Slow sampling speed with newer versions of PyMC

[Dekermanjian]

@jessegrabowski

import pymc as pm
import pytensor.tensor as pt
import pandas as pd
import numpy as np

df = pd.read_csv('https://raw.githubusercontent.com/dustinstansbury/statistical-rethinking-2023/main/data/Primates301.csv', delimiter=';')
distance_matrix = pd.read_csv('https://raw.githubusercontent.com/dustinstansbury/statistical-rethinking-2023/main/data/Primates301_distance_matrix.csv', delimiter=';')
distance_matrix.columns = distance_matrix.columns.map(int)

df.dropna(subset='brain', inplace=True)
distance_matrix = distance_matrix.loc[df.index, :].loc[:, df.index].copy()

D_mat = (distance_matrix / distance_matrix.max())

def log_standardize(x):
    x = np.log(x)
    return (x - x.mean()) / x.std()

coords = {'primate': df['name'].values}
with pm.Model(coords=coords) as naive_imputation_model:    
    G_obs = log_standardize(df.group_size.values)
    M_obs = log_standardize(df.body.values)
    B_obs = log_standardize(df.brain.values)
    
    # Priors
    alpha = pm.Normal("alpha", 0, 1)
    beta_G = pm.Normal("beta_G", 0, 0.5)
    beta_M = pm.Normal("beta_M", 0, 0.5)

    # Phylogenetic distance covariance prior, L1-kernel function
    eta_squared = pm.TruncatedNormal("eta_squared", 1, .25, lower=.001)
    rho = pm.TruncatedNormal("rho", 3, .25, lower=.001)
    K = pm.Deterministic('K', eta_squared * pt.exp(-rho * D_mat))

    # Naive imputation for G, M
    G = pm.Normal("G", 0, 1, observed=G_obs, dims='primate')
    M = pm.Normal("M", 0, 1, observed=M_obs, dims='primate')

    # Likelihood for B
    mu = alpha + beta_G * G + beta_M * M
    pm.MvNormal("B", mu=mu, cov=K, observed=B_obs)

    naive_imputation_inference = pm.sample()

I think we missed something when we pulled out the model from the notebook. I ran the pasteable model from above to match what we were doing with pixi and I am now getting the same divergences using the environment that has the downgraded openblas + sqlite. It is running a little bit faster, though.