def load_data():
URM_text = np.loadtxt('../data/train.csv', delimiter=',', dtype=int, skiprows=1)
user_list, item_list = zip(*URM_text)
rating_list = np.ones(len(user_list))
URM = sps.csr_matrix((rating_list, (user_list, item_list)))
urm_train, urm_test = train_test_holdout(URM)
icm = load_icm()
return { "URM_complete" : URM, "URM_test" : urm_test, "URM_train" : urm_train, "ICM" : icm}
def before():
model = LightFM(loss='warp')
train, test = train_test_holdout(load_urm())
model.fit(train, epochs=3, num_threads=2)
pid_array = np.arange(test.shape[1], dtype=np.int32)
a = model.predict(7, pid_array)
print(a)
test_auc = auc_score(model, test, train_interactions=train, num_threads=2).mean()
print(test_auc)
def load_data():
URM_text = np.loadtxt('../data/train.csv', delimiter=',', dtype=int, skiprows=1)
user_list, item_list = zip(*URM_text)
rating_list = np.ones(len(user_list))
URM = sps.csr_matrix((rating_list, (user_list, item_list)))
urm_train, urm_test = train_test_holdout(urm_all=URM)
topPop = create_top_pop_list()
urm_train.tocoo()
j = 0
for i in topPop:
# factor = (len(topPop)-j)**POPULARITY_SCALING_EXP
factor = (j)**POPULARITY_SCALING_EXP
urm_train[:,i].multiply(1/(1+factor))
j += 1
urm_train.tocsr()
icm = load_icm()
return { "URM_complete" : URM, "URM_test" : urm_test, "URM_train" : urm_train, "ICM" : icm}
def before():
model = LightFM(loss='warp')
train, test = train_test_holdout(load_urm())
model.fit(train, epochs=3, num_threads=2)
pid_array = np.arange(test.shape[1], dtype=np.int32)
a = model.predict(7, pid_array)
print(a)
test_auc = auc_score(model, test, train_interactions=train, num_threads=2).mean()
print(test_auc)
if __name__ == '__main__':
train, test = train_test_holdout(load_urm())
recsys = LightFM_Recommender(train, load_icm(), 200)
target = pd.read_csv('./data/target_playlists.csv', index_col=False)
recsys.fit(epochs=100)
recommended = recsys.recommendALL(target.values)
playlists = recommended[:, 0]
recommended = np.delete(recommended, 0, 1)
i = 0
res_fin = []
for j in recommended:
res = ''
for k in range(0, len(j)):
res = res + '{0} '.format(j[k])