Merge pull request #113 from Anand0295/add-rmse-metric

Add RMSE metric for regression evaluation (#111)

Author: ravin-d-27

pydeepflow/metrics.py

@@ -157,3 +157,25 @@ def r2_score(y_true, y_pred):
     ss_tot = np.sum((y_true - np.mean(y_true)) ** 2)
     return 1 - (ss_res / ss_tot)
 
+def rmse(y_true, y_pred):
+    """
+    Compute Root Mean Squared Error between true and predicted values.
+    
+    RMSE = sqrt((1/n) * Σ(y_true - y_pred)^2)
+    
+    Parameters
+    ----------
+    y_true : array-like
+        Ground truth (correct) target values.
+    y_pred : array-like
+        Estimated target values.
+    
+    Returns
+    -------
+    float
+        The RMSE score.
+    """
+    y_true = np.array(y_true)
+    y_pred = np.array(y_pred)
+    return np.sqrt(np.mean((y_true - y_pred) ** 2))
+

tests/test_metrics.py

@@ -2,7 +2,7 @@
 import numpy as np
 from pydeepflow.metrics import (
     precision_score, recall_score, f1_score, confusion_matrix,
-    mean_absolute_error, mean_squared_error, r2_score
+    mean_absolute_error, mean_squared_error, r2_score, rmse
 )
 
 class TestMetrics(unittest.TestCase):
@@ -54,5 +54,11 @@ def test_r2_score(self):
         # R^2 = 1 - (1.5 / 29.1875) = 1 - 0.051389... approx 0.9486
         self.assertAlmostEqual(r2_score(self.y_true_reg, self.y_pred_reg), 0.94861051, places=5)
 
+    def test_rmse(self):
+        y_true = np.array([1, 2, 3])
+        y_pred = np.array([2, 2, 4])
+        expected = np.sqrt(((1-2)**2 + (2-2)**2 + (3-4)**2) / 3)
+        self.assertAlmostEqual(rmse(y_true, y_pred), expected)
+
 if __name__ == '__main__':
     unittest.main()