How would I create a group level constraint on the sum of individual predictions per group?

Hi how’s it going?

I have a project I’m working on where I’m trying to model individual predictions, but they must fall within the bounds of a group total.

Here’s an example:

data = pd.DataFrame({'group':['group_1']*6+['group_2']*6,'user':['bob','phil','anthony']*2+['ben','nick','henry']*2,'var1':np.random.random_sample(12),'var2':np.random.random_sample(12),'y':[np.random.randint(5,20) for val in range(12)]})

Screenshot from 2020-07-20 16-06-29

Now say I make predictions on new data… I want to add a constraint that the sum of the predictions per group of the new data adds to the sum of the groups of the input data.

Screenshot from 2020-07-20 16-08-44

Here is my model for regression and predictions…

y = data['y']

with pm.Model() as m_5_1:
    a = pm.Normal("a", 10,5)
    bA = pm.Normal("bA",10,5)
    bB = pm.Normal("bB",10,5)
    sigma = pm.Uniform("sigma", 0,4)
    mu = pm.Deterministic("mu", a + bA * data['var1']) + bB * data['var2']

    result = pm.Normal(
        "result",mu=mu, sigma=sigma, observed=y.values
    trace = pm.sample()
newdata = pd.read_csv('newdata.csv')
number_of_rows_in_newdata = newdata.shape[0]

new_data_0 = xr.DataArray(

new_data_1 = xr.DataArray(

pred_mean = (
    trace["a"][:number_of_rows_in_newdata] +
    trace["bA"][:number_of_rows_in_newdata] * new_data_0 +
    trace["bB"][:number_of_rows_in_newdata] * new_data_1


predictions = xr.apply_ufunc(lambda mu, sd: rng.normal(mu, sd), pred_mean, trace["sigma"][:number_of_rows_in_newdata])

My question is how can I set some sort of constraint so that the model takes into account the group_1 and group_2 sum and makes sure that the predictions per group add to those numbers (63,74).

I hope this was a clear enough example, if not please let me know.

Thanks for your help.