How to use mutli-column functions in Patsy which return categorical output?

I am dealing with a situation where each item can have 1 or 2 labels both of which come from the same set. In this case I only want to have 1 set of categorical variables which encode if the values of each level are present or absent with respect to the reference. I also want to inversely weight the values of these categorical variables based on how many labels are present.
E.g. Let my categories be {A, B, C}

data = pd.DataFrame({"col1": ["A", "A", "B"], "col2": ["O", "B", "C"], "num_vals": [1,2,2]})

def inverse_val(x):
    return 1.0/x

# Using categorical coding will not give the correct output
X = patsy.dmatrix("(C(col1) + C(col2)):inverse_val(num_vals)", data, return_type="dataframe")

I tried to solve this issue using the following code:

def multival(*x, **kwargs):
  #raise Exception("Not Implemented")
  levels, reference = kwargs.get("levels", None), kwargs.get("reference", None)
  weights = kwargs.get("weights", None)
  if len(x[0].shape) != 1:
    raise Exception("Mismatching Shapes. All arrays should be 1d and should have the same shape")
  for k in x:
    if k.shape != x[0].shape:
      raise Exception("Mismatching Shapes. All arrays should be 1d and should have the same shape")
  if levels is None:
    levels = np.sort(np.unique(np.hstack(x))) # Sort the unique values and then use this ordering as levels
  if reference is None:
    reference = levels[0]
  #print "Levels: %s, reference: %s" % (levels, reference)
  levels = levels[levels != reference] # Remove reference from levels
  level_len = len(levels)
  #print x[0].shape[0], level_len
  out = np.zeros((x[0].shape[0], level_len))
  for i, v in enumerate(levels):
    # print i, v
    for col in x:
      out[np.where(np.array(col) == v), i] = 1
  #print "Created matrix with shape: ", out.shape
  colnames = ["T.%s" % k for k in levels]
  if weights is not None:
    weights = weights.values
    return pd.DataFrame(out, columns=colnames).divide(weights, axis=0)

  return pd.DataFrame(out, columns=colnames)

X = patsy.dmatrix("multival(col1, col2, weights=num_vals)", data, return_type="dataframe")

There is no way of identifying the column names generated by patsy:

E.g. The names are like this:

multival(col1, col2, weights=num_vals, reference='O')[0]
multival(col1, col2, weights=num_vals, reference='O')[1]
multival(col1, col2, weights=num_vals, reference='O')[2]

I would much rather prefer column names like the following:

multival(col1, col2, weights=num_vals, reference='O')[T.0]
multival(col1, col2, weights=num_vals, reference='O')[T.1]
multival(col1, col2, weights=num_vals, reference='O')[T.2]

OR if I am passing the levels variable:

multival(col1, col2, weights=num_vals, reference='O')[T.A]
multival(col1, col2, weights=num_vals, reference='O')[T.B]
multival(col1, col2, weights=num_vals, reference='O')[T.C]

Is there a way to achieve this in patsy ?

Duplicate of statsmodels/statsmodels#2843

Start with the patsy.dmatrix call and the custom multival function shown in the issue, then review the linked duplicate in statsmodels/statsmodels#2843. The work is complete when custom multi-column categorical output exposes meaningful level-based column names instead of numeric indices.