost(dr, eurm_ens)
eurm_ens = ab.boost_eurm(categories=[3, 4, 7, 9],
gamma=2,
top_k=[3, 3, 10, 40])
# SUBMISSION
rec_list = eurm_to_recommendation_list_submission(eurm_ens, datareader=dr)
sb.submit(rec_list, name='creative_track', track='creative')