swyft.prior

class swyft.prior.Prior(cdf, icdf, log_prob, n_parameters)

Fully factorizable prior.

Parameters:

• cdf (Callable) – cumulative density function, aka vu

• icdf (Callable) – inverse cumulative density function, aka ppf and uv

• log_prob (Callable) – log density function

• n_parameters (int) – number of parameters / dimensionality of the prior

Note

The prior is defined through the mapping \(u\to v\), from the Uniform distribution, \(u\sim \text{Unif}(0, 1)\) onto the parameters of interest, \(v\). This mapping corresponds to the inverse cummulative distribution function, and is internally used to perform inverse transform sampling. Sampling happens in the swyft.Bound object.

static conjugate_tensor_func(function)

conjugate a function by converting the input array to a tensor, apply function to the tensor, then convert the output tensor back to an array.

Parameters:

function (Callable[[Tensor], Tensor]) – callable which takes a torch tensor

Return type:

Callable[[ndarray], ndarray]

classmethod from_torch_distribution(distribution)

Create a prior from a batched pytorch distribution.

For example, distribution = torch.distributions.Uniform(-1 * torch.ones(5), 1 * torch.ones(5)).

Parameters:

distribution (Distribution) – pytorch distribution

Returns:

Prior

Return type:

PriorType

classmethod from_uv(icdf, n_parameters, n_grid_points=10000)

Create a prior which depends on InterpolatedTabulatedDistribution, i.e. an interpolated representation of the icdf, cdf, and log_prob.

Warning

Internally the mapping u -> v is tabulated on a linear grid on the interval [0, 1], with n grid points. In extreme cases, this can lead to approximation errors that can be mitigated by increasing n (in some cases).

Parameters:

• icdf (Callable) – map from hypercube: u -> v. inverse cumulative density function (icdf)

• n_parameters (int) – number of parameters / dimensionality of the prior

• n_grid_points (int) – number of grid points from which to interpolate the icdf, cdf, and log_prob

Returns:

Prior

Return type:

PriorType

class swyft.prior.PriorTruncator(prior, bound)

Samples from a truncated version of the prior and calculates the log_prob.

Parameters:

• prior (Prior) – Parameter prior

• bound (Bound) – Bound object

Note

The prior truncator is defined through a swyft.Bound object, which sample from (subregions of) the hypercube, with swyft.Prior, which maps the samples onto parameters of interest.

Instantiate prior truncator (combination of prior and bound).

Parameters:

• prior (Prior) – Prior object.

• bound (Bound) – Bound on hypercube. Set ‘None’ for untruncated priors.

log_prob(v)

Evaluate log probability.

Parameters:

v (ndarray) – (N, n_parameters) parameter points.

Returns:

(N,)

Return type:

log_prob

sample(n_samples)

Sample from truncated prior.

Parameters:

n_samples (int) – Number of samples to return

Returns:

(n_samples, n_parameters)

Return type:

Samples