def main(alpha, beta, URM_train, URM_test):
ICM = load_icm()
cf = GeneralEnsemble(URM_train, ICM, 50, epsilon=beta)
cf.fit(alpha)
target = pd.read_csv('../../data/target_playlists.csv', index_col=False)
recommended = cf.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])
res_fin.append(res)
i = i + 1
d = {'playlist_id': playlists, 'track_ids': res_fin}
df = pd.DataFrame(data=d, index=None)
df.to_csv("./results/resultsEnsembleAll.csv", index=None)
return evaluate_csv(URM_test, "./results/resultsEnsembleAll.csv")
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 = load_random_urms(0, 999)
icm = load_icm()
return { "URM_complete" : URM, "URM_test" : urm_test, "URM_train" : urm_train, "ICM" : icm}
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 main( write=True):
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 = load_random_urms()
icm = load_icm()
general = GeneralEnsemble(URM, urm_test, icm, recommendation_mode='linComb')
# alpha=alpha,
# beta=beta,
# gamma=gamma,
# epsilon=epsilon,
general.fit(0.001)
target = pd.read_csv('./data/target_playlists.csv', index_col=False)
#evaluator_MF = SequentialEvaluator(URM_test_list=urm_test, cutoff_list=[10])
#print(evaluator_MF.evaluateRecommender(general))
if write is True:
recommended = general.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])
res_fin.append(res)
i = i + 1
d = {'playlist_id': playlists, 'track_ids': res_fin}
df = pd.DataFrame(data=d, index=None)
df.to_csv("./results/TESTING_GENERAL_ENSEMBLE17_nocf.csv", index=None)
del general
return evaluate_csv(urm_test, "./results/TESTING_GENERAL_ENSEMBLE17_nocf.csv")
from Base.Evaluation.Evaluator import SequentialEvaluator
from SLIM_BPR.Cython.SLIM_BPR_Cython import SLIM_BPR_Cython
from refactored_code.utils import load_random_urms, load_icm
if __name__ == '__main__':
urm_train, urm_test = load_random_urms()
icm = load_icm(0.7, 0.3, 0.5)
slim = SLIM_BPR_Cython(urm_train, positive_threshold=0,
symmetric=True).fit(epochs=10,
topK=300,
batch_size=500,
sgd_mode='adagrad',
learning_rate=1e-4)
evaluator_MF = SequentialEvaluator(urm_test, cutoff_list=[10])
print(evaluator_MF.evaluateRecommender(slim))
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])
res_fin.append(res)