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Metaclass

pyrenew helper classes

Model 

Model(**kwargs)

Abstract base class for models

Source code in pyrenew/metaclass.py 
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@abstractmethod
def __init__(self, **kwargs) -> None:  # numpydoc ignore=GL08
    pass

model 

model(**kwargs) -> tuple

Alias for the sample method.

Parameters:

Name Type Description Default
**kwargs

Additional keyword arguments passed through to internal sample calls, should there be any.

 {}

Returns:

Type Description
tuple
Source code in pyrenew/metaclass.py 
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def model(self, **kwargs) -> tuple:
    """
    Alias for the sample method.

    Parameters
    ----------
    **kwargs
        Additional keyword arguments passed through to
        internal `sample` calls, should there be any.

    Returns
    -------
    tuple
    """
    return self.sample(**kwargs)

posterior_predictive 

posterior_predictive(
    rng_key: ArrayLike | None = None,
    numpyro_predictive_args: dict = {},
    **kwargs,
) -> dict

A wrapper of numpyro.infer.util.Predictive to generate posterior predictive samples.

Parameters:

Name Type Description Default
rng_key ArrayLike | None

Random key for the Predictive function call. Defaults to None.

 None
numpyro_predictive_args dict

Dictionary of arguments to be passed to the numpyro.infer.util.Predictive constructor.

 {}
**kwargs

Additional named arguments passed to the __call__() method of numpyro.infer.util.Predictive.

 {}

Returns:

Type Description
dict
Source code in pyrenew/metaclass.py 
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def posterior_predictive(
    self,
    rng_key: ArrayLike | None = None,
    numpyro_predictive_args: dict = {},
    **kwargs,
) -> dict:
    """
    A wrapper of [`numpyro.infer.util.Predictive`][] to generate
    posterior predictive samples.

    Parameters
    ----------
    rng_key
        Random key for the Predictive function call. Defaults to None.
    numpyro_predictive_args
        Dictionary of arguments to be passed to the
        [`numpyro.infer.util.Predictive`][] constructor.
    **kwargs
        Additional named arguments passed to the
        `__call__()` method of
        [`numpyro.infer.util.Predictive`][].

    Returns
    -------
    dict
    """
    if self.mcmc is None:
        raise ValueError(
            "No posterior samples available. Run model with model.run()."
        )

    if rng_key is None:
        rand_int = np.random.randint(np.iinfo(np.int64).min, np.iinfo(np.int64).max)
        rng_key = jr.key(rand_int)

    predictive = Predictive(
        model=self.model,
        posterior_samples=self.mcmc.get_samples(),
        **numpyro_predictive_args,
    )

    return predictive(rng_key, **kwargs)

print_summary 

print_summary(prob: float = 0.9, exclude_deterministic: bool = True) -> None

A wrapper of numpyro.infer.mcmc.MCMC.print_summary.

Parameters:

Name Type Description Default
prob float

The width of the credible interval to show. Default 0.9

 0.9
exclude_deterministic bool

Whether to print deterministic sites in the summary. Defaults to True.

 True

Returns:

Type Description
None
Source code in pyrenew/metaclass.py 
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def print_summary(
    self,
    prob: float = 0.9,
    exclude_deterministic: bool = True,
) -> None:
    """
    A wrapper of [`numpyro.infer.mcmc.MCMC.print_summary`][].

    Parameters
    ----------
    prob
        The width of the credible interval to show. Default 0.9
    exclude_deterministic
        Whether to print deterministic sites in the summary.
        Defaults to True.

    Returns
    -------
    None
    """
    return self.mcmc.print_summary(prob, exclude_deterministic)

prior_predictive 

prior_predictive(
    rng_key: ArrayLike | None = None,
    numpyro_predictive_args: dict = {},
    **kwargs,
) -> dict

A wrapper for numpyro.infer.util.Predictive to generate prior predictive samples.

Parameters:

Name Type Description Default
rng_key ArrayLike | None

Random key for the Predictive function call. Default None.

 None
numpyro_predictive_args dict

Dictionary of arguments to be passed to the numpyro.infer.util.Predictive constructor. Default None.

 {}
**kwargs

Additional named arguments passed to the __call__() method of numpyro.infer.util.Predictive.

 {}

Returns:

Type Description
dict
Source code in pyrenew/metaclass.py 
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def prior_predictive(
    self,
    rng_key: ArrayLike | None = None,
    numpyro_predictive_args: dict = {},
    **kwargs,
) -> dict:
    """
    A wrapper for [`numpyro.infer.util.Predictive`][]
    to generate prior predictive samples.

    Parameters
    ----------
    rng_key
        Random key for the Predictive function call.
        Default None.
    numpyro_predictive_args
        Dictionary of arguments to be passed to
        the [`numpyro.infer.util.Predictive`][]
        constructor. Default None.
    **kwargs
        Additional named arguments passed to the
        `__call__()` method of
        [`numpyro.infer.util.Predictive`][].

    Returns
    -------
    dict
    """

    if rng_key is None:
        rand_int = np.random.randint(np.iinfo(np.int64).min, np.iinfo(np.int64).max)
        rng_key = jr.key(rand_int)

    predictive = Predictive(
        model=self.model,
        posterior_samples=None,
        **numpyro_predictive_args,
    )

    return predictive(rng_key, **kwargs)

run 

run(
    num_warmup,
    num_samples,
    rng_key: ArrayLike | None = None,
    nuts_args: dict = None,
    mcmc_args: dict = None,
    **kwargs,
) -> None

Runs the model

Parameters:

Name Type Description Default
nuts_args dict

Dictionary of arguments passed to the kernel numpyro.infer.hmc.NUTS constructor. Defaults to None.

 None
mcmc_args dict

Dictionary of arguments passed to the MCMC runner numpyro.infer.mcmc.MCMC constructor. Defaults to None.

 None

Returns:

Type Description
None
Source code in pyrenew/metaclass.py 
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def run(
    self,
    num_warmup,
    num_samples,
    rng_key: ArrayLike | None = None,
    nuts_args: dict = None,
    mcmc_args: dict = None,
    **kwargs,
) -> None:
    """
    Runs the model

    Parameters
    ----------
    nuts_args
        Dictionary of arguments passed to the kernel
        [`numpyro.infer.hmc.NUTS`][] constructor.
        Defaults to None.
    mcmc_args
        Dictionary of arguments passed to the MCMC runner
        [`numpyro.infer.mcmc.MCMC`][] constructor.
        Defaults to None.

    Returns
    -------
    None
    """

    self._init_model(
        num_warmup=num_warmup,
        num_samples=num_samples,
        nuts_args=nuts_args,
        mcmc_args=mcmc_args,
    )
    if rng_key is None:
        rand_int = np.random.randint(np.iinfo(np.int64).min, np.iinfo(np.int64).max)
        rng_key = jr.key(rand_int)

    self.mcmc.run(rng_key=rng_key, **kwargs)

    return None

sample abstractmethod 

sample(**kwargs) -> tuple

Sample method of the model.

The method design in the class should have at least kwargs.

Parameters:

Name Type Description Default
**kwargs

Additional keyword arguments passed through to internal sample calls, should there be any.

 {}

Returns:

Type Description
tuple
Source code in pyrenew/metaclass.py 
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@abstractmethod
def sample(
    self,
    **kwargs,
) -> tuple:
    """
    Sample method of the model.

    The method design in the class should have at least kwargs.

    Parameters
    ----------
    **kwargs
        Additional keyword arguments passed through to internal
        `sample` calls, should there be any.

    Returns
    -------
    tuple
    """
    pass

RandomVariable 

RandomVariable(**kwargs)

Abstract base class for latent and observed random variables.

Default constructor

Source code in pyrenew/metaclass.py 
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def __init__(self, **kwargs):
    """
    Default constructor
    """
    pass

__call__ 

__call__(**kwargs)

Alias for sample.

Source code in pyrenew/metaclass.py 
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def __call__(self, **kwargs):
    """
    Alias for `sample`.
    """
    return self.sample(**kwargs)

sample abstractmethod 

sample(**kwargs) -> tuple

Sample method of the process

The method design in the class should have at least kwargs.

Parameters:

Name Type Description Default
**kwargs

Additional keyword arguments passed through to internal sample calls, should there be any.

 {}

Returns:

Type Description
tuple
Source code in pyrenew/metaclass.py 
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@abstractmethod
def sample(
    self,
    **kwargs,
) -> tuple:
    """
    Sample method of the process

    The method design in the class should have at least kwargs.

    Parameters
    ----------
    **kwargs
        Additional keyword arguments passed through to internal
        `sample` calls, should there be any.

    Returns
    -------
    tuple
    """
    pass

validate abstractmethod staticmethod 

validate(**kwargs) -> None

Validation of kwargs to be implemented in subclasses.

Source code in pyrenew/metaclass.py 
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@staticmethod
@abstractmethod
def validate(**kwargs) -> None:
    """
    Validation of kwargs to be implemented in subclasses.
    """
    pass