def feature_gen(M, labels, test_or_train, interval_start, interval_end,
label_interval_start, label_interval_end, row_M_start,
row_M_end):
return (append_cols(
M, M['relevent_feature_SUM'] *
2 if test_or_train == 'test' else M['relevent_feature_SUM'] *
3, 'mult'), labels)
def test_append_cols(self):
M = np.array([(1, 'a'), (2, 'b')], dtype=[('int', int), ('str', 'O')])
col1 = np.array([1.0, 2.0])
col2 = np.array([datetime(2015, 12, 12), datetime(2015, 12, 13)],
dtype='M8[us]')
ctrl = np.array(
[(1, 'a', 1.0), (2, 'b', 2.0)],
dtype=[('int', int), ('str', 'O'), ('float', float)])
res = utils.append_cols(M, col1, 'float')
self.assertTrue(np.array_equal(ctrl, res))
ctrl = np.array(
[(1, 'a', 1.0, datetime(2015, 12, 12)),
(2, 'b', 2.0, datetime(2015, 12, 13))],
dtype=[('int', int), ('str', 'O'), ('float', float),
('dt', 'M8[us]')])
res = utils.append_cols(M, [col1, col2], ['float', 'dt'])
self.assertTrue(np.array_equal(ctrl, res))
def feature_gen(M, test_or_train, interval_start, interval_end,
row_M_start, row_M_end):
return append_cols(M, M['relevent_feature'] * 2 if test_or_train == 'test'
else M['relevent_feature'] * 3, 'mult')
