Getting started with PyMC3¶
Authors: John Salvatier, Thomas V. Wiecki, Christopher Fonnesbeck
Note: This text is taken from the PeerJ CS publication on PyMC3.
Abstract¶
Probabilistic Programming allows for automatic Bayesian inference on userdefined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models. This class of MCMC, known as Hamliltonian Monte Carlo, requires gradient information which is often not readily available. PyMC3 is a new open source Probabilistic Programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs onthefly to C for increased speed. Contrary to other Probabilistic Programming languages, PyMC3 allows model specification directly in Python code. The lack of a domain specific language allows for great flexibility and direct interaction with the model. This paper is a tutorialstyle introduction to this software package.
Introduction¶
Probabilistic programming (PP) allows flexible specification of Bayesian statistical models in code. PyMC3 is a new, opensource PP framework with an intuitive and readable, yet powerful, syntax that is close to the natural syntax statisticians use to describe models. It features nextgeneration Markov chain Monte Carlo (MCMC) sampling algorithms such as the NoUTurn Sampler (NUTS; Hoffman, 2014), a selftuning variant of Hamiltonian Monte Carlo (HMC; Duane, 1987). This class of samplers works well on high dimensional and complex posterior distributions and allows many complex models to be fit without specialized knowledge about fitting algorithms. HMC and NUTS take advantage of gradient information from the likelihood to achieve much faster convergence than traditional sampling methods, especially for larger models. NUTS also has several selftuning strategies for adaptively setting the tunable parameters of Hamiltonian Monte Carlo, which means you usually don’t need to have specialized knowledge about how the algorithms work. PyMC3, Stan (Stan Development Team, 2014), and the LaplacesDemon package for R are currently the only PP packages to offer HMC.
Probabilistic programming in Python confers a number of advantages including multiplatform compatibility, an expressive yet clean and readable syntax, easy integration with other scientific libraries, and extensibility via C, C++, Fortran or Cython. These features make it relatively straightforward to write and use custom statistical distributions, samplers and transformation functions, as required by Bayesian analysis.
While most of PyMC3’s userfacing features are written in pure Python,
it leverages Theano (Bergstra et al., 2010) to transparently transcode
models to C and compile them to machine code, thereby boosting
performance. Theano is a library that allows expressions to be defined
using generalized vector data structures called tensors, which are
tightly integrated with the popular NumPy ndarray
data structure,
and similarly allow for broadcasting and advanced indexing, just as
NumPy arrays do. Theano also automatically optimizes the likelihood’s
computational graph for speed and provides simple GPU integration.
Here, we present a primer on the use of PyMC3 for solving general Bayesian statistical inference and prediction problems. We will first see the basics of how to use PyMC3, motivated by a simple example: installation, data creation, model definition, model fitting and posterior analysis. Then we will cover two case studies and use them to show how to define and fit more sophisticated models. Finally we will show how to extend PyMC3 and discuss other useful features: the Generalized Linear Models subpackage, custom distributions, custom transformations and alternative storage backends.
Installation¶
Running PyMC3 requires a working Python interpreter, either version 2.7
(or more recent) or 3.4 (or more recent); we recommend that new users
install version 3.4. A complete Python installation for Mac OSX, Linux
and Windows can most easily be obtained by downloading and installing
the free
`Anaconda Python Distribution
<https://store.continuum.io/cshop/anaconda/>`__
by ContinuumIO.
PyMC3
can be installed using pip
(https://pip.pypa.io/en/latest/installing.html):
pip install git+https://github.com/pymcdevs/pymc3
PyMC3 depends on several thirdparty Python packages which will be
automatically installed when installing via pip. The four required
dependencies are: Theano
, NumPy
, SciPy
, and Matplotlib
.
To take full advantage of PyMC3, the optional dependencies Pandas
and Patsy
should also be installed. These are not automatically
installed, but can be installed by:
pip install patsy pandas
The source code for PyMC3 is hosted on GitHub at https://github.com/pymcdevs/pymc3 and is distributed under the liberal Apache License 2.0. On the GitHub site, users may also report bugs and other issues, as well as contribute code to the project, which we actively encourage.
A Motivating Example: Linear Regression¶
To introduce model definition, fitting and posterior analysis, we first consider a simple Bayesian linear regression model with normal priors for the parameters. We are interested in predicting outcomes \(Y\) as normallydistributed observations with an expected value \(\mu\) that is a linear function of two predictor variables, \(X_1\) and \(X_2\).
where \(\alpha\) is the intercept, and \(\beta_i\) is the coefficient for covariate \(X_i\), while \(\sigma\) represents the observation error. Since we are constructing a Bayesian model, the unknown variables in the model must be assigned a prior distribution. We choose zeromean normal priors with variance of 100 for both regression coefficients, which corresponds to weak information regarding the true parameter values. We choose a halfnormal distribution (normal distribution bounded at zero) as the prior for \(\sigma\).
Generating data¶
We can simulate some artificial data from this model using only NumPy’s
random
module, and then use PyMC3 to try to recover the
corresponding parameters. We are intentionally generating the data to
closely correspond the PyMC3 model structure.
In [1]:
import numpy as np
import matplotlib.pyplot as plt
# Initialize random number generator
np.random.seed(123)
# True parameter values
alpha, sigma = 1, 1
beta = [1, 2.5]
# Size of dataset
size = 100
# Predictor variable
X1 = np.random.randn(size)
X2 = np.random.randn(size) * 0.2
# Simulate outcome variable
Y = alpha + beta[0]*X1 + beta[1]*X2 + np.random.randn(size)*sigma
Here is what the simulated data look like. We use the pylab
module
from the plotting library matplotlib.
In [2]:
%matplotlib inline
fig, axes = plt.subplots(1, 2, sharex=True, figsize=(10,4))
axes[0].scatter(X1, Y)
axes[1].scatter(X2, Y)
axes[0].set_ylabel('Y'); axes[0].set_xlabel('X1'); axes[1].set_xlabel('X2');
Model Specification¶
Specifying this model in PyMC3 is straightforward because the syntax is as close to the statistical notation. For the most part, each line of Python code corresponds to a line in the model notation above.
First, we import PyMC. We use the convention of importing it as pm
.
In [3]:
import pymc3 as pm
Now we build our model, which we will present in full first, then explain each part linebyline.
In [4]:
basic_model = pm.Model()
with basic_model:
# Priors for unknown model parameters
alpha = pm.Normal('alpha', mu=0, sd=10)
beta = pm.Normal('beta', mu=0, sd=10, shape=2)
sigma = pm.HalfNormal('sigma', sd=1)
# Expected value of outcome
mu = alpha + beta[0]*X1 + beta[1]*X2
# Likelihood (sampling distribution) of observations
Y_obs = pm.Normal('Y_obs', mu=mu, sd=sigma, observed=Y)
The first line,
basic_model = Model()
creates a new Model
object which is a container for the model random
variables.
Following instantiation of the model, the subsequent specification of
the model components is performed inside a with
statement:
with basic_model:
This creates a context manager, with our basic_model
as the
context, that includes all statements until the indented block ends.
This means all PyMC3 objects introduced in the indented code block below
the with
statement are added to the model behind the scenes. Absent
this context manager idiom, we would be forced to manually associate
each of the variables with basic_model
right after we create them.
If you try to create a new random variable without a with model:
statement, it will raise an error since there is no obvious model for
the variable to be added to.
The first three statements in the context manager:
alpha = Normal('alpha', mu=0, sd=10)
beta = Normal('beta', mu=0, sd=10, shape=2)
sigma = HalfNormal('sigma', sd=1)
create a stochastic random variables with a Normal prior distributions for the regression coefficients with a mean of 0 and standard deviation of 10 for the regression coefficients, and a halfnormal distribution for the standard deviation of the observations, \(\sigma\). These are stochastic because their values are partly determined by its parents in the dependency graph of random variables, which for priors are simple constants, and partly random (or stochastic).
We call the Normal
constructor to create a random variable to use as
a normal prior. The first argument is always the name of the random
variable, which should almost always match the name of the Python
variable being assigned to, since it sometimes used to retrieve the
variable from the model for summarizing output. The remaining required
arguments for a stochastic object are the parameters, in this case
mu
, the mean, and sd
, the standard deviation, which we assign
hyperparameter values for the model. In general, a distribution’s
parameters are values that determine the location, shape or scale of the
random variable, depending on the parameterization of the distribution.
Most commonly used distributions, such as Beta
, Exponential
,
Categorical
, Gamma
, Binomial
and many others, are available
in PyMC3.
The beta
variable has an additional shape
argument to denote it
as a vectorvalued parameter of size 2. The shape
argument is
available for all distributions and specifies the length or shape of the
random variable, but is optional for scalar variables, since it defaults
to a value of one. It can be an integer, to specify an array, or a
tuple, to specify a multidimensional array (e.g. shape=(5,7)
makes
random variable that takes on 5 by 7 matrix values).
Detailed notes about distributions, sampling methods and other PyMC3
functions are available via the help
function.
In [5]:
help(pm.Normal) #try help(Model), help(Uniform) or help(basic_model)
Help on class Normal in module pymc3.distributions.continuous:
class Normal(pymc3.distributions.distribution.Continuous)
 Univariate normal loglikelihood.

 .. math::

 f(x \mid \mu, \tau) =
 \sqrt{\frac{\tau}{2\pi}}
 \exp\left\{ \frac{\tau}{2} (x\mu)^2 \right\}

 ======== ==========================================
 Support :math:`x \in \mathbb{R}`
 Mean :math:`\mu`
 Variance :math:`\dfrac{1}{\tau}` or :math:`\sigma^2`
 ======== ==========================================

 Normal distribution can be parameterized either in terms of precision
 or standard deviation. The link between the two parametrizations is
 given by

 .. math::

 \tau = \dfrac{1}{\sigma^2}

 Parameters
 
 mu : float
 Mean.
 sd : float
 Standard deviation (sd > 0).
 tau : float
 Precision (tau > 0).

 Method resolution order:
 Normal
 pymc3.distributions.distribution.Continuous
 pymc3.distributions.distribution.Distribution
 builtins.object

 Methods defined here:

 __init__(self, mu=0, sd=None, tau=None, **kwargs)
 Initialize self. See help(type(self)) for accurate signature.

 logp(self, value)

 random(self, point=None, size=None, repeat=None)

 
 Methods inherited from pymc3.distributions.distribution.Distribution:

 __getnewargs__(self)

 default(self)

 get_test_val(self, val, defaults)

 getattr_value(self, val)

 
 Class methods inherited from pymc3.distributions.distribution.Distribution:

 dist(*args, **kwargs) from builtins.type

 
 Static methods inherited from pymc3.distributions.distribution.Distribution:

 __new__(cls, name, *args, **kwargs)
 Create and return a new object. See help(type) for accurate signature.

 
 Data descriptors inherited from pymc3.distributions.distribution.Distribution:

 __dict__
 dictionary for instance variables (if defined)

 __weakref__
 list of weak references to the object (if defined)
Having defined the priors, the next statement creates the expected value
mu
of the outcomes, specifying the linear relationship:
mu = alpha + beta[0]*X1 + beta[1]*X2
This creates a deterministic random variable, which implies that its
value is completely determined by its parents’ values. That is, there
is no uncertainty beyond that which is inherent in the parents’ values.
Here, mu
is just the sum of the intercept alpha
and the two
products of the coefficients in beta
and the predictor variables,
whatever their values may be.
PyMC3 random variables and data can be arbitrarily added, subtracted,
divided, multiplied together and indexedinto to create new random
variables. This allows for great model expressivity. Many common
mathematical functions like sum
, sin
, exp
and linear algebra
functions like dot
(for inner product) and inv
(for inverse) are
also provided.
The final line of the model, defines Y_obs
, the sampling
distribution of the outcomes in the dataset.
Y_obs = Normal('Y_obs', mu=mu, sd=sigma, observed=Y)
This is a special case of a stochastic variable that we call an
observed stochastic, and represents the data likelihood of the
model. It is identical to a standard stochastic, except that its
observed
argument, which passes the data to the variable, indicates
that the values for this variable were observed, and should not be
changed by any fitting algorithm applied to the model. The data can be
passed in the form of either a numpy.ndarray
or pandas.DataFrame
object.
Notice that, unlike for the priors of the model, the parameters for the
normal distribution of Y_obs
are not fixed values, but rather are
the deterministic object mu
and the stochastic sigma
. This
creates parentchild relationships between the likelihood and these two
variables.
Model fitting¶
Having completely specified our model, the next step is to obtain posterior estimates for the unknown variables in the model. Ideally, we could calculate the posterior estimates analytically, but for most nontrivial models, this is not feasible. We will consider two approaches, whose appropriateness depends on the structure of the model and the goals of the analysis: finding the maximum a posteriori (MAP) point using optimization methods, and computing summaries based on samples drawn from the posterior distribution using Markov Chain Monte Carlo (MCMC) sampling methods.
Maximum a posteriori methods¶
The maximum a posteriori (MAP) estimate for a model, is the mode of
the posterior distribution and is generally found using numerical
optimization methods. This is often fast and easy to do, but only gives
a point estimate for the parameters and can be biased if the mode isn’t
representative of the distribution. PyMC3 provides this functionality
with the find_MAP
function.
Below we find the MAP for our original model. The MAP is returned as a parameter point, which is always represented by a Python dictionary of variable names to NumPy arrays of parameter values.
In [6]:
map_estimate = pm.find_MAP(model=basic_model)
map_estimate
Optimization terminated successfully.
Current function value: 149.017982
Iterations: 16
Function evaluations: 21
Gradient evaluations: 21
Out[6]:
{'alpha': array(0.9065985497559482),
'beta': array([ 0.94848602, 2.60705514]),
'sigma_log__': array(0.03278147017403069)}
By default, find_MAP
uses the Broyden–Fletcher–Goldfarb–Shanno
(BFGS) optimization algorithm to find the maximum of the logposterior
but also allows selection of other optimization algorithms from the
scipy.optimize
module. For example, below we use Powell’s method to
find the MAP.
In [7]:
from scipy import optimize
map_estimate = pm.find_MAP(model=basic_model, fmin=optimize.fmin_powell)
map_estimate
Optimization terminated successfully.
Current function value: 149.019762
Iterations: 4
Function evaluations: 176
Out[7]:
{'alpha': array(0.9090521898977764),
'beta': array([ 0.95140146, 2.61437458]),
'sigma_log__': array(0.030009775203258385)}
It is important to note that the MAP estimate is not always reasonable, especially if the mode is at an extreme. This can be a subtle issue; with high dimensional posteriors, one can have areas of extremely high density but low total probability because the volume is very small. This will often occur in hierarchical models with the variance parameter for the random effect. If the individual group means are all the same, the posterior will have near infinite density if the scale parameter for the group means is almost zero, even though the probability of such a small scale parameter will be small since the group means must be extremely close together.
Most techniques for finding the MAP estimate also only find a local optimum (which is often good enough), but can fail badly for multimodal posteriors if the different modes are meaningfully different.
Sampling methods¶
Though finding the MAP is a fast and easy way of obtaining estimates of the unknown model parameters, it is limited because there is no associated estimate of uncertainty produced with the MAP estimates. Instead, a simulationbased approach such as Markov chain Monte Carlo (MCMC) can be used to obtain a Markov chain of values that, given the satisfaction of certain conditions, are indistinguishable from samples from the posterior distribution.
To conduct MCMC sampling to generate posterior samples in PyMC3, we
specify a step method object that corresponds to a particular MCMC
algorithm, such as Metropolis, Slice sampling, or the NoUTurn Sampler
(NUTS). PyMC3’s step_methods
submodule contains the following
samplers: NUTS
, Metropolis
, Slice
, HamiltonianMC
, and
BinaryMetropolis
. These step methods can be assigned manually, or
assigned automatically by PyMC3. Autoassignment is based on the
attributes of each variable in the model. In general:
 Binary variables will be assigned to
BinaryMetropolis
 Discrete variables will be assigned to
Metropolis
 Continuous variables will be assigned to
NUTS
Autoassignment can be overriden for any subset of variables by specifying them manually prior to sampling.
Gradientbased sampling methods¶
PyMC3 has the standard sampling algorithms like adaptive MetropolisHastings and adaptive slice sampling, but PyMC3’s most capable step method is the NoUTurn Sampler. NUTS is especially useful on models that have many continuous parameters, a situation where other MCMC algorithms work very slowly. It takes advantage of information about where regions of higher probability are, based on the gradient of the log posteriordensity. This helps it achieve dramatically faster convergence on large problems than traditional sampling methods achieve. PyMC3 relies on Theano to analytically compute model gradients via automatic differentiation of the posterior density. NUTS also has several selftuning strategies for adaptively setting the tunable parameters of Hamiltonian Monte Carlo. For random variables that are undifferentiable (namely, discrete variables) NUTS cannot be used, but it may still be used on the differentiable variables in a model that contains undifferentiable variables.
NUTS requires a scaling matrix parameter, which is analogous to the variance parameter for the jump proposal distribution in MetropolisHastings, although NUTS uses it somewhat differently. The matrix gives the rough shape of the distribution so that NUTS does not make jumps that are too large in some directions and too small in other directions. It is important to set this scaling parameter to a reasonable value to facilitate efficient sampling. This is especially true for models that have many unobserved stochastic random variables or models with highly nonnormal posterior distributions. Poor scaling parameters will slow down NUTS significantly, sometimes almost stopping it completely. A reasonable starting point for sampling can also be important for efficient sampling, but not as often.
Fortunately PyMC3
automatically initializes NUTS using another
inference algorithm called ADVI (autodiff variational inference).
Moreover, PyMC3
will automatically assign an appropriate sampler if
we don’t supply it via the step
keyword argument (see below for an
example of how to explicitly assign step methods).
In [8]:
from scipy import optimize
with basic_model:
# draw 500 posterior samples
trace = pm.sample()
Autoassigning NUTS sampler...
Initializing NUTS using ADVI...
Average Loss = 156.08: 5%▌  10932/200000 [00:01<00:31, 6082.63it/s]
Convergence archived at 11100
Interrupted at 11,100 [5%]: Average Loss = 237.04
100%██████████ 1000/1000 [00:01<00:00, 710.94it/s]
The sample
function runs the step method(s) assigned (or passed) to
it for the given number of iterations and returns a Trace
object
containing the samples collected, in the order they were collected. The
trace
object can be queried in a similar way to a dict
containing a map from variable names to numpy.array
s. The first
dimension of the array is the sampling index and the later dimensions
match the shape of the variable. We can see the last 5 values for the
alpha
variable as follows:
In [9]:
trace['alpha'][5:]
Out[9]:
array([ 0.80339734, 0.94747946, 0.93063514, 0.89569059, 0.89569059])
If we wanted to use the slice sampling algorithm to sigma
instead of
NUTS (which was assigned automatically), we could have specified this as
the step
argument for sample
.
In [11]:
with basic_model:
# obtain starting values via MAP
start = pm.find_MAP(fmin=optimize.fmin_powell)
# instantiate sampler
step = pm.Slice()
# draw 5000 posterior samples
trace = pm.sample(5000, step=step, start=start)
1%  38/5500 [00:00<00:14, 376.26it/s]
Optimization terminated successfully.
Current function value: 149.019762
Iterations: 4
Function evaluations: 176
100%██████████ 5500/5500 [00:10<00:00, 498.93it/s]
Posterior analysis¶
PyMC3
provides plotting and summarization functions for inspecting
the sampling output. A simple posterior plot can be created using
traceplot
.
In [12]:
_ = pm.traceplot(trace)
The left column consists of a smoothed histogram (using kernel density
estimation) of the marginal posteriors of each stochastic random
variable while the right column contains the samples of the Markov chain
plotted in sequential order. The beta
variable, being vectorvalued,
produces two histograms and two sample traces, corresponding to both
predictor coefficients.
In addition, the summary
function provides a textbased output of
common posterior statistics:
In [13]:
pm.summary(trace)
alpha:
Mean SD MC Error 95% HPD interval

0.905 0.099 0.001 [0.715, 1.103]
Posterior quantiles:
2.5 25 50 75 97.5
============================
0.711 0.838 0.904 0.971 1.101
beta:
Mean SD MC Error 95% HPD interval

0.949 0.086 0.002 [0.786, 1.118]
2.599 0.507 0.014 [1.590, 3.591]
Posterior quantiles:
2.5 25 50 75 97.5
============================
0.784 0.889 0.948 1.007 1.117
1.593 2.256 2.605 2.940 3.599
sigma:
Mean SD MC Error 95% HPD interval

0.991 0.073 0.001 [0.852, 1.134]
Posterior quantiles:
2.5 25 50 75 97.5
============================
0.859 0.941 0.986 1.037 1.147
Case study 1: Stochastic volatility¶
We present a case study of stochastic volatility, time varying stock market volatility, to illustrate PyMC3’s use in addressing a more realistic problem. The distribution of market returns is highly nonnormal, which makes sampling the volatilities significantly more difficult. This example has 400+ parameters so using common sampling algorithms like MetropolisHastings would get bogged down, generating highly autocorrelated samples. Instead, we use NUTS, which is dramatically more efficient.
The Model¶
Asset prices have timevarying volatility (variance of day over day
returns
). In some periods, returns are highly variable, while in
others they are very stable. Stochastic volatility models address this
with a latent volatility variable, which changes over time. The
following model is similar to the one described in the NUTS paper
(Hoffman 2014, p. 21).
Here, \(y\) is the daily return series which is modeled with a Studentt distribution with an unknown degrees of freedom parameter, and a scale parameter determined by a latent process \(s\). The individual \(s_i\) are the individual daily log volatilities in the latent log volatility process.
The Data¶
Our data consist of daily returns of the S&P 500 during the 2008
financial crisis. Here, we use pandasdatareader
to obtain the price
data from Yahoo!Finance; it can be installed with
pip install pandasdatareader
.
In [14]:
from pandas_datareader import data
In [15]:
import pandas as pd
returns = data.get_data_google('SPY', start='200851', end='2009121')['Close'].pct_change()
len(returns)
Out[15]:
400
In [16]:
returns.plot(figsize=(10, 6))
plt.ylabel('daily returns in %');
Model Specification¶
As with the linear regression example, specifying the model in PyMC3
mirrors its statistical specification. This model employs several new
distributions: the Exponential
distribution for the \(\nu\) and
\(\sigma\) priors, the StudentT (StudentT
) distribution for
distribution of returns, and the GaussianRandomWalk
for the prior
for the latent volatilities.
In PyMC3, variables with purely positive priors like Exponential
are
transformed with a log transform. This makes sampling more robust.
Behind the scenes, a variable in the unconstrained space (named
“variableName_log”) is added to the model for sampling. In this model
this happens behind the scenes for both the degrees of freedom, nu
,
and the scale parameter for the volatility process, sigma
, since
they both have exponential priors. Variables with priors that constrain
them on two sides, like Beta
or Uniform
, are also transformed to
be unconstrained but with a log odds transform.
Although, unlike model specification in PyMC2, we do not typically
provide starting points for variables at the model specification stage,
we can also provide an initial value for any distribution (called a
“test value”) using the testval
argument. This overrides the default
test value for the distribution (usually the mean, median or mode of the
distribution), and is most often useful if some values are illegal and
we want to ensure we select a legal one. The test values for the
distributions are also used as a starting point for sampling and
optimization by default, though this is easily overriden.
The vector of latent volatilities s
is given a prior distribution by
GaussianRandomWalk
. As its name suggests GaussianRandomWalk is a
vector valued distribution where the values of the vector form a random
normal walk of length n, as specified by the shape
argument. The
scale of the innovations of the random walk, sigma
, is specified in
terms of the precision of the normally distributed innovations and can
be a scalar or vector.
In [17]:
with pm.Model() as sp500_model:
nu = pm.Exponential('nu', 1./10, testval=5.)
sigma = pm.Exponential('sigma', 1./.02, testval=.1)
s = pm.GaussianRandomWalk('s', sigma**2, shape=len(returns))
volatility_process = pm.Deterministic('volatility_process', pm.math.exp(2*s))
r = pm.StudentT('r', nu, lam=1/volatility_process, observed=returns)
Notice that we transform the log volatility process s
into the
volatility process by exp(2*s)
. Here, exp
is a Theano function,
rather than the corresponding function in NumPy; Theano provides a large
subset of the mathematical functions that NumPy does.
Also note that we have declared the Model
name sp500_model
in
the first occurrence of the context manager, rather than splitting it
into two lines, as we did for the first example.
Fitting¶
In [18]:
with sp500_model:
trace = pm.sample(2000)
Autoassigning NUTS sampler...
Initializing NUTS using ADVI...
Average Loss = 868.62: 32%███▏  63550/200000 [00:23<00:50, 2717.77it/s]
Convergence archived at 63600
Interrupted at 63,600 [31%]: Average Loss = 559.54
100%██████████ 2500/2500 [02:37<00:00, 15.91it/s]
We can check our samples by looking at the traceplot for nu
and
sigma
.
In [19]:
pm.traceplot(trace, [nu, sigma]);
Finally we plot the distribution of volatility paths by plotting many of
our sampled volatility paths on the same graph. Each is rendered
partially transparent (via the alpha
argument in Matplotlib’s
plot
function) so the regions where many paths overlap are shaded
more darkly.
In [20]:
fig, ax = plt.subplots(figsize=(15, 8))
returns.plot(ax=ax)
ax.plot(returns.index, 1/np.exp(trace['s',::5].T), 'r', alpha=.03);
ax.set(title='volatility_process', xlabel='time', ylabel='volatility');
ax.legend(['S&P500', 'stochastic volatility process'])
Out[20]:
<matplotlib.legend.Legend at 0x12d6ddf98>
As you can see, the model correctly infers the increase in volatility during the 2008 financial crash. Moreover, note that this model is quite complex because of its high dimensionality and dependencystructure in the random walk distribution. NUTS as implemented in PyMC3, however, correctly infers the posterior distribution with ease.
Case study 2: Coal mining disasters¶
Consider the following time series of recorded coal mining disasters in the UK from 1851 to 1962 (Jarrett, 1979). The number of disasters is thought to have been affected by changes in safety regulations during this period. Unfortunately, we also have pair of years with missing data, identified as missing by a NumPy MaskedArray using 999 as the marker value.
Next we will build a model for this series and attempt to estimate when the change occurred. At the same time, we will see how to handle missing data, use multiple samplers and sample from discrete random variables.
In [21]:
disaster_data = np.ma.masked_values([4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,
3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,
2, 2, 3, 4, 2, 1, 3, 999, 2, 1, 1, 1, 1, 3, 0, 0,
1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,
0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,
3, 3, 1, 999, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,
0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1], value=999)
year = np.arange(1851, 1962)
plt.plot(year, disaster_data, 'o', markersize=8);
plt.ylabel("Disaster count")
plt.xlabel("Year")
Out[21]:
<matplotlib.text.Text at 0x131c71e10>
Occurrences of disasters in the time series is thought to follow a Poisson process with a large rate parameter in the early part of the time series, and from one with a smaller rate in the later part. We are interested in locating the change point in the series, which perhaps is related to changes in mining safety regulations.
In our model,
the parameters are defined as follows: * \(D_t\): The number of disasters in year \(t\) * \(r_t\): The rate parameter of the Poisson distribution of disasters in year \(t\). * \(s\): The year in which the rate parameter changes (the switchpoint). * \(e\): The rate parameter before the switchpoint \(s\). * \(l\): The rate parameter after the switchpoint \(s\). * \(t_l\), \(t_h\): The lower and upper boundaries of year \(t\).
This model is built much like our previous models. The major differences
are the introduction of discrete variables with the Poisson and
discreteuniform priors and the novel form of the deterministic random
variable rate
.
In [22]:
with pm.Model() as disaster_model:
switchpoint = pm.DiscreteUniform('switchpoint', lower=year.min(), upper=year.max(), testval=1900)
# Priors for pre and postswitch rates number of disasters
early_rate = pm.Exponential('early_rate', 1)
late_rate = pm.Exponential('late_rate', 1)
# Allocate appropriate Poisson rates to years before and after current
rate = pm.math.switch(switchpoint >= year, early_rate, late_rate)
disasters = pm.Poisson('disasters', rate, observed=disaster_data)
The logic for the rate random variable,
rate = switch(switchpoint >= year, early_rate, late_rate)
is implemented using switch
, a Theano function that works like an if
statement. It uses the first argument to switch between the next two
arguments.
Missing values are handled transparently by passing a MaskedArray
or
a pandas.DataFrame
with NaN values to the observed
argument when
creating an observed stochastic random variable. Behind the scenes,
another random variable, disasters.missing_values
is created to
model the missing values. All we need to do to handle the missing values
is ensure we sample this random variable as well.
Unfortunately because they are discrete variables and thus have no
meaningful gradient, we cannot use NUTS for sampling switchpoint
or
the missing disaster observations. Instead, we will sample using a
Metroplis
step method, which implements adaptive
MetropolisHastings, because it is designed to handle discrete values.
PyMC3
automatically assigns the correct sampling algorithms.
In [23]:
with disaster_model:
trace = pm.sample(10000)
Assigned Metropolis to switchpoint
Assigned NUTS to early_rate_log__
Assigned NUTS to late_rate_log__
Assigned Metropolis to disasters_missing
100%██████████ 10500/10500 [01:11<00:00, 146.76it/s]
In the trace plot below we can see that there’s about a 10 year span that’s plausible for a significant change in safety, but a 5 year span that contains most of the probability mass. The distribution is jagged because of the jumpy relationship between the year switchpoint and the likelihood and not due to sampling error.
In [24]:
pm.traceplot(trace);
Arbitrary deterministics¶
Due to its reliance on Theano, PyMC3 provides many mathematical
functions and operators for transforming random variables into new
random variables. However, the library of functions in Theano is not
exhaustive, therefore Theano and PyMC3 provide functionality for
creating arbitrary Theano functions in pure Python, and including these
functions in PyMC models. This is supported with the as_op
function
decorator.
Theano needs to know the types of the inputs and outputs of a function,
which are specified for as_op
by itypes
for inputs and
otypes
for outputs. The Theano documentation includes an overview
of the available
types.
In [25]:
import theano.tensor as tt
from theano.compile.ops import as_op
@as_op(itypes=[tt.lscalar], otypes=[tt.lscalar])
def crazy_modulo3(value):
if value > 0:
return value % 3
else :
return (value + 1) % 3
with pm.Model() as model_deterministic:
a = pm.Poisson('a', 1)
b = crazy_modulo3(a)
An important drawback of this approach is that it is not possible for
theano
to inspect these functions in order to compute the gradient
required for the Hamiltonianbased samplers. Therefore, it is not
possible to use the HMC or NUTS samplers for a model that uses such an
operator. However, it is possible to add a gradient if we inherit from
theano.Op
instead of using as_op
. The PyMC example set includes
a more elaborate example of the usage of
as_op.
Arbitrary distributions¶
Similarly, the library of statistical distributions in PyMC3 is not
exhaustive, but PyMC allows for the creation of userdefined functions
for an arbitrary probability distribution. For simple statistical
distributions, the DensityDist
function takes as an argument any
function that calculates a logprobability \(log(p(x))\). This
function may employ other random variables in its calculation. Here is
an example inspired by a blog post by Jake Vanderplas on which priors to
use for a linear regression (Vanderplas, 2014).
import theano.tensor as tt
with pm.Model() as model:
alpha = pm.Uniform('intercept', 100, 100)
# Create custom densities
beta = pm.DensityDist('beta', lambda value: 1.5 * tt.log(1 + value**2), testval=0)
eps = pm.DensityDist('eps', lambda value: tt.log(tt.abs_(value)), testval=1)
# Create likelihood
like = pm.Normal('y_est', mu=alpha + beta * X, sd=eps, observed=Y)
For more complex distributions, one can create a subclass of
Continuous
or Discrete
and provide the custom logp
function,
as required. This is how the builtin distributions in PyMC are
specified. As an example, fields like psychology and astrophysics have
complex likelihood functions for a particular process that may require
numerical approximation. In these cases, it is impossible to write the
function in terms of predefined theano operators and we must use a
custom theano operator using as_op
or inheriting from theano.Op
.
Implementing the beta
variable above as a Continuous
subclass is
shown below, along with a subfunction.
In [26]:
class Beta(pm.Continuous):
def __init__(self, mu, *args, **kwargs):
super(Beta, self).__init__(*args, **kwargs)
self.mu = mu
self.mode = mu
def logp(self, value):
mu = self.mu
return beta_logp(value  mu)
def beta_logp(value):
return 1.5 * np.log(1 + (value)**2)
with pm.Model() as model:
beta = Beta('slope', mu=0, testval=0)
If your logp can not be expressed in Theano, you can decorate the
function with as_op
as follows:
@as_op(itypes=[tt.dscalar], otypes=[tt.dscalar])
. Note, that this
will create a blackbox Python function that will be much slower and not
provide the gradients necessary for e.g. NUTS.
Generalized Linear Models¶
Generalized Linear Models (GLMs) are a class of flexible models that are
widely used to estimate regression relationships between a single
outcome variable and one or multiple predictors. Because these models
are so common, PyMC3
offers a glm
submodule that allows flexible
creation of various GLMs with an intuitive R
like syntax that is
implemented via the patsy
module.
The glm
submodule requires data to be included as a pandas
DataFrame
. Hence, for our linear regression example:
In [27]:
# Convert X and Y to a pandas DataFrame
import pandas
df = pandas.DataFrame({'x1': X1, 'x2': X2, 'y': Y})
The model can then be very concisely specified in one line of code.
In [28]:
from pymc3.glm import GLM
with pm.Model() as model_glm:
GLM.from_formula('y ~ x1 + x2', df)
trace = pm.sample()
Autoassigning NUTS sampler...
Initializing NUTS using ADVI...
Average Loss = 164.12: 5%▌  10845/200000 [00:01<00:21, 8809.19it/s]
Convergence archived at 11100
Interrupted at 11,100 [5%]: Average Loss = 220.44
100%██████████ 1000/1000 [00:01<00:00, 831.50it/s]
The error distribution, if not specified via the family
argument, is
assumed to be normal. In the case of logistic regression, this can be
modified by passing in a Binomial
family object.
In [29]:
from pymc3.glm.families import Binomial
df_logistic = pandas.DataFrame({'x1': X1, 'y': Y > np.median(Y)})
with pm.Model() as model_glm_logistic:
GLM.from_formula('y ~ x1', df_logistic, family=Binomial())
For a more complete and flexible formula interface, including hierarchical GLMs, see Bambi.
Discussion¶
Probabilistic programming is an emerging paradigm in statistical learning, of which Bayesian modeling is an important subdiscipline. The signature characteristics of probabilistic programming–specifying variables as probability distributions and conditioning variables on other variables and on observations–makes it a powerful tool for building models in a variety of settings, and over a range of model complexity. Accompanying the rise of probabilistic programming has been a burst of innovation in fitting methods for Bayesian models that represent notable improvement over existing MCMC methods. Yet, despite this expansion, there are few software packages available that have kept pace with the methodological innovation, and still fewer that allow nonexpert users to implement models.
PyMC3 provides a probabilistic programming platform for quantitative researchers to implement statistical models flexibly and succinctly. A large library of statistical distributions and several predefined fitting algorithms allows users to focus on the scientific problem at hand, rather than the implementation details of Bayesian modeling. The choice of Python as a development language, rather than a domainspecific language, means that PyMC3 users are able to work interactively to build models, introspect model objects, and debug or profile their work, using a dynamic, highlevel programming language that is easy to learn. The modular, objectoriented design of PyMC3 means that adding new fitting algorithms or other features is straightforward. In addition, PyMC3 comes with several features not found in most other packages, most notably Hamiltonianbased samplers as well as automatical transforms of constrained random variables which is only offered by STAN. Unlike STAN, however, PyMC3 supports discrete variables as well as nongradient based sampling algorithms like MetropolisHastings and Slice sampling.
Development of PyMC3 is an ongoing effort and several features are planned for future versions. Most notably, variational inference techniques are often more efficient than MCMC sampling, at the cost of generalizability. More recently, however, blackbox variational inference algorithms have been developed, such as automatic differentiation variational inference (ADVI; Kucukelbir et al., in prep). This algorithm is slated for addition to PyMC3. As an opensource scientific computing toolkit, we encourage researchers developing new fitting algorithms for Bayesian models to provide reference implementations in PyMC3. Since samplers can be written in pure Python code, they can be implemented generally to make them work on arbitrary PyMC3 models, giving authors a larger audience to put their methods into use.
References¶
Patil, A., D. Huard and C.J. Fonnesbeck. (2010) PyMC: Bayesian Stochastic Modelling in Python. Journal of Statistical Software, 35(4), pp. 181
Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., Bouchard, N., WardeFarley, D., and Bengio, Y. (2012) “Theano: new features and speed improvements”. NIPS 2012 deep learning workshop.
Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., WardeFarley, D., and Bengio, Y. (2010) “Theano: A CPU and GPU Math Expression Compiler”. Proceedings of the Python for Scientific Computing Conference (SciPy) 2010. June 30  July 3, Austin, TX
Lunn, D.J., Thomas, A., Best, N., and Spiegelhalter, D. (2000) WinBUGS – a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing, 10:325–337.
Neal, R.M. Slice sampling. Annals of Statistics. (2003). doi:10.2307/3448413.
van Rossum, G. The Python Library Reference Release 2.6.5., (2010). URL http://docs.python.org/library/.
Duane, S., Kennedy, A. D., Pendleton, B. J., and Roweth, D. (1987) “Hybrid Monte Carlo”, Physics Letters, vol. 195, pp. 216222.
Stan Development Team. (2014). Stan: A C++ Library for Probability and Sampling, Version 2.5.0. http://mcstan.org.
Gamerman, D. Markov Chain Monte Carlo: statistical simulation for Bayesian inference. Chapman and Hall, 1997.
Hoffman, M. D., & Gelman, A. (2014). The NoUTurn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. The Journal of Machine Learning Research, 30.
Kucukelbir A, Ranganath R, Gelman A, and Blei DM. Automatic Variational Inference in Stan http://arxiv.org/abs/1506.03431, in prep.
Vanderplas, Jake. “Frequentism and Bayesianism IV: How to be a Bayesian in Python.” Pythonic Perambulations. N.p., 14 Jun 2014. Web. 27 May. 2015. https://jakevdp.github.io/blog/2014/06/14/frequentismandbayesianism4bayesianinpython/.
R.G. Jarrett. A note on the intervals between coal mining disasters. Biometrika, 66:191–193, 1979.