Model¶

class
pymc3.model.
Model
(name='', model=None, theano_config=None)¶ Encapsulates the variables and likelihood factors of a model.
Model class can be used for creating class based models. To create a class based model you should inherit from
Model
and override__init__()
with arbitrary definitions (do not forget to call base class__init__()
first).Parameters: name : str
name that will be used as prefix for names of all random variables defined within model
model : Model
instance of Model that is supposed to be a parent for the new instance. If
None
, context will be used. All variables defined within instance will be passed to the parent instance. So that ‘nested’ model contributes to the variables and likelihood factors of parent model.theano_config : dict
A dictionary of theano config values that should be set temporarily in the model context. See the documentation of theano for a complete list. Set config key
compute_test_value
to raise if it is None.Examples
How to define a custom model
class CustomModel(Model): # 1) override init def __init__(self, mean=0, sigma=1, name='', model=None): # 2) call super's init first, passing model and name # to it name will be prefix for all variables here if # no name specified for model there will be no prefix super().__init__(name, model) # now you are in the context of instance, # `modelcontext` will return self you can define # variables in several ways note, that all variables # will get model's name prefix # 3) you can create variables with Var method self.Var('v1', Normal.dist(mu=mean, sigma=sd)) # this will create variable named like '{prefix_}v1' # and assign attribute 'v1' to instance created # variable can be accessed with self.v1 or self['v1'] # 4) this syntax will also work as we are in the # context of instance itself, names are given as usual Normal('v2', mu=mean, sigma=sd) # something more complex is allowed, too half_cauchy = HalfCauchy('sd', beta=10, testval=1.) Normal('v3', mu=mean, sigma=half_cauchy) # Deterministic variables can be used in usual way Deterministic('v3_sq', self.v3 ** 2) # Potentials too Potential('p1', tt.constant(1)) # After defining a class CustomModel you can use it in several # ways # I: # state the model within a context with Model() as model: CustomModel() # arbitrary actions # II: # use new class as entering point in context with CustomModel() as model: Normal('new_normal_var', mu=1, sigma=0) # III: # just get model instance with all that was defined in it model = CustomModel() # IV: # use many custom models within one context with Model() as model: CustomModel(mean=1, name='first') CustomModel(mean=2, name='second')

Var
(name, dist, data=None, total_size=None)¶ Create and add (un)observed random variable to the model with an appropriate prior distribution.
Parameters: name : str
dist : distribution for the random variable
data : array_like (optional)
If data is provided, the variable is observed. If None, the variable is unobserved.
total_size : scalar
upscales logp of variable with
coef = total_size/var.shape[0]
Returns: FreeRV or ObservedRV

add_random_variable
(var)¶ Add a random variable to the named variables of the model.

basic_RVs
¶ List of random variables the model is defined in terms of (which excludes deterministics).

check_test_point
(test_point=None, round_vals=2)¶ Checks log probability of test_point for all random variables in the model.
Parameters: test_point : Point
Point to be evaluated. if None, then all model.test_point is used
round_vals : int
Number of decimals to round logprobabilities
Returns: Pandas Series

cont_vars
¶ All the continuous variables in the model

disc_vars
¶ All the discrete variables in the model

fastfn
(outs, mode=None, *args, **kwargs)¶ Compiles a Theano function which returns
outs
and takes values of model vars as a dict as an argument.Parameters: outs : Theano variable or iterable of Theano variables
mode : Theano compilation mode
Returns: Compiled Theano function as point function.

flatten
(vars=None, order=None, inputvar=None)¶ Flattens model’s input and returns:
 FlatView with
 input vector variable
 replacements
input_var > vars
 view {variable: VarMap}
Parameters: vars : list of variables or None
if None, then all model.free_RVs are used for flattening input
order : ArrayOrdering
Optional, use predefined ordering
inputvar : tt.vector
Optional, use predefined inputvar
Returns: flat_view

fn
(outs, mode=None, *args, **kwargs)¶ Compiles a Theano function which returns the values of
outs
and takes values of model vars as arguments.Parameters: outs : Theano variable or iterable of Theano variables
mode : Theano compilation mode
Returns: Compiled Theano function

logp_nojact
¶ Theano scalar of logprobability of the model

logpt
¶ Theano scalar of logprobability of the model

makefn
(outs, mode=None, *args, **kwargs)¶ Compiles a Theano function which returns
outs
and takes the variable ancestors ofouts
as inputs.Parameters: outs : Theano variable or iterable of Theano variables
mode : Theano compilation mode
Returns: Compiled Theano function

name_for
(name)¶ Checks if name has prefix and adds if needed

name_of
(name)¶ Checks if name has prefix and deletes if needed

profile
(outs, n=1000, point=None, profile=True, *args, **kwargs)¶ Compiles and profiles a Theano function which returns
outs
and takes values of model vars as a dict as an argument.Parameters: outs : Theano variable or iterable of Theano variables
n : int, default 1000
Number of iterations to run
point : point
Point to pass to the function
profile : True or ProfileStats
args, kwargs
Compilation args
Returns: ProfileStats
Use .summary() to print stats.

test_point
¶ Test point used to check that the model doesn’t generate errors

unobserved_RVs
¶ List of all random variable, including deterministic ones.

varlogpt
¶ Theano scalar of logprobability of the unobserved random variables (excluding deterministic).

vars
¶ List of unobserved random variables used as inputs to the model (which excludes deterministics).


class
pymc3.model.
Factor
(*args, **kwargs)¶ Common functionality for objects with a log probability density associated with them.

d2logp
(vars=None)¶ Compiled log probability density hessian function

d2logp_nojac
(vars=None)¶ Compiled log density hessian function, without jacobian terms.

dlogp
(vars=None)¶ Compiled log probability density gradient function

dlogp_nojac
(vars=None)¶ Compiled log density gradient function, without jacobian terms.

fastd2logp
(vars=None)¶ Compiled log probability density hessian function

fastd2logp_nojac
(vars=None)¶ Compiled log density hessian function, without jacobian terms.

fastdlogp
(vars=None)¶ Compiled log probability density gradient function

fastdlogp_nojac
(vars=None)¶ Compiled log density gradient function, without jacobian terms.

fastlogp
¶ Compiled log probability density function

logp
¶ Compiled log probability density function

logp_nojact
¶ Theano scalar of logprobability, excluding jacobian terms.

logpt
¶ Theano scalar of logprobability of the model


pymc3.model.
compilef
(outs, mode=None, model=None)¶ Compiles a Theano function which returns
outs
and takes values of model vars as a dict as an argument.Parameters: outs : Theano variable or iterable of Theano variables
mode : Theano compilation mode
Returns: Compiled Theano function as point function.

pymc3.model.
fn
(outs, mode=None, model=None, *args, **kwargs)¶ Compiles a Theano function which returns the values of
outs
and takes values of model vars as arguments.Parameters: outs : Theano variable or iterable of Theano variables
mode : Theano compilation mode
Returns: Compiled Theano function

pymc3.model.
fastfn
(outs, mode=None, model=None)¶ Compiles a Theano function which returns
outs
and takes values of model vars as a dict as an argument.Parameters: outs : Theano variable or iterable of Theano variables
mode : Theano compilation mode
Returns: Compiled Theano function as point function.

pymc3.model.
modelcontext
(model)¶ return the given model or try to find it in the context if there was none supplied.

pymc3.model.
Point
(*args, **kwargs)¶ Build a point. Uses same args as dict() does. Filters out variables not in the model. All keys are strings.
Parameters: args, kwargs
arguments to build a dict

pymc3.model.
Deterministic
(name, var, model=None)¶ Create a named deterministic variable
Parameters: name : str
var : theano variables
Returns: var : var, with name attribute

pymc3.model.
Potential
(name, var, model=None)¶ Add an arbitrary factor potential to the model likelihood
Parameters: name : str
var : theano variables
Returns: var : var, with name attribute