A Framework for First Order Probabilistic Programming Languages
This is an improved version of the PyFOPPL. It is work in progress.
Write your FOPPL-model and save in a file, say, my_model.foppl. The file
should either be in the root folder of your project, or in a folder called
foppl-src or foppl-models. A simple model might look as follows:
(let [x (sample (normal 1.0 5.0))
y (+ x 1)]
(observe (normal y 2.0) 7.0)
y)You will find various models in the examples-folder in this project.
Once you have written your FOPPL-model, you can import it as a graphical model in your Python code like this:
from foppl import imports
# Import your model here:
from my_model import model
state = model.gen_prior_samples()
log_pdf = model.gen_pdf(state)
print(log_pdf)You can get a visual representation of the graphical model if you have
the Python packages networkx and matplotlib installed (preferably
also graphviz).
model.display_graph()The file example.py shows how you might import a FOPPL mode, print out the generated graphical model, or generate samples from it.
Options.debug: bool:
When set to True, PyFOPPL will print out additional debug information.
On the one hand, the output of the vertices will include addition
information. On the other hand, when running gen_prior_samples(), or
gen_pdf(), respectively, it will print out the performed computations
as they happen.
Options.log_file: str:
This lets you specify a possible log-file. When given a filename as a
string, the graph will print out the entire model and the generated
code for gen_prior_samples() as well as gen_pdf() to the specified
logfile.
In order to take effect during the import of any models, the options should be set before the actual import. You can, later on, deactivate the debug-option, if you do not need the runtime-output.
from foppl import Options
Options.debug = True
import my_model # Import your model here!
Options.debug = False
state = my_model.model.gen_prior_samples()
...This project is released unter the MIT-license. See LICENSE.