lint and add mypy to pre-commit

.pre-commit-config.yaml

@@ -69,3 +69,8 @@ repos:
   - id: rst-backticks
   - id: rst-directive-colons
   - id: rst-inline-touching-normal
+
+-   repo: https://github.com/pre-commit/mirrors-mypy
+    rev: ''  # Use the sha / tag you want to point at
+    hooks:
+    -   id: mypy

pyproject.toml

@@ -81,9 +81,6 @@ omit = ["*/__init__.py"]
 convention = "google"
 add_ignore = ["D107"]
 
-[tool.mypy]
-plugins = ["numpy.typing.mypy_plugin"]
-
 [[tool.mypy.overrides]]
 module = ["sklearn.*", "scipy.linalg"]
 ignore_missing_imports = true

src/sparselm/model/_adaptive_lasso.py

@@ -207,7 +207,7 @@ def _solve(
     ) -> NDArray[np.floating]:
         """Solve Lasso problem iteratively adaptive weights."""
         assert self.canonicals_.parameters is not None
-        previous_weights = self._get_weights_value(self.canonicals_.parameters)  
+        previous_weights = self._get_weights_value(self.canonicals_.parameters)
         for i in range(self.max_iter):
             self.canonicals_.problem.solve(
                 solver=self.solver, warm_start=self.warm_start, **solver_options
@@ -221,13 +221,13 @@ def _solve(
             self.n_iter_ = i + 1  # save number of iterations for sklearn
             self._iterative_update(
                 self.canonicals_.beta.value,
-                self.canonicals_.parameters,  
+                self.canonicals_.parameters,
                 self.canonicals_.auxiliaries,
             )
             # check convergence
-            if self._check_convergence(self.canonicals_.parameters, previous_weights):  
+            if self._check_convergence(self.canonicals_.parameters, previous_weights):
                 break
-            previous_weights = self._get_weights_value(self.canonicals_.parameters)  
+            previous_weights = self._get_weights_value(self.canonicals_.parameters)
         return self.canonicals_.beta.value
 
 
@@ -719,9 +719,7 @@ def _iterative_update(
         )
         parameters.adaptive_group_weights.value = (
             self.canonicals_.parameters.lambda2.value * parameters.group_weights
-        ) * update(
-            auxiliaries.group_norms.value, self.eps
-        )
+        ) * update(auxiliaries.group_norms.value, self.eps)
 
 
 class AdaptiveRidgedGroupLasso(AdaptiveGroupLasso, RidgedGroupLasso):

src/sparselm/model/_base.py

@@ -452,7 +452,7 @@ def generate_problem(
 
         beta = cp.Variable(X.shape[1])
         parameters = self._generate_params(X, y)
-        auxiliaries = self._generate_auxiliaries(X, y, beta, parameters)  
+        auxiliaries = self._generate_auxiliaries(X, y, beta, parameters)
         objective = self._generate_objective(X, y, beta, parameters, auxiliaries)
         constraints = self._generate_constraints(X, y, beta, parameters, auxiliaries)
         problem = cp.Problem(cp.Minimize(objective), constraints)
@@ -543,6 +543,6 @@ def _generate_objective(
         assert parameters is not None and hasattr(parameters, "eta")
         c0 = 2 * X.shape[0]  # keeps hyperparameter scale independent
         objective = super()._generate_objective(X, y, beta, parameters, auxiliaries)  # type: ignore
-        objective += c0 * parameters.eta * cp.sum_squares(tikhonov_w @ beta)  
+        objective += c0 * parameters.eta * cp.sum_squares(tikhonov_w @ beta)
 
         return objective

src/sparselm/model/_lasso.py

@@ -233,7 +233,7 @@ def _generate_params(self, X: NDArray, y: NDArray) -> SimpleNamespace:
         group_weights = (
             np.ones(n_groups) if self.group_weights is None else self.group_weights
         )
-        parameters.group_weights = group_weights  
+        parameters.group_weights = group_weights
         return parameters
 
     @staticmethod
@@ -272,7 +272,7 @@ def _generate_regularization(
         auxiliaries: SimpleNamespace | None = None,
     ) -> cp.Expression:
         assert auxiliaries is not None
-        return parameters.alpha * (parameters.group_weights @ auxiliaries.group_norms)  
+        return parameters.alpha * (parameters.group_weights @ auxiliaries.group_norms)
 
 
 # TODO this implementation is not efficient, reimplement, or simply deprecate.
@@ -401,7 +401,7 @@ def _generate_params(self, X: NDArray, y: NDArray) -> SimpleNamespace:
         group_weights = (
             np.ones(n_groups) if self.group_weights is None else self.group_weights
         )
-        parameters.group_weights = group_weights  
+        parameters.group_weights = group_weights
         return parameters
 
     def generate_problem(
@@ -493,7 +493,7 @@ def _solve(self, X, y, solver_options, *args, **kwargs) -> NDArray[np.floating]:
             [
                 sum(
                     self.canonicals_.beta.value[
-                        self.canonicals_.auxiliaries.extended_coef_indices == i  
+                        self.canonicals_.auxiliaries.extended_coef_indices == i
                     ]
                 )
                 for i in range(X.shape[1])
@@ -610,16 +610,16 @@ def _validate_params(self, X, y):
     def _set_param_values(self) -> None:
         super()._set_param_values()
         assert self.canonicals_.parameters is not None
-        self.canonicals_.parameters.lambda1.value = self.l1_ratio * self.alpha  
-        self.canonicals_.parameters.lambda2.value = (1 - self.l1_ratio) * self.alpha  
+        self.canonicals_.parameters.lambda1.value = self.l1_ratio * self.alpha
+        self.canonicals_.parameters.lambda2.value = (1 - self.l1_ratio) * self.alpha
 
     def _generate_params(self, X: NDArray, y: NDArray) -> SimpleNamespace:
         """Generate parameters."""
         parameters = super()._generate_params(X, y)
         # save for information purposes
-        parameters.l1_ratio = self.l1_ratio  
-        parameters.lambda1 = cp.Parameter(nonneg=True, value=self.l1_ratio * self.alpha)  
-        parameters.lambda2 = cp.Parameter(  
+        parameters.l1_ratio = self.l1_ratio
+        parameters.lambda1 = cp.Parameter(nonneg=True, value=self.l1_ratio * self.alpha)
+        parameters.lambda2 = cp.Parameter(
             nonneg=True, value=(1 - self.l1_ratio) * self.alpha
         )
         return parameters

src/sparselm/model/_miqp/_base.py

@@ -161,7 +161,7 @@ def _generate_hierarchy_constraints(
         z0_index = {gid: i for i, gid in enumerate(group_ids)}
         constraints = [
             z0[z0_index[high_id]] <= z0[z0_index[sub_id]]
-            for high_id, sub_ids in zip(group_ids, self.hierarchy)  
+            for high_id, sub_ids in zip(group_ids, self.hierarchy)
             for sub_id in sub_ids
         ]
         return constraints

src/sparselm/model/_miqp/_regularized_l0.py

@@ -157,9 +157,7 @@ def _generate_objective(
         c0 = 2 * X.shape[0]  # keeps hyperparameter scale independent
         objective = super()._generate_objective(
             X, y, beta, parameters, auxiliaries
-        ) + c0 * parameters.alpha * cp.sum(
-            auxiliaries.z0
-        )
+        ) + c0 * parameters.alpha * cp.sum(auxiliaries.z0)
         return objective
 
 

tests/test_common.py

@@ -17,7 +17,7 @@
 
 ESTIMATORS = getmembers(spm, isclass)
 ESTIMATOR_NAMES = [est[0] for est in ESTIMATORS]
-ESTIMATORS = [est[1] for est in ESTIMATORS]
+ESTIMATORS = [est[1] for est in ESTIMATORS]  # type: ignore
 
 
 @pytest.fixture(params=ESTIMATORS, ids=ESTIMATOR_NAMES)