def train(data_dir):
# data_dir = '/home/paula/Descargas/tagclouds-api/cstagclouds/extractkeywords/training/*'
x, y, words, qids, rake, groups = (np.array(l)
for l in load_data(data_dir))
model = RankSVM()
gss = GroupShuffleSplit(n_splits=1,
test_size=0.2,
random_state=randint(0, 30))
for train_index, test_index in gss.split(x, y, groups=groups):
x_train, x_test = x[train_index], x[test_index]
y_train, y_test = y[train_index], y[test_index]
q_train, q_test = qids[train_index], qids[test_index]
w_train, w_test = words[train_index], words[test_index]
rake_train, rake_test = rake[train_index], rake[test_index]
model.fit(x_train, y_train, q_train)
pred_test = model.predict(x_test)
a = list(zip(q_test, pred_test, w_test))
a2 = sorted(a, key=lambda x: (x[0], x[1]))
for word in a2:
print(word[0], word[1], word[2])
metric = pyltr.metrics.NDCG(len(x_test))
print('Random ranking:', metric.calc_mean_random(q_test, y_test))
print('Our model:', metric.calc_mean(q_test, y_test, pred_test))
print('Rake:', metric.calc_mean(q_test, y_test, rake_test))
def cross_val(data_dir, model_dir, save_model):
# data_dir = '/home/paula/Descargas/tagclouds-api/cstagclouds/extractkeywords/training/*'
# model_dir = '/home/paula/Descargas/tagclouds-api/cstagclouds/learningtorank/models/unfiltered/'
x, y, words, qids, rake, groups = (np.array(l)
for l in load_data(data_dir))
logo = LeaveOneGroupOut()
for train_index, test_index in logo.split(x, y, groups):
x_train, x_test = x[train_index], x[test_index]
y_train, y_test = y[train_index], y[test_index]
q_train, q_test = qids[train_index], qids[test_index]
rake_train, rake_test = rake[train_index], rake[test_index]
if save_model:
model = RankSVM(max_iter=5000)
model.fit(x_train, y_train, q_train)
pickle.dump(
model,
open(
'%sRankSVM/rankSVM_model_%s.sav' %
(model_dir, q_test[0].replace('.txt', '')), 'wb'))
else:
model = pickle.load(
open(
'%sRankSVM/rankSVM_model_%s.sav' %
(model_dir, q_test[0].replace('.txt', '')), 'rb'))
pred_test = model.predict(x_test)
metric = pyltr.metrics.NDCG(len(x_test))
print('%s' % (metric.calc_mean(q_test, y_test, pred_test)))