using tmp_path fixture for tests

tests/explainers/wrappers/test_captum_influence.py

@@ -1,5 +1,4 @@
 import os
-import shutil
 from collections import OrderedDict
 
 import pytest
@@ -28,7 +27,7 @@
     ],
 )
 # TODO: I think a good naming convention is "test_<function_name>..." or "test_<class_name>...".
-def test_self_influence(test_id, init_kwargs, request):
+def test_self_influence(test_id, init_kwargs, tmp_path):
     # TODO: this should be a fixture.
     model = torch.nn.Sequential(OrderedDict([("identity", torch.nn.Identity())]))
 
@@ -38,11 +37,12 @@ def test_self_influence(test_id, init_kwargs, request):
     y = torch.randint(0, 10, (100,))
     rand_dataset = TensorDataset(X, y)
 
+    # Using tmp_path pytest fixtures to create a temporary directory
     # TODO: One test should test one thing. This is test 1, ....
     self_influence_rank_functional = captum_similarity_self_influence(
         model=model,
         model_id="0",
-        cache_dir="temp_captum",
+        cache_dir=str(tmp_path),
         train_dataset=rand_dataset,
         init_kwargs=init_kwargs,
         device="cpu",
@@ -53,7 +53,7 @@ def test_self_influence(test_id, init_kwargs, request):
     explainer_obj = CaptumSimilarity(
         model=model,
         model_id="1",
-        cache_dir="temp_captum2",
+        cache_dir=str(tmp_path),
         train_dataset=rand_dataset,
         device="cpu",
         **init_kwargs,
@@ -63,13 +63,6 @@ def test_self_influence(test_id, init_kwargs, request):
     # TODO: here we then specifically test self_influence for CaptumSimilarity and should make it explicit in the name.
     self_influence_rank_stateful = explainer_obj.self_influence()
 
-    # TODO: we check "temp_captum2" but then remove os.path.join(os.getcwd(), "temp_captum2")?
-    # TODO: is there a reason to fear that the "temp_captum2" folder is not in os.getcwd()?
-    if os.path.isdir("temp_captum2"):
-        shutil.rmtree(os.path.join(os.getcwd(), "temp_captum2"))
-    if os.path.isdir("temp_captum"):
-        shutil.rmtree(os.path.join(os.getcwd(), "temp_captum"))
-
     # TODO: what if we pass a non-identity model? Then we don't expect torch.linalg.norm(X, dim=-1).argsort()
     # TODO: let's put expectations in the parametrisation of tests. We want to test different scenarios,
     #  and not some super-specific case. This specific case definitely can be tested as well.
@@ -96,7 +89,9 @@ def test_self_influence(test_id, init_kwargs, request):
 # TODO: I think a good naming convention is "test_<function_name>..." or "test_<class_name>...".
 # TODO: I would call it test_captum_similarity, because it is a test for the CaptumSimilarity class.
 # TODO: We could also make the explainer type (e.g. CaptumSimilarity) a param, then it would be test_explainer or something.
-def test_explain_stateful(test_id, model, dataset, explanations, test_tensor, test_labels, method_kwargs, request):
+def test_explain_stateful(
+    test_id, model, dataset, explanations, test_tensor, test_labels, method_kwargs, request, tmp_path
+):
     model = request.getfixturevalue(model)
     dataset = request.getfixturevalue(dataset)
     test_tensor = request.getfixturevalue(test_tensor)
@@ -106,7 +101,7 @@ def test_explain_stateful(test_id, model, dataset, explanations, test_tensor, te
     explainer = CaptumSimilarity(
         model=model,
         model_id="test_id",
-        cache_dir=os.path.join("./cache", "test_id"),
+        cache_dir=str(tmp_path),
         train_dataset=dataset,
         device="cpu",
         **method_kwargs,