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 write_results(id,
urm_test,
dict,
data_frame,
component_attributes,
encoder=None):
if encoder is None:
encoder = json.JSONEncoder()
filename = "../results/P3_ALPHA/result_{0}.csv".format(id)
data_frame.to_csv(filename, index=None)
eval = evaluate_csv(urm_test, filename)
eval_json = encoder.encode(eval)
param_json = encoder.encode(dict)
param_file = open('../results/P3_ALPHA/params_{0}.json'.format(id),
mode='w')
param_file.write(param_json)
param_file.close()
param_attributes_json = encoder.encode(component_attributes)
param_attributes_file = open(
'../results/P3_ALPHA/param_attributes_{0}.json'.format(id), mode='w')
param_attributes_file.write(param_attributes_json)
param_attributes_file.close()
eval_file = open('../results/P3_ALPHA/eval_{0}.json'.format(id), mode='w')
eval_file.write(eval_json)
eval_file.close()
return eval
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")
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)
i = i + 1
d = {'playlist_id': playlists, 'track_ids': res_fin}
df = pd.DataFrame(data=d, index=None)
df.to_csv("./results/lightfm_test.csv", index=None)
evaluate_csv(test, "./results/lightfm_test.csv")