Blackbox version of ordered logit

11

I fixed it myself with a code rewrite, borrowing some from the docs of OrderedLogistic. The following samples just fine, though I did notice in my obsessive checking that univariate_ordered didn’t actually order the cutpoints, but multivariate_ordered did. The following intercept-only model seems to work fine.

c = {'K': range(d['y'].nunique() - 1),
     'Kt': range(d['y'].nunique()),
     'N': range(len(d))
    }

with pm.Model(coords=c) as ordinal:
    
    # Priors for cutpoints
    cut = pm.Normal('cut',
                    mu=np.linspace(-3, 3, num=len(c['K'])),
                    sigma=1,
                    transform=pm.distributions.transforms.multivariate_ordered,
                    dims='K')
    
    # Prior for intercept
    β0 = pm.Normal('β0', mu=0, sigma=1)
    
    # Linear predictor
    η = pm.Deterministic('η', pm.invlogit(cut - (β0 * d['intercept'].to_numpy()[:, None])), dims=('N', 'K'))
    
    # Get probabilities now via subtraction
    pa = pt.concatenate(
        [
            pt.zeros_like(η[:, [0]]),
            η,
            pt.ones_like(η[:, [0]])
        ],
        axis=1)
    
    p = pm.Deterministic('p', 
                         pa[..., 1:] - pa[..., :-1], 
                         dims=('N', 'Kt'))
    
    # Likelihood
    pm.Categorical('y', p=p, observed=d['y'].to_numpy())

Ordinal regression is destined to be my nemesis.

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