rename variables

hidimstat/cpi.py

@@ -150,7 +150,7 @@ def _joblib_predict_one_group(imputation_model, X, j):
             Compute the prediction of the model with the permuted data for a
             single group of covariates.
             """
-            list_y_pred_perm = []
+
             if isinstance(X, pd.DataFrame):
                 X_j = X[self.groups[j]].copy().values
                 X_minus_j = X.drop(columns=self.groups[j]).values
@@ -169,15 +169,17 @@ def _joblib_predict_one_group(imputation_model, X, j):
             X_j_hat = imputation_model.predict(X_minus_j).reshape(X_j.shape)
             residual_j = X_j - X_j_hat
 
-            X_perm_all = np.empty((self.n_permutations, X.shape[0], X.shape[1]))
-            X_perm_all[:, :, non_group_ids] = X_minus_j
-            # n_permutations x n_samples x n_features_j
+            # Create an array X_perm_j of shape (n_permutations, n_samples, n_features)
+            # where the j-th group of covariates is (conditionally) permuted
+            X_perm_j = np.empty((self.n_permutations, X.shape[0], X.shape[1]))
+            X_perm_j[:, :, non_group_ids] = X_minus_j
+            # Create the permuted data for the j-th group of covariates
             residual_j_perm = np.array(
                 [self.rng.permutation(residual_j) for _ in range(self.n_permutations)]
             )
-            X_perm_all[:, :, group_ids] = X_j_hat[np.newaxis, :, :] + residual_j_perm
-
-            X_perm_batch = X_perm_all.reshape(-1, X.shape[1])
+            X_perm_j[:, :, group_ids] = X_j_hat[np.newaxis, :, :] + residual_j_perm
+            # Reshape X_perm_j to allow for batch prediction
+            X_perm_batch = X_perm_j.reshape(-1, X.shape[1])
             if isinstance(X, pd.DataFrame):
                 X_perm_batch = pd.DataFrame(
                     X_perm_batch.reshape(-1, X.shape[1]), columns=X.columns
@@ -193,19 +195,6 @@ def _joblib_predict_one_group(imputation_model, X, j):
                 )
             return y_pred_perm
 
-            # for _ in range(self.n_permutations):
-            #     X_j_perm = X_j_hat + self.rng.permutation(residual_j)
-            #     X_perm = np.empty_like(X)
-            #     X_perm[:, non_group_ids] = X_minus_j
-            #     X_perm[:, group_ids] = X_j_perm
-            #     if isinstance(X, pd.DataFrame):
-            #         X_perm = pd.DataFrame(X_perm, columns=X.columns)
-
-            #     y_pred_perm = getattr(self.estimator, self.method)(X_perm)
-            #     list_y_pred_perm.append(y_pred_perm)
-
-            # return np.array(list_y_pred_perm)
-
         # Parallelize the computation of the importance scores for each group
         out_list = Parallel(n_jobs=self.n_jobs)(
             delayed(_joblib_predict_one_group)(imputation_model, X, j)