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Make SVM visualization mesh step size configurable #18

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Make SVM visualization mesh step size configurable #18

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Initial plan
Copilot Dec 13, 2025
eb8bd15
Make mesh step size configurable with clear documentation
Copilot Dec 13, 2025
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3 changes: 2 additions & 1 deletion config.yaml
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Expand Up @@ -28,4 +28,5 @@ models:
kernel: "rbf" # or 'linear', 'poly', etc.
C: 1.0 # Must be > 0
probability: true # Necessary for AUC and predict_with_details
class_weight: "balanced" # or null (write null without quotes in YAML for None)
class_weight: "balanced" # or null (write null without quotes in YAML for None)
mesh_step_size: 0.02 # Step size for decision boundary visualization mesh grid (smaller = higher resolution)
17 changes: 15 additions & 2 deletions src/models/svm_classifier.py
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Expand Up @@ -34,11 +34,18 @@ def _build_model(self):
random_state=self.params.get('random_state', default_params.get('random_state', 42))
)

def plot_decision_boundary(self, X, y, title="SVM Decision Boundary"):
def plot_decision_boundary(self, X, y, title="SVM Decision Boundary", step_size=None):
"""
Visualizes the decision boundary, margins, and support vectors.

NOTE: This method strictly requires 2D feature data.

Args:
X: Feature data (must be 2D)
y: Target labels
title: Plot title (default: "SVM Decision Boundary")
step_size: Mesh grid step size for visualization resolution (default: from config.yaml).
Smaller values increase resolution but may slow down plotting.
"""
if self.model is None:
raise RuntimeError("Model not trained. Call train() first.")
Expand All @@ -54,7 +61,13 @@ def plot_decision_boundary(self, X, y, title="SVM Decision Boundary"):
"Tip: Use PCA to reduce to 2D or select only 2 columns."
)

h = 0.02 # Step size
# Get step size from config if not provided
if step_size is None:
config = get_config()
default_params = config.get_param('models.svm', {})
step_size = default_params.get('mesh_step_size', 0.02)

h = step_size # Mesh grid step size for visualization resolution
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
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