bayesflow.helper_classes module

class bayesflow.helper_classes.EarlyStopper(patience=5, tolerance=0.05)

Bases: object

This class will track the total validation loss and trigger an early stopping recommendation based on its hyperparameters.

patienceint, optional, default: 5

How many successive times the tolerance value is reached before triggering an early stopping recommendation.

tolerancefloat, optional, default: 0.05

The minimum reduction of validation loss to be considered significant.

update_and_recommend(current_val_loss)

Adds loss to history and check difference between sequential losses.

class bayesflow.helper_classes.LossHistory

Bases: object

Helper class to keep track of losses during training.

add_entry(epoch, current_loss)

Adds loss entry for current epoch into internal memory data structure.

add_val_entry(epoch, val_loss)

Add validation entry to loss structure. Assume loss_names already exists as an attribute, so no attempt will be made to create names.

file_name = 'history'

flush()

Returns current history and removes all existing loss history, but keeps loss names.

get_plottable()

Returns the losses as a nicely formatted pandas DataFrame, in case only train losses were collected, otherwise a dict of data frames.

get_running_losses(epoch)

Compute and return running means of the losses for current epoch.

last_total_loss()

last_total_val_loss()

load_from_file(file_path)

Loads the most recent saved LossHistory object from file_path.

save_to_file(file_path, max_to_keep)

Saves a LossHistory object to a pickled dictionary in file_path. If max_to_keep saved loss history files are found in file_path, the oldest is deleted before a new one is saved.

start_new_run()

property total_loss

property total_val_loss

class bayesflow.helper_classes.MemoryReplayBuffer(capacity_in_batches=500)

Bases: object

Implements a memory replay buffer for simulation-based inference.

Creates a circular buffer following the logic of experience replay.

Parameters:

capacity_in_batchesint, optional, default: 500

The capacity of the buffer in batches of simulations. Could potentially grow very large, so make sure you pick a reasonable number!

sample()

Samples batch_size number of parameter vectors and simulations from buffer.

Returns:

forward_dictdict

The (raw or configured) outputs of the forward model.

store(forward_dict)

Stores simulation outputs, if internal buffer is not full.

Parameters:

forward_dictdict

The confogired outputs of the forward model.

class bayesflow.helper_classes.MultiSimulationDataset(forward_dict, batch_size, buffer_size=1024)

Bases: object

Helper class for model comparison training with multiple generative models.

Will create multiple SimulationDataset instances, each parsing their own simulation dictionaries and returning these as expected by BayesFlow amortizers.

Creates a wrapper holding multiple tf.data.Dataset instances for offline training in an amortized model comparison context.

Parameters:

forward_dictdict

The outputs from a MultiGenerativeModel or a custom function, stored in a dictionary with at least the following keys: model_outputs - a list with length equal to the number of models, each element representing a batched output of a single model model_indices - a list with integer model indices, which will later be one-hot-encoded for the model comparison learning problem.

batch_sizeint

The total number of simulations from all models in a given batch. The batch size per model will be calculated as batch_size // num_models

buffer_sizeint, optional, default: 1024

The buffer size for shuffling elements in a tf.data.Dataset

class bayesflow.helper_classes.RegressionLRAdjuster(optimizer, period=1000, wait_between_fits=10, patience=10, tolerance=-0.05, reduction_factor=0.25, cooldown_factor=2, num_resets=3, **kwargs)

Bases: object

This class will compute the slope of the loss trajectory and inform learning rate decay.

Creates an instance with given hyperparameters which will track the slope of the loss trajectory according to specified hyperparameters and then issue an optional stopping suggestion.

Parameters:

optimizertf.keras.optimizers.Optimizer instance

An optimizer implementing a lr() method

periodint, optional, default: 1000

How much loss values to consider from the past

wait_between_fitsint, optional, default: 10

How many backpropagation updates to wait between two successive fits

patienceint, optional, default: 10

How many successive times the tolerance value is reached before lr update.

tolerancefloat, optional, default: -0.05

The minimum slope to be considered substantial for training.

reduction_factorfloat in [0, 1], optional, default: 0.25

The factor by which the learning rate is reduced upon hitting the tolerance threshold for patience number of times

cooldown_factorfloat, optional, default: 2

The factor by which the period is multiplied to arrive at a cooldown period.

num_resetsint, optional, default: 3

How many times to reduce the learning rate before issuing an optional stopping

**kwargsdict, optional, default {}

Additional keyword arguments passed to the HuberRegression class.

file_name = 'lr_adjuster'

get_slope(losses)

Fits a Huber regression on the provided loss trajectory or returns None if not enough data points present.

load_from_file(file_path)

Loads the saved LRAdjuster object from file_path.

reset()

Resets all stateful variables in preparation for a new start.

save_to_file(file_path)

Saves the state parameters of a RegressionLRAdjuster object to a pickled dictionary in file_path.

class bayesflow.helper_classes.SimulationDataset(forward_dict, batch_size, buffer_size=1024)

Bases: object

Helper class to create a tensorflow.data.Dataset which parses simulation dictionaries and returns simulation dictionaries as expected by BayesFlow amortizers.

Creates a wrapper holding a tf.data.Dataset instance for offline training in an amortized estimation context.

Parameters:

forward_dictdict

The outputs from a GenerativeModel or a custom function, stored in a dictionary with at least the following keys: sim_data - an array representing the batched output of the model prior_draws - an array with prior generated from the model’s prior

batch_sizeint

The total number of simulations from all models in a given batch. The batch size per model will be calculated as batch_size // num_models

buffer_sizeint, optional, default: 1024

The buffer size for shuffling elements in a tf.data.Dataset

class bayesflow.helper_classes.SimulationMemory(stores_raw=True, capacity_in_batches=50)

Bases: object

Helper class to keep track of a pre-determined number of simulations during training.

file_name = 'memory'

get_memory()

is_full()

Returns True if the buffer is full, otherwise False.

load_from_file(file_path)

Loads the saved SimulationMemory object from file_path.

save_to_file(file_path)

Saves a SimulationMemory object to a pickled dictionary in file_path.

store(forward_dict)

Stores simulation outputs in forward_dict, if internal buffer is not full.

Parameters:

forward_dictdict

The configured outputs of the forward model.