From c9b0046210593e3c30bf080ccfd5fbfcdd6b6770 Mon Sep 17 00:00:00 2001
From: Vibhanshu Botke <113599107+Vibhanshu230@users.noreply.github.com>
Date: Mon, 30 Mar 2026 20:50:01 +0530
Subject: [PATCH] vectorised rmse blend weights loop approach and removed
 unecessary extratrees GridSearchCV refit

---
 .../inst/python/pecan_debias/debias.py             | 14 ++++++--------
 1 file changed, 6 insertions(+), 8 deletions(-)

diff --git a/modules/assim.sequential/inst/python/pecan_debias/debias.py b/modules/assim.sequential/inst/python/pecan_debias/debias.py
index 287ddeef07c..6a70d029ad8 100644
--- a/modules/assim.sequential/inst/python/pecan_debias/debias.py
+++ b/modules/assim.sequential/inst/python/pecan_debias/debias.py
@@ -14,7 +14,7 @@ def _fit_knn(X, y):
     grid = GridSearchCV(
         pipe,
         {'kneighborsregressor__n_neighbors': list(range(1, 31))},
-        cv=max(2, min(5, len(y))),
+        cv=min(5, max(2, len(y)//2)),
         scoring='neg_root_mean_squared_error',
         n_jobs=-1
     )
@@ -35,13 +35,12 @@ def _fit_extratrees(X, y):
     base = ExtraTreesRegressor(random_state=42, n_jobs=-1)
     grid = GridSearchCV(
         base, param_grid,
-        cv=max(2, min(5, len(y))),
+        cv=min(5, max(2, len(y)//2)),
         scoring='neg_root_mean_squared_error',
         n_jobs=-1
     )
     grid.fit(X, y)
     tree = grid.best_estimator_
-    tree.fit(X, y)
     return tree
 
 def _fit_one(X, y):
@@ -51,11 +50,10 @@ def _fit_one(X, y):
     knn_pred  = knn.predict(X)
     tree_pred = tree.predict(X)
     weights = np.linspace(0, 1, 101)
-    best_w, best_rmse = 0.5, np.inf
-    for w in weights:
-        rmse = np.sqrt(mean_squared_error(y, w*knn_pred + (1-w)*tree_pred))
-        if rmse < best_rmse:
-            best_rmse, best_w = rmse, w
+    # replacing the loop based RMSE search over blend weights with a vectorised approach.
+    preds = weights[:, None]*knn_pred[None, :]+(1-weights[:, None])*tree_pred[None, :]
+    rmses = np.sqrt(((preds-y)**2).mean(axis=1))
+    best_w = weights[np.argmin(rmses)]
     return knn, tree, float(best_w)
 
 def train_full_model(name, X, y, feature_names=None):