tests: update resource setup in tests

tests/test_classical_classifier.py

@@ -20,23 +20,24 @@ class TestClassicalClassifier(ClassifierMixin):
         unittest (_type_): unittest module
     """
 
-    def setup(self) -> None:
+    @classmethod
+    def setup_class(cls):
         """Set up basic classifier and data.
 
         Prepares inputs and expected outputs for testing.
         """
-        self.x_train = pd.DataFrame(
+        cls.x_train = pd.DataFrame(
             [[1, 2], [3, 4], [1, 2], [3, 4]], columns=["BEST_ASK", "BEST_BID"]
         )
-        self.y_train = pd.Series([1, 1, -1, -1])
-        self.x_test = pd.DataFrame(
+        cls.y_train = pd.Series([1, 1, -1, -1])
+        cls.x_test = pd.DataFrame(
             [[1, 2], [3, 4], [1, 2], [3, 4]], columns=["BEST_ASK", "BEST_BID"]
         )
-        self.y_test = pd.Series([1, -1, 1, -1])
-        self.clf = ClassicalClassifier(
+        cls.y_test = pd.Series([1, -1, 1, -1])
+        cls.clf = ClassicalClassifier(
             layers=[("nan", "ex")],
             random_state=7,
-        ).fit(self.x_train, self.y_train)
+        ).fit(cls.x_train, cls.y_train)
 
     def test_random_state(self) -> None:
         """Test, if random state is correctly set.

tests/test_fttransformer.py

@@ -32,35 +32,36 @@ class TestFTTransformer(NeuralNetTestsMixin):
         NeuralNetTestsMixin (neural net mixin): mixin
     """
 
-    def setup(self) -> None:
+    @classmethod
+    def setup_class(cls):
         """Set up basic network and data.
 
         Prepares inputs and expected outputs for testing.
         """
-        self.num_features_cont = 5
-        self.num_features_cat = 1
-        self.cat_cardinalities = [2]
-        self.batch_size = 64
+        cls.num_features_cont = 5
+        cls.num_features_cat = 1
+        cls.cat_cardinalities = [2]
+        cls.batch_size = 64
 
         set_seed()
 
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-        self.x_cat = torch.randint(0, 1, (self.batch_size, self.num_features_cat)).to(
+        cls.x_cat = torch.randint(0, 1, (cls.batch_size, cls.num_features_cat)).to(
             device
         )
-        self.x_cont = (
-            torch.randn(self.batch_size, self.num_features_cont).float().to(device)
+        cls.x_cont = (
+            torch.randn(cls.batch_size, cls.num_features_cont).float().to(device)
         )
-        self.expected_outputs = (
-            torch.randint(0, 1, (self.batch_size, 1)).float().to(device)
+        cls.expected_outputs = (
+            torch.randint(0, 1, (cls.batch_size, 1)).float().to(device)
         )
 
         # https://github.com/Yura52/rtdl/blob/main/rtdl/modules.py
 
         params_feature_tokenizer: Dict[str, Any] = {
-            "num_continous": self.num_features_cont,
-            "cat_cardinalities": self.cat_cardinalities,
+            "num_continous": cls.num_features_cont,
+            "cat_cardinalities": cls.cat_cardinalities,
             "d_token": 96,
         }
         feature_tokenizer = FeatureTokenizer(**params_feature_tokenizer)
@@ -89,7 +90,7 @@ def setup(self) -> None:
 
         transformer = Transformer(**params_transformer)
 
-        self.net = FTTransformer(feature_tokenizer, transformer).to(device)
+        cls.net = FTTransformer(feature_tokenizer, transformer).to(device)
 
     def test_numerical_feature_tokenizer(self) -> None:
         """Test numerical feature tokenizer.

tests/test_objective.py

@@ -24,23 +24,24 @@ class TestObjectives:
         metaclass (_type_, optional): parent. Defaults to abc.ABCMeta.
     """
 
-    def setup(self) -> None:
+    @classmethod
+    def setup_class(cls):
         """Set up basic data.
 
         Construct feature matrix and target.
         """
-        self._old_cwd = Path.cwd()
+        cls._old_cwd = Path.cwd()
         start = dt.datetime(2020, 1, 1).replace(tzinfo=dt.timezone.utc)
         end = dt.datetime(2021, 12, 31).replace(tzinfo=dt.timezone.utc)
         index = pd.date_range(start=start, end=end, freq="15min")
 
         # make 1 const feature and 1 non-const feature, as catboost requires non-const
-        self._x_train = pd.DataFrame(data={"feature_1": 1}, index=index)
+        cls._x_train = pd.DataFrame(data={"feature_1": 1}, index=index)
         rng = np.random.default_rng()
-        self._x_train["feature_2"] = rng.integers(1, 6, self._x_train.shape[0])
-        self._y_train = self._x_train["feature_2"]
-        self._x_val = self._x_train.copy()
-        self._y_val = self._y_train.copy()
+        cls._x_train["feature_2"] = rng.integers(1, 6, cls._x_train.shape[0])
+        cls._y_train = cls._x_train["feature_2"]
+        cls._x_val = cls._x_train.copy()
+        cls._y_val = cls._y_train.copy()
 
     def test_classical_objective(self) -> None:
         """Test if classical objective returns a valid value.