Model¶

pymc3.model.Deterministic(name, var, model=None, dims=None)¶

Create a named deterministic variable

Parameters

name: str
var: theano variables

Returns

var: var, with name attribute

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.

property fastlogp¶: Compiled log probability density function

property logp¶: Compiled log probability density function

property logp_nojact¶: Theano scalar of log-probability, excluding jacobian terms.

property logpt¶: Theano scalar of log-probability of the model

class pymc3.model.Model(*args, **kwargs)¶

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.
check_bounds: bool: Ensure that input parameters to distributions are in a valid range. If your model is built in a way where you know your parameters can only take on valid values you can set this to False for increased speed.

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, dims=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]
dimstuple: Dimension names for the variable.

Returns

FreeRV or ObservedRV

add_random_variable(var, dims=None)¶: Add a random variable to the named variables of the model.

property 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 log-probabilities

Returns

Pandas Series

property cont_vars¶: All the continuous variables in the model

property 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_dlogp_function(grad_vars=None, tempered=False, **kwargs)¶

Compile a theano function that computes logp and gradient.

Parameters

grad_vars: list of random variables, optional: Compute the gradient with respect to those variables. If None, use all free random variables of this model.
tempered: bool: Compute the tempered logp free_logp + alpha * observed_logp. alpha can be changed using ValueGradFunction.set_weights([alpha]).

property logp_nojact¶: Theano scalar of log-probability of the model but without the jacobian if transformed Random Variable is presented. Note that If there is no transformed variable in the model, logp_nojact will be the same as logpt as there is no need for Jacobian correction.

property logpt¶: Theano scalar of log-probability of the model

makefn(outs, mode=None, *args, **kwargs)¶

Compiles a Theano function which returns outs and takes the variable ancestors of outs 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.

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

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

property varlogpt¶: Theano scalar of log-probability of the unobserved random variables (excluding deterministic).

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

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.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

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.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.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.modelcontext(model: Optional[pymc3.model.Model]) → pymc3.model.Model ¶: Return the given model or, if none was supplied, try to find one in the context stack.

pymc3.model.set_data(new_data, model=None)¶

Sets the value of one or more data container variables.

Parameters

new_data: dict: New values for the data containers. The keys of the dictionary are the variables’ names in the model and the values are the objects with which to update.
model: Model (optional if in `with` context)

Examples

>>> import pymc3 as pm
>>> with pm.Model() as model:
...     x = pm.Data('x', [1., 2., 3.])
...     y = pm.Data('y', [1., 2., 3.])
...     beta = pm.Normal('beta', 0, 1)
...     obs = pm.Normal('obs', x * beta, 1, observed=y)
...     trace = pm.sample(1000, tune=1000)

Set the value of x to predict on new data.

>>> with model:
...     pm.set_data({'x': [5., 6., 9.]})
...     y_test = pm.sample_posterior_predictive(trace)
>>> y_test['obs'].mean(axis=0)
array([4.6088569 , 5.54128318, 8.32953844])