Fixed no-else-return message in probnum.linalg module

src/probnum/linalg/solvers/beliefs/_linear_system_belief.py

@@ -157,8 +157,7 @@ def x(self) -> randvars.RandomVariable:
         """Belief about the solution."""
         if self._x is None:
             return self._induced_x()
-        else:
-            return self._x
+        return self._x
 
     @property
     def A(self) -> randvars.RandomVariable:

src/probnum/linalg/solvers/matrixbased.py

@@ -149,8 +149,7 @@ def has_converged(self, iter, maxiter, **kwargs):
                 "Iteration terminated. Solver reached the maximum number of iterations."
             )
             return True, "maxiter"
-        else:
-            return False, ""
+        return False, ""
 
     def solve(self, callback=None, **kwargs):
         """Solve the linear system :math:`Ax=b`.
@@ -315,15 +314,15 @@ def _get_prior_params(self, A0, Ainv0, x0, b):
                 A0_covfactor = self.A
                 return A0_mean, A0_covfactor, Ainv0_mean, Ainv0_covfactor
             # Construct matrix priors from initial guess x0
-            elif isinstance(x0, np.ndarray):
+            if isinstance(x0, np.ndarray):
                 A0_mean, Ainv0_mean = self._construct_symmetric_matrix_prior_means(
                     A=self.A, x0=x0, b=b
                 )
                 Ainv0_covfactor = Ainv0_mean
                 # Symmetric posterior correspondence
                 A0_covfactor = self.A
                 return A0_mean, A0_covfactor, Ainv0_mean, Ainv0_covfactor
-            elif isinstance(x0, randvars.RandomVariable):
+            if isinstance(x0, randvars.RandomVariable):
                 raise NotImplementedError
 
         # Prior on Ainv specified
@@ -360,7 +359,7 @@ def _get_prior_params(self, A0, Ainv0, x0, b):
             return A0_mean, A0_covfactor, Ainv0_mean, Ainv0_covfactor
 
         # Prior on A specified
-        elif A0 is not None and not isinstance(Ainv0, randvars.RandomVariable):
+        if A0 is not None and not isinstance(Ainv0, randvars.RandomVariable):
             if isinstance(A0, randvars.RandomVariable):
                 A0_mean = A0.mean
                 A0_covfactor = A0.cov.A
@@ -392,16 +391,15 @@ def _get_prior_params(self, A0, Ainv0, x0, b):
             Ainv0_covfactor = Ainv0_mean
             return A0_mean, A0_covfactor, Ainv0_mean, Ainv0_covfactor
         # Both matrix priors on A and H specified via random variables
-        elif isinstance(A0, randvars.RandomVariable) and isinstance(
+        if isinstance(A0, randvars.RandomVariable) and isinstance(
             Ainv0, randvars.RandomVariable
         ):
             A0_mean = A0.mean
             A0_covfactor = A0.cov.A
             Ainv0_mean = Ainv0.mean
             Ainv0_covfactor = Ainv0.cov.A
             return A0_mean, A0_covfactor, Ainv0_mean, Ainv0_covfactor
-        else:
-            raise NotImplementedError
+        raise NotImplementedError
 
     def _compute_trace_Ainv_covfactor0(self, Y, unc_scale):
         """Computes the trace of the prior covariance factor for the inverse view.
@@ -512,15 +510,14 @@ def has_converged(self, iter, maxiter, resid=None, atol=None, rtol=None):
         b_norm = np.linalg.norm(self.b)
         if resid_norm <= atol:
             return True, "resid_atol"
-        elif resid_norm <= rtol * b_norm:
+        if resid_norm <= rtol * b_norm:
             return True, "resid_rtol"
         # uncertainty-based
         if np.sqrt(self.trace_sol_cov) <= atol:
             return True, "tracecov_atol"
-        elif np.sqrt(self.trace_sol_cov) <= rtol * b_norm:
+        if np.sqrt(self.trace_sol_cov) <= rtol * b_norm:
             return True, "tracecov_rtol"
-        else:
-            return False, ""
+        return False, ""
 
     def _calibrate_uncertainty(self, S, sy, method):
         """Calibrate uncertainty based on the Rayleigh coefficients.

src/probnum/linalg/solvers/policies/_conjugate_gradient.py

@@ -61,26 +61,25 @@ def __call__(
                 )
 
             return residual
+        # Reorthogonalization of the residual
+        if self._reorthogonalization_fn_residual is not None:
+            residual, prev_residual = self._reorthogonalized_residual(
+                solver_state=solver_state
+            )
         else:
-            # Reorthogonalization of the residual
-            if self._reorthogonalization_fn_residual is not None:
-                residual, prev_residual = self._reorthogonalized_residual(
-                    solver_state=solver_state
-                )
-            else:
-                prev_residual = solver_state.residuals[solver_state.step - 1]
+            prev_residual = solver_state.residuals[solver_state.step - 1]
 
-            # A-conjugacy correction (in exact arithmetic)
-            beta = (np.linalg.norm(residual) / np.linalg.norm(prev_residual)) ** 2
-            action = residual + beta * solver_state.actions[solver_state.step - 1]
+        # A-conjugacy correction (in exact arithmetic)
+        beta = (np.linalg.norm(residual) / np.linalg.norm(prev_residual)) ** 2
+        action = residual + beta * solver_state.actions[solver_state.step - 1]
 
-            # Reorthogonalization of the resulting action
-            if self._reorthogonalization_fn_action is not None:
-                action = self._reorthogonalized_action(
-                    action=action, solver_state=solver_state
-                )
+        # Reorthogonalization of the resulting action
+        if self._reorthogonalization_fn_action is not None:
+            action = self._reorthogonalized_action(
+                action=action, solver_state=solver_state
+            )
 
-            return action
+        return action
 
     def _reorthogonalized_residual(
         self,

tox.ini

@@ -68,7 +68,7 @@ commands =
     # Per-package Linting Passes
     pylint src/probnum/diffeq --disable="redefined-outer-name,too-many-instance-attributes,too-many-arguments,too-many-locals,too-few-public-methods,protected-access,unnecessary-pass,unused-variable,unused-argument,no-self-use,duplicate-code,missing-function-docstring,missing-param-doc,missing-type-doc,missing-raises-doc,missing-return-type-doc" --jobs=0
     pylint src/probnum/filtsmooth --disable="no-member,arguments-differ,too-many-arguments,too-many-locals,too-few-public-methods,protected-access,unused-variable,unused-argument,no-self-use,duplicate-code,useless-param-doc" --jobs=0
-    pylint src/probnum/linalg --disable="no-member,abstract-method,arguments-differ,else-if-used,redefined-builtin,too-many-instance-attributes,too-many-arguments,too-many-locals,too-many-lines,too-many-statements,too-many-branches,too-complex,too-few-public-methods,protected-access,unused-argument,attribute-defined-outside-init,no-else-return,no-else-raise,no-self-use,duplicate-code,missing-module-docstring,missing-param-doc,missing-type-doc,missing-raises-doc,missing-return-type-doc" --jobs=0
+    pylint src/probnum/linalg --disable="no-member,abstract-method,arguments-differ,redefined-builtin,too-many-instance-attributes,too-many-arguments,too-many-locals,too-many-lines,too-many-statements,too-many-branches,too-complex,too-few-public-methods,protected-access,unused-argument,attribute-defined-outside-init,no-else-raise,no-self-use,else-if-used,duplicate-code,missing-module-docstring,missing-param-doc,missing-type-doc,missing-raises-doc,missing-return-type-doc" --jobs=0
     pylint src/probnum/linops --disable="too-many-instance-attributes,too-many-arguments,too-many-locals,protected-access,no-else-return,no-else-raise,else-if-used,missing-class-docstring,missing-function-docstring,missing-raises-doc,duplicate-code" --jobs=0
     pylint src/probnum/problems --disable="too-many-arguments,too-many-locals,unused-variable,unused-argument,consider-using-from-import,duplicate-code,missing-module-docstring,missing-function-docstring,missing-param-doc,missing-type-doc,missing-raises-doc" --jobs=0
     pylint src/probnum/quad --disable="too-many-arguments,missing-module-docstring" --jobs=0