# Save and Load a BART model

**URL:** <https://discourse.pymc.io/t/save-and-load-a-bart-model/13135>\
**Category:** v5\
**Tags:** bart\
**Created:** [October 19, 2023, 3:49pm UTC](https://discourse.pymc.io/t/save-and-load-a-bart-model/13135 "2023-10-19T15:49:40Z")\
**Posts on this page:** 10\
**Page:** 1

<div class="post-metadata">

**Author:** ![JTIEGS](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/jtiegs/32/7028_2.png) [@JTIEGS](https://discourse.pymc.io/u/JTIEGS)\
**Post date:** [October 19, 2023, 3:49pm UTC](https://discourse.pymc.io/t/save-and-load-a-bart-model/13135/1 "2023-10-19T15:49:40Z")

</div>

Hi, I am trying to save and load a trained model for posterior predictions with a model that contains a BART RV. I am running into multiple issues that is making this task seem not possible. If anyone could provide me with some input on what I might be doing wrong I would appreciate it.

```auto
from pathlib import Path
import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc as pm
import pymc_bart as pmb
import cloudpickle as cpkl
print(f"Running on PyMC v{pm. __version__ }")
print(f"Running on PyMC-BART v{pmb. __version__ }")

```

VERSION 5 for both PYMC and PYMC-BART

Using the bikes example for simplicity.

```auto
try:
    bikes = pd.read_csv(Path("..", "data", "bikes.csv"))
except FileNotFoundError:
    bikes = pd.read_csv(pm.get_data("bikes.csv"))

features = ["hour", "temperature", "humidity", "workingday"]

X = bikes[features]
Y = bikes["count"]

xt = X[0:10]
yt = Y[0:10]

```

Baseline model

```auto
with pm.Model() as model_bikes:
    xdata = pm.MutableData("xdata", X)
    a = pm.Exponential("a", 1)
    mu_ = pmb.BART("mu_", xdata, np.log(Y), m=20)
    mu = pm.Deterministic("mu", pm.math.exp(mu_))
    y = pm.NegativeBinomial("y", mu=mu, alpha=a, observed=Y, shape=xdata.shape[0])
    idata_bikes = pm.sample(random_seed=99, draws=100, tune=100, compute_convergence_checks=False)
idata_bikes

```

Used the clouldpickle to save the trace. I was having some weird issues with to\_netcdf (basically the file stayed open and I couldn’t overwrite it when trying things out, it was easier to just pickle).

```auto
with open('test4.pkl', mode='wb') as file:
   cpkl.dump(idata_bikes, file)

with open("test4.pkl", mode="rb") as file:
    idata4 = cpkl.load(file)

```

Test the posterior predictions on the baseline model using the baseline idata and the saved/reload idata.  
Both work fine.  
It also works to set the new data when using the baseline model.

```auto
with model_bikes:
    pm.set_data({"xdata": xt})
    post1 = pm.sample_posterior_predictive(idata_bikes, var_names=["mu", "y"])

with model_bikes:
    pm.set_data({"xdata": xt})
    post2 = pm.sample_posterior_predictive(idata4, var_names=["mu", "y"])

```

This is where things start to get difficult. Based on other examples I should be able to create a new model (untrained) and then get posterior predictions with the saved traced.

New model  
Input data is the same as the input data in the baseline model.

```auto
with pm.Model() as model2:
    xdata2 = pm.MutableData("xdata", X)
    a2 = pm.Exponential("a", 1)
    mu_2 = pmb.BART("mu_", xdata2, np.log(Y), m=50)
    mu2 = pm.Deterministic("mu", pm.math.exp(mu_2))
    y2 = pm.NegativeBinomial("y", mu=mu2, alpha=a2, observed=Y, shape=xdata2.shape[0])

```

NOTE I named the new variable with suffix 2 because I thought there maybe was a chance that variables from the baseline model were causing issues, but that didn’t make a difference.

Getting the posterior predictive on the full data input into the model using the baseline trace and the saved trace.

```auto
with model2:
    post3 = pm.sample_posterior_predictive(idata_bikes, var_names=["mu", "y"], )

with model2:
    # pm.set_data({"xdata": xt})
    post5 = pm.sample_posterior_predictive(idata4, var_names=["mu", "y"], )

```

This seems to work well enough, with outputs being relatively close to the outputs of the baseline model.

However, when I try to set new data with the new model I an unable too and get a shape error. This seems odd, because setting new data on the baseline model doesn’t throw a shape error, so I am not sure where this error is arising from.

```auto
with model2:
    pm.set_data({"xdata": xt})
    post4 = pm.sample_posterior_predictive(idata_bikes, var_names=["mu", "y"])

with model2:
    pm.set_data({"xdata": xt})
    post6 = pm.sample_posterior_predictive(idata4, var_names=["mu", "y"])

```

error:

```auto
{
	"name": "ValueError",
	"message": "size does not match the broadcast shape of the parameters. (10,), (10,), (348,)\nApply node that caused the error: nbinom_rv{0, (0, 0), int64, True}
...

```

So I tried instantiating the model with the new data size

```auto
with pm.Model() as model3:
    xdata2 = pm.MutableData("xdata", xt)
    a2 = pm.Exponential("a", 1)
    mu_2 = pmb.BART("mu_", xdata2, np.log(yt), m=50)
    mu2 = pm.Deterministic("mu", pm.math.exp(mu_2))
    y2 = pm.NegativeBinomial("y", mu=mu2, alpha=a2, observed=yt, shape=xdata2.shape[0])

```

And this runs

```auto
with model3:
    # pm.set_data({"xdata": xt})
    post6 = pm.sample_posterior_predictive(idata4, var_names=["mu", "y"], )
with model3:
    # pm.set_data({"xdata": xt})
    post4 = pm.sample_posterior_predictive(idata_bikes, var_names=["mu", "y"], )

```

However the output predictions are not relatively close to what I expect. In fact the output of the BART RV is the same across all observations, so it doesn’t appear that the BART tree structure from the prior training is being used at all.

Finally I tried to pickle the whole model. The model with save and load, but when I try to do anything with the model I receive an error that I believe is associated with the multiprocessing in training the BART model.  
NOTE: it says file not found error, but the file will load, it is only one I try to do something with it that it starts having issues.

```auto
"name": "FileNotFoundError",
"message": "[Errno 2] No such file or directory\nApply node that caused the error: BART_rv{1, (2, 1, 0, 0, 0, 1), floatX, True}(RandomGeneratorSharedVariable(<Generator(PCG64) at 0x7FF34E558900>), [], 11, xdata, [2.7725887 ... .49650756], 20, 0.95, 2.0, [])\nToposort index: 1\nInputs types: [RandomGeneratorType, TensorType(int64, shape=(0,)), TensorType(int64, shape=()), TensorType(float64, shape=(None, None)), TensorType(float64, shape=(348,)), TensorType(int8, shape=()), TensorType(float64, shape=()), TensorType(float32, shape=()), TensorType(float64, shape=(0,))]\nInputs shapes: ['No shapes', (0,), (), (10, 4), (348,), (), (), (), (0,)]\nInputs strides: ['No strides', (8,), (), (8, 80), (8,), (), (), (), (8,)]\nInputs values: 

```

If anyone has had success in saving and reloading a model that has a BART RV and wouldn’t mind sharing how they did it, that would be amazing.

Also of note I have been able to successfully save and load with other models that don’t include the BART RV, so I am not convinced it is a OS problem.

Thanks!

---

<div class="post-metadata">

**Author:** ![JTIEGS](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/jtiegs/32/7028_2.png) [@JTIEGS](https://discourse.pymc.io/u/JTIEGS)\
**Post date:** [October 23, 2023, 2:43pm UTC](https://discourse.pymc.io/t/save-and-load-a-bart-model/13135/2 "2023-10-23T14:43:35Z")

</div>

Hi, just to follow-up I think I found a work around to this. I was able to convert to list and pickle the all\_trees object from the RV. This object does not have any issues w/ multiprocessing/connection that I see with the full BARTRV or the Model when pickled and loaded. Then I can use the `pmb.utilites._sample_posterior(...)` function to get draws from the tree structure, which as it appears is how the pdp utility function works.

```auto
all_trees = list(model_bikes.mu_.owner.op.all_trees)
with open('test_all_tree.pkl', mode='wb') as file:
   cpkl.dump(all_trees, file)

```

Restart session and reload modules

```auto
with open('test_all_tree.pkl', mode='rb') as file:
   all_tree2 = cpkl.load(file)

rng = np.random.default_rng()
xt_2 = pt.constant(xt)
smpl = pmbu._sample_posterior(all_tree2, xt_2, rng, size = 400, shape=1)
mu_smpl = smpl.mean(0)
ex_mu = pm.math.exp(mu_smpl).eval()

```

ex\_mu appears to appropriately match the `pm.sample_posterior_distribution(...)` from the trained model and with the updated covariate matrix.

For my actual project, I only care about the mu value output from the BARTRV, so I think that is appropriate method to predict mu with a new covariate matrix. If I needed the y value I believe it could be extended with a sample from whatever the final distribution is w/ the mu value inputed, as I have seen in other examples.

If anyone can confirm that this is an appropriate way to save/load/predict from the BARTRV or provide me with an alternative method that would be great!

Thanks!

---

<div class="post-metadata">

**Author:** ![cluhmann](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/cluhmann/32/3083_2.png) [@cluhmann](https://discourse.pymc.io/u/cluhmann)\
**Post date:** [October 24, 2023, 7:04pm UTC](https://discourse.pymc.io/t/save-and-load-a-bart-model/13135/3 "2023-10-24T19:04:56Z")

</div>

@aloctavodia ?

---

<div class="post-metadata">

**Author:** ![aloctavodia](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/aloctavodia/32/8642_2.png) [@aloctavodia](https://discourse.pymc.io/u/aloctavodia)\
**Post date:** [October 25, 2023, 3:43pm UTC](https://discourse.pymc.io/t/save-and-load-a-bart-model/13135/4 "2023-10-25T15:43:55Z")

</div>

Sorry for the late reply, I was very busy with other stuff, still are actually so I am just answering to tell you that the workaround seems fine to me. I will try next week to inspect this issue with more time and see if I can provide a better answer or a more general solution. I will do it on GitHub [BART model save, reload and new predictions · Issue #123 · pymc-devs/pymc-bart · GitHub](https://github.com/pymc-devs/pymc-bart/issues/123)

---

<div class="post-metadata">

**Author:** ![JJ\_Py](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/jj_py/32/8183_2.png) [@JJ\_Py](https://discourse.pymc.io/u/JJ_Py)\
**Post date:** [August 21, 2024, 8:00am UTC](https://discourse.pymc.io/t/save-and-load-a-bart-model/13135/5 "2024-08-21T08:00:53Z")

</div>

> [@JTIEGS](#):
>
> `smpl = pmbu._sample_posterior(all_tree2, xt_2, rng, size = 400, shape=1)`

```auto
smpl = pmbu._sample_posterior(all_tree2, xt_2, rng, size = 400, shape=1)

```

hi, I haven’t find the where did you defined the _pmbu_. And _pm_ or _pmb_ also do not have the _\_sample\_posterior_ method.

---

<div class="post-metadata">

**Author:** ![JTIEGS](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/jtiegs/32/7028_2.png) [@JTIEGS](https://discourse.pymc.io/u/JTIEGS)\
**Post date:** [August 21, 2024, 1:17pm UTC](https://discourse.pymc.io/t/save-and-load-a-bart-model/13135/6 "2024-08-21T13:17:24Z")

</div>

Hi,  
I belive it is from `import pymc_bart.utilities as pmbu`. I have it mentioned in the paragraph but not defined in the code block.

The `_sample_posterior` is an internal utility function but should be accessible for this purpose.

Let me know if that helps!

---

<div class="post-metadata">

**Author:** ![JJ\_Py](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/jj_py/32/8183_2.png) [@JJ\_Py](https://discourse.pymc.io/u/JJ_Py)\
**Post date:** [August 22, 2024, 11:31am UTC](https://discourse.pymc.io/t/save-and-load-a-bart-model/13135/7 "2024-08-22T11:31:21Z")

</div>

> [@JTIEGS](#):
>
> .utilites.

Thank you for your reply. But pymc\_bart has no attribute ‘utilites’. My pymc version is v5.15.0, pymc\_bart version is v0.5.14.  
Thank you very much for taking the time to answer my questions. I’m trying to save the model, and deploy it on a platform. It’s my thesis.

```auto
import pymc_bart.utilities as pmbu

```

```auto
ModuleNotFoundError Traceback (most recent call last)
Cell In[11], line 1
----> 1 import pymc_bart.utilities as pmbu

ModuleNotFoundError: No module named 'pymc_bart.utilities'

```

---

<div class="post-metadata">

**Author:** ![Genesis](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/genesis/32/6017_2.png) [@Genesis](https://discourse.pymc.io/u/Genesis)\
**Post date:** [August 22, 2024, 12:30pm UTC](https://discourse.pymc.io/t/save-and-load-a-bart-model/13135/8 "2024-08-22T12:30:04Z")

</div>

Try `.utils`? It’s there: [pymc-bart/pymc\_bart/utils.py at main · pymc-devs/pymc-bart · GitHub](https://github.com/pymc-devs/pymc-bart/blob/main/pymc_bart/utils.py)

---

<div class="post-metadata">

**Author:** ![JTIEGS](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/jtiegs/32/7028_2.png) [@JTIEGS](https://discourse.pymc.io/u/JTIEGS)\
**Post date:** [August 22, 2024, 12:34pm UTC](https://discourse.pymc.io/t/save-and-load-a-bart-model/13135/9 "2024-08-22T12:34:46Z")

</div>

Try pymc\_bart.utils.

The utils.py file is still additionally accessible from the GitHub repo. If you still can’t load the utils file you could try to pull out the \_sample\_posterior function from the utils.py file directly.

This would require some additional work to get the function to work without the other dependencies, but it should provide you the logic as to what you need to sample the posterior trees.

---

<div class="post-metadata">

**Author:** ![Vitorio\_Mexas](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/vitorio_mexas/32/9739_2.png) [@Vitorio\_Mexas](https://discourse.pymc.io/u/Vitorio_Mexas)\
**Post date:** [September 28, 2026, 1:18pm UTC](https://discourse.pymc.io/t/save-and-load-a-bart-model/13135/10 "2026-09-28T13:18:54Z")

</div>

I made this for my use-case: [GitHub - vitoriomexas311/bart-save-load: Save and reload scalar PyMC BART posterior trees for prediction without retraining. · GitHub](https://github.com/vitoriomexas311/bart-save-load).