dimenwarper/simppl

A simple probabilistic programming language

simppl

The simple (?) probabilistic programming language™

simppl is not your typical probabilistic programming language. It does not include the latest and greatest inference methods. In fact, it only has exhaustive/enumerative inference. It does not have your favorite distribution; it only includes a couple of very simplified, discretized distribution. It doesn't use that new awesome computational graph/deep learning framework, it has minimal dependencies beyond the python data stack. Because of this, models will be dead simple and inference should hopefully be fast and straightforward. Rather than a library you should use for production, it is a sandbox for quirky Bayesian inference ideas and for starting conversations around the theme of "Bayesian inference for users with deadlines" (with apologies to Django). Read the blogpost here.

Install

With pip:

pip install git+git://github.com/dimenwarper/simppl.git@master

A simppl model

A simple model illustrating that simppl flavor:

from simppl.distributions import SomeValue
from simppl.utils import capture_locals
from simppl.inference import Exhaustive

import numpy as np

def sigmoid_response(toxicity, log_concentration):
    return 1 / (1 + np.exp(toxicity * log_concentration))

log_concentration = 1
true_toxicity = 0.6
inhibitions = (sigmoid_response(true_toxicity, log_concentration) + np.random.randn(2) * 0.001).reshape(-1, 1)

def cytotoxicity_model(inibitions=inhibitions):
    toxicity = SomeValue('toxicity', between=[0, 2], around=[0, 1.5], mostly=1.3)
    inhibition = SomeValue(
        'inibition', 
        between=[0, 1], 
        mostly=sigmoid_response(toxicity, log_concentration),
        observations=inhibitions
    )
                        
    capture_locals()
    return inhibition

env = Exhaustive(cytotoxicity_model)
(env
 .executions
 .assign(toxicity=lambda df: np.round(df['toxicity'], 2))
 .groupby('toxicity')['_probability_']
 .sum()
 .plot.bar()
);

This and other examples are in the showcase.ipynb.

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