Various pylint changes

pose_evaluation/metrics/base_embedding_metric.py

@@ -1,6 +1,7 @@
 from typing import TypeVar
-from pose_evaluation.metrics.base import BaseMetric
 import torch
+from pose_evaluation.metrics.base import BaseMetric
+
 
 # Define a type alias for embeddings (e.g., torch.Tensor)
 Embedding = TypeVar("Embedding", bound=torch.Tensor)

pose_evaluation/metrics/conftest.py

@@ -1,12 +1,12 @@
-# conftest.py
-import pytest
 import shutil
 from pathlib import Path
 from typing import Callable, Union
 import torch
 import numpy as np
+import pytest
 
-@pytest.fixture(scope="session", autouse=True)  
+
+@pytest.fixture(scope="session", autouse=True)
 def clean_test_artifacts():
     """Fixture to clean up test artifacts before each test session."""
     test_artifacts_dir = Path(__file__).parent / "tests"  # Using Path
@@ -17,19 +17,18 @@ def clean_test_artifacts():
     # (Optional) You can add cleanup logic here to run after the session if needed
 
 
-# conftest.py
-from typing import Callable, Union
-import torch
-import numpy as np
-
-@pytest.fixture
-def distance_range_checker() -> Callable[[Union[torch.Tensor, np.ndarray], float, float], None]:
+@pytest.fixture(name="distance_range_checker")
+def fixture_distance_range_checker() -> Callable[[Union[torch.Tensor, np.ndarray], float, float], None]:
     def _check_range(distances: Union[torch.Tensor, np.ndarray], min_val: float = 0, max_val: float = 2) -> None:
         max_distance = distances.max().item()
         min_distance = distances.min().item()
 
         # Use np.isclose for comparisons with tolerance
-        assert np.isclose(min_distance, min_val, atol=1e-6) or min_val <= min_distance <= max_val, f"Minimum distance ({min_distance}) is outside the expected range [{min_val}, {max_val}]"
-        assert np.isclose(max_distance, max_val, atol=1e-6) or min_val <= max_distance <= max_val, f"Maximum distance ({max_distance}) is outside the expected range [{min_val}, {max_val}]"
+        assert (
+            np.isclose(min_distance, min_val, atol=1e-6) or min_val <= min_distance <= max_val
+        ), f"Minimum distance ({min_distance}) is outside the expected range [{min_val}, {max_val}]"
+        assert (
+            np.isclose(max_distance, max_val, atol=1e-6) or min_val <= max_distance <= max_val
+        ), f"Maximum distance ({max_distance}) is outside the expected range [{min_val}, {max_val}]"
 
-    return _check_range
+    return _check_range

pose_evaluation/metrics/test_embedding_distance_metric.py

@@ -1,12 +1,15 @@
+from pathlib import Path
+from typing import List
+import logging
 import pytest
 import numpy as np
+import matplotlib.pyplot as plt
 import torch
 from pose_evaluation.metrics.embedding_distance_metric import EmbeddingDistanceMetric
-from pose_evaluation.metrics.conftest import distance_range_checker
-import matplotlib.pyplot as plt
-import logging
-from typing import List
-from pathlib import Path
+
+# no need to import. https://github.com/pylint-dev/pylint/issues/3493#issuecomment-616761997
+# from pose_evaluation.metrics.conftest import distance_range_checker
+
 
 # TODO: many fixes. Including the fact that we test cosine but not Euclidean,
 
@@ -19,14 +22,16 @@
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
-@pytest.fixture
-def cosine_metric():
+# named the fixture this way to solve many pylint W0621
+# https://stackoverflow.com/questions/46089480/pytest-fixtures-redefining-name-from-outer-scope-pylint
+@pytest.fixture(name="cosine_metric")
+def fixture_cosine_metric():
     """Fixture to create an EmbeddingDistanceMetric instance."""
     return EmbeddingDistanceMetric(kind="cosine")
 
 
-@pytest.fixture
-def embeddings() -> List[torch.Tensor]:
+@pytest.fixture(name="embeddings")
+def fixture_embeddings() -> List[torch.Tensor]:
     """Fixture to create dummy embeddings for testing."""
     return [random_tensor(768) for _ in range(5)]
 
@@ -185,7 +190,7 @@ def test_score_all_against_self(
     scores = cosine_metric.score_all(embeddings, embeddings)
     assert scores.shape == (len(embeddings), len(embeddings)), "Output shape mismatch for score_all."
     assert torch.allclose(
-        torch.diagonal(scores), torch.zeros(len(embeddings),dtype=scores.dtype), atol=1e-6
+        torch.diagonal(scores), torch.zeros(len(embeddings), dtype=scores.dtype), atol=1e-6
     ), "Self-comparison scores should be zero for cosine distance."
     distance_range_checker(scores, min_val=0, max_val=2)
     logger.info(f"Score matrix shape: {scores.shape}, Diagonal values: {torch.diagonal(scores)}")

pyproject.toml

@@ -18,7 +18,7 @@ dependencies = [
     # For reading .csv files, etc
     "pandas", 
     # For segment similarity
-    #"sign_language_segmentation @ git+https://github.com/sign-language-processing/segmentation"
+    "sign_language_segmentation @ git+https://github.com/sign-language-processing/segmentation"
 ]
 
 [project.optional-dependencies]
@@ -40,6 +40,7 @@ disable = [
     "C0115", # Missing class docstring
     "C0116", # Missing function or method docstring
     "W0511", # TODO
+    "W1203", # use lazy % formatting in logging functions
 ]
 
 [tool.setuptools]