Use the weights in the bootstrap to draw a weighted sample

src/elexsolver/TransitionMatrixSolver.py

@@ -111,32 +111,21 @@ def fit_predict(self, X, Y, weights=None):
             X = X.to_numpy()
         if not isinstance(Y, np.ndarray):
             Y = Y.to_numpy()
-        # assuming pandas.Series
-        if weights is not None and not isinstance(weights, np.ndarray):
-            weights = weights.values
 
         tm = TransitionMatrixSolver(strict=self._strict, verbose=False)
         predicted_transitions.append(tm.fit_predict(X, Y, weights=weights))
         maes.append(tm.MAE)
 
-        from sklearn.utils import resample  # to be replaced
-
         for b in tqdm(range(0, self._B - 1), desc="Bootstrapping"):
-            X_resampled = []
-            Y_resampled = []
-            weights_resampled = []
-            for i in resample(range(0, len(X)), replace=True, random_state=b):
-                X_resampled.append(X[i])
-                Y_resampled.append(Y[i])
-                if weights is not None:
-                    weights_resampled.append(weights[i])
-            if weights is None:
-                weights_resampled = None
-            else:
-                weights_resampled = np.array(weights_resampled)
-            predicted_transitions.append(
-                tm.fit_predict(np.array(X_resampled), np.array(Y_resampled), weights=weights_resampled)
+            rng = np.random.default_rng(seed=b)
+            X_resampled = rng.choice(
+                X, len(X), replace=True, axis=0, p=(weights / weights.sum() if weights is not None else None)
             )
+            Y_resampled = []
+            for x in X_resampled:
+                index = np.where((X == x).all(axis=1))[0][0]
+                Y_resampled.append(Y[index])
+            predicted_transitions.append(tm.fit_predict(X_resampled, np.array(Y_resampled), weights=None))
             maes.append(tm.MAE)
 
         self._mae = np.mean(maes)