def icm():
datareader = Datareader(mode='offline', only_load=True)
evaluator = Evaluator(datareader)
print('NLP...')
stopwords = STOP_WORDS
token_weights = np.array(TOKEN_WEIGHTS)
test_playlists = datareader.get_test_pids()
nlp = NLP(datareader=datareader, stopwords=[], mode='tracks')
print('Getting ucm and icm...')
icm = nlp.get_icm()
icm = bm25_row(icm)
print('Computing similarity...')
start = time.time()
# Compute similarity
similarity = tversky_similarity(icm, shrink=200, alpha=0.1, beta=1)
similarity = similarity.tocsr()
print(time.time() - start)
urm = datareader.get_urm()
print('Computing eurm...')
start = time.time()
# Compute eurm
eurm_nlp = dot_product(urm[test_playlists, :], similarity, k=500)
eurm_nlp = eurm_nlp.tocsr()
# sparse.save_npz(ROOT_DIR + '/data/eurm_nlp_weighted_offline.npz', eurm_nlp)
evaluator.evaluate(eurm_to_recommendation_list(eurm_nlp),
name='nlp_enriched')
def online():
datareader = Datareader(mode='online', only_load=True)
print('NLP...')
stopwords = STOP_WORDS
token_weights = np.array(TOKEN_WEIGHTS)
nlp = NLP(datareader, stopwords=[])
ucm = nlp.get_ucm()
#ucm = bm25_row(ucm)
#inplace_csr_column_scale(ucm, token_weights)
urm = datareader.get_urm_shrinked()[0]
print('Computing similarity...')
start = time.time()
# Compute similarity
similarity = tversky_similarity(ucm, shrink=200, alpha=0.1, beta=1)
similarity = similarity.tocsr()
print(time.time() - start)
print('Computing eurm...')
start = time.time()
# Compute eurm
eurm_nlp = dot_product(similarity, urm, k=500)
eurm_nlp = eurm_nlp.tocsr()
print(eurm_nlp.shape)
eurm_nlp = eurm_nlp[-10000:, :]
sparse.save_npz(ROOT_DIR + '/data/eurm_nlp_no_stop_online.npz', eurm_nlp)
def fitnessFunction(self, individual):
# Convert list into a numpy array
individual = np.array(individual)
# Make a copy of the UCM and filter it for each column
if self.verbose:
print('Filtering UCM...')
start = time.time()
UCM_filtered = self.UCM.copy()
UCM_filtered = UCM_filtered.astype(np.float64)
inplace_csr_column_scale(UCM_filtered, individual)
if self.verbose:
print('UCM filtered in', time.time() - start, 'sec')
# Compute similarity
if self.verbose:
print('Computing similarity...')
start = time.time()
similarity = tversky_similarity(UCM_filtered, shrink=200, alpha=0.1,
beta=1, target_items=self.test_playlists_indices,
binary=False)
similarity = similarity.tocsr()
if self.verbose:
print('Similarity computed in', time.time() - start, 'sec')
# Compute eurm
if self.verbose:
print('Computing eurm...')
start = time.time()
eurm = dot_product(similarity, self.URM_train, k=500)
if self.verbose:
print('eurm computed in', time.time() - start, 'sec')
print('Converting eurm in csr...')
start = time.time()
eurm = eurm.tocsr()
eurm = eurm[self.test_playlists_indices, :]
if self.verbose:
print('eurm converted in', time.time() - start, 'sec')
# Evaluate
rec_list = eurm_to_recommendation_list(eurm)
print('current', self.current)
score_cat_1 = self.evaluator.evaluate_single_metric(rec_list, name='Genetic', metric='prec',
level='track', cat=1, verbose=False)
score_cat_2 = self.evaluator.evaluate_single_metric(rec_list, name='Genetic', metric='prec',
level='track', cat=2, verbose=False)
score = (score_cat_1 + score_cat_2) / 2
self.current += 1
if self.verbose:
print(score)
print("Numfeatures {}".format(np.sum(individual)))
print('\n')
return score,
def get_similarity_from_icm(self):
self.get_icm()
if self.verbose:
print('Computing similarity from icm...')
self.similarity_icm = tversky_similarity(self.icm,
shrink=200,
alpha=0.1,
beta=1)
self.similarity_icm = self.similarity_icm.tocsr()
return self.similarity_icm
def prova():
dr = Datareader(mode='offline', only_load=True)
print(dr.get_artist_to_tracks_dict())
exit()
dr = Datareader(mode='offline', only_load=True, verbose=False)
test_playlists = dr.get_test_pids()
stopwords = STOP_WORDS
token_weights = np.array(TOKEN_WEIGHTS)
nlp = NLP(mode='playlists', datareader=dr, stopwords=STOP_WORDS)
s = nlp.get_ucm()
print(s.shape)
evaluator = Evaluator(dr)
ucm = nlp.get_ucm()
sim = sparse.load_npz(ROOT_DIR + '/data/cf_user_similarity.npz')
print('Computing dot...')
ucm = dot_product(sim, ucm, k=200)
print('NNZ', ucm.nnz)
exit()
urm = dr.get_urm()
# ucm = ucm.astype(np.float64)
# inplace_csr_column_scale(ucm, token_weights)
print('Computing similarity...')
start = time.time()
# Compute similarity
similarity = tversky_similarity(ucm, shrink=200, alpha=0.1, beta=1)
similarity = similarity.tocsr()
print(time.time() - start)
print('Computing eurm...')
start = time.time()
# Compute eurm
eurm_nlp = dot_product(similarity, urm, k=500)
eurm_nlp = eurm_nlp.tocsr()
eurm_nlp = eurm_nlp[test_playlists, :]
#sparse.save_npz(ROOT_DIR + '/data/eurm_nlp_weighted_offline.npz', eurm_nlp)
evaluator.evaluate(eurm_to_recommendation_list(eurm_nlp),
name='nlp_enriched')
nlp = NLP2(dr, stopwords=[], norm=norm,work=work,split=split,date=date, skip_words=skip_words,
porter=porter,porter2=porter2,lanca=lanca,lanca2=lanca2)
# new_titles, occ_full, occ_single = nlp.fit( verbose=False, workout=True, normalize=True, date=True, lancaster=False,
# porter=False, underscore=True, double_fit=False)
ucm = nlp.get_UCM(data1=data1)
urm = dr.get_urm()
test_playlists = dr.get_test_pids()
print('ucm', ucm.shape)
print('Computing similarity...')
start = time.time()
# Compute similarity
ucm= bm25_row(ucm)
similarity = tversky_similarity(ucm, binary=False, shrink=1, alpha=0.1, beta=1)
similarity = similarity.tocsr()
print(time.time() - start)
print('Computing eurm...')
start = time.time()
# Compute eurm
eurm = dot_product(similarity, urm, k=500)
eurm = eurm.tocsr()
eurm = eurm[test_playlists, :]
print('eurm', eurm.shape)
print(time.time() - start)
# Evaluating
