Add ruff linter and format code

- Add ruff as dev dependency
- Format code according to ruff standards
- All checks passing

model.py

@@ -23,15 +23,20 @@ class SonarModel:
         random_state : int, default=42
             Random state for reproducibility
         """
-        self.pipeline = Pipeline([
-            ('scaler', StandardScaler()),
-            ('classifier', RandomForestClassifier(
-                n_estimators=n_estimators,
-                max_depth=max_depth,
-                random_state=random_state,
-                n_jobs=-1
-            ))
-        ])
+        self.pipeline = Pipeline(
+            [
+                ("scaler", StandardScaler()),
+                (
+                    "classifier",
+                    RandomForestClassifier(
+                        n_estimators=n_estimators,
+                        max_depth=max_depth,
+                        random_state=random_state,
+                        n_jobs=-1,
+                    ),
+                ),
+            ]
+        )
         self.is_fitted = False
 
     def fit(self, X, y):
@@ -52,7 +57,7 @@ class SonarModel:
         """
         self.pipeline.fit(X, y)
         self.is_fitted = True
-        self.classes_ = self.pipeline.named_steps['classifier'].classes_
+        self.classes_ = self.pipeline.named_steps["classifier"].classes_
         self.n_features_ = X.shape[1]
         return self
 
@@ -121,7 +126,7 @@ class SonarModel:
         filepath : str
             Path to save the model
         """
-        with open(filepath, 'wb') as f:
+        with open(filepath, "wb") as f:
             pickle.dump(self, f)
 
     @classmethod
@@ -139,7 +144,7 @@ class SonarModel:
         model : SonarModel
             Loaded model instance
         """
-        with open(filepath, 'rb') as f:
+        with open(filepath, "rb") as f:
             return pickle.load(f)
 
     def get_feature_importance(self):
@@ -153,4 +158,4 @@ class SonarModel:
         """
         if not self.is_fitted:
             raise ValueError("Model must be fitted to get feature importances")
-        return self.pipeline.named_steps['classifier'].feature_importances_
+        return self.pipeline.named_steps["classifier"].feature_importances_

pyproject.toml

@@ -8,3 +8,8 @@ dependencies = [
     "scikit-learn>=1.3.0",
     "numpy>=1.24.0",
 ]
+
+[dependency-groups]
+dev = [
+    "ruff>=0.13.1",
+]

train.py

@@ -25,7 +25,7 @@ def generate_sample_data(n_samples=1000, n_features=60, n_classes=2):
         n_clusters_per_class=2,
         weights=[0.5, 0.5],
         flip_y=0.01,
-        random_state=42
+        random_state=42,
     )
     return X, y
 
@@ -87,14 +87,16 @@ def main():
     y_proba = loaded_model.predict_proba(X_samples)
 
     for i in range(len(sample_indices)):
-        print(f"   Sample {i+1}:")
+        print(f"   Sample {i + 1}:")
         print(f"     True class: {y_true[i]}")
         print(f"     Predicted class: {y_pred[i]}")
-        print(f"     Probabilities: Class 0={y_proba[i][0]:.3f}, Class 1={y_proba[i][1]:.3f}")
+        print(
+            f"     Probabilities: Class 0={y_proba[i][0]:.3f}, Class 1={y_proba[i][1]:.3f}"
+        )
 
     print("\n" + "=" * 50)
     print("Training completed successfully!")
 
 
 if __name__ == "__main__":
-    main()
+    main()