Advanced usage of Theano in PyMC3

Using shared variables

Shared variables allow us to use values in theano functions that are not considered an input to the function, but can still be changed later. They are very similar to global variables in may ways:

a = tt.scalar('a')
# Create a new shared variable with initial value of 0.1
b = theano.shared(0.1)
func = theano.function([a], a * b)
assert func(2.) == 0.2

assert func(2.) == 20.

Shared variables can also contain arrays, and are allowed to change their shape as long as the number of dimensions stays the same.

We can use shared variables in PyMC3 to fit the same model to several datasets without the need to recreate the model each time (which can be time consuming if the number of datasets is large):

# We generate 10 datasets
true_mu = [np.random.randn() for _ in range(10)]
observed_data = [mu + np.random.randn(20) for mu in true_mu]

data = theano.shared(observed_data[0])
with pm.Model() as model:
    mu = pm.Normal('mu', 0, 10)
    pm.Normal('y', mu=mu, sigma=1, observed=data)

# Generate one trace for each dataset
traces = []
for data_vals in observed_data:
    # Switch out the observed dataset
    with model:

We can also sometimes use shared variables to work around limitations in the current PyMC3 api. A common task in Machine Learning is to predict values for unseen data, and one way to achieve this is to use a shared variable for our observations:

x = np.random.randn(100)
y = x > 0

x_shared = theano.shared(x)

with pm.Model() as model:
  coeff = pm.Normal('x', mu=0, sigma=1)
  logistic = pm.math.sigmoid(coeff * x_shared)
  pm.Bernoulli('obs', p=logistic, observed=y)

  # fit the model
  trace = pm.sample()

  # Switch out the observations and use `sample_posterior_predictive` to predict
  x_shared.set_value([-1, 0, 1.])
  post_pred = pm.sample_posterior_predictive(trace, samples=500)

However, due to the way we handle shapes at the moment, it is not possible to change the shape of a shared variable if that would also change the shape of one of the variables.

Writing custom Theano Ops

While Theano includes a wide range of operations, there are cases where it makes sense to write your own. But before doing this it is a good idea to think hard if it is actually necessary. Especially if you want to use algorithms that need gradient information — this includes NUTS and all variational methods, and you probably should want to use those — this is often quite a bit of work and also requires some math and debugging skills for the gradients.

Good reasons for defining a custom Op might be the following:

  • You require an operation that is not available in Theano and can’t be build up out of existing Theano operations. This could for example include models where you need to solve differential equations or integrals, or find a root or minimum of a function that depends on your parameters.

  • You want to connect your PyMC3 model to some existing external code.

  • After carefully considering different parametrizations and a lot of profiling your model is still too slow, but you know of a faster way to compute the gradient than what theano is doing. This faster way might be anything from clever maths to using more hardware. There is nothing stopping anyone from using a cluster via MPI in a custom node, if a part of the gradient computation is slow enough and sufficiently parallelizable to make the cost worth it. We would definitely like to hear about any such examples.

Theano has extensive documentation, about how to write new Ops.

Finding the root of a function

We’ll use finding the root of a function as a simple example. Let’s say we want to define a model where a parameter is defined implicitly as the root of a function, that depends on another parameter:

\[\begin{split}\theta \sim N^+(0, 1)\\ \text{$\mu\in \mathbb{R}^+$ such that $R(\mu, \theta) = \mu + \mu e^{\theta \mu} - 1= 0$}\\ y \sim N(\mu, 0.1^2)\end{split}\]

First, we observe that this problem is well-defined, because \(R(\cdot, \theta)\) is monotone and has the image \((-1, \infty)\) for \(\mu, \theta \in \mathbb{R}^+\). To avoid overflows in \(\exp(\mu \theta)\) for large values of \(\mu\theta\) we instead find the root of

\[R'(\mu, \theta) = \log(R(\mu, \theta) + 1) = \log(\mu) + \log(1 + e^{\theta\mu}).\]

Also, we have

\[\frac{\partial}{\partial\mu}R'(\mu, \theta) = \theta\, \text{logit}^{-1}(\theta\mu) + \mu^{-1}.\]

We can now use scipy.optimize.newton to find the root:

from scipy import optimize, special
import numpy as np

def func(mu, theta):
    thetamu = theta * mu
    value = np.log(mu) + np.logaddexp(0, thetamu)
    return value

def jac(mu, theta):
    thetamu = theta * mu
    jac = theta * special.expit(thetamu) + 1 / mu
    return jac

def mu_from_theta(theta):
    return optimize.newton(func, 1, fprime=jac, args=(theta,))

We could wrap mu_from_theta with theano.compile.ops.as_op and use gradient-free methods like Metropolis, but to get NUTS and ADVI working, we also need to define the derivative of mu_from_theta. We can find this derivative using the implicit function theorem, or equivalently we take the derivative with respect of \(\theta\) for both sides of \(R(\mu(\theta), \theta) = 0\) and solve for \(\frac{d\mu}{d\theta}\). This isn’t hard to do by hand, but for the fun of it, let’s do it using sympy:

import sympy

mu = sympy.Function('mu')
theta = sympy.Symbol('theta')
R = mu(theta) + mu(theta) * sympy.exp(theta * mu(theta)) - 1
solution = sympy.solve(R.diff(theta), mu(theta).diff(theta))[0]

We get

\[\frac{d}{d\theta}\mu(\theta) = - \frac{\mu(\theta)^2}{1 + \theta\mu(\theta) + e^{-\theta\mu(\theta)}}\]

Now, we use this to define a theano op, that also computes the gradient:

import theano
import theano.tensor as tt
import theano.tests.unittest_tools
from theano.graph.op import Op

class MuFromTheta(Op):
    itypes = [tt.dscalar]
    otypes = [tt.dscalar]

    def perform(self, node, inputs, outputs):
        theta, = inputs
        mu = mu_from_theta(theta)
        outputs[0][0] = np.array(mu)

    def grad(self, inputs, g):
        theta, = inputs
        mu = self(theta)
        thetamu = theta * mu
        return [- g[0] * mu ** 2 / (1 + thetamu + tt.exp(-thetamu))]

If you value your sanity, always check that the gradient is ok:

theano.tests.unittest_tools.verify_grad(MuFromTheta(), [np.array(0.2)])
theano.tests.unittest_tools.verify_grad(MuFromTheta(), [np.array(1e-5)])
theano.tests.unittest_tools.verify_grad(MuFromTheta(), [np.array(1e5)])

We can now define our model using this new op:

import pymc3 as pm

tt_mu_from_theta = MuFromTheta()

with pm.Model() as model:
    theta = pm.HalfNormal('theta', sigma=1)
    mu = pm.Deterministic('mu', tt_mu_from_theta(theta))
    pm.Normal('y', mu=mu, sigma=0.1, observed=[0.2, 0.21, 0.3])

    trace = pm.sample()