From 87c85e46645f8b63ca5b5fbece4372c876c084c3 Mon Sep 17 00:00:00 2001
From: Axel Nilsson <Karlaxel.n@outlook.com>
Date: Sun, 4 Feb 2024 20:19:08 +0100
Subject: [PATCH] Updated mypy configuration.

---
 mypy.ini                   | 4 ++--
 src/abacus/models/garch.py | 8 ++++----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/mypy.ini b/mypy.ini
index b7e131d..ff6c62b 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -1,6 +1,6 @@
 [mypy]
 warn_unused_configs = True
-files = src/models
+files = src
 ignore_missing_imports = True
 check_untyped_defs = True
 explicit_package_bases = True
@@ -8,5 +8,5 @@ warn_unreachable = True
 warn_redundant_casts = True
 warn_unused_ignores = True
 follow_imports_for_stubs = False
-disable_error_code=assignment, has-type, union-attr, arg-type, attr-defined
+disable_error_code=assignment, has-type, union-attr, arg-type, attr-defined, return
 plugins = numpy.typing.mypy_plugin
diff --git a/src/abacus/models/garch.py b/src/abacus/models/garch.py
index 669e470..8ca1d5e 100644
--- a/src/abacus/models/garch.py
+++ b/src/abacus/models/garch.py
@@ -16,7 +16,7 @@ def __init__(self, data: torch.Tensor):
         super().__init__(data)
 
     @property
-    def parameters(self) -> tuple[float, 3]:
+    def parameters(self) -> tuple[float, ...]:
         self._check_calibration()
 
         optimal_parameters = self._optimal_parameters
@@ -31,11 +31,11 @@ def _number_of_parameters(self) -> int:
         return len(self.parameters)
 
     @property
-    def _optimal_parameters(self) -> np.array:
+    def _optimal_parameters(self) -> np.ndarray:
         return self._uncondition_parameters(parameters=torch.tensor(self._solution.x)).numpy()
 
     @property
-    def _inital_solution(self) -> np.array:
+    def _inital_solution(self) -> np.ndarray:
         return np.array(self._precondition_parameters(parameters=torch.tensor(INITIAL_GARCH_PARAMETERS)))
 
     @property
@@ -83,7 +83,7 @@ def transform_to_uniform(self):
 
         return Normal(0, 1).cdf(residuals)
 
-    def _cost_function(self, parameters: np.array) -> tuple[float, 2]:
+    def _cost_function(self, parameters: np.array) -> tuple[float, ...]:
         """
         Defines the conditional log loss for the GARCH model. Calculates the loss recursively.