Pymc models to turing jl

When working with Julia, it is common to encounter situations where you need to convert code from one package to another. In this article, we will explore different ways to convert Pymc models to Turing.jl, a popular package for probabilistic programming in Julia.

Option 1: Manual Conversion

The first option is to manually convert the Pymc model to Turing.jl. This involves understanding the structure and syntax of both packages and rewriting the code accordingly. Let’s take a look at an example:

# Pymc model
with pm.Model() as model:
    x = pm.Normal('x', mu=0, sigma=1)
    y = pm.Normal('y', mu=x, sigma=1, observed=data)
    trace = pm.sample(1000)

# Turing.jl model
@model function turing_model(data)
    x ~ Normal(0, 1)
    y ~ Normal(x, 1)
end

chain = sample(turing_model(data), NUTS(), 1000)

In this example, we manually converted the Pymc model to Turing.jl by replacing the Pymc syntax with the equivalent Turing.jl syntax. This option requires a good understanding of both packages and can be time-consuming for complex models.

Option 2: Automatic Conversion

If manual conversion seems daunting, an alternative is to use an automatic conversion tool. There are several tools available that can help with this process. One such tool is the pymc2turing package, which can convert Pymc models to Turing.jl code. Here’s how you can use it:

using pymc2turing

# Pymc model
with pm.Model() as model:
    x = pm.Normal('x', mu=0, sigma=1)
    y = pm.Normal('y', mu=x, sigma=1, observed=data)
    trace = pm.sample(1000)

# Automatic conversion
turing_code = pymc2turing.convert(model)
eval(Meta.parse(turing_code))

chain = sample(turing_model(data), NUTS(), 1000)

In this example, we used the pymc2turing package to automatically convert the Pymc model to Turing.jl code. This option can save time and effort, especially for complex models. However, it is important to note that automatic conversion tools may not always produce perfect results, and manual adjustments may still be required.

Option 3: Rewrite from Scratch

If the Pymc model is relatively simple or if you prefer a fresh start, you can choose to rewrite the model from scratch using Turing.jl syntax. This option allows you to fully leverage the features and capabilities of Turing.jl without being constrained by the Pymc syntax. Here’s an example:

@model function turing_model(data)
    x ~ Normal(0, 1)
    y ~ Normal(x, 1)
end

chain = sample(turing_model(data), NUTS(), 1000)

In this example, we rewrote the model from scratch using Turing.jl syntax. This option provides the most flexibility and control over the model, but it requires a good understanding of Turing.jl and may not be suitable for complex models.

After exploring these three options, it is clear that the best option depends on the specific situation. If you are familiar with both Pymc and Turing.jl syntax and have a relatively simple model, manual conversion (Option 1) may be the most efficient choice. If you prefer an automated approach, using a conversion tool like pymc2turing (Option 2) can save time and effort. Finally, if you want to fully leverage the features of Turing.jl or have a simple model, rewriting from scratch (Option 3) may be the best option.

Ultimately, the choice depends on your familiarity with the packages, the complexity of the model, and your personal preferences. It is recommended to try out different options and choose the one that works best for your specific needs.