I was wondering how to create a class using pm.Model’s class. Since I am coming from Torch and Tensorflow based models, I like to know how to create a class inheritance based on pymc.Model’s class like the keras.Model or torch.nn.Module option. The documentation for the Model’s class is without a clear example, unfortunately. Is there any example post on creating a class using the pm.Model’s class and how to use it?
Link: [pymc.Model](pymc.Model — PyMC 4.4.0 documentation)
The API for PyMC is a bit different from Torch. You don’t ever need to subclass
pm.Model to create a model. Instead, it is used as a context manager, as follows:
basic_model = pm.Model()
# Priors for unknown model parameters
alpha = pm.Normal("alpha", mu=0, sigma=10)
beta = pm.Normal("beta", mu=0, sigma=10, shape=2)
sigma = pm.HalfNormal("sigma", sigma=1)
# Expected value of outcome
mu = alpha + beta * X1 + beta * X2
# Likelihood (sampling distribution) of observations
Y_obs = pm.Normal("Y_obs", mu=mu, sigma=sigma, observed=Y)
This example comes from the introduction and overview example notebook in the examples gallery, which I highly recommend checking out.
If you wanted to write a class wrapper around a PyMC model, perhaps to get some additional functionality (like a
.fit method), you wouldn’t subclass
pm.Model. Instead just write a generic class with a
pm.Model as a member variable and proceed as normal.