# 8. Extending PyMC¶

PyMC tries to make standard things easy, but keep unusual things possible. Its openness, combined with Python’s flexibility, invite extensions from using new step methods to exotic stochastic processes (see the Gaussian process module). This chapter briefly reviews the ways PyMC is designed to be extended.

## 8.1. Nonstandard Stochastics¶

The simplest way to create a Stochastic object with a nonstandard distribution is to use the medium or long decorator syntax. See Chapter Building models. If you want to create many stochastics with the same nonstandard distribution, the decorator syntax can become cumbersome. An actual subclass of Stochastic can be created using the class factory stochastic_from_dist. This function takes the following arguments:

• The name of the new class,
• A logp function,
• A random function,
• The NumPy datatype of the new class (for continuous distributions, this should be float; for discrete distributions, int; for variables valued as non-numerical objects, object),
• A flag indicating whether the resulting class represents a vector-valued variable.

The necessary parent labels are read from the logp function, and a docstring for the new class is automatically generated. Instances of the new class can be created in one line.

Full subclasses of Stochastic may be necessary to provide nonstandard behaviors (see gp.GP).

## 8.2. User-defined step methods¶

The StepMethod class is meant to be subclassed. There are an enormous number of MCMC step methods in the literature, whereas PyMC provides only about half a dozen. Most user-defined step methods will be either Metropolis-Hastings or Gibbs step methods, and these should subclass Metropolis or Gibbs respectively. More unusual step methods should subclass StepMethod directly.

### 8.2.1. Example: an asymmetric Metropolis step¶

Consider the probability model in examples/custom_step.py:

mu = pymc.Normal('mu',0,.01, value=0)
tau = pymc.Exponential('tau',.01, value=1)
cutoff = pymc.Exponential('cutoff',1, value=1.3)
D = pymc.Truncnorm('D',mu,tau,-np.inf,cutoff,value=data,observed=True)


The stochastic variable cutoff cannot be smaller than the largest element of $$D$$, otherwise $$D$$‘s density would be zero. The standard Metropolis step method can handle this case without problems; it will propose illegal values occasionally, but these will be rejected.

Suppose we want to handle cutoff with a smarter step method that doesn’t propose illegal values. Specifically, we want to use the nonsymmetric proposal distribution:

\begin{eqnarray*} x_p | x \sim \textup{Truncnorm}(x, \sigma, \max(D), \infty). \end{eqnarray*}

We can implement this Metropolis-Hastings algorithm with the following step method class:

class TruncatedMetropolis(pymc.Metropolis):
def __init__(self, stochastic, low_bound, up_bound, *args, **kwargs):
self.low_bound = low_bound
self.up_bound = up_bound
pymc.Metropolis.__init__(self, stochastic, *args, **kwargs)

def propose(self):
self.stochastic.value = \
pymc.rtruncnorm(self.stochastic.value, tau, self.low_bound, self.up_bound)

def hastings_factor(self):
cur_val = self.stochastic.value
last_val = self.stochastic.last_value

lp_for = pymc.truncnorm_like(cur_val, last_val, tau, self.low_bound, self.up_bound)
lp_bak = pymc.truncnorm_like(last_val, cur_val, tau, self.low_bound, self.up_bound)

if self.verbose > 1:
print self._id + ': Hastings factor %f'%(lp_bak - lp_for)
return lp_bak - lp_for


The propose method sets the step method’s stochastic’s value to a new value, drawn from a truncated normal distribution. The precision of this distribution is computed from two factors: self.proposal_sd, which can be set with an input argument to Metropolis, and self.adaptive_scale_factor. Metropolis step methods’ default tuning behavior is to reduce adaptive_scale_factor if the acceptance rate is too low, and to increase adaptive_scale_factor if it is too high. By incorporating adaptive_scale_factor into the proposal standard deviation, we avoid having to write our own tuning infrastructure. If we don’t want the proposal to tune, we don’t have to use adaptive_scale_factor.

The hastings_factor method adjusts for the asymmetric proposal distribution [Gelman2004]. It computes the log of the quotient of the ‘backward’ density and the ‘forward’ density. For symmetric proposal distributions, this quotient is 1, so its log is zero.

Having created our custom step method, we need to tell MCMC instances to use it to handle the variable cutoff. This is done in custom_step.py with the following line:

M.use_step_method(TruncatedMetropolis, cutoff, D.value.max(), np.inf)


This call causes $$M$$ to pass the arguments cutoff, D.value.max(), and np.inf to a TruncatedMetropolis object’s __init__ method, and use the object to handle cutoff.

Its often convenient to get a handle to a custom step method instance directly for debugging purposes. M.step_method_dict[cutoff] returns a list of all the step methods $$M$$ will use to handle cutoff:

>>> M.step_method_dict[cutoff]
[<custom_step.TruncatedMetropolis object at 0x3c91130>]


There may be more than one, and conversely step methods may handle more than one stochastic variable. To see which variables step method $$S$$ is handling, try:

>>> S.stochastics
set([<pymc.distributions.Exponential 'cutoff' at 0x3cd6b90>])


### 8.2.2. General step methods¶

All step methods must implement the following methods:

step():
Updates the values of self.stochastics.
tune():

Tunes the jumping strategy based on performance so far. A default method is available that increases self.adaptive_scale_factor (see below) when acceptance rate is high, and decreases it when acceptance rate is low. This method should return True if additional tuning will be required later,

and False otherwise.
competence(s):
A class method that examines stochastic variable $$s$$ and returns a
value from 0 to 3 expressing the step method’s ability to handle the variable. This method is used by MCMC instances when automatically assigning step methods. Conventions are:
0
I cannot safely handle this variable.
1
I can handle the variable about as well as the standard Metropolis step method.
2
I can do better than Metropolis.
3
I am the best step method you are likely to find for this variable in most cases.

For example, if you write a step method that can handle MyStochasticSubclass well, the competence method might look like this:

class MyStepMethod(pymc.StepMethod):
def __init__(self, stochastic, *args, **kwargs):
...

@classmethod
def competence(self, stochastic):
if isinstance(stochastic, MyStochasticSubclass):
return 3
else:
return 0


Note that PyMC will not even attempt to assign a step method automatically if its __init__ method cannot be called with a single stochastic instance, that is MyStepMethod(x) is a legal call. The list of step methods that PyMC will consider assigning automatically is called pymc.StepMethodRegistry.

current_state():

This method is easiest to explain by showing the code:

state = {}
for s in self._state:
state[s] = getattr(self, s)
return state


self._state should be a list containing the names of the attributes needed to reproduce the current jumping strategy. If an MCMC object writes its state out to a database, these attributes will be preserved. If an MCMC object restores its state from the database later, the corresponding step method will have these attributes set to their saved values.

Step methods should also maintain the following attributes:

_id:
A string that can identify each step method uniquely (usually something
like <class_name>_<stochastic_name>).
adaptive_scale_factor:
An ‘adaptive scale factor’. This attribute is only needed if the default tune() method is used.
_tuning_info:
A list of strings giving the names of any tuning parameters. For
Metropolis instances, this would be adaptive_scale_factor. This list is used to keep traces of tuning parameters in order to verify ‘diminishing tuning’ [Roberts2007].

All step methods have a property called loglike, which returns the sum of the log-probabilities of the union of the extended children of self.stochastics. This quantity is one term in the log of the Metropolis- Hastings acceptance ratio. The logp_plus_loglike property gives the sum of that and the log-probabilities of self.stochastics.

### 8.2.3. Metropolis-Hastings step methods¶

A Metropolis-Hastings step method only needs to implement the following methods, which are called by Metropolis.step():

reject():

Usually just

def reject(self):
self.rejected += 1
[s.value = s.last_value for s in self.stochastics]

propose():
Sets the values of all self.stochastics to new, proposed values. This method may use the adaptive_scale_factor attribute to take advantage of the standard tuning scheme.

Metropolis-Hastings step methods may also override the tune and competence methods.

Metropolis-Hastings step methods with asymmetric jumping distributions may implement a method called hastings_factor(), which returns the log of the ratio of the ‘reverse’ and ‘forward’ proposal probabilities. Note that no accept() method is needed or used.

By convention, Metropolis-Hastings step methods use attributes called accepted and rejected to log their performance.

### 8.2.4. Gibbs step methods¶

Gibbs step methods handle conjugate submodels. These models usually have two components: the ‘parent’ and the ‘children’. For example, a gamma-distributed variable serving as the precision of several normally-distributed variables is a conjugate submodel; the gamma variable is the parent and the normal variables are the children.

This section describes PyMC’s current scheme for Gibbs step methods, several of which are in a semi-working state in the sandbox directory. It is meant to be as generic as possible to minimize code duplication, but it is admittedly complicated. Feel free to subclass StepMethod directly when writing Gibbs step methods if you prefer.

Gibbs step methods that subclass PyMC’s Gibbs should define the following class attributes:

child_class:
The class of the children in the submodels the step method can handle.
parent_class:
The class of the parent.
parent_label:
The label the children would apply to the parent in a conjugate submodel.
In the gamma-normal example, this would be tau.
linear_OK:
A flag indicating whether the children can use linear combinations
involving the parent as their actual parent without destroying the conjugacy.

A subclass of Gibbs that defines these attributes only needs to implement a propose() method, which will be called by Gibbs.step(). The resulting step method will be able to handle both conjugate and ‘non-conjugate’ cases. The conjugate case corresponds to an actual conjugate submodel. In the non-conjugate case all the children are of the required class, but the parent is not. In this case the parent’s value is proposed from the likelihood and accepted based on its prior. The acceptance rate in the non-conjugate case will be less than one.

The inherited class method Gibbs.competence will determine the new step method’s ability to handle a variable $$x$$ by checking whether:

• all $$x$$‘s children are of class child_class, and either apply parent_label to $$x$$ directly or (if linear_OK=True) to a LinearCombination object (chapter Building models), one of whose parents contains $$x$$.
• $$x$$ is of class parent_class

If both conditions are met, pymc.conjugate_Gibbs_competence will be returned. If only the first is met, pymc.nonconjugate_Gibbs_competence will be returned.

## 8.3. New fitting algorithms¶

PyMC provides a convenient platform for non-MCMC fitting algorithms in addition to MCMC. All fitting algorithms should be implemented by subclasses of Model. There are virtually no restrictions on fitting algorithms, but many of Model’s behaviors may be useful. See Chapter Fitting Models.

### 8.3.1. Monte Carlo fitting algorithms¶

Unless there is a good reason to do otherwise, Monte Carlo fitting algorithms should be implemented by subclasses of Sampler to take advantage of the interactive sampling feature and database backends. Subclasses using the standard sample() and isample() methods must define one of two methods:

draw():
If it is possible to generate an independent sample from the posterior at
every iteration, the draw method should do so. The default _loop method can be used in this case.
_loop():
If it is not possible to implement a draw() method, but you want to
take advantage of the interactive sampling option, you should override _loop(). This method is responsible for generating the posterior samples and calling tally() when it is appropriate to save the model’s state. In addition, _loop should monitor the sampler’s status attribute at every iteration and respond appropriately. The possible values of status are:
'ready':
'running':
Sampling should continue as normal.
'halt':
Sampling should halt as soon as possible. _loop should call the halt() method and return control. _loop can set the status to 'halt' itself if appropriate (eg the database is full or a KeyboardInterrupt has been caught).
'paused':
Sampling should pause as soon as possible. _loop should return, but should be able to pick up where it left off next time it’s called.

Samplers may alternatively want to override the default sample() method. In that case, they should call the tally() method whenever it is appropriate to save the current model state. Like custom _loop() methods, custom sample() methods should handle KeyboardInterrupts and call the halt() method when sampling terminates to finalize the traces.

## 8.4. A second warning: Don’t update stochastic variables’ values in-place¶

If you’re going to implement a new step method, fitting algorithm or unusual (non-numeric-valued) Stochastic subclass, you should understand the issues related to in-place updates of Stochastic objects’ values. Fitting methods should never update variables’ values in-place for two reasons:

• In algorithms that involve accepting and rejecting proposals, the ‘pre-proposal’ value needs to be preserved uncorrupted. It would be possible to make a copy of the pre-proposal value and then allow in-place updates, but in PyMC we have chosen to store the pre-proposal value as Stochastic.last_value and require proposed values to be new objects. In-place updates would corrupt Stochastic.last_value, and this would cause problems.
• LazyFunction’s caching scheme checks variables’ current values against its internal cache by reference. That means if you update a variable’s value in-place, it or its child may miss the update and incorrectly skip recomputing its value or log-probability.

However, a Stochastic object’s value can make in-place updates to itself if the updates don’t change its identity. For example, the Stochastic subclass gp.GP is valued as a gp.Realization object. GP realizations represent random functions, which are infinite-dimensional stochastic processes, as literally as possible. The strategy they employ is to ‘self-discover’ on demand: when they are evaluated, they generate the required value conditional on previous evaluations and then make an internal note of it. This is an in-place update, but it is done to provide the same behavior as a single random function whose value everywhere has been determined since it was created.