Fix warnings in solver

Author: FilippoOlivo

.pylintrc

@@ -395,7 +395,7 @@ max-branches=12
 max-locals=15
 
 # Maximum number of parents for a class (see R0901).
-max-parents=7
+max-parents=12
 
 # Maximum number of public methods for a class (see R0904).
 max-public-methods=20

pina/solver/__init__.py

@@ -1,3 +1,7 @@
+"""
+TODO
+"""
+
 __all__ = [
     "SolverInterface",
     "SingleSolverInterface",

pina/solver/garom.py

@@ -1,14 +1,11 @@
 """Module for GAROM"""
 
 import torch
-
+from torch.nn.modules.loss import _Loss
 from .solver import MultiSolverInterface
-from ..utils import check_consistency
-from ..loss.loss_interface import LossInterface
 from ..condition import InputTargetCondition
 from ..utils import check_consistency
 from ..loss import LossInterface, PowerLoss
-from torch.nn.modules.loss import _Loss
 
 
 class GAROM(MultiSolverInterface):
@@ -60,18 +57,22 @@ def __init__(
             rate scheduler for the generator.
         :param Scheduler scheduler_discriminator: Learning
             rate scheduler for the discriminator.
-        :param dict scheduler_discriminator_kwargs: LR scheduler constructor keyword args.
-        :param gamma: Ratio of expected loss for generator and discriminator, defaults to 0.3.
+        :param dict scheduler_discriminator_kwargs: LR scheduler constructor
+            keyword args.
+        :param gamma: Ratio of expected loss for generator and discriminator,
+            defaults to 0.3.
         :type gamma: float
-        :param lambda_k: Learning rate for control theory optimization, defaults to 0.001.
+        :param lambda_k: Learning rate for control theory optimization,
+            defaults to 0.001.
         :type lambda_k: float
-        :param regularizer: Regularization term in the GAROM loss, defaults to False.
+        :param regularizer: Regularization term in the GAROM loss,
+            defaults to False.
         :type regularizer: bool
 
         .. warning::
-            The algorithm works only for data-driven model. Hence in the ``problem`` definition
-            the codition must only contain ``input`` (e.g. coefficient parameters, time
-            parameters), and ``target``.
+            The algorithm works only for data-driven model. Hence in the
+            ``problem`` definition the codition must only contain ``input``
+            (e.g. coefficient parameters, time parameters), and ``target``.
         """
 
         # set loss
@@ -118,9 +119,11 @@ def forward(self, x, mc_steps=20, variance=False):
         :param mc_steps: Number of montecarlo samples to approximate the
             expected value, defaults to 20.
         :type mc_steps: int
-        :param variance: Returining also the sample variance of the solution, defaults to False.
+        :param variance: Returining also the sample variance of the solution,
+            defaults to False.
         :type variance: bool
-        :return: The expected value of the generator distribution. If ``variance=True`` also the
+        :return: The expected value of the generator distribution. If
+            ``variance=True`` also the
             sample variance is returned.
         :rtype: torch.Tensor | tuple(torch.Tensor, torch.Tensor)
         """
@@ -139,6 +142,7 @@ def forward(self, x, mc_steps=20, variance=False):
         return mean
 
     def sample(self, x):
+        """TODO"""
         # sampling
         return self.generator(x)
 
@@ -285,24 +289,30 @@ def test_step(self, batch):
 
     @property
     def generator(self):
+        """TODO"""
         return self.models[0]
 
     @property
     def discriminator(self):
+        """TODO"""
         return self.models[1]
 
     @property
     def optimizer_generator(self):
+        """TODO"""
         return self.optimizers[0].instance
 
     @property
     def optimizer_discriminator(self):
+        """TODO"""
         return self.optimizers[1].instance
 
     @property
     def scheduler_generator(self):
+        """TODO"""
         return self.schedulers[0].instance
 
     @property
     def scheduler_discriminator(self):
+        """TODO"""
         return self.schedulers[1].instance

pina/solver/physic_informed_solver/__init__.py

@@ -1,3 +1,5 @@
+"""TODO"""
+
 __all__ = [
     "PINNInterface",
     "PINN",

pina/solver/physic_informed_solver/causal_pinn.py

@@ -2,9 +2,9 @@
 
 import torch
 
-from pina.problem import TimeDependentProblem
+from ...problem import TimeDependentProblem
 from .pinn import PINN
-from pina.utils import check_consistency
+from ...utils import check_consistency
 
 
 class CausalPINN(PINN):

pina/solver/physic_informed_solver/competitive_pinn.py

@@ -1,7 +1,7 @@
 """Module for Competitive PINN."""
 
-import torch
 import copy
+import torch
 
 from ...problem import InverseProblem
 from .pinn_interface import PINNInterface
@@ -211,6 +211,8 @@ def on_train_batch_end(self, outputs, batch, batch_idx):
         """
         # increase by one the counter of optimization to save loggers
         (
+            # Unavoidable long line
+            # pylint: disable=line-too-long
             self.trainer.fit_loop.epoch_loop.manual_optimization.optim_step_progress.total.completed
         ) += 1
 
@@ -236,6 +238,9 @@ def discriminator(self):
         """
         return self.models[1]
 
+    # In this case self.optimizers is a list of two optimizers!!!
+    # pylint: disable=unsubscriptable-object
+
     @property
     def optimizer_model(self):
         """

pina/solver/physic_informed_solver/gradient_pinn.py

@@ -3,8 +3,8 @@
 import torch
 
 from .pinn import PINN
-from pina.operator import grad
-from pina.problem import SpatialProblem
+from ...operator import grad
+from ...problem import SpatialProblem
 
 
 class GradientPINN(PINN):

pina/solver/physic_informed_solver/pinn_interface.py

@@ -106,7 +106,6 @@ def loss_phys(self, samples, equation):
             samples and equation.
         :rtype: LabelTensor
         """
-        pass
 
     def compute_residual(self, samples, equation):
         """

pina/solver/physic_informed_solver/self_adaptive_pinn.py

@@ -1,10 +1,10 @@
 """Module for Self-Adaptive PINN."""
 
-import torch
 from copy import deepcopy
+import torch
 
-from pina.utils import check_consistency
-from pina.problem import InverseProblem
+from ...utils import check_consistency
+from ...problem import InverseProblem
 from ..solver import MultiSolverInterface
 from .pinn_interface import PINNInterface
 
@@ -155,7 +155,7 @@ def __init__(
         self._vectorial_loss.reduction = "none"
 
     def forward(self, x):
-        """
+        r"""
         Forward pass implementation for the PINN
         solver. It returns the function
         evaluation :math:`\mathbf{u}(\mathbf{x})` at the control points
@@ -234,6 +234,8 @@ def on_train_batch_end(self, outputs, batch, batch_idx):
         """
         # increase by one the counter of optimization to save loggers
         (
+            # Unavoidable pylint error
+            # pylint: disable=line-too-long
             self.trainer.fit_loop.epoch_loop.manual_optimization.optim_step_progress.total.completed
         ) += 1
 
@@ -370,6 +372,9 @@ def scheduler_weights(self):
         """
         return self.schedulers[1]
 
+    # In this case self.optimizers is a list of two optimizers!!!
+    # pylint: disable=unsubscriptable-object
+
     @property
     def optimizer_model(self):
         """

pina/solver/reduced_order_model.py

@@ -39,7 +39,8 @@ class ReducedOrderModelSolver(SupervisedSolver):
             \mathcal{D}_{\rm{net}}[\mathcal{E}_{\rm{net}}[\mathbf{u}(\mu_i)]] -
             \mathbf{u}(\mu_i))
 
-    where :math:`\mathcal{L}` is a specific loss function, default Mean Square Error:
+    where :math:`\mathcal{L}` is a specific loss function, default 
+    Mean Square Error:
 
     .. math::
         \mathcal{L}(v) = \| v \|^2_2.