Fix linting errors in tests/

tests/test_criterions.py

@@ -293,7 +293,7 @@ def _sample_dist_matrices(seed):
 
 
 @pytest.mark.parametrize("seed", [42, 4242, 424242])
-def test_infonce(seed):
+def test_infonce_check_output_parts(seed):
     pos_dist, neg_dist = _sample_dist_matrices(seed)
 
     ref_loss, ref_align, ref_uniform = _reference_infonce(pos_dist, neg_dist)

tests/test_distributions.py

@@ -43,7 +43,7 @@ def prepare(N=1000, n=128, d=5, probs=[0.3, 0.1, 0.6], device="cpu"):
     continuous = torch.randn(N, d).to(device)
 
     rand = torch.from_numpy(np.random.randint(0, N, (n,))).to(device)
-    qidx = discrete[rand].to(device)
+    _ = discrete[rand].to(device)
     query = continuous[rand] + 0.1 * torch.randn(n, d).to(device)
     query = query.to(device)
 
@@ -173,7 +173,7 @@ def test_mixed():
         discrete, continuous)
 
     reference_idx = distribution.sample_prior(10)
-    positive_idx = distribution.sample_conditional(reference_idx)
+    _ = distribution.sample_conditional(reference_idx)
 
     # The conditional distribution p(· | disc, cont) should yield
     # samples where the label exactly matches the reference sample.
@@ -193,7 +193,7 @@ def test_continuous(benchmark):
     def _test_distribution(dist):
         distribution = dist(continuous)
         reference_idx = distribution.sample_prior(10)
-        positive_idx = distribution.sample_conditional(reference_idx)
+        _ = distribution.sample_conditional(reference_idx)
         return distribution
 
     distribution = _test_distribution(

tests/test_load.py

@@ -122,7 +122,7 @@ def generate_numpy_confounder(filename, dtype):
 
 
 @register("npz")
-def generate_numpy_path(filename, dtype):
+def generate_numpy_path_2(filename, dtype):
     A = np.arange(1000, dtype=dtype).reshape(10, 100)
     np.savez(filename, array=A, other_data="test")
     loaded_A = cebra_load.load(pathlib.Path(filename))
@@ -415,7 +415,7 @@ def generate_csv_path(filename, dtype):
 
 @register_error("csv")
 def generate_csv_empty_file(filename, dtype):
-    with open(filename, "w") as creating_new_csv_file:
+    with open(filename, "w") as _:
         pass
     _ = cebra_load.load(filename)
 
@@ -616,7 +616,6 @@ def generate_pickle_invalid_key(filename, dtype):
 
 @register_error("pkl", "p")
 def generate_pickle_no_array(filename, dtype):
-    A = np.arange(1000, dtype=dtype).reshape(10, 100)
     with open(filename, "wb") as f:
         pickle.dump({"A": "test_1", "B": "test_2"}, f)
     _ = cebra_load.load(filename)

tests/test_models.py

@@ -155,7 +155,7 @@ def test_version_check(version, raises):
             cebra.models.model._check_torch_version(raise_error=True)
 
 
-def test_version_check():
+def test_version_check_2():
     raises = not cebra.models.model._check_torch_version(raise_error=False)
     if raises:
         assert len(cebra.models.get_options("*dropout*")) == 0

tests/test_plot.py

@@ -239,7 +239,7 @@ def test_compare_models():
         _ = cebra_plot.compare_models(models, labels=long_labels, ax=ax)
     with pytest.raises(ValueError, match="Invalid.*labels"):
         invalid_labels = copy.deepcopy(labels)
-        ele = invalid_labels.pop()
+        _ = invalid_labels.pop()
         invalid_labels.append(["a"])
         _ = cebra_plot.compare_models(models, labels=invalid_labels, ax=ax)
 

tests/test_sklearn.py

@@ -276,7 +276,6 @@ def test_api(estimator, check):
         pytest.skip(f"Model architecture {estimator.model_architecture} "
                     f"requires longer input sizes than 20 samples.")
 
-    success = True
     exception = None
     num_successful = 0
     total_runs = 0
@@ -334,7 +333,6 @@ def test_sklearn(model_architecture, device):
     y_c1 = np.random.uniform(0, 1, (1000, 5))
     y_c1_s2 = np.random.uniform(0, 1, (800, 5))
     y_c2 = np.random.uniform(0, 1, (1000, 2))
-    y_c2_s2 = np.random.uniform(0, 1, (800, 2))
     y_d = np.random.randint(0, 10, (1000,))
     y_d_s2 = np.random.randint(0, 10, (800,))
 
@@ -817,7 +815,6 @@ def test_sklearn_full(model_architecture, device, pad_before_transform):
     X = np.random.uniform(0, 1, (1000, 50))
     y_c1 = np.random.uniform(0, 1, (1000, 5))
     y_c2 = np.random.uniform(0, 1, (1000, 2))
-    y_d = np.random.randint(0, 10, (1000,))
 
     # time contrastive
     cebra_model.fit(X)
@@ -883,7 +880,7 @@ def test_sklearn_resampling_model_not_yet_supported(model_architecture, device):
 
     with pytest.raises(ValueError):
         cebra_model.fit(X, y_c1)
-        output = cebra_model.transform(X)
+        _ = cebra_model.transform(X)
 
 
 def _iterate_actions():

tests/test_solver.py

@@ -99,11 +99,12 @@ def test_single_session(data_name, loader_initfunc, solver_initfunc):
 @pytest.mark.parametrize("data_name, loader_initfunc, solver_initfunc",
                          single_session_tests)
 def test_single_session_auxvar(data_name, loader_initfunc, solver_initfunc):
-    return  # TODO
+
+    pytest.skip("Not yet supported")
 
     loader = _get_loader(data_name, loader_initfunc)
     model = _make_model(loader.dataset)
-    behavior_model = _make_behavior_model(loader.dataset)
+    behavior_model = _make_behavior_model(loader.dataset)  # noqa: F841
 
     criterion = cebra.models.InfoNCE()
     optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
@@ -171,7 +172,7 @@ def test_multi_session(data_name, loader_initfunc, solver_initfunc):
 
 @pytest.mark.parametrize("data_name, loader_initfunc, solver_initfunc",
                          multi_session_tests)
-def test_multi_session(data_name, loader_initfunc, solver_initfunc):
+def test_multi_session_2(data_name, loader_initfunc, solver_initfunc):
     loader = _get_loader(data_name, loader_initfunc)
     criterion = cebra.models.InfoNCE()
     model = nn.ModuleList(