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Computes and reports classification metrics for the trained LinearSVC model.
Metrics reported:
- Overall accuracy : fraction of test samples correctly classified.
- Per-class precision: of all samples predicted as class C, how many truly are C?
- Per-class recall : of all true samples of class C, how many were predicted C?
- Per-class F1-score : harmonic mean of precision and recall per class.
- Confusion matrix : raw counts of (true label, predicted label) pairs;
passed to the visualizer for heatmap generation.
"""
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
def evaluate(model, X_test, y_test, label_encoder) -> dict:
"""
Run the trained model on the test set and compute all evaluation metrics.
Args:
model : Fitted LinearSVC classifier.
X_test : Sparse test feature matrix.
y_test : True integer labels for the test set.
label_encoder : LabelEncoder fitted during preprocessing; used to
convert integer predictions back to language strings.
Returns:
Dictionary with the following keys:
'accuracy' : float, overall test accuracy.
'report' : str, full per-class metrics table.
'confusion_matrix': ndarray of shape (n_classes, n_classes).
'class_names' : list of string class names in label order.
'y_pred' : ndarray of integer predictions.
'y_test' : ndarray of true integer labels (same object passed in).
"""
y_pred = model.predict(X_test)
class_names = label_encoder.classes_
accuracy = accuracy_score(y_test, y_pred)
# zero_division=0 silences warnings for classes that never appear in predictions
report = classification_report(
y_test, y_pred, target_names=class_names, zero_division=0
)
cm = confusion_matrix(y_test, y_pred)
_print_results(accuracy, report)
return {
"accuracy": accuracy,
"report": report,
"confusion_matrix": cm,
"class_names": class_names,
"y_pred": y_pred,
"y_test": y_test,
}
def _print_results(accuracy: float, report: str):
"""Print a formatted summary of the evaluation results to stdout."""
divider = "=" * 52
print(f"\n{divider}")
print(" Classification Results")
print(divider)
print(f" Overall Accuracy : {accuracy:.4f} ({accuracy * 100:.2f}%)")
print("\n Per-Class Metrics:\n")
print(report)
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