gensbi.recipes#

Cookie cutter modules for creating and training SBI models.

Submodules#

Classes#

`FluxDiffusionPipeline`	Abstract base class for GenSBI training pipelines.
`FluxFlowPipeline`	Abstract base class for GenSBI training pipelines.
`SimformerDiffusionPipeline`	Abstract base class for GenSBI training pipelines.
`SimformerFlowPipeline`	Abstract base class for GenSBI training pipelines.

Package Contents#

class gensbi.recipes.FluxDiffusionPipeline(train_dataset, val_dataset, dim_theta, dim_x, params=None, training_config=None)[source]#

Bases: gensbi.recipes.pipeline.AbstractPipeline

Abstract base class for GenSBI training pipelines.

This class provides a template for implementing training and evaluation pipelines for conditional generative models. Subclasses should implement model creation, default parameter setup, loss function, sampling, and evaluation methods.

Parameters:

train_dataset (iterable) – Training dataset, should yield batches of data.
val_dataset (iterable) – Validation dataset, should yield batches of data.
dim_theta (int) – Dimensionality of the parameter (theta) space.
dim_x (int) – Dimensionality of the observation (x) space.
params (dict, optional) – Model parameters. If None, uses defaults from _get_default_params.
training_config (dict, optional) – Training configuration. If None, uses defaults from _get_default_training_config.

_get_default_params()[source]#: Return default parameters for the Flux model.

classmethod _get_default_training_config()[source]#

Return a dictionary of default training configuration parameters.

Returns:: training_config – Default training configuration.
Return type:: dict

_make_model()[source]#: Create and return the Flux model to be trained.

_wrap_model()[source]#: Wrap the model for evaluation (either using SimformerWrapper or Flux1Wrapper).

get_loss_fn()[source]#: Return the loss function for training/validation.

sample(rng, x_o, nsamples=10000, nsteps=18, use_ema=True)[source]#

Generate samples from the trained model.

Parameters:

rng (jax.random.PRNGKey) – Random number generator key.
x_o (array-like) – Conditioning variable (e.g., observed data).
nsamples (int, optional) – Number of samples to generate.
step_size (float, optional) – Step size for the sampler.

Returns:

samples – Generated samples.

Return type:

array-like

cond_ids#

loss_fn#

obs_ids#

path#

class gensbi.recipes.FluxFlowPipeline(train_dataset, val_dataset, dim_theta, dim_x, params=None, training_config=None)[source]#

Bases: gensbi.recipes.pipeline.AbstractPipeline

Abstract base class for GenSBI training pipelines.

This class provides a template for implementing training and evaluation pipelines for conditional generative models. Subclasses should implement model creation, default parameter setup, loss function, sampling, and evaluation methods.

Parameters:

train_dataset (iterable) – Training dataset, should yield batches of data.
val_dataset (iterable) – Validation dataset, should yield batches of data.
dim_theta (int) – Dimensionality of the parameter (theta) space.
dim_x (int) – Dimensionality of the observation (x) space.
params (dict, optional) – Model parameters. If None, uses defaults from _get_default_params.
training_config (dict, optional) – Training configuration. If None, uses defaults from _get_default_training_config.

_get_default_params()[source]#: Return default parameters for the Flux model.

_make_model()[source]#: Create and return the Flux model to be trained.

_wrap_model()[source]#: Wrap the model for evaluation (either using SimformerWrapper or Flux1Wrapper).

get_loss_fn()[source]#: Return the loss function for training/validation.

sample(rng, x_o, nsamples=10000, step_size=0.01, use_ema=True)[source]#

Generate samples from the trained model.

Parameters:

rng (jax.random.PRNGKey) – Random number generator key.
x_o (array-like) – Conditioning variable (e.g., observed data).
nsamples (int, optional) – Number of samples to generate.
step_size (float, optional) – Step size for the sampler.

Returns:

samples – Generated samples.

Return type:

array-like

cond_ids#

loss_fn#

obs_ids#

p0_dist_model#

path#

class gensbi.recipes.SimformerDiffusionPipeline(train_dataset, val_dataset, dim_theta, dim_x, params=None, training_config=None)[source]#

Bases: gensbi.recipes.pipeline.AbstractPipeline

Abstract base class for GenSBI training pipelines.

This class provides a template for implementing training and evaluation pipelines for conditional generative models. Subclasses should implement model creation, default parameter setup, loss function, sampling, and evaluation methods.

Parameters:

train_dataset (iterable) – Training dataset, should yield batches of data.
val_dataset (iterable) – Validation dataset, should yield batches of data.
dim_theta (int) – Dimensionality of the parameter (theta) space.
dim_x (int) – Dimensionality of the observation (x) space.
params (dict, optional) – Model parameters. If None, uses defaults from _get_default_params.
training_config (dict, optional) – Training configuration. If None, uses defaults from _get_default_training_config.

_get_default_params()[source]#: Return default parameters for the Simformer model.

classmethod _get_default_training_config()[source]#

Return a dictionary of default training configuration parameters.

Returns:: training_config – Default training configuration.
Return type:: dict

_make_model()[source]#: Create and return the Simformer model to be trained.

_wrap_model()[source]#: Wrap the model for evaluation (either using SimformerWrapper or Flux1Wrapper).

get_loss_fn()[source]#: Return the loss function for training/validation.

sample(key, x_o, nsamples=10000, nsteps=18, use_ema=True)[source]#

Generate samples from the trained model.

Parameters:

rng (jax.random.PRNGKey) – Random number generator key.
x_o (array-like) – Conditioning variable (e.g., observed data).
nsamples (int, optional) – Number of samples to generate.
step_size (float, optional) – Step size for the sampler.

Returns:

samples – Generated samples.

Return type:

array-like

loss_fn#

path#

undirected_edge_mask#

class gensbi.recipes.SimformerFlowPipeline(train_dataset, val_dataset, dim_theta, dim_x, params=None, training_config=None)[source]#

Bases: gensbi.recipes.pipeline.AbstractPipeline

Abstract base class for GenSBI training pipelines.

This class provides a template for implementing training and evaluation pipelines for conditional generative models. Subclasses should implement model creation, default parameter setup, loss function, sampling, and evaluation methods.

Parameters:

train_dataset (iterable) – Training dataset, should yield batches of data.
val_dataset (iterable) – Validation dataset, should yield batches of data.
dim_theta (int) – Dimensionality of the parameter (theta) space.
dim_x (int) – Dimensionality of the observation (x) space.
params (dict, optional) – Model parameters. If None, uses defaults from _get_default_params.
training_config (dict, optional) – Training configuration. If None, uses defaults from _get_default_training_config.

_get_default_params()[source]#: Return default parameters for the Simformer model.

_make_model()[source]#: Create and return the Simformer model to be trained.

_wrap_model()[source]#: Wrap the model for evaluation (either using SimformerWrapper or Flux1Wrapper).

get_loss_fn()[source]#: Return the loss function for training/validation.

sample(key, x_o, nsamples=10000, step_size=0.01, use_ema=True)[source]#

Generate samples from the trained model.

Parameters:

rng (jax.random.PRNGKey) – Random number generator key.
x_o (array-like) – Conditioning variable (e.g., observed data).
nsamples (int, optional) – Number of samples to generate.
step_size (float, optional) – Step size for the sampler.

Returns:

samples – Generated samples.

Return type:

array-like

loss_fn#

p0_dist_model#

path#

undirected_edge_mask#