API Reference¶
Inference¶
sbi.inference.base.infer(simulator, prior, method, num_simulations, num_workers=1)
¶
Runs simulationbased inference and returns the posterior.
This function provides a simple interface to run sbi. Inference is run for a single round and hence the returned posterior \(p(\thetax)\) can be sampled and evaluated for any \(x\) (i.e. it is amortized).
The scope of this function is limited to the most essential features of sbi. For more flexibility (e.g. multiround inference, different density estimators) please use the flexible interface described here: https://www.mackelab.org/sbi/tutorial/02_flexible_interface/
Parameters:
Name  Type  Description  Default 

simulator 
Callable

A function that takes parameters \(\theta\) and maps them to
simulations, or observations, 
required 
prior 
Distribution

A probability distribution that expresses prior knowledge about the
parameters, e.g. which ranges are meaningful for them. Any
object with 
required 
method 
str

What inference method to use. Either of SNPE, SNLE or SNRE. 
required 
num_simulations 
int

Number of simulation calls. More simulations means a longer runtime, but a better posterior estimate. 
required 
num_workers 
int

Number of parallel workers to use for simulations. 
1

Source code in sbi/inference/base.py
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sbi.utils.user_input_checks.prepare_for_sbi(simulator, prior)
¶
Prepare simulator and prior for usage in sbi.
NOTE: This is a wrapper around process_prior
and process_simulator
which can be
used in isolation as well.
Attempts to meet the following requirements by reshaping and typecasting:
 the simulator function receives as input and returns a Tensor.
 the simulator can simulate batches of parameters and return batches of data.
 the prior does not produce batches and samples and evaluates to Tensor.
 the output shape is a
torch.Size((1,N))
(i.e, has a leading batch dimension 1).
If this is not possible, a suitable exception will be raised.
Parameters:
Name  Type  Description  Default 

simulator 
Callable

Simulator as provided by the user. 
required 
prior 
Prior as provided by the user. 
required 
Returns:
Type  Description 

Tuple[Callable, Distribution]

Tuple (simulator, prior) checked and matching the requirements of sbi. 
Source code in sbi/utils/user_input_checks.py
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sbi.inference.base.simulate_for_sbi(simulator, proposal, num_simulations, num_workers=1, simulation_batch_size=1, show_progress_bar=True)
¶
Returns (\(\theta, x\)) pairs obtained from sampling the proposal and simulating.
This function performs two steps:
 Sample parameters \(\theta\) from the
proposal
.  Simulate these parameters to obtain \(x\).
Parameters:
Name  Type  Description  Default 

simulator 
Callable

A function that takes parameters \(\theta\) and maps them to
simulations, or observations, 
required 
proposal 
Any

Probability distribution that the parameters \(\theta\) are sampled from. 
required 
num_simulations 
int

Number of simulations that are run. 
required 
num_workers 
int

Number of parallel workers to use for simulations. 
1

simulation_batch_size 
int

Number of parameter sets that the simulator maps to data x at once. If None, we simulate all parameter sets at the same time. If >= 1, the simulator has to process data of shape (simulation_batch_size, parameter_dimension). 
1

show_progress_bar 
bool

Whether to show a progress bar for simulating. This will not affect whether there will be a progressbar while drawing samples from the proposal. 
True

Source code in sbi/inference/base.py
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sbi.inference.snpe.snpe_a.SNPE_A
¶
Bases: PosteriorEstimator
Source code in sbi/inference/snpe/snpe_a.py
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__init__(prior=None, density_estimator='mdn_snpe_a', num_components=10, device='cpu', logging_level='WARNING', summary_writer=None, show_progress_bars=True)
¶
SNPEA [1].
[1] Fast epsilonfree Inference of Simulation Models with Bayesian Conditional Density Estimation, Papamakarios et al., NeurIPS 2016, https://arxiv.org/abs/1605.06376.
This class implements SNPEA. SNPEA trains across multiple rounds with a maximumlikelihoodloss. This will make training converge to the proposal posterior instead of the true posterior. To correct for this, SNPEA applies a posthoc correction after training. This correction has to be performed analytically. Thus, SNPEA is limited to Gaussian distributions for all but the last round. In the last round, SNPEA can use a Mixture of Gaussians.
Parameters:
Name  Type  Description  Default 

prior 
Optional[Distribution]

A probability distribution that expresses prior knowledge about the
parameters, e.g. which ranges are meaningful for them. Any
object with 
None

density_estimator 
Union[str, Callable]

If it is a string (only “mdn_snpe_a” is valid), use a
preconfigured mixture of densities network. Alternatively, a function
that builds a custom neural network can be provided. The function will
be called with the first batch of simulations (theta, x), which can
thus be used for shape inference and potentially for zscoring. It
needs to return a PyTorch 
'mdn_snpe_a'

num_components 
int

Number of components of the mixture of Gaussians in the
last round. This overrides the 
10

device 
str

Training device, e.g., “cpu”, “cuda” or “cuda:{0, 1, …}”. 
'cpu'

logging_level 
Union[int, str]

Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL. 
'WARNING'

summary_writer 
Optional[TensorboardSummaryWriter]

A tensorboard 
None

show_progress_bars 
bool

Whether to show a progressbar during training. 
True

Source code in sbi/inference/snpe/snpe_a.py
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build_posterior(density_estimator=None, prior=None)
¶
Build posterior from the neural density estimator.
This method first corrects the estimated density with correct_for_proposal
and then returns a DirectPosterior
.
Parameters:
Name  Type  Description  Default 

density_estimator 
Optional[TorchModule]

The density estimator that the posterior is based on.
If 
None

prior 
Optional[Distribution]

Prior distribution. 
None

Returns:
Type  Description 

DirectPosterior

Posterior \(p(\thetax)\) with 
Source code in sbi/inference/snpe/snpe_a.py
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correct_for_proposal(density_estimator=None)
¶
Build mixture of Gaussians that approximates the posterior.
Returns a SNPE_A_MDN
object, which applies the posthoccorrection required in
SNPEA.
Parameters:
Name  Type  Description  Default 

density_estimator 
Optional[TorchModule]

The density estimator that the posterior is based on.
If 
None

Returns:
Type  Description 

SNPE_A_MDN

Posterior \(p(\thetax)\) with 
Source code in sbi/inference/snpe/snpe_a.py
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train(final_round=False, training_batch_size=50, learning_rate=0.0005, validation_fraction=0.1, stop_after_epochs=20, max_num_epochs=2 ** 31  1, clip_max_norm=5.0, calibration_kernel=None, resume_training=False, retrain_from_scratch=False, show_train_summary=False, dataloader_kwargs=None, component_perturbation=0.005)
¶
Return density estimator that approximates the proposal posterior.
[1] Fast epsilonfree Inference of Simulation Models with Bayesian Conditional Density Estimation, Papamakarios et al., NeurIPS 2016, https://arxiv.org/abs/1605.06376.
Training is performed with maximum likelihood on samples from the latest round, which leads the algorithm to converge to the proposal posterior.
Parameters:
Name  Type  Description  Default 

final_round 
bool

Whether we are in the last round of training or not. For all but the last round, Algorithm 1 from [1] is executed. In last the round, Algorithm 2 from [1] is executed once. 
False

training_batch_size 
int

Training batch size. 
50

learning_rate 
float

Learning rate for Adam optimizer. 
0.0005

validation_fraction 
float

The fraction of data to use for validation. 
0.1

stop_after_epochs 
int

The number of epochs to wait for improvement on the validation set before terminating training. 
20

max_num_epochs 
int

Maximum number of epochs to run. If reached, we stop
training even when the validation loss is still decreasing. Otherwise,
we train until validation loss increases (see also 
2 ** 31  1

clip_max_norm 
Optional[float]

Value at which to clip the total gradient norm in order to prevent exploding gradients. Use None for no clipping. 
5.0

calibration_kernel 
Optional[Callable]

A function to calibrate the loss with respect to the
simulations 
None

resume_training 
bool

Can be used in case training time is limited, e.g. on a
cluster. If 
False

force_first_round_loss 
If 
required  
retrain_from_scratch 
bool

Whether to retrain the conditional density estimator for the posterior from scratch each round. Not supported for SNPEA. 
False

show_train_summary 
bool

Whether to print the number of epochs and validation loss and leakage after the training. 
False

dataloader_kwargs 
Optional[Dict]

Additional or updated kwargs to be passed to the training and validation dataloaders (like, e.g., a collate_fn) 
None

component_perturbation 
float

The standard deviation applied to all weights and biases when, in the last round, the Mixture of Gaussians is build from a single Gaussian. This value can be problemspecific and also depends on the number of mixture components. 
0.005

Returns:
Type  Description 

nn.Module

Density estimator that approximates the distribution \(p(\thetax)\). 
Source code in sbi/inference/snpe/snpe_a.py
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sbi.inference.snpe.snpe_c.SNPE_C
¶
Bases: PosteriorEstimator
Source code in sbi/inference/snpe/snpe_c.py
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__init__(prior=None, density_estimator='maf', device='cpu', logging_level='WARNING', summary_writer=None, show_progress_bars=True)
¶
SNPEC / APT [1].
[1] Automatic Posterior Transformation for Likelihoodfree Inference, Greenberg et al., ICML 2019, https://arxiv.org/abs/1905.07488.
This class implements two loss variants of SNPEC: the nonatomic and the atomic version. The atomic loss of SNPEC can be used for any density estimator, i.e. also for normalizing flows. However, it suffers from leakage issues. On the other hand, the nonatomic loss can only be used only if the proposal distribution is a mixture of Gaussians, the density estimator is a mixture of Gaussians, and the prior is either Gaussian or Uniform. It does not suffer from leakage issues. At the beginning of each round, we print whether the nonatomic or the atomic version is used.
In this codebase, we will automatically switch to the nonatomic loss if the
following criteria are fulfilled:
 proposal is a DirectPosterior
with density_estimator mdn
, as built
with utils.sbi.posterior_nn()
.
 the density estimator is a mdn
, as built with
utils.sbi.posterior_nn()
.
 isinstance(prior, MultivariateNormal)
(from torch.distributions
) or
isinstance(prior, sbi.utils.BoxUniform)
Note that custom implementations of any of these densities (or estimators) will not trigger the nonatomic loss, and the algorithm will fall back onto using the atomic loss.
Parameters:
Name  Type  Description  Default 

prior 
Optional[Distribution]

A probability distribution that expresses prior knowledge about the parameters, e.g. which ranges are meaningful for them. 
None

density_estimator 
Union[str, Callable]

If it is a string, use a preconfigured network of the
provided type (one of nsf, maf, mdn, made). Alternatively, a function
that builds a custom neural network can be provided. The function will
be called with the first batch of simulations (theta, x), which can
thus be used for shape inference and potentially for zscoring. It
needs to return a PyTorch 
'maf'

device 
str

Training device, e.g., “cpu”, “cuda” or “cuda:{0, 1, …}”. 
'cpu'

logging_level 
Union[int, str]

Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL. 
'WARNING'

summary_writer 
Optional[TensorboardSummaryWriter]

A tensorboard 
None

show_progress_bars 
bool

Whether to show a progressbar during training. 
True

Source code in sbi/inference/snpe/snpe_c.py
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train(num_atoms=10, training_batch_size=50, learning_rate=0.0005, validation_fraction=0.1, stop_after_epochs=20, max_num_epochs=2 ** 31  1, clip_max_norm=5.0, calibration_kernel=None, resume_training=False, force_first_round_loss=False, discard_prior_samples=False, use_combined_loss=False, retrain_from_scratch=False, show_train_summary=False, dataloader_kwargs=None)
¶
Return density estimator that approximates the distribution \(p(\thetax)\).
Parameters:
Name  Type  Description  Default 

num_atoms 
int

Number of atoms to use for classification. 
10

training_batch_size 
int

Training batch size. 
50

learning_rate 
float

Learning rate for Adam optimizer. 
0.0005

validation_fraction 
float

The fraction of data to use for validation. 
0.1

stop_after_epochs 
int

The number of epochs to wait for improvement on the validation set before terminating training. 
20

max_num_epochs 
int

Maximum number of epochs to run. If reached, we stop
training even when the validation loss is still decreasing. Otherwise,
we train until validation loss increases (see also 
2 ** 31  1

clip_max_norm 
Optional[float]

Value at which to clip the total gradient norm in order to prevent exploding gradients. Use None for no clipping. 
5.0

calibration_kernel 
Optional[Callable]

A function to calibrate the loss with respect to the
simulations 
None

resume_training 
bool

Can be used in case training time is limited, e.g. on a
cluster. If 
False

force_first_round_loss 
bool

If 
False

discard_prior_samples 
bool

Whether to discard samples simulated in round 1, i.e. from the prior. Training may be sped up by ignoring such less targeted samples. 
False

use_combined_loss 
bool

Whether to train the neural net also on prior samples using maximum likelihood in addition to training it on all samples using atomic loss. The extra MLE loss helps prevent density leaking with bounded priors. 
False

retrain_from_scratch 
bool

Whether to retrain the conditional density estimator for the posterior from scratch each round. 
False

show_train_summary 
bool

Whether to print the number of epochs and validation loss and leakage after the training. 
False

dataloader_kwargs 
Optional[Dict]

Additional or updated kwargs to be passed to the training and validation dataloaders (like, e.g., a collate_fn) 
None

Returns:
Type  Description 

nn.Module

Density estimator that approximates the distribution \(p(\thetax)\). 
Source code in sbi/inference/snpe/snpe_c.py
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sbi.inference.snle.snle_a.SNLE_A
¶
Bases: LikelihoodEstimator
Source code in sbi/inference/snle/snle_a.py
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__init__(prior=None, density_estimator='maf', device='cpu', logging_level='WARNING', summary_writer=None, show_progress_bars=True)
¶
Sequential Neural Likelihood [1].
[1] Sequential Neural Likelihood: Fast Likelihoodfree Inference with Autoregressive Flows_, Papamakarios et al., AISTATS 2019, https://arxiv.org/abs/1805.07226
Parameters:
Name  Type  Description  Default 

prior 
Optional[Distribution]

A probability distribution that expresses prior knowledge about the
parameters, e.g. which ranges are meaningful for them. If 
None

density_estimator 
Union[str, Callable]

If it is a string, use a preconfigured network of the
provided type (one of nsf, maf, mdn, made). Alternatively, a function
that builds a custom neural network can be provided. The function will
be called with the first batch of simulations (theta, x), which can
thus be used for shape inference and potentially for zscoring. It
needs to return a PyTorch 
'maf'

device 
str

Training device, e.g., “cpu”, “cuda” or “cuda:{0, 1, …}”. 
'cpu'

logging_level 
Union[int, str]

Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL. 
'WARNING'

summary_writer 
Optional[TensorboardSummaryWriter]

A tensorboard 
None

show_progress_bars 
bool

Whether to show a progressbar during simulation and sampling. 
True

Source code in sbi/inference/snle/snle_a.py
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sbi.inference.snre.snre_a.SNRE_A
¶
Bases: RatioEstimator
Source code in sbi/inference/snre/snre_a.py
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__init__(prior=None, classifier='resnet', device='cpu', logging_level='warning', summary_writer=None, show_progress_bars=True)
¶
AALR[1], here known as SNRE_A.
[1] Likelihoodfree MCMC with Amortized Approximate Likelihood Ratios, Hermans et al., ICML 2020, https://arxiv.org/abs/1903.04057
Parameters:
Name  Type  Description  Default 

prior 
Optional[Distribution]

A probability distribution that expresses prior knowledge about the
parameters, e.g. which ranges are meaningful for them. If 
None

classifier 
Union[str, Callable]

Classifier trained to approximate likelihood ratios. If it is
a string, use a preconfigured network of the provided type (one of
linear, mlp, resnet). Alternatively, a function that builds a custom
neural network can be provided. The function will be called with the
first batch of simulations (theta, x), which can thus be used for shape
inference and potentially for zscoring. It needs to return a PyTorch

'resnet'

device 
str

Training device, e.g., “cpu”, “cuda” or “cuda:{0, 1, …}”. 
'cpu'

logging_level 
Union[int, str]

Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL. 
'warning'

summary_writer 
Optional[TensorboardSummaryWriter]

A tensorboard 
None

show_progress_bars 
bool

Whether to show a progressbar during simulation and sampling. 
True

Source code in sbi/inference/snre/snre_a.py
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train(training_batch_size=50, learning_rate=0.0005, validation_fraction=0.1, stop_after_epochs=20, max_num_epochs=2 ** 31  1, clip_max_norm=5.0, resume_training=False, discard_prior_samples=False, retrain_from_scratch=False, show_train_summary=False, dataloader_kwargs=None, loss_kwargs={})
¶
Return classifier that approximates the ratio \(p(\theta,x)/p(\theta)p(x)\).
Parameters:
Name  Type  Description  Default 

training_batch_size 
int

Training batch size. 
50

learning_rate 
float

Learning rate for Adam optimizer. 
0.0005

validation_fraction 
float

The fraction of data to use for validation. 
0.1

stop_after_epochs 
int

The number of epochs to wait for improvement on the validation set before terminating training. 
20

max_num_epochs 
int

Maximum number of epochs to run. If reached, we stop
training even when the validation loss is still decreasing. Otherwise,
we train until validation loss increases (see also 
2 ** 31  1

clip_max_norm 
Optional[float]

Value at which to clip the total gradient norm in order to prevent exploding gradients. Use None for no clipping. 
5.0

resume_training 
bool

Can be used in case training time is limited, e.g. on a
cluster. If 
False

discard_prior_samples 
bool

Whether to discard samples simulated in round 1, i.e. from the prior. Training may be sped up by ignoring such less targeted samples. 
False

retrain_from_scratch 
bool

Whether to retrain the conditional density estimator for the posterior from scratch each round. 
False

show_train_summary 
bool

Whether to print the number of epochs and validation loss and leakage after the training. 
False

dataloader_kwargs 
Optional[Dict]

Additional or updated kwargs to be passed to the training and validation dataloaders (like, e.g., a collate_fn) 
None

loss_kwargs 
Dict[str, Any]

Additional or updated kwargs to be passed to the self._loss fn. 
{}

Returns:
Type  Description 

nn.Module

Classifier that approximates the ratio \(p(\theta,x)/p(\theta)p(x)\). 
Source code in sbi/inference/snre/snre_a.py
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sbi.inference.snre.snre_b.SNRE_B
¶
Bases: RatioEstimator
Source code in sbi/inference/snre/snre_b.py
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__init__(prior=None, classifier='resnet', device='cpu', logging_level='warning', summary_writer=None, show_progress_bars=True)
¶
SRE[1], here known as SNRE_B.
[1] On Contrastive Learning for Likelihoodfree Inference, Durkan et al., ICML 2020, https://arxiv.org/pdf/2002.03712
Parameters:
Name  Type  Description  Default 

prior 
Optional[Distribution]

A probability distribution that expresses prior knowledge about the
parameters, e.g. which ranges are meaningful for them. If 
None

classifier 
Union[str, Callable]

Classifier trained to approximate likelihood ratios. If it is
a string, use a preconfigured network of the provided type (one of
linear, mlp, resnet). Alternatively, a function that builds a custom
neural network can be provided. The function will be called with the
first batch of simulations (theta, x), which can thus be used for shape
inference and potentially for zscoring. It needs to return a PyTorch

'resnet'

device 
str

Training device, e.g., “cpu”, “cuda” or “cuda:{0, 1, …}”. 
'cpu'

logging_level 
Union[int, str]

Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL. 
'warning'

summary_writer 
Optional[TensorboardSummaryWriter]

A tensorboard 
None

show_progress_bars 
bool

Whether to show a progressbar during simulation and sampling. 
True

Source code in sbi/inference/snre/snre_b.py
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train(num_atoms=10, training_batch_size=50, learning_rate=0.0005, validation_fraction=0.1, stop_after_epochs=20, max_num_epochs=2 ** 31  1, clip_max_norm=5.0, resume_training=False, discard_prior_samples=False, retrain_from_scratch=False, show_train_summary=False, dataloader_kwargs=None)
¶
Return classifier that approximates the ratio \(p(\theta,x)/p(\theta)p(x)\).
Parameters:
Name  Type  Description  Default 

num_atoms 
int

Number of atoms to use for classification. 
10

training_batch_size 
int

Training batch size. 
50

learning_rate 
float

Learning rate for Adam optimizer. 
0.0005

validation_fraction 
float

The fraction of data to use for validation. 
0.1

stop_after_epochs 
int

The number of epochs to wait for improvement on the validation set before terminating training. 
20

max_num_epochs 
int

Maximum number of epochs to run. If reached, we stop
training even when the validation loss is still decreasing. Otherwise,
we train until validation loss increases (see also 
2 ** 31  1

clip_max_norm 
Optional[float]

Value at which to clip the total gradient norm in order to prevent exploding gradients. Use None for no clipping. 
5.0

resume_training 
bool

Can be used in case training time is limited, e.g. on a
cluster. If 
False

discard_prior_samples 
bool

Whether to discard samples simulated in round 1, i.e. from the prior. Training may be sped up by ignoring such less targeted samples. 
False

retrain_from_scratch 
bool

Whether to retrain the conditional density estimator for the posterior from scratch each round. 
False

show_train_summary 
bool

Whether to print the number of epochs and validation loss and leakage after the training. 
False

dataloader_kwargs 
Optional[Dict]

Additional or updated kwargs to be passed to the training and validation dataloaders (like, e.g., a collate_fn) 
None

Returns:
Type  Description 

nn.Module

Classifier that approximates the ratio \(p(\theta,x)/p(\theta)p(x)\). 
Source code in sbi/inference/snre/snre_b.py
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sbi.inference.snre.snre_c.SNRE_C
¶
Bases: RatioEstimator
Source code in sbi/inference/snre/snre_c.py
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__init__(prior=None, classifier='resnet', device='cpu', logging_level='warning', summary_writer=None, show_progress_bars=True)
¶
NREC[1] is a generalization of the nonsequential (amortized) versions of
SNRE_A and SNRE_B. We call the algorithm SNRE_C within sbi
.
NREC:
(1) like SNRE_B, features a “multiclass” loss function where several marginally
drawn parameterdata pairs are contrasted against a jointly drawn pair.
(2) like AALR/NRE_A, i.e., the nonsequential version of SNRE_A, it encourages
the approximate ratio \(p(\theta,x)/p(\theta)p(x)\), accessed through
.potential()
within sbi
, to be exact at optimum. This addresses the
issue that SNRE_B estimates this ratio only up to an arbitrary function
(normalizing constant) of the data \(x\).
Just like for all ratio estimation algorithms, the sequential version of SNRE_C will be estimated only up to a function (normalizing constant) of the data \(x\) in rounds after the first.
[1] Contrastive Neural Ratio Estimation, Benajmin Kurt Miller, et. al., NeurIPS 2022, https://arxiv.org/abs/2210.06170
Parameters:
Name  Type  Description  Default 

prior 
Optional[Distribution]

A probability distribution that expresses prior knowledge about the
parameters, e.g. which ranges are meaningful for them. If 
None

classifier 
Union[str, Callable]

Classifier trained to approximate likelihood ratios. If it is
a string, use a preconfigured network of the provided type (one of
linear, mlp, resnet). Alternatively, a function that builds a custom
neural network can be provided. The function will be called with the
first batch of simulations (theta, x), which can thus be used for shape
inference and potentially for zscoring. It needs to return a PyTorch

'resnet'

device 
str

Training device, e.g., “cpu”, “cuda” or “cuda:{0, 1, …}”. 
'cpu'

logging_level 
Union[int, str]

Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL. 
'warning'

summary_writer 
Optional[TensorboardSummaryWriter]

A tensorboard 
None

show_progress_bars 
bool

Whether to show a progressbar during simulation and sampling. 
True

Source code in sbi/inference/snre/snre_c.py
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train(num_classes=5, gamma=1.0, training_batch_size=50, learning_rate=0.0005, validation_fraction=0.1, stop_after_epochs=20, max_num_epochs=2 ** 31  1, clip_max_norm=5.0, resume_training=False, discard_prior_samples=False, retrain_from_scratch=False, show_train_summary=False, dataloader_kwargs=None)
¶
Return classifier that approximates the ratio \(p(\theta,x)/p(\theta)p(x)\).
Parameters:
Name  Type  Description  Default 

num_classes 
int

Number of theta to classify against, corresponds to \(K\) in
Contrastive Neural Ratio Estimation. Minimum value is 1. Similar to

5

gamma 
float

Determines the relative weight of the sum of all \(K\) dependently drawn classes against the marginally drawn one. Specifically, \(p(y=k) :=p_K\), \(p(y=0) := p_0\), \(p_0 = 1  K p_K\), and finally \(\gamma := K p_K / p_0\). 
1.0

training_batch_size 
int

Training batch size. 
50

learning_rate 
float

Learning rate for Adam optimizer. 
0.0005

validation_fraction 
float

The fraction of data to use for validation. 
0.1

stop_after_epochs 
int

The number of epochs to wait for improvement on the validation set before terminating training. 
20

max_num_epochs 
int

Maximum number of epochs to run. If reached, we stop
training even when the validation loss is still decreasing. Otherwise,
we train until validation loss increases (see also 
2 ** 31  1

clip_max_norm 
Optional[float]

Value at which to clip the total gradient norm in order to prevent exploding gradients. Use None for no clipping. 
5.0

exclude_invalid_x 
Whether to exclude simulation outputs 
required  
resume_training 
bool

Can be used in case training time is limited, e.g. on a
cluster. If 
False

discard_prior_samples 
bool

Whether to discard samples simulated in round 1, i.e. from the prior. Training may be sped up by ignoring such less targeted samples. 
False

retrain_from_scratch 
bool

Whether to retrain the conditional density estimator for the posterior from scratch each round. 
False

show_train_summary 
bool

Whether to print the number of epochs and validation loss and leakage after the training. 
False

dataloader_kwargs 
Optional[Dict]

Additional or updated kwargs to be passed to the training and validation dataloaders (like, e.g., a collate_fn) 
None

Returns:
Type  Description 

nn.Module

Classifier that approximates the ratio \(p(\theta,x)/p(\theta)p(x)\). 
Source code in sbi/inference/snre/snre_c.py
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sbi.inference.snre.bnre.BNRE
¶
Bases: SNRE_A
Source code in sbi/inference/snre/bnre.py
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__init__(prior=None, classifier='resnet', device='cpu', logging_level='warning', summary_writer=None, show_progress_bars=True)
¶
Balanced neural ratio estimation (BNRE)[1]. BNRE is a variation of NRE aiming to produce more conservative posterior approximations
[1] Delaunoy, A., Hermans, J., Rozet, F., Wehenkel, A., & Louppe, G.. Towards Reliable SimulationBased Inference with Balanced Neural Ratio Estimation. NeurIPS 2022. https://arxiv.org/abs/2208.13624
Parameters:
Name  Type  Description  Default 

prior 
Optional[Distribution]

A probability distribution that expresses prior knowledge about the
parameters, e.g. which ranges are meaningful for them. If 
None

classifier 
Union[str, Callable]

Classifier trained to approximate likelihood ratios. If it is
a string, use a preconfigured network of the provided type (one of
linear, mlp, resnet). Alternatively, a function that builds a custom
neural network can be provided. The function will be called with the
first batch of simulations \((\theta, x)\), which can thus be used for
shape inference and potentially for zscoring. It needs to return a
PyTorch 
'resnet'

device 
str

Training device, e.g., “cpu”, “cuda” or “cuda:{0, 1, …}”. 
'cpu'

logging_level 
Union[int, str]

Minimum severity of messages to log. One of the strings INFO, WARNING, DEBUG, ERROR and CRITICAL. 
'warning'

summary_writer 
Optional[TensorboardSummaryWriter]

A tensorboard 
None

show_progress_bars 
bool

Whether to show a progressbar during simulation and sampling. 
True

Source code in sbi/inference/snre/bnre.py
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train(regularization_strength=100.0, training_batch_size=50, learning_rate=0.0005, validation_fraction=0.1, stop_after_epochs=20, max_num_epochs=2 ** 31  1, clip_max_norm=5.0, resume_training=False, discard_prior_samples=False, retrain_from_scratch=False, show_train_summary=False, dataloader_kwargs=None)
¶
Return classifier that approximates the ratio \(p(\theta,x)/p(\theta)p(x)\).
Parameters:
Name  Type  Description  Default 

regularization_strength 
float

The multiplicative coefficient applied to the balancing regularizer (\(\lambda\)). 
100.0

training_batch_size 
int

Training batch size. 
50

learning_rate 
float

Learning rate for Adam optimizer. 
0.0005

validation_fraction 
float

The fraction of data to use for validation. 
0.1

stop_after_epochs 
int

The number of epochs to wait for improvement on the validation set before terminating training. 
20

max_num_epochs 
int

Maximum number of epochs to run. If reached, we stop
training even when the validation loss is still decreasing. Otherwise,
we train until validation loss increases (see also 
2 ** 31  1

clip_max_norm 
Optional[float]

Value at which to clip the total gradient norm in order to prevent exploding gradients. Use None for no clipping. 
5.0

exclude_invalid_x 
Whether to exclude simulation outputs 
required  
resume_training 
bool

Can be used in case training time is limited, e.g. on a
cluster. If 
False

discard_prior_samples 
bool

Whether to discard samples simulated in round 1, i.e. from the prior. Training may be sped up by ignoring such less targeted samples. 
False

retrain_from_scratch 
bool

Whether to retrain the conditional density estimator for the posterior from scratch each round. 
False

show_train_summary 
bool

Whether to print the number of epochs and validation loss and leakage after the training. 
False

dataloader_kwargs 
Optional[Dict]

Additional or updated kwargs to be passed to the training and validation dataloaders (like, e.g., a collate_fn) 
None

Returns:
Type  Description 

nn.Module

Classifier that approximates the ratio \(p(\theta,x)/p(\theta)p(x)\). 
Source code in sbi/inference/snre/bnre.py
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sbi.inference.abc.mcabc.MCABC
¶
Bases: ABCBASE
Source code in sbi/inference/abc/mcabc.py
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__call__(x_o, num_simulations, eps=None, quantile=None, lra=False, sass=False, sass_fraction=0.25, sass_expansion_degree=1, kde=False, kde_kwargs={}, return_summary=False)
¶
Run MCABC and return accepted parameters or KDE object fitted on them.
Parameters:
Name  Type  Description  Default 

x_o 
Union[Tensor, ndarray]

Observed data. 
required 
num_simulations 
int

Number of simulations to run. 
required 
eps 
Optional[float]

Acceptance threshold \(\epsilon\) for distance between observed and simulated data. 
None

quantile 
Optional[float]

Upper quantile of smallest distances for which the corresponding
parameters are returned, e.g, q=0.01 will return the top 1%. Exactly
one of quantile or 
None

lra 
bool

Whether to run linear regression adjustment as in Beaumont et al. 2002 
False

sass 
bool

Whether to determine semiautomatic summary statistics as in Fearnhead & Prangle 2012. 
False

sass_fraction 
float

Fraction of simulation budget used for the initial sass run. 
0.25

sass_expansion_degree 
int

Degree of the polynomial feature expansion for the sass regression, default 1  no expansion. 
1

kde 
bool

Whether to run KDE on the accepted parameters to return a KDE object from which one can sample. 
False

kde_kwargs 
Dict[str, Any]

kwargs for performing KDE: ‘bandwidth=’; either a float, or a string naming a bandwidth heuristics, e.g., ‘cv’ (cross validation), ‘silvermann’ or ‘scott’, default ‘cv’. ‘transform’: transform applied to the parameters before doing KDE. ‘sample_weights’: weights associated with samples. See ‘get_kde’ for more details 
{}

return_summary 
bool

Whether to return the distances and data corresponding to the accepted parameters. 
False

Returns:
Name  Type  Description 

theta 
if kde False

accepted parameters 
kde 
if kde True

KDE object based on accepted parameters from which one can .sample() and .log_prob(). 
summary 
if summary True

dictionary containing the accepted paramters (if kde True), distances and simulated data x. 
Source code in sbi/inference/abc/mcabc.py
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__init__(simulator, prior, distance='l2', num_workers=1, simulation_batch_size=1, show_progress_bars=True)
¶
MonteCarlo Approximate Bayesian Computation (Rejection ABC) [1].
[1] Pritchard, J. K., Seielstad, M. T., PerezLezaun, A., & Feldman, M. W. (1999). Population growth of human Y chromosomes: a study of Y chromosome microsatellites. Molecular biology and evolution, 16(12), 17911798.
Parameters:
Name  Type  Description  Default 

simulator 
Callable

A function that takes parameters \(\theta\) and maps them to
simulations, or observations, 
required 
prior 
A probability distribution that expresses prior knowledge about the
parameters, e.g. which ranges are meaningful for them. Any
object with 
required  
distance 
Union[str, Callable]

Distance function to compare observed and simulated data. Can be
a custom function or one of 
'l2'

num_workers 
int

Number of parallel workers to use for simulations. 
1

simulation_batch_size 
int

Number of parameter sets that the simulator maps to data x at once. If None, we simulate all parameter sets at the same time. If >= 1, the simulator has to process data of shape (simulation_batch_size, parameter_dimension). 
1

show_progress_bars 
bool

Whether to show a progressbar during simulation and sampling. 
True

Source code in sbi/inference/abc/mcabc.py
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sbi.inference.abc.smcabc.SMCABC
¶
Bases: ABCBASE
Source code in sbi/inference/abc/smcabc.py
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__call__(x_o, num_particles, num_initial_pop, num_simulations, epsilon_decay, distance_based_decay=False, ess_min=None, kernel_variance_scale=1.0, use_last_pop_samples=True, return_summary=False, kde=False, kde_kwargs={}, kde_sample_weights=False, lra=False, lra_with_weights=False, sass=False, sass_fraction=0.25, sass_expansion_degree=1)
¶
Run SMCABC and return accepted parameters or KDE object fitted on them.
Parameters:
Name  Type  Description  Default 

x_o 
Union[Tensor, ndarray]

Observed data. 
required 
num_particles 
int

Number of particles in each population. 
required 
num_initial_pop 
int

Number of simulations used for initial population. 
required 
num_simulations 
int

Total number of possible simulations. 
required 
epsilon_decay 
float

Factor with which the acceptance threshold \(\epsilon\) decays. 
required 
distance_based_decay 
bool

Whether the \(\epsilon\) decay is constant over populations or calculated from the previous populations distribution of distances. 
False

ess_min 
Optional[float]

Threshold of effective sampling size for resampling weights. Not used when None (default). 
None

kernel_variance_scale 
float

Factor for scaling the perturbation kernel variance. 
1.0

use_last_pop_samples 
bool

Whether to fill up the current population with samples from the previous population when the budget is used up. If False, the current population is discarded and the previous population is returned. 
True

lra 
bool

Whether to run linear regression adjustment as in Beaumont et al. 2002 
False

lra_with_weights 
bool

Whether to run lra as weighted linear regression with SMC weights 
False

sass 
bool

Whether to determine semiautomatic summary statistics as in Fearnhead & Prangle 2012. 
False

sass_fraction 
float

Fraction of simulation budget used for the initial sass run. 
0.25

sass_expansion_degree 
int

Degree of the polynomial feature expansion for the sass regression, default 1  no expansion. 
1

kde 
bool

Whether to run KDE on the accepted parameters to return a KDE object from which one can sample. 
False

kde_kwargs 
Dict[str, Any]

kwargs for performing KDE: ‘bandwidth=’; either a float, or a string naming a bandwidth heuristics, e.g., ‘cv’ (cross validation), ‘silvermann’ or ‘scott’, default ‘cv’. ‘transform’: transform applied to the parameters before doing KDE. ‘sample_weights’: weights associated with samples. See ‘get_kde’ for more details 
{}

kde_sample_weights 
bool

Whether perform weighted KDE with SMC weights or on raw particles. 
False

return_summary 
bool

Whether to return a dictionary with all accepted particles, weights, etc. at the end. 
False

Returns:
Name  Type  Description 

theta 
if kde False

accepted parameters of the last population. 
kde 
if kde True

KDE object fitted on accepted parameters, from which one can .sample() and .log_prob(). 
summary 
if return_summary True

dictionary containing the accepted paramters (if kde True), distances and simulated data x of all populations. 
Source code in sbi/inference/abc/smcabc.py
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__init__(simulator, prior, distance='l2', num_workers=1, simulation_batch_size=1, show_progress_bars=True, kernel='gaussian', algorithm_variant='C')
¶
Sequential Monte Carlo Approximate Bayesian Computation.
We distinguish between three different SMC methods here
 A: Toni et al. 2010 (Phd Thesis)
 B: Sisson et al. 2007 (with correction from 2009)
 C: Beaumont et al. 2009
In Toni et al. 2010 we find an overview of the differences on page 34:  B: same as A except for resampling of weights if the effective sampling size is too small.  C: same as A except for calculation of the covariance of the perturbation kernel: the kernel covariance is a scaled version of the covariance of the previous population.
Parameters:
Name  Type  Description  Default 

simulator 
Callable

A function that takes parameters \(\theta\) and maps them to
simulations, or observations, 
required 
prior 
Distribution

A probability distribution that expresses prior knowledge about the
parameters, e.g. which ranges are meaningful for them. Any
object with 
required 
distance 
Union[str, Callable]

Distance function to compare observed and simulated data. Can be
a custom function or one of 
'l2'

num_workers 
int

Number of parallel workers to use for simulations. 
1

simulation_batch_size 
int

Number of parameter sets that the simulator maps to data x at once. If None, we simulate all parameter sets at the same time. If >= 1, the simulator has to process data of shape (simulation_batch_size, parameter_dimension). 
1

show_progress_bars 
bool

Whether to show a progressbar during simulation and sampling. 
True

kernel 
Optional[str]

Perturbation kernel. 
'gaussian'

algorithm_variant 
str

Indicating the choice of algorithm variant, A, B, or C. 
'C'

Source code in sbi/inference/abc/smcabc.py
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get_new_kernel(thetas)
¶
Return new kernel distribution for a given set of paramters.
Source code in sbi/inference/abc/smcabc.py
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get_particle_ranges(particles, weights, samples_per_dim=100)
¶
Return range of particles in each parameter dimension.
Source code in sbi/inference/abc/smcabc.py
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resample_if_ess_too_small(particles, log_weights, ess_min, pop_idx)
¶
Return resampled particles and uniform weights if effectice sampling size is too small.
Source code in sbi/inference/abc/smcabc.py
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run_lra_update_weights(particles, xs, observation, log_weights, lra_with_weights)
¶
Return particles and weights adjusted with LRA.
Runs (weighted) linear regression from xs onto particles to adjust the particles.
Updates the SMC weights according to the new particles.
Source code in sbi/inference/abc/smcabc.py
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run_sass_set_xo(num_particles, num_pilot_simulations, x_o, lra=False, sass_expansion_degree=1)
¶
Return transform for semiautomatic summary statistics.
Runs an single round of rejection abc with fixed budget and accepts num_particles simulations to run the regression for sass.
Sets self.x_o once the x_shape can be derived from simulations.
Source code in sbi/inference/abc/smcabc.py
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sample_from_population_with_weights(particles, weights, num_samples=1)
staticmethod
¶
Return samples from particles sampled with weights.
Source code in sbi/inference/abc/smcabc.py
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Posteriors¶
sbi.inference.posteriors.direct_posterior.DirectPosterior
¶
Bases: NeuralPosterior
Posterior \(p(\thetax_o)\) with log_prob()
and sample()
methods, only
applicable to SNPE.
SNPE trains a neural network to directly approximate the posterior distribution.
However, for bounded priors, the neural network can have leakage: it puts nonzero
mass in regions where the prior is zero. The DirectPosterior
class wraps the
trained network to deal with these cases.
Specifically, this class offers the following functionality:
 correct the calculation of the log probability such that it compensates for the
leakage.
 reject samples that lie outside of the prior bounds.
This class can not be used in combination with SNLE or SNRE.
Source code in sbi/inference/posteriors/direct_posterior.py
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__init__(posterior_estimator, prior, max_sampling_batch_size=10000, device=None, x_shape=None, enable_transform=True)
¶
Parameters:
Name  Type  Description  Default 

prior 
Distribution

Prior distribution with 
required 
posterior_estimator 
flows.Flow

The trained neural posterior. 
required 
max_sampling_batch_size 
int

Batchsize of samples being drawn from the proposal at every iteration. 
10000

device 
Optional[str]

Training device, e.g., “cpu”, “cuda” or “cuda:0”. If None,

None

x_shape 
Optional[torch.Size]

Shape of a single simulator output. If passed, it is used to check the shape of the observed data and give a descriptive error. 
None

enable_transform 
bool

Whether to transform parameters to unconstrained space
during MAP optimization. When False, an identity transform will be returned
for 
True

Source code in sbi/inference/posteriors/direct_posterior.py
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leakage_correction(x, num_rejection_samples=10000, force_update=False, show_progress_bars=False, rejection_sampling_batch_size=10000)
¶
Return leakage correction factor for a leaky posterior density estimate.
The factor is estimated from the acceptance probability during rejection sampling from the posterior.
This is to avoid reestimating the acceptance probability from scratch
whenever log_prob
is called and norm_posterior=True
. Here, it
is estimated only once for self.default_x
and saved for later. We
reevaluate only whenever a new x
is passed.
Parameters:
Name  Type  Description  Default 

num_rejection_samples 
int

Number of samples used to estimate correction factor. 
10000

show_progress_bars 
bool

Whether to show a progress bar during sampling. 
False

rejection_sampling_batch_size 
int

Batch size for rejection sampling. 
10000

Returns:
Type  Description 

Tensor

Saved or newlyestimated correction factor (as a scalar 
Source code in sbi/inference/posteriors/direct_posterior.py
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log_prob(theta, x=None, norm_posterior=True, track_gradients=False, leakage_correction_params=None)
¶
Returns the logprobability of the posterior \(p(\thetax)\).
Parameters:
Name  Type  Description  Default 

theta 
Tensor

Parameters \(\theta\). 
required 
norm_posterior 
bool

Whether to enforce a normalized posterior density.
Renormalization of the posterior is useful when some
probability falls out or leaks out of the prescribed prior support.
The normalizing factor is calculated via rejection sampling, so if you
need speedier but unnormalized log posterior estimates set here

True

track_gradients 
bool

Whether the returned tensor supports tracking gradients. This can be helpful for e.g. sensitivity analysis, but increases memory consumption. 
False

leakage_correction_params 
Optional[dict]

A 
None

Returns:
Type  Description 

Tensor


Tensor

support of the prior, âˆž (corresponding to 0 probability) outside. 
Source code in sbi/inference/posteriors/direct_posterior.py
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map(x=None, num_iter=1000, num_to_optimize=100, learning_rate=0.01, init_method='posterior', num_init_samples=1000, save_best_every=10, show_progress_bars=False, force_update=False)
¶
Returns the maximumaposteriori estimate (MAP).
The method can be interrupted (CtrlC) when the user sees that the
logprobability converges. The best estimate will be saved in self._map
and
can be accessed with self.map()
. The MAP is obtained by running gradient
ascent from a given number of starting positions (samples from the posterior
with the highest logprobability). After the optimization is done, we select the
parameter set that has the highest logprobability after the optimization.
Warning: The default values used by this function are not welltested. They might require handtuning for the problem at hand.
For developers: if the prior is a BoxUniform
, we carry out the optimization
in unbounded space and transform the result back into bounded space.
Parameters:
Name  Type  Description  Default 

x 
Optional[Tensor]

Deprecated  use 
None

num_iter 
int

Number of optimization steps that the algorithm takes to find the MAP. 
1000

learning_rate 
float

Learning rate of the optimizer. 
0.01

init_method 
Union[str, Tensor]

How to select the starting parameters for the optimization. If
it is a string, it can be either [ 
'posterior'

num_init_samples 
int

Draw this number of samples from the posterior and evaluate the logprobability of all of them. 
1000

num_to_optimize 
int

From the drawn 
100

save_best_every 
int

The best logprobability is computed, saved in the

10

show_progress_bars 
bool

Whether to show a progressbar during sampling from the posterior. 
False

force_update 
bool

Whether to recalculate the MAP when x is unchanged and have a cached value. 
False

log_prob_kwargs 
Will be empty for SNLE and SNRE. Will contain {‘norm_posterior’: True} for SNPE. 
required 
Returns:
Type  Description 

Tensor

The MAP estimate. 
Source code in sbi/inference/posteriors/direct_posterior.py
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sample(sample_shape=torch.Size(), x=None, max_sampling_batch_size=10000, sample_with=None, show_progress_bars=True)
¶
Return samples from posterior distribution \(p(\thetax)\).
Parameters:
Name  Type  Description  Default 

sample_shape 
Shape

Desired shape of samples that are drawn from posterior. If
sample_shape is multidimensional we simply draw 
torch.Size()

sample_with 
Optional[str]

This argument only exists to keep backwardcompatibility with

None

show_progress_bars 
bool

Whether to show sampling progress monitor. 
True

Source code in sbi/inference/posteriors/direct_posterior.py
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sbi.inference.posteriors.importance_posterior.ImportanceSamplingPosterior
¶
Bases: NeuralPosterior
Provides importance sampling to sample from the posterior.
SNLE or SNRE train neural networks to approximate the likelihood(ratios).
ImportanceSamplingPosterior
allows to estimate the posterior logprobability by
estimating the normlalization constant with importance sampling. It also allows to
perform importance sampling (with .sample()
) and to draw approximate samples with
samplingimportanceresampling (SIR) (with .sir_sample()
)
Source code in sbi/inference/posteriors/importance_posterior.py
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__init__(potential_fn, proposal, theta_transform=None, method='sir', oversampling_factor=32, max_sampling_batch_size=10000, device=None, x_shape=None)
¶
Parameters:
Name  Type  Description  Default 

potential_fn 
Callable

The potential function from which to draw samples. 
required 
proposal 
Any

The proposal distribution. 
required 
theta_transform 
Optional[TorchTransform]

Transformation that is applied to parameters. Is not used
during but only when calling 
None

method 
str

Either of [ 
'sir'

oversampling_factor 
int

Number of proposed samples from which only one is selected based on its importance weight. 
32

max_sampling_batch_size 
int

The batch size of samples being drawn from the proposal at every iteration. 
10000

device 
Optional[str]

Device on which to sample, e.g., “cpu”, “cuda” or “cuda:0”. If
None, 
None

x_shape 
Optional[torch.Size]

Shape of a single simulator output. If passed, it is used to check the shape of the observed data and give a descriptive error. 
None

Source code in sbi/inference/posteriors/importance_posterior.py
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estimate_normalization_constant(x, num_samples=10000, force_update=False)
¶
Returns the normalization constant via importance sampling.
Parameters:
Name  Type  Description  Default 

num_samples 
int

Number of importance samples used for the estimate. 
10000

force_update 
bool

Whether to recalculate the normlization constant when x is unchanged and have a cached value. 
False

Source code in sbi/inference/posteriors/importance_posterior.py
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log_prob(theta, x=None, track_gradients=False, normalization_constant_params=None)
¶
Returns the logprobability of theta under the posterior.
The normalization constant is estimated with importance sampling.
Parameters:
Name  Type  Description  Default 

theta 
Tensor

Parameters \(\theta\). 
required 
track_gradients 
bool

Whether the returned tensor supports tracking gradients. This can be helpful for e.g. sensitivity analysis, but increases memory consumption. 
False

normalization_constant_params 
Optional[dict]

Parameters passed on to

None

Returns:
Type  Description 

Tensor


Source code in sbi/inference/posteriors/importance_posterior.py
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map(x=None, num_iter=1000, num_to_optimize=100, learning_rate=0.01, init_method='proposal', num_init_samples=1000, save_best_every=10, show_progress_bars=False, force_update=False)
¶
Returns the maximumaposteriori estimate (MAP).
The method can be interrupted (CtrlC) when the user sees that the
logprobability converges. The best estimate will be saved in self._map
and
can be accessed with self.map()
. The MAP is obtained by running gradient
ascent from a given number of starting positions (samples from the posterior
with the highest logprobability). After the optimization is done, we select the
parameter set that has the highest logprobability after the optimization.
Warning: The default values used by this function are not welltested. They might require handtuning for the problem at hand.
For developers: if the prior is a BoxUniform
, we carry out the optimization
in unbounded space and transform the result back into bounded space.
Parameters:
Name  Type  Description  Default 

x 
Optional[Tensor]

Deprecated  use 
None

num_iter 
int

Number of optimization steps that the algorithm takes to find the MAP. 
1000

learning_rate 
float

Learning rate of the optimizer. 
0.01

init_method 
Union[str, Tensor]

How to select the starting parameters for the optimization. If
it is a string, it can be either [ 
'proposal'

num_init_samples 
int

Draw this number of samples from the posterior and evaluate the logprobability of all of them. 
1000

num_to_optimize 
int

From the drawn 
100

save_best_every 
int

The best logprobability is computed, saved in the

10

show_progress_bars 
bool

Whether to show a progressbar during sampling from the posterior. 
False

force_update 
bool

Whether to recalculate the MAP when x is unchanged and have a cached value. 
False

log_prob_kwargs 
Will be empty for SNLE and SNRE. Will contain {‘norm_posterior’: True} for SNPE. 
required 
Returns:
Type  Description 

Tensor

The MAP estimate. 
Source code in sbi/inference/posteriors/importance_posterior.py
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sample(sample_shape=torch.Size(), x=None, oversampling_factor=32, max_sampling_batch_size=10000, sample_with=None)
¶
Return samples from the approximate posterior distribution.
Parameters:
Name  Type  Description  Default 

sample_shape 
Shape

description 
torch.Size()

x 
Optional[Tensor]

description 
None

Source code in sbi/inference/posteriors/importance_posterior.py
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sbi.inference.posteriors.mcmc_posterior.MCMCPosterior
¶
Bases: NeuralPosterior
Provides MCMC to sample from the posterior.
SNLE or SNRE train neural networks to approximate the likelihood(ratios).
MCMCPosterior
allows to sample from the posterior with MCMC.
Source code in sbi/inference/posteriors/mcmc_posterior.py
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