From d95fce6904b636c8a3d154520081bb85905f9fbb Mon Sep 17 00:00:00 2001
From: ViktorC <viktor.csomor@gmail.com>
Date: Sun, 1 Jan 2023 01:35:06 +0000
Subject: [PATCH] Rename some instance methods of PIDeepONet

---
 pararealml/operators/ml/pidon/pi_deeponet.py  | 40 ++++++++++---------
 .../operators/ml/pidon/pidon_operator.py      |  4 +-
 2 files changed, 24 insertions(+), 20 deletions(-)

diff --git a/pararealml/operators/ml/pidon/pi_deeponet.py b/pararealml/operators/ml/pidon/pi_deeponet.py
index ad5b3f8..27b7b16 100644
--- a/pararealml/operators/ml/pidon/pi_deeponet.py
+++ b/pararealml/operators/ml/pidon/pi_deeponet.py
@@ -156,7 +156,7 @@ def boundary_condition_loss_weights(self) -> Sequence[float]:
         """
         return self._bc_loss_weights
 
-    def fit(
+    def train(
         self,
         epochs: int,
         optimizer: Union[str, Dict[str, Any], tf.optimizers.Optimizer],
@@ -165,10 +165,10 @@ def fit(
         restore_best_weights: bool = True,
     ) -> Tuple[List[Loss], Optional[List[Loss]]]:
         """
-        Fits the branch and trunk net parameters by minimising the
-        physics-informed loss function over the provided training data set. It
-        also evaluates the loss over both the training data and the test data,
-        if provided, for every epoch.
+        Fits the model by minimising the physics-informed loss function over
+        the provided training data set with respect to the parameters of the
+        branch, trunk, and combiner networks. It also evaluates the loss over
+        both the training data and the test data, if provided, for every epoch.
 
         :param epochs: the number of epochs over the training data
         :param optimizer: the optimizer to use to minimize the loss function
@@ -228,7 +228,7 @@ def fit(
 
         return training_loss_history, test_loss_history
 
-    def fit_with_lbfgs(
+    def train_with_lbfgs(
         self,
         training_data: DataSetIterator,
         max_iterations: int,
@@ -238,9 +238,10 @@ def fit_with_lbfgs(
         gradient_tol: float,
     ):
         """
-        Fits the branch and trunk net parameters by minimising the
-        physics-informed loss function over the provided training data set
-        using the L-BFGS optimization method.
+        Fits the model by minimising the physics-informed loss function over
+        the provided training data set with respect to the parameters of the
+        branch, trunk, and combiner networks using the L-BFGS optimization
+        method.
 
         :param training_data: the data set providing the full training batch
         :param max_iterations: the maximum number of iterations to perform the
@@ -360,12 +361,14 @@ def _compute_total_loss(
         :param data: the data set to compute the loss over
         :param optimizer: an optional optimizer instance; if one is provided,
             the model parameters are updated after each batch
-        :return: the mean physics-informed loss
+        :return: the mean loss over the data set
         """
         loss_function = (
             partial(self._compute_batch_loss, training=False)
             if optimizer is None
-            else partial(self._train, optimizer=optimizer)
+            else partial(
+                self._compute_and_minimize_batch_loss, optimizer=optimizer
+            )
         )
 
         batch_losses = []
@@ -384,17 +387,17 @@ def _compute_total_loss(
         )
 
     @tf.function
-    def _train(
+    def _compute_and_minimize_batch_loss(
         self, batch: DataBatch, optimizer: tf.optimizers.Optimizer
     ) -> Loss:
         """
-        Performs a forward pass on the batch, computes the batch loss, and
+        Performs a forward pass over the batch, computes the batch loss, and
         updates the model parameters.
 
         :param batch: the batch to compute the losses over
         :param optimizer: the optimizer to use to update parameters of the
             model
-        :return: the various losses over the batch
+        :return: the mean loss over the batch
         """
         with AutoDifferentiator() as auto_diff:
             loss = self._compute_batch_loss(batch, True)
@@ -408,15 +411,16 @@ def _train(
     @tf.function
     def _compute_batch_loss(self, batch: DataBatch, training: bool) -> Loss:
         """
-        Computes and returns the total physics-informed loss over the batch
+        Computes and returns the physics-informed loss over the batch
         consisting of the mean squared differential equation error, the mean
-        squared initial condition error, and in the case of PDEs, the mean
-        squared Dirichlet and Neumann boundary condition errors.
+        squared initial condition error, in the case of PDEs, the mean
+        squared Dirichlet and Neumann boundary condition errors, and if
+        applicable, the regularization error.
 
         :param batch: the batch to compute the losses over
         :param training: whether to call the underlying DeepONet in training
             mode
-        :return: the total physics-informed loss over the batch
+        :return: the mean loss over the batch
         """
         domain_batch, initial_batch, boundary_batch = batch
         diff_eq_loss = self._compute_differential_equation_loss(
diff --git a/pararealml/operators/ml/pidon/pidon_operator.py b/pararealml/operators/ml/pidon/pidon_operator.py
index 8eab76a..21fad5d 100644
--- a/pararealml/operators/ml/pidon/pidon_operator.py
+++ b/pararealml/operators/ml/pidon/pidon_operator.py
@@ -245,14 +245,14 @@ def train(
             )
         )
 
-        training_loss_history, test_loss_history = model.fit(
+        training_loss_history, test_loss_history = model.train(
             training_data=training_data,
             test_data=test_data,
             **optimization_args._asdict(),
         )
 
         if secondary_optimization_args:
-            model.fit_with_lbfgs(
+            model.train_with_lbfgs(
                 training_data=training_data,
                 **secondary_optimization_args._asdict(),
             )