From 877c24f3f4e528c419f7ddbb706ea91a87ce7ee2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ricardo=20Montan=CC=83ana?= <rmontanana@gmail.com>
Date: Fri, 25 Feb 2022 19:24:44 +0100
Subject: [PATCH] fix rc1

---
 odte/Odte.py | 71 +++++++++++++++++++++++++---------------------------
 1 file changed, 34 insertions(+), 37 deletions(-)

diff --git a/odte/Odte.py b/odte/Odte.py
index a396da7..8c9c059 100644
--- a/odte/Odte.py
+++ b/odte/Odte.py
@@ -26,35 +26,6 @@ from stree import Stree  # type: ignore
 from ._version import __version__
 
 
-def _parallel_build_tree(
-    base_estimator_: Stree,
-    X: np.ndarray,
-    y: np.ndarray,
-    weights: np.ndarray,
-    random_seed: int,
-    boot_samples: int,
-    max_features: int,
-    hyperparams: str,
-) -> Tuple[BaseEstimator, Tuple[int, ...]]:
-    clf = base_estimator_
-    hyperparams_ = json.loads(hyperparams)
-    hyperparams_.update(dict(random_state=random_seed))
-    clf.set_params(**hyperparams_)
-    n_samples = X.shape[0]
-    # bootstrap
-    random_box = check_random_state(random_seed)
-    indices = random_box.randint(0, n_samples, boot_samples)
-    # update weights with the chosen samples
-    weights_update = np.bincount(indices, minlength=n_samples)
-    current_weights = weights * weights_update
-    # random subspace
-    features = Odte._get_random_subspace(X, y, max_features)
-    # train the classifier
-    bootstrap = X[indices, :]
-    clf.fit(bootstrap[:, features], y[indices], current_weights[indices])
-    return (clf, features)
-
-
 class Odte(BaseEnsemble, ClassifierMixin):
     def __init__(
         self,
@@ -135,15 +106,12 @@ class Odte(BaseEnsemble, ClassifierMixin):
     def _train(
         self, X: np.ndarray, y: np.ndarray, weights: np.ndarray
     ) -> Tuple[List[BaseEstimator], List[Tuple[int, ...]]]:
-        # np.random.RandomState(seed)
         n_samples = X.shape[0]
         boot_samples = self._get_bootstrap_n_samples(n_samples)
-        estimator = []
-        for i in range(self.n_estimators):
-            estimator.append(clone(self.base_estimator_))
+        estimator = clone(self.base_estimator_)
         return Parallel(n_jobs=self.n_jobs, prefer="threads")(  # type: ignore
-            delayed(_parallel_build_tree)(
-                estimator[i],
+            delayed(Odte._parallel_build_tree)(
+                estimator,
                 X,
                 y,
                 weights,
@@ -152,11 +120,40 @@ class Odte(BaseEnsemble, ClassifierMixin):
                 self.max_features_,
                 self.be_hyperparams,
             )
-            for i, random_seed in enumerate(
-                range(self.random_state, self.random_state + self.n_estimators)
+            for random_seed in range(
+                self.random_state, self.random_state + self.n_estimators
             )
         )
 
+    @staticmethod
+    def _parallel_build_tree(
+        base_estimator_: BaseEstimator,
+        X: np.ndarray,
+        y: np.ndarray,
+        weights: np.ndarray,
+        random_seed: int,
+        boot_samples: int,
+        max_features: int,
+        hyperparams: str,
+    ) -> Tuple[BaseEstimator, Tuple[int, ...]]:
+        clf = clone(base_estimator_)
+        hyperparams_ = json.loads(hyperparams)
+        hyperparams_.update(dict(random_state=random_seed))
+        clf.set_params(**hyperparams_)
+        n_samples = X.shape[0]
+        # bootstrap
+        random_box = check_random_state(random_seed)
+        indices = random_box.randint(0, n_samples, boot_samples)
+        # update weights with the chosen samples
+        weights_update = np.bincount(indices, minlength=n_samples)
+        current_weights = weights * weights_update
+        # random subspace
+        features = Odte._get_random_subspace(X, y, max_features)
+        # train the classifier
+        bootstrap = X[indices, :]
+        clf.fit(bootstrap[:, features], y[indices], current_weights[indices])
+        return (clf, features)
+
     def _get_bootstrap_n_samples(self, n_samples: int) -> int:
         if self.max_samples is None:
             return n_samples