Sanity check: How fast should sampling be?

I’m trying to understand what’s normal. This model hovers around 2 seconds per draw and which results in expected 1-3 hours ETA on the progress bar.

How long does sampling usually take for simple models on datasets of this size (1 million observations)?

import pymc3
import numpy as np
import pandas as pd

def standardize(x):
    return (x - x.mean()) / x.std()

if __name__ == "__main__":

    diamonds = pd.read_csv(
    diamonds = diamonds.sample(n=1_000_000, replace=True)
    diamonds = diamonds.assign(price_std=lambda x: standardize(x["price"]))

    model = pymc3.glm.GLM.from_formula(
        "price_std ~ C(cut) + C(color) + C(clarity) + carat + depth + table + x + z",
        priors={"Intercept": pymc3.Normal.dist(), "Regressor": pymc3.Normal.dist(),},
    fit = pymc3.sample(init="adapt_diag", model=model,)

I think this is a reasonable sampling speed. I ran your script on my own computer and also got a projected runtime of 2 hours after finishing the burn-in phase.

I also see about 1.5 expected total hours after 25% done so far.

A few points:

  • Within the formula you can have standardize(price) directly without manually calculating it
  • You could also try the log of price, np.log(price) within the formula
  • I dont know what x, y, z represent for diamonds. You’ve included x and z without y, is that on purpose?