Mixtures not refactored for V4

[larryshamalama]

The following snipet and error uses v4:

import numpy as np
import pandas as pd
import pymc3 as pm

from aesara import tensor as at

old_faithful_df = pd.read_csv(pm.get_data("old_faithful.csv"))
waiting_times = old_faithful_df["waiting"]
waiting_times = ((waiting_times - waiting_times.mean())/waiting_times.std()).values

def stick_breaking(betas):
    '''
    betas is a K-vector of iid draws from a Beta distribution
    '''
    sticks = tt.concatenate(
        [
            [1],
            (1 - betas[:-1])
        ]
    )
    
    return tt.mul(betas, tt.cumprod(sticks))

with pm.Model() as model:
    alpha = pm.Gamma(name="alpha", alpha=1, beta=1)
    v = pm.Beta(name="v", alpha=1, beta=alpha, shape=(K,)) # beta=alpha kinda confusing here
    
    w = pm.Deterministic(name="w", var=stick_breaking(v))
    mu = pm.Normal(name="mu", mu=0, sigma=5)
    sigma = pm.InverseGamma(name="sigma", alpha=1, beta=1, shape=(K,))
    obs = pm.NormalMixture(name="theta", w=w, mu=mu, tau=1/sigma, observed=waiting_times)

yields

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-8-9959a48ef76f> in <module>
      6     mu = pm.Normal(name="mu", mu=0, sigma=5)
      7     sigma = pm.InverseGamma(name="sigma", alpha=1, beta=1, shape=(K,))
----> 8     obs = pm.NormalMixture(name="theta", w=w, mu=mu, tau=1/sigma, observed=waiting_times)

~/anaconda3/envs/pymc3-dev-py39/lib/python3.9/site-packages/pymc3/distributions/distribution.py in __new__(cls, name, rng, dims, initval, observed, total_size, transform, *args, **kwargs)
    205         # Create the RV without specifying initval, because the initval may have a shape
    206         # that only matches after replicating with a size implied by dims (see below).
--> 207         rv_out = cls.dist(*args, rng=rng, initval=None, **kwargs)
    208         ndim_actual = rv_out.ndim
    209         resize_shape = None

TypeError: dist() missing 1 required positional argument: 'dist_params'

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-8-9959a48ef76f> in <module>
      6     mu = pm.Normal(name="mu", mu=0, sigma=5)
      7     sigma = pm.InverseGamma(name="sigma", alpha=1, beta=1, shape=(K,))
----> 8     obs = pm.NormalMixture(name="theta", w=w, mu=mu, tau=1/sigma, observed=waiting_times)

~/anaconda3/envs/pymc3-dev-py39/lib/python3.9/site-packages/pymc3/distributions/distribution.py in __new__(cls, name, rng, dims, initval, observed, total_size, transform, *args, **kwargs)
    205         # Create the RV without specifying initval, because the initval may have a shape
    206         # that only matches after replicating with a size implied by dims (see below).
--> 207         rv_out = cls.dist(*args, rng=rng, initval=None, **kwargs)
    208         ndim_actual = rv_out.ndim
    209         resize_shape = None

TypeError: dist() missing 1 required positional argument: 'dist_params'

However, when using v3.11.2, my model works fine. Is this because we have yet to port Mixture distributions in v4? (Similar to issue 4642?)

[ricardoV94]

Yes it hasn't been refactored yet