# What’s new in PyMC3 3.1

BlogPythonTools & Languagesposted by Thomas Wiecki, PhD July 17, 2017

*Thomas originally posted this article here at http://twiecki.github.io *

We recently released PyMC3 3.1 after the first stable 3.0 release in January 2017. You can update either via `pip install pymc3`

or via `conda install -c conda-forge pymc3`

.

A lot is happening in PyMC3-land. One thing I am particularily proud of is the developer community we have built. We now have around 10 active core contributors from the US, Germany, Russia, Japan and Switzerland. Specifically, since 3.0, Adrian Seyboldt, Junpeng Lao and Hannes Bathke have joined the team. Moreover, we have 3 Google Summer of Code students: Maxime Kochurov, who is working on Variational Inference; Bill Engels, who is working on Gaussian Processes, and Bhargav Srinivasa is implementing Riemannian HMC.

Moreover, PyMC3 is being seeing increased adoption in academia, as well as in industry.

Here, I want to highlight some of the new features of PyMC3 3.1.

## Discourse forum + better docs

To facilitate the community building process and give users a place to ask questions we have a launched a discourse forum: http://discourse.pymc.io. Bug reports should still onto the Github issue tracker, but for all PyMC3 questions or modeling discussions, please use the discourse forum.

There are also some improvements to the documentation. Mainly, a quick-start to the general PyMC3 API, and a quick-start to the variational API.

## Gaussian Processes

PyMC3 now as high-level support for GPs which allow for very flexible non-linear curve-fitting (among other things). This work was mainly done by Bill Engels with help from Chris Fonnesbeck. Here, we highlight the basic API, but for more information see the full introduction.

```
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.cm as cmap
cm = cmap.inferno
import numpy as np
import scipy as sp
import seaborn as sns
import theano
import theano.tensor as tt
import theano.tensor.nlinalg
import pymc3 as pm
```

```
np.random.seed(20090425)
n = 20
X = pm.floatX(np.sort(3*np.random.rand(n))[:,None])
# generate fake data from GP with white noise (with variance sigma2)
y = pm.floatX(
np.array([ 1.36653628, 1.15196999, 0.82142869, 0.85243384, 0.63436304,
0.14416139, 0.09454237, 0.32878065, 0.51946622, 0.58603513,
0.46938673, 0.63876778, 0.48415033, 1.28011185, 1.52401102,
1.38430047, 0.47455605, -0.21110139, -0.49443319, -0.25518805])
)
```

```
Z = pm.floatX(np.linspace(0, 3, 100)[:, None])
with pm.Model() as model:
# priors on the covariance function hyperparameters and noise
l = pm.Uniform('l', 0, 10)
log_s2_f = pm.Uniform('log_s2_f', lower=-10, upper=5)
log_s2_n = pm.Uniform('log_s2_n', lower=-10, upper=5)
f_cov = tt.exp(log_s2_f) * pm.gp.cov.ExpQuad(1, l)
# Instantiate GP
y_obs = pm.gp.GP('y_obs', cov_func=f_cov, sigma=tt.exp(log_s2_n),
observed={'X': X, 'Y': y})
trace = pm.sample()
# Draw samples from GP
gp_samples = pm.gp.sample_gp(trace, y_obs, Z, samples=50, random_seed=42)
```

Auto-assigning NUTS sampler... Initializing NUTS using ADVI... Average Loss = 27.649: 6%|▌ | 12091/200000 [00:09<02:15, 1386.23it/s] Convergence archived at 12100 Interrupted at 12,100 [6%]: Average Loss = 9,348 100%|██████████| 1000/1000 [00:20<00:00, 49.74it/s] 100%|██████████| 50/50 [00:12<00:00, 3.93it/s]

```
fig, ax = plt.subplots(figsize=(9, 5))
[ax.plot(Z, x, color=cm(0.3), alpha=0.3) for x in gp_samples]
# overlay the observed data
ax.plot(X, y, 'ok', ms=10);
ax.set(xlabel="x", ylabel="f(x)", title="Posterior predictive distribution");
```

## Improvements to NUTS

NUTS is now identical to Stan’s implementation and also much much faster. In addition, Adrian Seyboldt added higher-order integrators, which promise to be more efficient in higher dimensions, and sampler statistics that help identify problems with NUTS sampling.

In addition, we changed the default kwargs of `pm.sample()`

. By default, the sampler is run for 500 iterations with tuning enabled (you can change this with the `tune`

kwarg), these samples are then discarded from the returned trace. Moreover, if no arguments are specified, `sample()`

will draw 500 samples in addition to the tuning samples. So for almost all models, just calling `pm.sample()`

should be sufficient.

```
with pm.Model():
mu1 = pm.Normal("mu1", mu=0, sd=1, shape=1000)
trace = pm.sample(discard_tuned_samples=False) # do not remove tuned samples for the plot below
```

Auto-assigning NUTS sampler... Initializing NUTS using ADVI... Average Loss = 7.279: 14%|█▍ | 28648/200000 [00:08<00:53, 3176.47it/s] Convergence archived at 28900 Interrupted at 28,900 [14%]: Average Loss = 8.9536 100%|██████████| 1000/1000 [00:03<00:00, 263.60it/s]

`trace`

now has a bunch of extra parameters pertaining to statistics of the sampler:

```
fig, ax = plt.subplots(figsize=(8, 5))
ax.plot(trace['step_size_bar']); ax.set(xlabel='iteration', ylabel='step size');
```

## Variational Inference

Maxim “Ferrine” Kochurov has done outstanding contributions to improve support for Variational Inference. Essentially, Ferrine has implemented Operator Variational Inference (OPVI) which is a framework to express many existing VI approaches in a modular fashion. He has also made it much easier to supply mini-batches. See here for a full overview of the capabilities.

Specifically, PyMC3 supports the following VI methods:

- Auto-diff Variational Inference (ADVI) mean-field
- ADVI full rank
- Stein Variational Gradient Descent (SVGD)
- Armortized SVGD

In addition, Ferrine is making great progress on adding Flows which allows learning very flexible transformations of the VI approximation to learn more complex (i.e. non-normal) posterior distributions.

```
x = np.random.randn(10000)
x_mini = pm.Minibatch(x, batch_size=100)
with pm.Model():
mu = pm.Normal('x', mu=0, sd=1)
sd = pm.HalfNormal('sd', sd=1)
obs = pm.Normal('obs', mu=mu, sd=sd, observed=x_mini)
vi_est = pm.fit() # Run ADVI
vi_trace = vi_est.sample() # sample from VI posterior
```

Average Loss = 149.38: 100%|██████████| 10000/10000 [00:01<00:00, 9014.13it/s] Finished [100%]: Average Loss = 149.33

As you can see, we have also added a new high-level API in the spirit of `sample`

: `pymc3.fit()`

with many configuration options:

```
help(pm.fit)
```

Help on function fit in module pymc3.variational.inference: fit(n=10000, local_rv=None, method='advi', model=None, random_seed=None, start=None, inf_kwargs=None, **kwargs) Handy shortcut for using inference methods in functional way Parameters ---------- n : `int` number of iterations local_rv : dict[var->tuple] mapping {model_variable -> local_variable (:math:`mu`, :math:`rho`)} Local Vars are used for Autoencoding Variational Bayes See (AEVB; Kingma and Welling, 2014) for details method : str or :class:`Inference` string name is case insensitive in {'advi', 'fullrank_advi', 'advi->fullrank_advi', 'svgd', 'asvgd'} model : :class:`pymc3.Model` PyMC3 model for inference random_seed : None or int leave None to use package global RandomStream or other valid value to create instance specific one inf_kwargs : dict additional kwargs passed to :class:`Inference` start : `Point` starting point for inference Other Parameters ---------------- frac : `float` if method is 'advi->fullrank_advi' represents advi fraction when training kwargs : kwargs additional kwargs for :func:`Inference.fit` Returns ------- :class:`Approximation`

SVGD for example is an algorithm that updates multiple particles and is thus well suited for multi-modal posteriors.

```
with pm.Model():
pm.NormalMixture('m',
mu=np.array([0., .5]),
w=np.array([.4, .6]),
sd=np.array([.1, .1]))
vi_est = pm.fit(method='SVGD')
vi_est = vi_est.sample(5000)
```

100%|██████████| 10000/10000 [00:24<00:00, 407.10it/s]

```
sns.distplot(vi_est['m'])
```

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