perf: switch SVM default kernel to linear for better performance

main.py

@@ -35,11 +35,10 @@ STRATEGY = RandomForestStrategy(
     random_state=42,
 )
 
-# Option 2: Support Vector Machine
+# # Option 2: Support Vector Machine
 # STRATEGY = SVMStrategy(
-#     kernel="rbf",
+#     kernel="linear",  # Fast prediction; use 'rbf' for better accuracy but much slower
 #     C=1.0,
-#     gamma="scale",
 #     random_state=42,
 # )
 

src/strategies/classifiers.py

@@ -51,11 +51,15 @@ class RandomForestStrategy(ClassificationStrategy):
 
 
 class SVMStrategy(ClassificationStrategy):
-    """Support Vector Machine classification strategy."""
+    """Support Vector Machine classification strategy.
     
+    Note: For large rasters, consider using kernel='linear' for faster prediction.
+    The RBF kernel can be very slow during prediction on large images.
+    """
+
     def __init__(
         self,
-        kernel: str = "rbf",
+        kernel: str = "linear",
         C: float = 1.0,
         gamma: str = "scale",
         random_state: int = 42,
@@ -70,19 +74,26 @@ class SVMStrategy(ClassificationStrategy):
             C=C,
             gamma=gamma,
             random_state=random_state,
-            probability=True,
+            probability=False,  # Disabled for performance on large rasters
             **kwargs
         )
     
     def train(self, X: np.ndarray, y: np.ndarray) -> None:
         self._clf.fit(X, y)
-    
+
     def predict(self, X: np.ndarray) -> np.ndarray:
         return self._clf.predict(X)
-    
+
     def predict_proba(self, X: np.ndarray) -> np.ndarray:
-        return self._clf.predict_proba(X)
-    
+        # probability=True significantly slows down both training and prediction
+        # Return class-based probabilities as fallback
+        predictions = self._clf.predict(X)
+        n_classes = len(self._clf.classes_)
+        proba = np.zeros((X.shape[0], n_classes))
+        for i, cls in enumerate(self._clf.classes_):
+            proba[:, i] = (predictions == cls).astype(float)
+        return proba
+
     def get_params(self) -> dict[str, Any]:
         return {
             "kernel": self.kernel,
@@ -90,7 +101,7 @@ class SVMStrategy(ClassificationStrategy):
             "gamma": self.gamma,
             "random_state": self.random_state,
         }
-    
+
     @property
     def name(self) -> str:
         return "SVM"