import arviz as az import pymc as pm # 0.4.3 import pymc_bart as pmb # 0.2.1 import pandas as pd df_train_features = # select data here, about 850k samples df_train_labels = # select labels here, highly unbalanced with about 98% zeros with pm.Model() as model_bart: mu = pmb.BART("mu", df_train_features, df_train_labels, m=200) theta = pm.Deterministic("theta", pm.math.invprobit(mu)) y = pm.Bernoulli("y", p=theta, observed=df_train_labels) idata = pm.sample(random_seed=0, tune=200)
When I run the above code, the output of Jupyter notebook shows the progress like below:
Multiprocess sampling (4 chains in 4 jobs) PGBART: [mu] <progress bar> 100.00% [4800/4800 <time> Sampling 4 chains, 0 divergences] Sampling 4 chains for 200 tune and 1_000 draw iterations (800 + 4_000 draws total) took <time> seconds.
So I have run into a few issues:
- If I use the entire training data as mentioned above, at some point during the progress bar, the kernel just died without any further warning nor error. This happened a few times when progress was at 50%, 80% and even 100%. When I tried with just 1% of the data (so 8500 rows), then it worked fine. How can I force verbose output to see the precise error message?
- Is the above model setup correct for BART classification? I am basing it against chapter 4 of the original BART paper.
- In an online setting, assuming the above code doesn’t run into any error, how can I feed further training data into the model without retraining it from scratch? Which variable above should I pickle? And how do I resume training?
- Relating to question 1 and 3, if it’s because of memory constraintI split up the data into chunks of 10% and do the training 10 times sequentially?
- I see lots of mentions of steps and potential for NUTS sampler in this forum from various google search queries. Is it something I should concern myself with, and change the above code accordingly?
My apology if my questions are naive. I am completely new to both PyMC and Bayesian inference in general so I am still learning.
Even if you have insight into just one of the questions, I’d really appreciate your inputs.