MAINT: Remove np.int_ and np.uint (scikit-learn#27523)

Author: mtsokol

sklearn/ensemble/_hist_gradient_boosting/grower.py

@@ -39,7 +39,7 @@ class TreeNode:
     ----------
     depth : int
         The depth of the node, i.e. its distance from the root.
-    sample_indices : ndarray of shape (n_samples_at_node,), dtype=np.uint
+    sample_indices : ndarray of shape (n_samples_at_node,), dtype=np.uint32
         The indices of the samples at the node.
     sum_gradients : float
         The sum of the gradients of the samples at the node.
@@ -50,7 +50,7 @@ class TreeNode:
     ----------
     depth : int
         The depth of the node, i.e. its distance from the root.
-    sample_indices : ndarray of shape (n_samples_at_node,), dtype=np.uint
+    sample_indices : ndarray of shape (n_samples_at_node,), dtype=np.uint32
         The indices of the samples at the node.
     sum_gradients : float
         The sum of the gradients of the samples at the node.

sklearn/preprocessing/_discretization.py

@@ -98,7 +98,7 @@ class KBinsDiscretizer(TransformerMixin, BaseEstimator):
         The edges of each bin. Contain arrays of varying shapes ``(n_bins_, )``
         Ignored features will have empty arrays.
 
-    n_bins_ : ndarray of shape (n_features,), dtype=np.int_
+    n_bins_ : ndarray of shape (n_features,), dtype=np.int64
         Number of bins per feature. Bins whose width are too small
         (i.e., <= 1e-8) are removed with a warning.
 
@@ -439,7 +439,7 @@ def inverse_transform(self, Xt):
         for jj in range(n_features):
             bin_edges = self.bin_edges_[jj]
             bin_centers = (bin_edges[1:] + bin_edges[:-1]) * 0.5
-            Xinv[:, jj] = bin_centers[np.int_(Xinv[:, jj])]
+            Xinv[:, jj] = bin_centers[(Xinv[:, jj]).astype(np.int64)]
 
         return Xinv