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')