Update _Dist/NeuralNetworks

Author: carefree0910

_Dist/NeuralNetworks/Base.py

@@ -133,13 +133,10 @@ def gen_batch(self, n_batch, re_shuffle=True):
         if next_cursor >= self.n_valid:
             next_cursor = self.n_valid
             end = True
-        rs, w = self._get_data(indices[self._batch_cursor:next_cursor])
-        if end:
-            self._batch_cursor = -1
-        else:
-            self._batch_cursor = next_cursor
+        data, w = self._get_data(indices[self._batch_cursor:next_cursor])
+        self._batch_cursor = -1 if end else next_cursor
         logger.debug("Done")
-        return rs, w
+        return data, w
 
     def gen_random_subset(self, n):
         n = min(n, self.n_valid)

_Dist/NeuralNetworks/NNUtil.py

@@ -313,8 +313,8 @@ def get_feature_info(data, numerical_idx, is_regression, logger=None):
         else:
             shrink_features = data_t
         feature_sets = [
-            set() if idx is None or idx else set(shrink_feat)
-            for idx, shrink_feat in zip(numerical_idx, shrink_features)
+            set() if idx is None or idx else set(shrink_feature)
+            for idx, shrink_feature in zip(numerical_idx, shrink_features)
         ]
         n_features = [len(feature_set) for feature_set in feature_sets]
         all_num_idx = [
@@ -349,9 +349,7 @@ def get_feature_info(data, numerical_idx, is_regression, logger=None):
                     all_num_idx[i] = numerical_idx[i] = None
                 elif numerical_idx[i]:
                     shrink_feature = np.asarray(shrink_feature, np.float32)
-                    if np.isnan(shrink_feature[-1]):
-                        shrink_feature = shrink_feature[:-1]
-                    if np.max(shrink_feature) < 2 ** 30:
+                    if np.max(shrink_feature[~np.isnan(shrink_feature)]) < 2 ** 30:
                         if np.allclose(shrink_feature, np.array(shrink_feature, np.int32)):
                             if Toolbox.all_unique(shrink_feature):
                                 Toolbox.warn_all_unique(i, logger)