Lasso regression with block updatingΒΆ

Sometimes, it is very useful to update a set of parameters together. For example, variables that are highly correlated are often good to update together. In PyMC 3 block updating is simple, as example will demonstrate.

Here we have a LASSO regression model where the two coefficients are strongly correlated. Normally, we would define the coefficient parameters as a single random variable, but here we define them separately to show how to do block updates.

First we generate some fake data.

%matplotlib inline
from matplotlib.pylab import *
from pymc3 import *
import numpy as np

d = np.random.normal(size=(3, 30))
d1 = d[0] + 4
d2 = d[1] + 4
yd = .2*d1 +.3*d2 + d[2]

Then define the random variables.

lam = 3

with Model() as model:
    s = Exponential('s', 1)
    tau = Uniform('tau', 0, 1000)
    b = lam * tau
    m1 = Laplace('m1', 0, b)
    m2 = Laplace('m2', 0, b)

    p = d1*m1 + d2*m2

    y = Normal('y', mu=p, sigma=s, observed=yd)

For most samplers, including Metropolis and HamiltonianMC, simply pass a list of variables to sample as a block. This works with both scalar and array parameters.

with model:
    start = find_MAP()

    step1 = Metropolis([m1, m2])

    step2 = Slice([s, tau])

    trace = sample(10000, [step1, step2], start=start)
100.00% [112/112 00:00<00:00 logp = -53.335, ||grad|| = 2.0806]

Multiprocess sampling (4 chains in 4 jobs)
>>Metropolis: [m2]
>>Metropolis: [m1]
>>Slice: [tau]
>>Slice: [s]
100.00% [44000/44000 00:51<00:00 Sampling 4 chains, 0 divergences]
Sampling 4 chains for 1_000 tune and 10_000 draw iterations (4_000 + 40_000 draws total) took 53 seconds.
The number of effective samples is smaller than 10% for some parameters.
/dependencies/arviz/arviz/data/ FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.
hexbin(trace[m1],trace[m2], gridsize = 50)
%load_ext watermark
%watermark -n -u -v -iv -w
platform   1.0.8
matplotlib 3.2.1
re         2.2.1
numpy      1.18.5
last updated: Fri Jun 12 2020

CPython 3.7.7
IPython 7.15.0
watermark 2.0.2