Remove high-level imports

Author: stefanradev93

bayesflow/__init__.py

@@ -1,26 +0,0 @@
-# Copyright (c) 2022 The BayesFlow Developers
-
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-"""Imports bayesflow core interface modules."""
-from bayesflow import simulation
-from bayesflow import networks
-from bayesflow import amortizers
-from bayesflow import diagnostics
-from bayesflow import trainers

bayesflow/trainers.py

@@ -566,7 +566,9 @@ def train_from_presimulation(self, presimulation_path, max_epochs=None, save_che
 
     def train_experience_replay(self, epochs, iterations_per_epoch, batch_size, 
                                 capacity_in_batches=1000, save_checkpoint=True, **kwargs):
-        """Trains the network(s) via experience replay using a memory replay buffer.
+        """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
         ----------
@@ -580,9 +582,7 @@ def train_experience_replay(self, epochs, iterations_per_epoch, batch_size,
             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!
-
         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.