DBSCAN always triggers and EfficiencyWarning

### Describe the bug

Calling dbscan always triggers an efficiency warning. There is no apparent way to either call it correctly or disable the warning. 

This was originally reported as an issue in SemiBin, which uses DBSCAN under the hood: https://github.com/BigDataBiology/SemiBin/issues/175

### Steps/Code to Reproduce

```python
import numpy as np
from sklearn.cluster import dbscan
from sklearn.neighbors import kneighbors_graph, sort_graph_by_row_values

f = np.random.randn(10_000, 240)
dist_matrix = kneighbors_graph(
    f,
    n_neighbors=200,
    mode='distance',
    p=2,
    n_jobs=3)

_, labels = dbscan(dist_matrix,
        eps=0.1, min_samples=5, n_jobs=4, metric='precomputed')


dist_matrix = sort_graph_by_row_values(dist_matrix)
_, labels = dbscan(dist_matrix,
        eps=0.1, min_samples=5, n_jobs=4, metric='precomputed')
```

### Expected Results

No warning, at least in second call

### Actual Results

```
/home/luispedro/.mambaforge/envs/py3.11/lib/python3.11/site-packages/sklearn/neighbors/_base.py:248: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.
  warnings.warn(
/home/luispedro/.mambaforge/envs/py3.11/lib/python3.11/site-packages/sklearn/neighbors/_base.py:248: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning.
  warnings.warn(
```

### Versions

```shell
I tested on the current main branch, 5cdbbf15e3fade7cc2462ef66dc4ea0f37f390e3, but it has been going on for a while (see original SemiBin report from September 2024):


System:
    python: 3.11.9 | packaged by conda-forge | (main, Apr 19 2024, 18:36:13) [GCC 12.3.0]
executable: /home/luispedro/.mambaforge/envs/py3.11/bin/python3.11
   machine: Linux-6.8.0-55-generic-x86_64-with-glibc2.39

Python dependencies:
      sklearn: 1.7.dev0
          pip: 24.0
   setuptools: 70.0.0
        numpy: 1.26.4
        scipy: 1.13.1
       Cython: None
       pandas: 2.2.2
   matplotlib: 3.8.4
       joblib: 1.4.2
threadpoolctl: 3.5.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 16
         prefix: libopenblas
       filepath: /home/luispedro/.mambaforge/envs/py3.11/lib/libopenblasp-r0.3.27.so
        version: 0.3.27
threading_layer: pthreads
   architecture: Haswell

       user_api: openmp
   internal_api: openmp
    num_threads: 16
         prefix: libgomp
       filepath: /home/luispedro/.mambaforge/envs/py3.11/lib/libgomp.so.1.0.0
        version: None
```

COMMENT:
@yuwei-1, for the more detailed explanation you requested:

```
         # Calculate neighborhood for all samples. This leaves the original
         # point in, which needs to be considered later (i.e. point i is in the
         # neighborhood of point i. While True, its useless information)
-        if self.metric == "precomputed" and sparse.issparse(X):
-            # set the diagonal to explicit values, as a point is its own
-            # neighbor
-            X = X.copy()  # copy to avoid in-place modification
-            with warnings.catch_warnings():
-                warnings.simplefilter("ignore", sparse.SparseEfficiencyWarning)
-                X.setdiag(X.diagonal())
-
         neighbors_model = NearestNeighbors(
             radius=self.eps,
             algorithm=self.algorithm,
@@ -420,6 +412,13 @@ class DBSCAN(ClusterMixin, BaseEstimator):
         # This has worst case O(n^2) memory complexity
         neighborhoods = neighbors_model.radius_neighbors(X, return_distance=False)
 
+        # Each point is its own neighbor, so update the neighborhoods
+        # accordingly after the initial fitting
+        if self.metric == "precomputed" and sparse.issparse(X):
+            for i, neighborhood in enumerate(neighborhoods):
+                if i not in neighborhoods[i]:
+                    neighborhoods[i] = np.append(neighborhood, i)
+
```

As before, we use `neighbors_model` to fit the `X` matrix and thereafter return data on the neighbors of each point. Whereas we previously manually updated the `X` matrix to reflect that each point is in its own neighborhood (when `self.metric == "precomputed" and sparse.issparse(X)`, of course), we now do it after the model fitting, directly modifying the `neighborhoods` variable. Not only does this sidestep the `EfficiencyWarning`, it also avoids the previous copying and reallocation of the `X` matrix.

Your thoughts?

Labels: Bug, help wanted, Needs Investigation