https://github.com/suriyadeepan/pm-pyro
PyMC3-like Interface for Pyro
https://github.com/suriyadeepan/pm-pyro
probabilistic-programming pymc3 pyro
Last synced: 2 months ago
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PyMC3-like Interface for Pyro
- Host: GitHub
- URL: https://github.com/suriyadeepan/pm-pyro
- Owner: suriyadeepan
- License: gpl-3.0
- Created: 2019-11-17T07:14:07.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-12-01T02:50:55.000Z (over 6 years ago)
- Last Synced: 2025-11-27T19:10:16.329Z (4 months ago)
- Topics: probabilistic-programming, pymc3, pyro
- Language: Jupyter Notebook
- Size: 1.34 MB
- Stars: 38
- Watchers: 1
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
PyMC3-like abstractions for pyro's stochastic function.
Define a model as a stochastic function in pyro.
Use `pm_like` wrapper to create a PyMC3-esque `Model`.
Random variables are exposed to user as attributes of `Model`.
pm-pyro provides abstractions for sampling-based inference methods (NUTS - The No-U-Turn Sampler, HMC - Hamiltonion Monte Carlo), as well as Variational Inference (SVI with autoguides), trace plots, posterior plot and posterior predictive plots.
## Install
Install from pypi
```bash
pip install pm-pyro
```
Developer setup
```bash
# install requirements
pip install -r requirements-dev.txt
# run tests
python -m pytest pmpyro/tests.py
```
## Example
Borrowed the example from a [PyMC3 tutorial](https://docs.pymc.io/notebooks/getting_started.html). The outcome variable `Y` is dependent on 2 features `X_1` and `X_2`. The notebook for this example is available [here](notebooks/motivating-example.ipynb)
## Model Specification
We design a simple Bayesian Linear Regression model.
## Stochastic Function
The model specification is implemented as a stochastic function.
```python
import pyro.distributions as dist
import pyro
import torch
def pyro_model(x1, x2, y):
alpha = pyro.sample('alpha', dist.Normal(0, 10))
beta = pyro.sample('beta',pdist.Normal(torch.zeros(2,), torch.ones(2,) * 10.))
sigma = pyro.sample('sigma', dist.HalfNormal(1.))
# Expected value of outcome
mu = alpha + beta[0] * x1 + beta[1] * x2
# Likelihood (sampling distribution) of observations
return pyro.sample('y_obs', dist.Normal(mu, sigma), obs=y)
```
## Context-manager Syntax
The `pm_like` wrapper creates a PyMC3-esque `Model`.
We can use the context manager syntax for running inference.
`pm.sample` samples from the model using the NUTS sampler.
The trace is a python dictionary which contains the samples.
```python
from pmpyro import pm_like
import pmpyro as pm
with pm_like(pyro_model, X1, X2, Y) as model:
trace = pm.sample(1000)
```
```
sample: 100%|██████████| 1300/1300 [00:16, 80.42it/s, step size=7.49e-01, acc. prob=0.911]
```
## Traceplot
We can visualize the samples using `traceplot`.
Select random variables by passing them as a list via `var_names = [ 'alpha' ... ]` argument.
```python
pm.traceplot(trace)
```
## Plot Posterior
Visualize posterior of random variables using `plot_posterior`.
```python
pm.plot_posterior(trace, var_names=['beta'])
```
## Posterior Predictive Samples
We can sample from the posterior by running `plot_posterior_predictive` or `sample_posterior_predictive` with the same
function signatures as the stochastic function `def pyro_model(x1, x2, y)`, replacing observed variable `Y` with `None`.
```python
ppc = pm.plot_posterior_predictive(X1, X2, None,
trace=trace, model=model, samples=60,
alpha=0.08, obs={'y_obs' : Y})
```
## Trace Summary
The summary of random variables is available as a pandas array.
```python
pm.summary()
```
## License
This project is licensed under the GPL v3 License - see the [LICENSE.md](LICENSE.md) file for details