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import logging
import os
from pickle import load as pickle_load
import numpy as np
import tensorflow as tf
from tqdm.autonotebook import tqdm
from bayesflow.amortizers import (
AmortizedLikelihood,
AmortizedModelComparison,
AmortizedPosterior,
AmortizedPosteriorLikelihood,
)
from bayesflow.computational_utilities import maximum_mean_discrepancy
from bayesflow.configuration import *
from bayesflow.default_settings import DEFAULT_KEYS, OPTIMIZER_DEFAULTS, TQDM_MININTERVAL
from bayesflow.diagnostics import plot_latent_space_2d, plot_sbc_histograms
from bayesflow.exceptions import ArgumentError, SimulationError
from bayesflow.helper_classes import (
EarlyStopper,
LossHistory,
MemoryReplayBuffer,
MultiSimulationDataset,
SimulationDataset,
SimulationMemory,
)
from bayesflow.helper_functions import backprop_step, extract_current_lr, format_loss_string, loss_to_string
from bayesflow.simulation import GenerativeModel, MultiGenerativeModel
logging.basicConfig()
[docs]
class Trainer:
"""This class connects a generative model (or, already simulated data from a model) with
a configurator and a neural inference architecture for amortized inference (amortizer). A Trainer
instance is responsible for optimizing the amortizer via various forms of simulation-based training.
At the very minimum, the trainer must be initialized with an `amortizer` instance, which is capable
of processing the (configured) outputs of a generative model. A `configurator` will then process
the outputs of the generative model and convert them into suitable inputs for the amortizer. Users
can choose from a palette of default configurators or create their own configurators, essentially
building a modularized pipeline `GenerativeModel` -> `Configurator` -> `Amortizer`. Most complex models
will require custom configurators.
Notes
-----
Currently, the trainer supports the following simulation-based training regimes, based on efficiency
considerations:
* Online training
>>> trainer.train_online(epochs, iterations_per_epoch, batch_size, **kwargs)
This training regime is optimal for fast generative models which can efficiently simulated data on-the-fly.
In order for this training regime to be efficient, on-the-fly batch simulations should not take longer
than 2-3 seconds.
* Experience replay training
>>> trainer.train_experience_replay(epochs, iterations_per_epoch, batch_size, **kwargs)
This training regime is also good for fast generative models capable of efficiently simulating data on-the-fly.
Compare to pure online training, this training will keep an experience replay buffer from which simulations
are randomly sampled, so the networks will likely see some simulations multiple times.
* Round-based training
>>> trainer.train_rounds(rounds, sim_per_round, epochs, batch_size, **kwargs)
This training regime is optimal for slow, but still reasonably performant generative models.
In order for this training regime to be efficient, on-the-fly batch simulations should not take
longer than 2-3 minutes.
.. note:: overfitting presents a danger when using small numbers of simulated data sets, so it is recommended
to use some amount of regularization for the neural amortizer(s).
* Offline training
>>> trainer.train_offline(simulations_dict, epochs, batch_size, **kwargs)
This training regime is optimal for very slow, external simulators, which take several minutes for a
single simulation. It assumes that all training data has been already simulated and stored on disk.
.. warning:: Overfitting presents a danger when using a small simulated data set, so it is recommended to use
some amount of regularization for the neural amortizer(s).
.. note::
For extremely slow simulators (i.e., more than an hour of a single simulation), the BayesFlow framework
might not be the ideal choice and should probably be considered in combination with a black-box surrogate
optimization method, such as Bayesian optimization.
"""
[docs]
def __init__(
self,
amortizer,
generative_model=None,
configurator=None,
checkpoint_path=None,
max_to_keep=3,
default_lr=0.0005,
skip_checks=False,
memory=False,
**kwargs,
):
"""Creates a trainer which will use a generative model (or data simulated from it) to optimize
a neural architecture (amortizer) for amortized posterior inference, likelihood inference, or both.
Parameters
----------
amortizer : `bayesflow.amortizers.Amortizer`
The neural architecture to be optimized.
generative_model : `bayesflow.forward_inference.GenerativeModel`
A generative model returning a dictionary with randomly sampled parameters, data, and optional context
configurator : callable or None, optional, default: None
A callable object transforming and combining the outputs of the generative model into inputs for a BayesFlow
amortizer.
checkpoint_path : string or None, optional, default: None
Optional file path for storing the trained amortizer, loss history and optional memory.
max_to_keep : int, optional, default: 3
Number of checkpoints and loss history snapshots to keep.
default_lr : float, optional, default: 0.0005
The default learning rate to use for default optimizers.
skip_checks : bool, optional, default: False
If True, do not perform consistency checks, i.e., simulator runs and passed through nets
memory : bool or bayesflow.SimulationMemory, optional, default: False
If ``True``, store a pre-defined amount of simulations for later use (validation, etc.).
If `SimulationMemory` instance provided, stores a reference to the instance.
Otherwise the corresponding attribute will be set to None.
Other Parameters:
-----------------
memory_kwargs : dict
Keyword arguments to be passed to the `SimulationMemory` instance, if ``memory=True``
num_models : int
The number of models in an amortized model comparison scenario, in case of a custom model comparison
amortizer which does not have a num_models attribute.
"""
# Set-up logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
self.amortizer = amortizer
self.generative_model = generative_model
if self.generative_model is None:
logger.info(
"Trainer initialization: No generative model provided. Only offline learning mode is available!"
)
# Determine n models in case model comparison mode
if type(generative_model) is MultiGenerativeModel:
_num_models = generative_model.num_models
elif type(amortizer) is AmortizedModelComparison:
_num_models = amortizer.num_models
else:
_num_models = kwargs.get("num_models")
# Set-up configurator
self.configurator = self._manage_configurator(configurator, num_models=_num_models)
# Set-up memory classes
self.loss_history = LossHistory()
if memory is True:
self.simulation_memory = SimulationMemory(**kwargs.pop("memory_kwargs", {}))
elif type(memory) is SimulationMemory:
self.simulation_memory = memory
else:
self.simulation_memory = None
# Set-up replay buffer and optimizer attributes
self.replay_buffer = None
self.optimizer = None
self.default_lr = default_lr
# Checkpoint and helper classes settings
self.max_to_keep = max_to_keep
if checkpoint_path is not None:
self.checkpoint = tf.train.Checkpoint(model=self.amortizer)
self.manager = tf.train.CheckpointManager(self.checkpoint, checkpoint_path, max_to_keep=max_to_keep)
self.checkpoint.restore(self.manager.latest_checkpoint)
self.loss_history.load_from_file(checkpoint_path)
if self.simulation_memory is not None:
self.simulation_memory.load_from_file(checkpoint_path)
if self.manager.latest_checkpoint:
logger.info("Networks loaded from {}".format(self.manager.latest_checkpoint))
else:
logger.info("Initialized networks from scratch.")
else:
self.checkpoint = None
self.manager = None
self.checkpoint_path = checkpoint_path
# Perform a sanity check with provided components
if not skip_checks:
self._check_consistency()
[docs]
def diagnose_latent2d(self, inputs=None, **kwargs):
"""Performs visual pre-inference diagnostics of latent space on either provided validation data
(new simulations) or internal simulation memory.
If ``inputs is not None``, then diagnostics will be performed on the inputs, regardless
whether the `simulation_memory` of the trainer is empty or not. If ``inputs is None``, then
the trainer will try to access is memory or raise a `ConfigurationError`.
Parameters
----------
inputs : None, list, or dict, optional, default: None
The optional inputs to use
Other Parameters
----------------
conf_args :
optional keyword arguments passed to the configurator
net_args :
optional keyword arguments passed to the amortizer
plot_args :
optional keyword arguments passed to `plot_latent_space_2d`
Returns
-------
fig : plt.Figure
The figure object which can be readily saved to disk using `fig.savefig()`.
"""
if type(self.amortizer) is AmortizedPosterior:
# If no inputs, try memory and throw if no memory
if inputs is None:
if self.simulation_memory is None:
raise ConfigurationError(
"You should either enable simulation memory or supply the inputs argument."
)
else:
inputs = self.simulation_memory.get_memory()
else:
inputs = self.configurator(inputs, **kwargs.pop("conf_args", {}))
# Do inference
if type(inputs) is list:
z, _ = self.amortizer.call_loop(inputs, **kwargs.pop("net_args", {}))
else:
z, _ = self.amortizer(inputs, **kwargs.pop("net_args", {}))
return plot_latent_space_2d(z, **kwargs.pop("plot_args", {}))
else:
raise NotImplementedError("Latent space diagnostics are only available for type AmortizedPosterior!")
[docs]
def diagnose_sbc_histograms(self, inputs=None, n_samples=None, **kwargs):
"""Performs visual pre-inference diagnostics via simulation-based calibration (SBC)
(new simulations) or internal simulation memory.
If ``inputs is not None``, then diagnostics will be performed on the inputs, regardless
whether the `simulation_memory` of the trainer is empty or not. If ``inputs is None``, then
the trainer will try to access is memory or raise a `ConfigurationError`.
Parameters
----------
inputs : None, list or dict, optional, default: None
The optional inputs to use
n_samples : int or None, optional, default: None
The number of posterior samples to draw for each simulated data set.
If None, the number will be heuristically determined so that n_sim / n_draws is approximately equal to 20
Other Parameters
----------------
conf_args :
optional keyword arguments passed to the configurator
net_args :
optional keyword arguments passed to the amortizer
plot_args :
optional keyword arguments passed to `plot_sbc()`
Returns
-------
fig : plt.Figure
The figure object which can be readily saved to disk using `fig.savefig()`.
"""
if type(self.amortizer) is AmortizedPosterior:
# If no inputs, try memory and throw if no memory
if inputs is None:
if self.simulation_memory is None:
raise ConfigurationError("You should either ")
else:
inputs = self.simulation_memory.get_memory()
else:
inputs = self.configurator(inputs, **kwargs.pop("conf_args", {}))
# Heuristically determine the number of posterior samples
if n_samples is None:
if type(inputs) is list:
n_sim = np.sum([inp["parameters"].shape[0] for inp in inputs])
n_samples = int(np.ceil(n_sim / 20))
else:
n_samples = int(np.ceil(inputs["parameters"].shape[0] / 20))
# Do inference
if type(inputs) is list:
post_samples = self.amortizer.sample_loop(inputs, n_samples=n_samples, **kwargs.pop("net_args", {}))
prior_samples = np.concatenate([inp["parameters"] for inp in inputs], axis=0)
else:
post_samples = self.amortizer(inputs, n_samples, n_samples, **kwargs.pop("net_args", {}))
prior_samples = inputs["parameters"]
# Check for prior names and override keyword if available
plot_kwargs = kwargs.pop("plot_args", {})
if type(self.generative_model) is GenerativeModel and plot_kwargs.get("param_names") is None:
plot_kwargs["param_names"] = self.generative_model.param_names
return plot_sbc_histograms(post_samples, prior_samples, **plot_kwargs)
else:
raise NotImplementedError("SBC diagnostics are only available for type AmortizedPosterior!")
[docs]
def load_pretrained_network(self):
"""Attempts to load a pre-trained network if checkpoint path is provided and a checkpoint manager exists."""
if self.manager is None or self.checkpoint is None:
return False
status = self.checkpoint.restore(self.manager.latest_checkpoint)
return status
[docs]
def train_online(
self,
epochs,
iterations_per_epoch,
batch_size,
save_checkpoint=True,
optimizer=None,
reuse_optimizer=False,
early_stopping=False,
use_autograph=True,
validation_sims=None,
**kwargs,
):
"""Trains an amortizer via online learning. Additional keyword arguments
are passed to the generative mode, configurator, and amortizer.
Parameters
----------
epochs : int
Number of epochs (and number of times a checkpoint is stored)
iterations_per_epoch : int
Number of batch simulations to perform per epoch
batch_size : int
Number of simulations to perform at each backprop step
save_checkpoint : bool, default: True
A flag to decide whether to save checkpoints after each epoch,
if a checkpoint_path provided during initialization, otherwise ignored.
optimizer : tf.keras.optimizer.Optimizer or None
Optimizer for the neural network. ``None`` will result in ``tf.keras.optimizers.Adam``
using a learning rate of 5e-4 and a cosine decay from 5e-4 to 0. A custom optimizer
will override default learning rate and schedule settings.
reuse_optimizer : bool, optional, default: False
A flag indicating whether the optimizer instance should be treated as persistent or not.
If ``False``, the optimizer and its states are not stored after training has finished.
Otherwise, the optimizer will be stored as ``self.optimizer` and re-used in further training runs.
early_stopping : bool, optional, default: False
Whether to use optional stopping or not during training. Could speed up training.
Only works if ``validation_sims is not None``, i.e., validation data has been provided.
use_autograph : bool, optional, default: True
Whether to use autograph for the backprop step. Could lead to enormous speed-ups but
could also be harder to debug.
validation_sims : dict or None, optional, default: None
Simulations used as a "validation set".
If ``dict``, will assume it's the output of a generative model and try
``amortizer.compute_loss(configurator(validation_sims))``
after each epoch.
If ``int``, will assume it's the number of sims to generate from the generative
model before starting training. Only considered if a generative model has been
provided during initialization.
If ``None`` (default), no validation set will be used.
Other Parameters
----------------
model_args :
optional kwargs passed to the generative model
val_model_args:
optional kwargs passed to the generative model for generating validation data. Only useful if
``type(validation_sims) is int``.
conf_args :
optional kwargs passed to the configurator before each backprop (update) step.
val_conf_args :
optional kwargs passed to the configurator then configuring the validation data.
net_args :
optional kwargs passed to the amortizer
early_stopping_args :
optional kwargs passed to the `EarlyStopper`
Returns
-------
losses : dict or pandas.DataFrame
A dictionary storing the losses across epochs and iterations
"""
assert self.generative_model is not None, "No generative model found. Only offline training is possible!"
# Compile update function, if specified
if use_autograph:
_backprop_step = tf.function(backprop_step, reduce_retracing=True)
else:
_backprop_step = backprop_step
# Create new optimizer and initialize loss history
self._setup_optimizer(optimizer, epochs, iterations_per_epoch)
self.loss_history.start_new_run()
validation_sims = self._config_validation(validation_sims, **kwargs.pop("val_model_args", {}))
# Create early stopper, if conditions met, otherwise None returned
early_stopper = self._config_early_stopping(early_stopping, validation_sims, **kwargs)
# Loop through training epochs
for ep in range(1, epochs + 1):
with tqdm(total=iterations_per_epoch, desc=f"Training epoch {ep}", mininterval=TQDM_MININTERVAL) as p_bar:
for it in range(1, iterations_per_epoch + 1):
# Perform one training step and obtain current loss value
loss = self._train_step(batch_size, update_step=_backprop_step, **kwargs)
# Store returned loss
self.loss_history.add_entry(ep, loss)
# Compute running loss
avg_dict = self.loss_history.get_running_losses(ep)
# Extract current learning rate
lr = extract_current_lr(self.optimizer)
# Format for display on progress bar
disp_str = format_loss_string(ep, it, loss, avg_dict, lr=lr)
# Update progress bar
p_bar.set_postfix_str(disp_str, refresh=False)
p_bar.update(1)
# Store and compute validation loss, if specified
self._validation(ep, validation_sims, **kwargs)
self._save_trainer(save_checkpoint)
# Check early stopping, if specified
if self._check_early_stopping(early_stopper):
break
# Remove optimizer reference, if not set as persistent
if not reuse_optimizer:
self.optimizer = None
return self.loss_history.get_plottable()
[docs]
def train_offline(
self,
simulations_dict,
epochs,
batch_size,
save_checkpoint=True,
optimizer=None,
reuse_optimizer=False,
early_stopping=False,
validation_sims=None,
use_autograph=True,
**kwargs,
):
"""Trains an amortizer via offline learning. Assume parameters, data and optional
context have already been simulated (i.e., forward inference has been performed).
Parameters
----------
simulations_dict : dict
A dictionary containing the simulated data / context, if using the default keys,
the method expects at least the mandatory keys ``sim_data`` and ``prior_draws`` to be present
epochs : int
Number of epochs (and number of times a checkpoint is stored)
batch_size : int
Number of simulations to perform at each backpropagation step
save_checkpoint : bool, default: True
Determines whether to save checkpoints after each epoch,
if a checkpoint_path provided during initialization, otherwise ignored.
optimizer : tf.keras.optimizer.Optimizer or None
Optimizer for the neural network. ``None`` will result in ``tf.keras.optimizers.Adam``
using a learning rate of 5e-4 and a cosine decay from 5e-4 to 0. A custom optimizer
will override default learning rate and schedule settings.
reuse_optimizer : bool, optional, default: False
A flag indicating whether the optimizer instance should be treated as persistent or not.
If ``False``, the optimizer and its states are not stored after training has finished.
Otherwise, the optimizer will be stored as ``self.optimizer`` and re-used in further training runs.
early_stopping : bool, optional, default: False
Whether to use optional stopping or not during training. Could speed up training.
Only works if ``validation_sims is not None``, i.e., validation data has been provided.
use_autograph : bool, optional, default: True
Whether to use autograph for the backprop step. Could lead to enormous speed-ups but
could also be harder to debug.
validation_sims : dict, int, or None, optional, default: None
Simulations used as a "validation set".
If ``dict``, will assume it's the output of a generative model and try
``amortizer.compute_loss(configurator(validation_sims))`` after each epoch.
If ``int``, will assume it's the number of sims to generate from the generative
model before starting training. Only considered if a generative model has been
provided during initialization.
If ``None`` (default), no validation set will be used.
Other Parameters
----------------
val_model_args :
optional kwargs passed to the generative model for generating validation data.
Only useful if ``type(validation_sims) is int``.
conf_args :
optional kwargs passed to the configurator before each backprop (update) step.
val_conf_args :
optional kwargs passed to the configurator then configuring the validation data.
net_args :
optional kwargs passed to the amortizer
early_stopping_args :
optional kwargs passed to the `EarlyStopper`
Returns
-------
losses : ``dict`` or ``pandas.DataFrame``
A dictionary or a data frame storing the losses across epochs and iterations
"""
# Compile update function, if specified
if use_autograph:
_backprop_step = tf.function(backprop_step, reduce_retracing=True)
else:
_backprop_step = backprop_step
# Inits
if isinstance(self.amortizer, AmortizedModelComparison):
data_set = MultiSimulationDataset(simulations_dict, batch_size)
else:
data_set = SimulationDataset(simulations_dict, batch_size)
self._setup_optimizer(optimizer, epochs, data_set.num_batches)
self.loss_history.start_new_run()
validation_sims = self._config_validation(validation_sims, **kwargs.pop("val_model_args", {}))
# Create early stopper, if conditions met, otherwise None returned
early_stopper = self._config_early_stopping(early_stopping, validation_sims, **kwargs)
# Loop through epochs
for ep in range(1, epochs + 1):
with tqdm(
total=data_set.num_batches, desc="Training epoch {}".format(ep), mininterval=TQDM_MININTERVAL
) as p_bar:
# Loop through dataset
for bi, forward_dict in enumerate(data_set, start=1):
# Perform one training step and obtain current loss value
input_dict = self.configurator(forward_dict, **kwargs.pop("conf_args", {}))
loss = self._train_step(batch_size, _backprop_step, input_dict, **kwargs)
# Store returned loss
self.loss_history.add_entry(ep, loss)
# Compute running loss
avg_dict = self.loss_history.get_running_losses(ep)
# Extract current learning rate
lr = extract_current_lr(self.optimizer)
# Format for display on progress bar
disp_str = format_loss_string(ep, bi, loss, avg_dict, lr=lr, it_str="Batch")
# Update progress bar
p_bar.set_postfix_str(disp_str, refresh=False)
p_bar.update(1)
# Store and compute validation loss, if specified
self._validation(ep, validation_sims, **kwargs)
self._save_trainer(save_checkpoint)
# Check early stopping, if specified
if self._check_early_stopping(early_stopper):
break
# Remove optimizer reference, if not set as persistent
if not reuse_optimizer:
self.optimizer = None
return self.loss_history.get_plottable()
[docs]
def train_from_presimulation(
self,
presimulation_path,
optimizer,
save_checkpoint=True,
max_epochs=None,
reuse_optimizer=False,
custom_loader=None,
early_stopping=False,
validation_sims=None,
use_autograph=True,
**kwargs,
):
"""Trains an amortizer via a modified form of offline training.
Like regular offline training, it assumes that parameters, data and optional context have already
been simulated (i.e., forward inference has been performed).
Also like regular offline training, it is faster than online training in scenarios where simulations are slow.
Unlike regular offline training, it uses each batch from the presimulated dataset only once during training,
if not otherwise specified by a higher maximal number of epochs. Then, presimulated data is reused in a cyclic
manner to achieve the desired number of epochs.
A larger presimulated dataset is therefore required than for offline training, and the increase in speed
gained by loading simulations instead of generating them on the fly comes at a cost:
a large presimulated dataset takes up a large amount of hard drive space.
Parameters
----------
presimulation_path : str
File path to the folder containing the files from the precomputed simulation.
Ideally generated using a GenerativeModel's presimulate_and_save method, otherwise must match
the structure produced by that method.
Each file contains the data for one epoch (i.e. a number of batches), and must be compatible
with the custom_loader provided.
The custom_loader must read each file into a collection (either a dictionary or a list) of simulation_dict
objects.
This is easily achieved with the pickle library: if the files were generated from collections of
simulation_dict objects using pickle.dump, the _default_loader (default for custom_load) will
load them using pickle.load.
Training parameters like number of iterations and batch size are inferred from the files during training.
optimizer : tf.keras.optimizer.Optimizer
Optimizer for the neural network training. Since for this training, it is impossible to guess the number of
iterations beforehead, an optimizer must be provided.
save_checkpoint : bool, optional, default : True
Determines whether to save checkpoints after each epoch,
if a checkpoint_path provided during initialization, otherwise ignored.
max_epochs : int or None, optional, default: None
An optional parameter to limit or extend the number of epochs. If number of epochs is larger than the files
of the dataset, presimulations will be reused.
reuse_optimizer : bool, optional, default: False
A flag indicating whether the optimizer instance should be treated as persistent or not.
If ``False``, the optimizer and its states are not stored after training has finished.
Otherwise, the optimizer will be stored as ``self.optimizer`` and re-used in further training runs.
custom_loader : callable, optional, default: self._default_loader
Must take a string file_path as an input and output a collection (dictionary or list) of
simulation_dict objects. A simulation_dict has the keys ``prior_non_batchable_context``,
``prior_batchable_context``, ``prior_draws``, ``sim_non_batchable_context``, ``sim_batchable_context``, and
``sim_data``.
Here, ``prior_draws`` and ``sim_data`` must have actual data as values, the rest are optional.
early_stopping : bool, optional, default: False
Whether to use optional stopping or not during training. Could speed up training.
validation_sims : dict, int, or None, optional, default: None
Simulations used as a validation set.
If ``dict``, will assume it's the output of a generative model and try
``amortizer.compute_loss(configurator(validation_sims))``
after each epoch.
If ``int``, will assume it's the number of sims to generate from the generative
model before starting training. Only considered if a generative model has been
provided during initialization.
If ``None`` (default), no validation set will be used.
use_autograph : bool, optional, default: True
Whether to use autograph for the backprop step. Could lead to enormous speed-ups but
could also be harder to debug.
Other Parameters
----------------
conf_args :
optional keyword arguments passed to the configurator
net_args :
optional keyword arguments passed to the amortizer
Returns
-------
losses : ``dict`` or ``pandas.DataFrame``
A dictionary or a data frame storing the losses across epochs and iterations
"""
self.optimizer = optimizer
# Compile update function, if specified
if use_autograph:
_backprop_step = tf.function(backprop_step, reduce_retracing=True)
else:
_backprop_step = backprop_step
# Inits
self.loss_history.start_new_run()
validation_sims = self._config_validation(validation_sims, **kwargs.pop("val_model_args", {}))
# Create early stopper, if conditions met, otherwise None returned
early_stopper = self._config_early_stopping(early_stopping, validation_sims, **kwargs)
# Loop over the presimulated dataset.
file_list = os.listdir(presimulation_path)
# Use default loading function if none is provided
if custom_loader is None:
custom_loader = self._default_loader
# Remove non-pickle files from the list
file_list = [f for f in file_list if f.endswith(".pkl")]
if max_epochs is not None:
# Limit number of epochs to max_epochs
if len(file_list) > max_epochs:
file_list = file_list[:max_epochs]
# If the number of files is smaller than the number of epochs, repeat the files until max_epochs is reached
elif len(file_list) < max_epochs:
file_list = file_list * int(np.ceil(max_epochs / len(file_list)))
file_list = file_list[:max_epochs]
for ep, current_filename in enumerate(file_list, start=1):
# Read single file into memory as a dictionary or list
file_path = os.path.join(presimulation_path, current_filename)
epoch_data = custom_loader(file_path)
# For each epoch, the number of iterations is inferred from the presimulated dictionary or
# list used for that epoch
if isinstance(epoch_data, dict):
index_list = list(epoch_data.keys())
elif isinstance(epoch_data, list):
index_list = np.arange(len(epoch_data))
else:
raise ValueError(
f"Loading a simulation file resulted in a {type(epoch_data)}. Must be a dictionary or a list!"
)
with tqdm(total=len(index_list), desc=f"Training epoch {ep}", mininterval=TQDM_MININTERVAL) as p_bar:
for it, index in enumerate(index_list, start=1):
# Perform one training step and obtain current loss value
input_dict = self.configurator(epoch_data[index])
# Like the number of iterations, the batch size is inferred from presimulated dictionary or list
if isinstance(self.amortizer, AmortizedModelComparison):
batch_size = input_dict[DEFAULT_KEYS["summary_conditions"]].shape[0]
else:
batch_size = epoch_data[index][DEFAULT_KEYS["sim_data"]].shape[0]
loss = self._train_step(batch_size, _backprop_step, input_dict, **kwargs)
# Store returned loss
self.loss_history.add_entry(ep, loss)
# Compute running loss
avg_dict = self.loss_history.get_running_losses(ep)
# Extract current learning rate
lr = extract_current_lr(self.optimizer)
# Format for display on progress bar
disp_str = format_loss_string(ep, it, loss, avg_dict, lr=lr)
# Update progress bar
p_bar.set_postfix_str(disp_str, refresh=False)
p_bar.update(1)
# Store after each epoch, if specified
self._validation(ep, validation_sims, **kwargs)
self._save_trainer(save_checkpoint)
# Check early stopping, if specified
if self._check_early_stopping(early_stopper):
break
# Remove optimizer reference, if not set as persistent
if not reuse_optimizer:
self.optimizer = None
return self.loss_history.get_plottable()
[docs]
def train_experience_replay(
self,
epochs,
iterations_per_epoch,
batch_size,
save_checkpoint=True,
optimizer=None,
reuse_optimizer=False,
buffer_capacity=1000,
early_stopping=False,
use_autograph=True,
validation_sims=None,
**kwargs,
):
"""Trains the network(s) via experience replay using a memory replay buffer, as utilized
in reinforcement learning. Additional keyword arguments are passed to the generative mode,
configurator, and amortizer. Read below for signature.
Parameters
----------
epochs : int
Number of epochs (and number of times a checkpoint is stored)
iterations_per_epoch : int
Number of batch simulations to perform per epoch
batch_size : int
Number of simulations to perform at each backpropagation step.
save_checkpoint : bool, optional, default: True
A flag to decide whether to save checkpoints after each epoch,
if a ``checkpoint_path`` provided during initialization, otherwise ignored.
optimizer : tf.keras.optimizer.Optimizer or None
Optimizer for the neural network. ``None`` will result in ``tf.keras.optimizers.Adam``
using a learning rate of 5e-4 and a cosine decay from 5e-4 to 0. A custom optimizer
will override default learning rate and schedule settings.
reuse_optimizer : bool, optional, default: False
A flag indicating whether the optimizer instance should be treated as persistent or not.
If ``False``, the optimizer and its states are not stored after training has finished.
Otherwise, the optimizer will be stored as ``self.optimizer`` and re-used in further training runs.
buffer_capacity : int, optional, default: 1000
Max number of batches to store in buffer. For instance, if ``batch_size=32``
and ``capacity_in_batches=1000``, then the buffer will hold a maximum of
32 * 1000 = 32000 simulations. Be careful with memory!
Important! Argument will be ignored if buffer has previously been initialized!
early_stopping : bool, optional, default: True
Whether to use optional stopping or not during training. Could speed up training.
Only works if ``validation_sims is not None``, i.e., validation data has been provided.
use_autograph : bool, optional, default: True
Whether to use autograph for the backprop step. Could lead to enormous speed-ups but
could also be harder to debug.
validation_sims : dict or None, optional, default: None
Simulations used as a "validation set".
If ``dict``, will assume it's the output of a generative model and try
``amortizer.compute_loss(configurator(validation_sims))``
after each epoch.
If ``int``, will assume it's the number of sims to generate from the generative
model before starting training. Only considered if a generative model has been
provided during initialization.
If ``None`` (default), no validation set will be used.
Other Parameters
----------------
model_args :
optional kwargs passed to the generative model
val_model_args :
optional kwargs passed to the generative model for generating validation data. Only useful if
``type(validation_sims) is int``.
conf_args :
optional kwargs passed to the configurator before each backprop (update) step.
val_conf_args :
optional kwargs passed to the configurator then configuring the validation data.
net_args :
optional kwargs passed to the amortizer
early_stopping_args:
optional kwargs passed to the `EarlyStopper`
Returns
-------
losses : ``dict`` or ``pandas.DataFrame``
A dictionary or a data frame storing the losses across epochs and iterations.
"""
assert self.generative_model is not None, "No generative model found. Only offline training is possible!"
# Compile update function, if specified
if use_autograph:
_backprop_step = tf.function(backprop_step, reduce_retracing=True)
else:
_backprop_step = backprop_step
# Inits
self._setup_optimizer(optimizer, epochs, iterations_per_epoch)
self.loss_history.start_new_run()
if self.replay_buffer is None:
self.replay_buffer = MemoryReplayBuffer(buffer_capacity)
validation_sims = self._config_validation(validation_sims)
# Create early stopper, if conditions met, otherwise None returned
early_stopper = self._config_early_stopping(early_stopping, validation_sims, **kwargs)
# Loop through epochs
for ep in range(1, epochs + 1):
with tqdm(total=iterations_per_epoch, desc=f"Training epoch {ep}", mininterval=TQDM_MININTERVAL) as p_bar:
for it in range(1, iterations_per_epoch + 1):
# Simulate a batch of data and store into buffer
input_dict = self._forward_inference(
batch_size, **kwargs.pop("conf_args", {}), **kwargs.pop("model_args", {})
)
self.replay_buffer.store(input_dict)
# Sample from buffer
input_dict = self.replay_buffer.sample()
# One step backprop
loss = _backprop_step(input_dict, self.amortizer, self.optimizer, **kwargs.pop("net_args", {}))
# Store returned loss
self.loss_history.add_entry(ep, loss)
# Compute running loss
avg_dict = self.loss_history.get_running_losses(ep)
# Extract current learning rate
lr = extract_current_lr(self.optimizer)
# Format for display on progress bar
disp_str = format_loss_string(ep, it, loss, avg_dict, lr=lr)
# Update progress bar
p_bar.set_postfix_str(disp_str, refresh=False)
p_bar.update(1)
# Store and compute validation loss, if specified
self._validation(ep, validation_sims, **kwargs)
self._save_trainer(save_checkpoint)
# Check early stopping, if specified
if self._check_early_stopping(early_stopper):
break
# Remove optimizer reference, if not set as persistent
if not reuse_optimizer:
self.optimizer = None
return self.loss_history.get_plottable()
[docs]
def train_rounds(
self,
rounds,
sim_per_round,
epochs,
batch_size,
save_checkpoint=True,
optimizer=None,
reuse_optimizer=False,
early_stopping=False,
use_autograph=True,
validation_sims=None,
**kwargs,
):
"""Trains an amortizer via round-based learning. In each round, ``sim_per_round`` data sets
are simulated from the generative model and added to the data sets simulated in previous
round. Then, the networks are trained for ``epochs`` on the augmented set of data sets.
.. note::
Training time will increase from round to round, since the number of simulations
increases correspondingly. The final round will then train the networks on ``rounds * sim_per_round``
data sets, so make sure this number does not eat up all available memory.
Parameters
----------
rounds : int
Number of rounds to perform (outer loop)
sim_per_round : int
Number of simulations per round
epochs : int
Number of epochs (and number of times a checkpoint is stored, inner loop) within a round.
batch_size : int
Number of simulations to use at each backpropagation step
save_checkpoint : bool, optional, default: True
A flag to decide whether to save checkpoints after each epoch,
if a checkpoint_path provided during initialization, otherwise ignored.
optimizer : tf.keras.optimizer.Optimizer or None
Optimizer for the neural network training. ``None`` will result in ``tf.keras.optimizers.Adam``
using a learning rate of 5e-4 and a cosine decay from 5e-4 to 0. A custom optimizer
will override default learning rate and schedule settings.
reuse_optimizer : bool, optional, default: False
A flag indicating whether the optimizer instance should be treated as persistent or not.
If ``False``, the optimizer and its states are not stored after training has finished.
Otherwise, the optimizer will be stored as ``self.optimizer`` and re-used in further training runs.
early_stopping : bool, optional, default: False
Whether to use optional stopping or not during training. Could speed up training.
Only works if ``validation_sims is not None``, i.e., validation data has been provided.
Will be performed within rounds, not between rounds!
use_autograph : bool, optional, default: True
Whether to use autograph for the backprop step. Could lead to enormous speed-ups but
could also be harder to debug.
validation_sims : dict or None, optional, default: None
Simulations used as a "validation set".
If ``dict``, will assume it's the output of a generative model and try
``amortizer.compute_loss(configurator(validation_sims))``
after each epoch.
If ``int``, will assume it's the number of sims to generate from the generative
model before starting training. Only considered if a generative model has been
provided during initialization.
If ``None`` (default), no validation set will be used.
Other Parameters
----------------
model_args :
optional kwargs passed to the generative model
val_model_args :
optional kwargs passed to the generative model for generating validation data. Only useful if
``type(validation_sims) is int``.
conf_args :
optional kwargs passed to the configurator before each backprop (update) step.
val_conf_args :
optional kwargs passed to the configurator then configuring the validation data.
net_args :
optional kwargs passed to the amortizer
early_stopping_args :
optional kwargs passed to the `EarlyStopper`
Returns
-------
losses : ``dict`` or ``pandas.DataFrame``
A dictionary or a data frame storing the losses across epochs and iterations
"""
assert self.generative_model is not None, "No generative model found. Only offline training is possible!"
# Prepare logger
logger = logging.getLogger()
# Create new optimizer and initialize loss history, needs to calculate iters per epoch
batches_per_sim = np.ceil(sim_per_round / batch_size)
sum_total = (rounds + rounds**2) / 2
iterations_per_epoch = int(batches_per_sim * sum_total)
self._setup_optimizer(optimizer, epochs, iterations_per_epoch)
validation_sims = self._config_validation(validation_sims)
# Loop for each round
first_round = True
for r in range(1, rounds + 1):
# Data generation step
if first_round:
# Simulate initial data
logger.info(f"Simulating initial {sim_per_round} data sets for training...")
simulations_dict = self._forward_inference(sim_per_round, configure=False, **kwargs)
first_round = False
else:
# Simulate further data
logger.info(f"Simulating new {sim_per_round} data sets and appending to previous...")
logger.info(f"New total number of simulated data sets for training: {sim_per_round * r}")
simulations_dict_r = self._forward_inference(sim_per_round, configure=False, **kwargs)
# Attempt to concatenate data sets
for k in simulations_dict.keys():
if simulations_dict[k] is not None:
simulations_dict[k] = np.concatenate((simulations_dict[k], simulations_dict_r[k]), axis=0)
# Train offline with generated stuff
_ = self.train_offline(
simulations_dict,
epochs,
batch_size,
save_checkpoint,
reuse_optimizer=True,
early_stopping=early_stopping,
use_autograph=use_autograph,
validation_sims=validation_sims,
**kwargs,
)
# Remove optimizer reference, if not set as persistent
if not reuse_optimizer:
self.optimizer = None
return self.loss_history.get_plottable()
[docs]
def mmd_hypothesis_test(
self, observed_data, reference_data=None, num_reference_simulations=1000, num_null_samples=100, bootstrap=False
):
"""Performs a sampling-based hypothesis test for detecting Out-Of-Simulation (model misspecification).
Parameters
----------
observed_data : np.ndarray
Observed data, shape (num_observed, ...)
reference_data : np.ndarray
Reference data representing samples from the well-specified model, shape (num_reference, ...)
num_reference_simulations : int, default: 1000
Number of reference simulations (M) simulated from the trainer's generative model
if no `reference_data` are provided.
num_null_samples : int, default: 100
Number of draws from the MMD sampling distribution under the null hypothesis "the trainer's generative
model is well-specified"
bootstrap : bool, default: False
If true, the reference data (see above) are bootstrapped for each sample from the MMD sampling distribution.
If false, a new data set is simulated for computing each draw from the MMD sampling distribution.
Returns
-------
mmd_null_samples : np.ndarray
samples from the H0 sampling distribution ("well-specified model")
mmd_observed : float
summary MMD estimate for the observed data sets
"""
if reference_data is None:
if self.generative_model is None:
raise ArgumentError("If you do not provide reference data, your trainer must have a generative model!")
reference_data = self.configurator(self.generative_model(num_reference_simulations))
if type(reference_data) == dict and "summary_conditions" in reference_data.keys():
reference_summary = self.amortizer.summary_net(reference_data["summary_conditions"])
else:
reference_summary = self.amortizer.summary_net(reference_data)
if type(observed_data) == dict and "summary_conditions" in observed_data.keys():
observed_summary = self.amortizer.summary_net(observed_data["summary_conditions"])
else:
observed_summary = self.amortizer.summary_net(observed_data)
num_observed = observed_summary.shape[0]
num_reference = reference_summary.shape[0]
mmd_null_samples = np.empty(num_null_samples, dtype=np.float32)
for i in tqdm(range(num_null_samples), mininterval=TQDM_MININTERVAL):
if bootstrap:
bootstrap_idx = np.random.randint(0, num_reference, size=num_observed)
simulated_summary = tf.gather(reference_summary, bootstrap_idx, axis=0)
else:
simulated_data = self.configurator(self.generative_model(num_observed))
simulated_summary = self.amortizer.summary_net(simulated_data["summary_conditions"])
mmd_null_samples[i] = np.sqrt(maximum_mean_discrepancy(reference_summary, simulated_summary).numpy())
mmd_observed = np.sqrt(maximum_mean_discrepancy(reference_summary, observed_summary).numpy())
return mmd_null_samples, mmd_observed
def _config_validation(self, validation_sims, **kwargs):
"""Helper method to prepare validation set based on user input."""
logger = logging.getLogger()
if validation_sims is None:
return None
if type(validation_sims) is dict:
return validation_sims
if type(validation_sims) is int:
if self.generative_model is not None:
vals = self.generative_model(validation_sims, **kwargs)
logger.info(f"Generated {validation_sims} simulations for validation.")
return vals
else:
logger.warning(
"Validation simulations can only be generated if the Trainer is initialized "
+ "with a generative model."
)
return None
logger.warning('Type of argument "validation_sims" not understood. No validation simulations were created.')
def _config_early_stopping(self, early_stopping, validation_sims, **kwargs):
"""Helper method to configure early stopping or warn user for."""
if early_stopping:
if validation_sims is not None:
early_stopper = EarlyStopper(**kwargs.pop("early_stopping_args", {}))
else:
logger = logging.getLogger()
logger.warning("No early stopping will be used, since validation_sims were not provided.")
early_stopper = None
return early_stopper
return None
def _setup_optimizer(self, optimizer, epochs, iterations_per_epoch):
"""Helper method to prepare optimizer based on user input."""
if optimizer is None:
# No optimizer so far and None provided
if self.optimizer is None:
# Calculate decay steps for default cosine decay
schedule = tf.keras.optimizers.schedules.CosineDecay(
self.default_lr, iterations_per_epoch * epochs, name="lr_decay"
)
self.optimizer = tf.keras.optimizers.Adam(schedule, **OPTIMIZER_DEFAULTS)
# No optimizer provided, but optimizer exists, that is,
# has been declared as persistent, so do nothing
else:
pass
else:
self.optimizer = optimizer
def _save_trainer(self, save_checkpoint):
"""Helper method to take care of IO operations."""
if self.manager is not None and save_checkpoint:
self.manager.save()
self.loss_history.save_to_file(file_path=self.checkpoint_path, max_to_keep=self.max_to_keep)
if self.simulation_memory is not None:
self.simulation_memory.save_to_file(file_path=self.checkpoint_path)
def _validation(self, ep, validation_sims, **kwargs):
"""Helper method to take care of computing the validation loss(es)."""
if validation_sims is not None:
conf = self.configurator(validation_sims, **kwargs.pop("val_conf_args", {}))
val_loss = self.amortizer.compute_loss(conf, **kwargs.pop("net_args", {}))
self.loss_history.add_val_entry(ep, val_loss)
val_loss_str = loss_to_string(ep, val_loss)
logger = logging.getLogger()
logger.info(val_loss_str)
def _check_early_stopping(self, early_stopper):
"""Helper method to check improvement in validation loss."""
if early_stopper is not None:
if early_stopper.update_and_recommend(self.loss_history.last_total_val_loss()):
logger = logging.getLogger()
logger.info("Early stopping triggered.")
return True
return False
def _train_step(self, batch_size, update_step, input_dict=None, **kwargs):
"""Performs forward inference -> configuration -> network -> loss pipeline.
Parameters
----------
batch_size : int
Number of simulations to perform at each backprop step
update_step : callable
The function which will perform one backprop step on a batch. Should have the following signature:
``update_step(input_dict, amortizer, optimizer, **kwargs)``
input_dict : dict
The optional pre-configured forward dict from a generative model, simulated, if None
Other Parameters
----------------
model_args :
optional keyword arguments passed to the generative model
conf_args :
optional keyword arguments passed to the configurator
net_args :
optional keyword arguments passed to the amortizer
"""
if input_dict is None:
input_dict = self._forward_inference(
batch_size, **kwargs.pop("conf_args", {}), **kwargs.pop("model_args", {})
)
if self.simulation_memory is not None:
self.simulation_memory.store(input_dict)
loss = update_step(input_dict, self.amortizer, self.optimizer, **kwargs.pop("net_args", {}))
return loss
def _forward_inference(self, n_sim, configure=True, **kwargs):
"""Performs one step of single-model forward inference.
Parameters
----------
n_sim : int
Number of simulations to perform at the given step (i.e., batch size)
configure : bool, optional, default: True
Determines whether to pass the forward inputs through a configurator.
**kwargs : dict
Optional keyword arguments passed to the generative model
Returns
-------
out_dict : dict
The outputs of the generative model.
Raises
------
SimulationError
If the trainer has no generative model but ``trainer._forward_inference``
is called (i.e., needs to simulate data from the generative model)
"""
if self.generative_model is None:
raise SimulationError("No generative model specified. Only offline learning is available!")
out_dict = self.generative_model(n_sim, **kwargs.pop("model_args", {}))
if configure:
out_dict = self.configurator(out_dict, **kwargs.pop("conf_args", {}))
return out_dict
def _manage_configurator(self, config_fun, **kwargs):
"""Determines which configurator to use if None specified during construction."""
# Do nothing if callable provided
if callable(config_fun):
return config_fun
# If None of something else (default), infer default config based on amortizer type
else:
# Amortized posterior
if isinstance(self.amortizer, AmortizedPosterior):
default_config = DefaultPosteriorConfigurator()
# Amortized lieklihood
elif isinstance(self.amortizer, AmortizedLikelihood):
default_config = DefaultLikelihoodConfigurator()
# Joint amortizer
elif isinstance(self.amortizer, AmortizedPosteriorLikelihood):
default_config = DefaultJointConfigurator()
# Model comparison amortizer
elif isinstance(self.amortizer, AmortizedModelComparison):
if kwargs.get("num_models") is None:
raise ConfigurationError(
'Either your generative model or amortizer should have "num_models" attribute, or '
+ "you need initialize Trainer with num_models explicitly!"
)
default_config = DefaultModelComparisonConfigurator(kwargs.get("num_models"))
# Unknown raises an error
else:
raise NotImplementedError(
f"Could not initialize configurator based on " + f"amortizer type {type(self.amortizer)}!"
)
return default_config
def _check_consistency(self):
"""Attempts to run one step generative_model -> configurator -> amortizer -> loss with
batch_size=2. Should be skipped if generative model has non-standard behavior.
Raises
------
ConfigurationError
If any operation along the above chain fails.
"""
logger = logging.getLogger()
logger.setLevel(logging.INFO)
if self.generative_model is not None:
_n_sim = 2
try:
logger.info("Performing a consistency check with provided components...")
_ = self.amortizer.compute_loss(self.configurator(self.generative_model(_n_sim)))
logger.info("Done.")
except Exception as err:
raise ConfigurationError(
"Could not carry out computations of generative_model ->"
+ f"configurator -> amortizer -> loss! Error trace:\n {err}"
)
def _default_loader(self, file_path):
"""Uses pickle to load as a default."""
with open(file_path, "rb+") as f:
loaded_file = pickle_load(f)
return loaded_file
