fix and add tests

tests/explainers/test_self_influence.py

@@ -4,8 +4,8 @@
 import torch
 from torch.utils.data import TensorDataset
 
-from src.explainers.self_influence import get_self_influence_ranking
-from src.utils.explain_wrapper import explain
+from src.explainers.captum.similarity import CaptumSimilarityExplainer
+from src.explainers.functional import captum_similarity_self_influence_ranking
 from src.utils.functions.similarities import dot_product_similarity
 
 
@@ -15,22 +15,43 @@
     [
         (
             "random_data",
-            {"method": "SimilarityInfluence", "layer": "identity", "similarity_metric": dot_product_similarity},
+            {"layer": "identity", "similarity_metric": dot_product_similarity},
         ),
     ],
 )
-def test_self_influence_ranking(test_id, explain_kwargs, request):
+def test_self_influence(test_id, init_kwargs, request):
     model = torch.nn.Sequential(OrderedDict([("identity", torch.nn.Identity())]))
+    # X=torch.randn(1,200)
+    import os
+    import shutil
+
+    os.mkdir("temp_captum")
+    os.mkdir("temp_captum2")
+    torch.random.manual_seed(42)
     X = torch.randn(100, 200)
-    rand_dataset = TensorDataset(X, torch.randint(0, 10, (100,)))
+    # rand_dataset = TensorDataset(X,torch.randint(0,10,(1,)))
+    y = torch.randint(0, 10, (100,))
+    rand_dataset = TensorDataset(X, y)
+    init_kwargs = {"layer": "identity", "similarity_metric": dot_product_similarity}
 
-    self_influence_rank = get_self_influence_ranking(
+    self_influence_rank_functional = captum_similarity_self_influence_ranking(
         model=model,
         model_id="0",
         cache_dir="temp_captum",
-        training_data=rand_dataset,
-        explain_fn=explain,
-        explain_fn_kwargs=explain_kwargs,
+        train_dataset=rand_dataset,
+        init_kwargs=init_kwargs,
+        device="cpu",
+    )
+
+    explainer_obj = CaptumSimilarityExplainer(
+        model=model, model_id="1", cache_dir="temp_captum2", train_dataset=rand_dataset, device="cpu", **init_kwargs
     )
+    self_influence_rank_stateful = explainer_obj.self_influence_ranking()
+
+    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"))
 
-    assert torch.allclose(self_influence_rank, torch.linalg.norm(X, dim=-1).argsort())
+    assert torch.allclose(self_influence_rank_functional, torch.linalg.norm(X, dim=-1).argsort())
+    assert torch.allclose(self_influence_rank_functional, self_influence_rank_stateful)

tests/metrics/test_unnamed_metrics.py

@@ -7,7 +7,7 @@
 @pytest.mark.parametrize(
     "test_id, model, dataset, top_k, batch_size, explanations, expected_score",
     [
-        ("mnist", "load_mnist_model", "load_mnist_dataset", 3, 8, "load_mnist_explanations_1", 8),
+        ("mnist", "load_mnist_model", "load_mnist_dataset", 3, 8, "load_mnist_explanations_1", 7),
     ],
 )
 def test_top_k_overlap_metrics(test_id, model, dataset, top_k, batch_size, explanations, expected_score, request):