def test_model_class(c):
global test_pred
global svmC
global c0
os.system('tar -zxf dir/tmp_%d_%d.tar.gz ' % (c, c0))
clf = joblib.load('dir/tmp_%d_%d/model.pkl' % (c, c0))
(test_pred[:, c], temp, bags) = testSVM(N, 'dir/tfile_%d' % c, clf)
def test_model_class(c):
global test_pred
global test_sent_pred
global temp_sent
global svmC
global c0
global slbld_list
os.system('tar -zxf dir/tmp_%d_%d.tar.gz ' % (c, c0))
clf = joblib.load('dir/tmp_%d_%d/model.pkl' % (c, c0))
(test_pred[:, c], temp_sent[:, c], bags) = testSVM(N, 'dir/tfile_%d' % c,
clf)
test_sent_pred[:, c] = temp_sent[slbld_list, c].copy()
def train_model_class(c):
global svmC
global c0
global valid_pred
(bag_pred_f, ypred_f, clf, bags) = MISVM(N, 'dir/trfile_' + str(c), svmC)
#save model
os.system('mkdir -p dir/tmp_%d_%d' % (c, c0))
joblib.dump(clf, 'dir/tmp_%d_%d/model.pkl' % (c, c0))
# validation
(bag_pred_f, ypred_f, bags) = testSVM(N, 'dir/vfile_%d' % c, clf)
valid_pred[:, c] = np.round(bag_pred_f)
os.system('tar -zcf dir/tmp_%d_%d.tar.gz dir/tmp_%d_%d' % (c, c0, c, c0))
os.system('rm -r dir/tmp_%d_%d' % (c, c0))
def train_model_class(c):
global svmC
global c0
global valid_pred
if os.path.isfile('dir/tmp_%d_%d.tar.gz' % (c, c0)):
os.system('tar -zxf dir/tmp_%d_%d.tar.gz ' % (c, c0))
clf = joblib.load('dir/tmp_%d_%d/model.pkl' % (c, c0))
else:
(bag_pred_f, ypred_f, clf, bags) = miSVM(N, 'dir/trfile_' + str(c),
svmC)
#save model
os.system('mkdir -p dir/tmp_%d_%d' % (c, c0))
joblib.dump(clf, 'dir/tmp_%d_%d/model.pkl' % (c, c0))
# validation
(bag_pred_f, ypred_f, bags) = testSVM(N, 'dir/vfile_%d' % c, clf)
valid_pred[:, c] = np.round(bag_pred_f)
if not os.path.isfile('dir/tmp_%d_%d.tar.gz' % (c, c0)):
os.system('tar -zcf dir/tmp_%d_%d.tar.gz dir/tmp_%d_%d' %
(c, c0, c, c0))
os.system('rm -r dir/tmp_%d_%d' % (c, c0))