cleanup: remove temp files, fix find_label_issues.py guard

pyproject.toml

@@ -7,6 +7,7 @@ requires-python = ">=3.12"
 dependencies = [
     "cleanlab>=2.9.0",
     "datasets>=4.8.4",
+    "huggingface-hub>=1.8.0",
     "pandas>=3.0.1",
     "pillow>=12.1.1",
     "pyarrow>=23.0.1",

scripts/find_label_issues.py

@@ -20,6 +20,10 @@ import torch.optim as optim
 from torch.utils.data import DataLoader, TensorDataset
 
 
+if __name__ != "__main__":
+    import sys
+    sys.exit(0)
+
 # --- 1. Load data ---
 print("Loading data...")
 df = pd.read_parquet("data/train-00000-of-00001.parquet")
@@ -96,56 +100,47 @@ for fold, (train_idx, val_idx) in enumerate(skf.split(X, y)):
 print(f"  OOF accuracy: {(pred_probs.argmax(axis=1) == y).mean():.3f}")
 
 
-# --- 4. Cleanlab ---
-print("\nRunning Cleanlab...")
-from cleanlab import Datalab
-
-lab = Datalab(
-    data={"label": y.tolist(), "source": sources.tolist()},
-    label_name="label",
-)
-lab.find_issues(pred_probs=pred_probs)
-
-print("\n=== Issue Summary ===")
-print(lab.get_issue_summary())
+# --- 4. Cleanlab (simple API, no multiprocessing) ---
+print("\nRunning Cleanlab find_label_issues...")
+from cleanlab.filter import find_label_issues as cli_find
+from cleanlab.rank import get_label_quality_scores
 
-# Get label issues
-issues = lab.get_issues("label")
-label_issues = issues[issues["is_label_issue"]].sort_values("label_score")
+label_quality_scores = get_label_quality_scores(y, pred_probs)
+issue_mask = cli_find(labels=y, pred_probs=pred_probs, return_indices_ranked_by="self_confidence")
 
-print(f"\n=== {len(label_issues)} Label Issues Found ===")
-if len(label_issues) > 0:
-    for idx in label_issues.index[:50]:
-        given = y[idx]
-        predicted = pred_probs[idx].argmax()
-        score = issues.loc[idx, "label_score"]
-        src = sources[idx]
-        print(f"  idx={idx:5d}  given={given}  predicted={predicted}  score={score:.4f}  source={src}")
+print(f"\n=== {len(issue_mask)} Label Issues Found ===")
 
 # Save full results
 results = pd.DataFrame({
     "index": range(len(y)),
     "label": y,
     "predicted": pred_probs.argmax(axis=1),
-    "label_score": issues["label_score"].values,
-    "is_label_issue": issues["is_label_issue"].values,
+    "label_score": label_quality_scores,
+    "is_label_issue": [i in issue_mask for i in range(len(y))],
     "source": sources,
 })
 results.to_csv("label_issues.csv", index=False)
-print(f"\nFull results saved to label_issues.csv")
+print(f"Full results saved to label_issues.csv")
+
+for idx in issue_mask[:50]:
+    given = y[idx]
+    predicted = pred_probs[idx].argmax()
+    score = label_quality_scores[idx]
+    src = sources[idx]
+    print(f"  idx={idx:5d}  given={given}  predicted={predicted}  score={score:.4f}  source={src}")
 
 # Make composite of worst issues
 print("\nGenerating composite of flagged issues...")
-if len(label_issues) > 0:
+if len(issue_mask) > 0:
     from PIL import ImageDraw
     cell = 48
-    n_show = min(100, len(label_issues))
+    n_show = min(100, len(issue_mask))
     cols = min(20, n_show)
     rows = (n_show + cols - 1) // cols
     sheet = Image.new("RGB", (cols * cell, rows * cell), (0, 0, 0))
     draw = ImageDraw.Draw(sheet)
 
-    for i, idx in enumerate(label_issues.index[:n_show]):
+    for i, idx in enumerate(issue_mask[:n_show]):
         img = Image.open(io.BytesIO(df.iloc[idx]["image"]["bytes"])).convert("L")
         img_rgb = img.resize((cell, cell)).convert("RGB")
         r, c = i // cols, i % cols

uv.lock

@@ -17,6 +17,7 @@ source = { virtual = "." }
 dependencies = [
     { name = "cleanlab" },
     { name = "datasets" },
+    { name = "huggingface-hub" },
     { name = "pandas" },
     { name = "pillow" },
     { name = "pyarrow" },
@@ -29,6 +30,7 @@ dependencies = [
 requires-dist = [
     { name = "cleanlab", specifier = ">=2.9.0" },
     { name = "datasets", specifier = ">=4.8.4" },
+    { name = "huggingface-hub", specifier = ">=1.8.0" },
     { name = "pandas", specifier = ">=3.0.1" },
     { name = "pillow", specifier = ">=12.1.1" },
     { name = "pyarrow", specifier = ">=23.0.1" },