Talip7/scikit-learn-issues-multilabel huggingface dataset:
BallTree.query returns inconsistent indices between scikit-learn versions 0.24.1 and 1.1.1

### Describe the bug

For the same data, `BallTree.query` can return different nearest neighbour indices if these neighbours are equally distanced from a point depending on scikit-learn version. Specifically, this can be observed in 0.24.1 vs 1.1.1.  

### Steps/Code to Reproduce

```
import numpy as np
from sklearn.neighbors import BallTree

np.random.seed(61)

X = np.random.randint(0, 3, size=(10,2))  # generated dataset

tree = BallTree(X, 4, 'hamming')
distances, indices = tree.query(X, 3, return_distance=True, dualtree=False, breadth_first=False, sort_results=True)

for i, (point, distances, indices) in enumerate(zip(X, distances, indices)):
    print(f'index {i}: datapoint {point} distances: {[round(dist,2) for dist in distances]} indices: {indices}')

```


### Expected Results

scikit-learn versions 0.24.1 and 1.1.1 would be expected to consistently show the same nearest neighbour indices whenever multiple neighbours are equally distanced from a data point

### Actual Results

scikit-learn v. 0.24.1:

<img width="549" alt="scikit-0 24 1" src="https://user-images.githubusercontent.com/21366061/174240375-d7215db5-6137-4ed6-a0d3-647d41761e19.png">

scikit-learn v. 1.1.1:

<img width="549" alt="scikit-1 1 1" src="https://user-images.githubusercontent.com/21366061/174240420-e4684662-26b1-4808-ae72-8e4ccff8e751.png">




### Versions

```shell
System:
  python: 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15) [Clang 6.0 (clang-600.0.57)]
executable: /Users/korigo/Downloads/scikit-0.24/bin/python3
  machine: macOS-10.16-x86_64-i386-64bit
Python dependencies:
     pip: 22.0.4
  setuptools: 62.1.0
   sklearn: 0.24.1
    numpy: 1.22.4
    scipy: 1.8.1
    Cython: None
    pandas: None
  matplotlib: None
    joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True

System:
  python: 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15) [Clang 6.0 (clang-600.0.57)]
executable: /Users/korigo/Downloads/scikit-1.1.1/bin/python3
  machine: macOS-10.16-x86_64-i386-64bit
Python dependencies:
   sklearn: 1.1.1
     pip: 22.0.4
  setuptools: 62.1.0
    numpy: 1.22.4
    scipy: 1.8.1
    Cython: None
    pandas: None
  matplotlib: None
    joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True
```

COMMENT:
The tie-breaking logic of 0.24.1 were not any less arbitrary either: the indices (that represent the order of the samples) weren't always sorted for the position of tied neighbors (see e.g. the results for index #4).

Labels: Bug, module:neighbors
BallTree.query returns inconsistent indices between scikit-learn versions 0.24.1 and 1.1.1

### Describe the bug

For the same data, `BallTree.query` can return different nearest neighbour indices if these neighbours are equally distanced from a point depending on scikit-learn version. Specifically, this can be observed in 0.24.1 vs 1.1.1.  

### Steps/Code to Reproduce

```
import numpy as np
from sklearn.neighbors import BallTree

np.random.seed(61)

X = np.random.randint(0, 3, size=(10,2))  # generated dataset

tree = BallTree(X, 4, 'hamming')
distances, indices = tree.query(X, 3, return_distance=True, dualtree=False, breadth_first=False, sort_results=True)

for i, (point, distances, indices) in enumerate(zip(X, distances, indices)):
    print(f'index {i}: datapoint {point} distances: {[round(dist,2) for dist in distances]} indices: {indices}')

```


### Expected Results

scikit-learn versions 0.24.1 and 1.1.1 would be expected to consistently show the same nearest neighbour indices whenever multiple neighbours are equally distanced from a data point

### Actual Results

scikit-learn v. 0.24.1:

<img width="549" alt="scikit-0 24 1" src="https://user-images.githubusercontent.com/21366061/174240375-d7215db5-6137-4ed6-a0d3-647d41761e19.png">

scikit-learn v. 1.1.1:

<img width="549" alt="scikit-1 1 1" src="https://user-images.githubusercontent.com/21366061/174240420-e4684662-26b1-4808-ae72-8e4ccff8e751.png">




### Versions

```shell
System:
  python: 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15) [Clang 6.0 (clang-600.0.57)]
executable: /Users/korigo/Downloads/scikit-0.24/bin/python3
  machine: macOS-10.16-x86_64-i386-64bit
Python dependencies:
     pip: 22.0.4
  setuptools: 62.1.0
   sklearn: 0.24.1
    numpy: 1.22.4
    scipy: 1.8.1
    Cython: None
    pandas: None
  matplotlib: None
    joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True

System:
  python: 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15) [Clang 6.0 (clang-600.0.57)]
executable: /Users/korigo/Downloads/scikit-1.1.1/bin/python3
  machine: macOS-10.16-x86_64-i386-64bit
Python dependencies:
   sklearn: 1.1.1
     pip: 22.0.4
  setuptools: 62.1.0
    numpy: 1.22.4
    scipy: 1.8.1
    Cython: None
    pandas: None
  matplotlib: None
    joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True
```

COMMENT:
@eeghor do you see any practical advantage to the way of handling ties in 0.24? Both seem arbitrary to me.

Since this behavior was already released in 1.0 and 1.1, I don't think 1.2 should restore the tie breaking behavior of 0.24 as it seems as arbitrary as the released in 1.0/1.1.

Labels: Bug, module:neighbors
BallTree.query returns inconsistent indices between scikit-learn versions 0.24.1 and 1.1.1

### Describe the bug

For the same data, `BallTree.query` can return different nearest neighbour indices if these neighbours are equally distanced from a point depending on scikit-learn version. Specifically, this can be observed in 0.24.1 vs 1.1.1.  

### Steps/Code to Reproduce

```
import numpy as np
from sklearn.neighbors import BallTree

np.random.seed(61)

X = np.random.randint(0, 3, size=(10,2))  # generated dataset

tree = BallTree(X, 4, 'hamming')
distances, indices = tree.query(X, 3, return_distance=True, dualtree=False, breadth_first=False, sort_results=True)

for i, (point, distances, indices) in enumerate(zip(X, distances, indices)):
    print(f'index {i}: datapoint {point} distances: {[round(dist,2) for dist in distances]} indices: {indices}')

```


### Expected Results

scikit-learn versions 0.24.1 and 1.1.1 would be expected to consistently show the same nearest neighbour indices whenever multiple neighbours are equally distanced from a data point

### Actual Results

scikit-learn v. 0.24.1:

<img width="549" alt="scikit-0 24 1" src="https://user-images.githubusercontent.com/21366061/174240375-d7215db5-6137-4ed6-a0d3-647d41761e19.png">

scikit-learn v. 1.1.1:

<img width="549" alt="scikit-1 1 1" src="https://user-images.githubusercontent.com/21366061/174240420-e4684662-26b1-4808-ae72-8e4ccff8e751.png">




### Versions

```shell
System:
  python: 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15) [Clang 6.0 (clang-600.0.57)]
executable: /Users/korigo/Downloads/scikit-0.24/bin/python3
  machine: macOS-10.16-x86_64-i386-64bit
Python dependencies:
     pip: 22.0.4
  setuptools: 62.1.0
   sklearn: 0.24.1
    numpy: 1.22.4
    scipy: 1.8.1
    Cython: None
    pandas: None
  matplotlib: None
    joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True

System:
  python: 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15) [Clang 6.0 (clang-600.0.57)]
executable: /Users/korigo/Downloads/scikit-1.1.1/bin/python3
  machine: macOS-10.16-x86_64-i386-64bit
Python dependencies:
   sklearn: 1.1.1
     pip: 22.0.4
  setuptools: 62.1.0
    numpy: 1.22.4
    scipy: 1.8.1
    Cython: None
    pandas: None
  matplotlib: None
    joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True
```

COMMENT:
https://github.com/scikit-learn/scikit-learn/pull/23725 resolves the issue.

Labels: Bug, module:neighbors
BallTree.query returns inconsistent indices between scikit-learn versions 0.24.1 and 1.1.1

### Describe the bug

For the same data, `BallTree.query` can return different nearest neighbour indices if these neighbours are equally distanced from a point depending on scikit-learn version. Specifically, this can be observed in 0.24.1 vs 1.1.1.  

### Steps/Code to Reproduce

```
import numpy as np
from sklearn.neighbors import BallTree

np.random.seed(61)

X = np.random.randint(0, 3, size=(10,2))  # generated dataset

tree = BallTree(X, 4, 'hamming')
distances, indices = tree.query(X, 3, return_distance=True, dualtree=False, breadth_first=False, sort_results=True)

for i, (point, distances, indices) in enumerate(zip(X, distances, indices)):
    print(f'index {i}: datapoint {point} distances: {[round(dist,2) for dist in distances]} indices: {indices}')

```


### Expected Results

scikit-learn versions 0.24.1 and 1.1.1 would be expected to consistently show the same nearest neighbour indices whenever multiple neighbours are equally distanced from a data point

### Actual Results

scikit-learn v. 0.24.1:

<img width="549" alt="scikit-0 24 1" src="https://user-images.githubusercontent.com/21366061/174240375-d7215db5-6137-4ed6-a0d3-647d41761e19.png">

scikit-learn v. 1.1.1:

<img width="549" alt="scikit-1 1 1" src="https://user-images.githubusercontent.com/21366061/174240420-e4684662-26b1-4808-ae72-8e4ccff8e751.png">




### Versions

```shell
System:
  python: 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15) [Clang 6.0 (clang-600.0.57)]
executable: /Users/korigo/Downloads/scikit-0.24/bin/python3
  machine: macOS-10.16-x86_64-i386-64bit
Python dependencies:
     pip: 22.0.4
  setuptools: 62.1.0
   sklearn: 0.24.1
    numpy: 1.22.4
    scipy: 1.8.1
    Cython: None
    pandas: None
  matplotlib: None
    joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True

System:
  python: 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15) [Clang 6.0 (clang-600.0.57)]
executable: /Users/korigo/Downloads/scikit-1.1.1/bin/python3
  machine: macOS-10.16-x86_64-i386-64bit
Python dependencies:
   sklearn: 1.1.1
     pip: 22.0.4
  setuptools: 62.1.0
    numpy: 1.22.4
    scipy: 1.8.1
    Cython: None
    pandas: None
  matplotlib: None
    joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True
```

COMMENT:
I opened #23728 to discuss how randomization could be a better way to break ties throughout scikit-learn in general.

Labels: Bug, module:neighbors
BallTree.query returns inconsistent indices between scikit-learn versions 0.24.1 and 1.1.1

### Describe the bug

For the same data, `BallTree.query` can return different nearest neighbour indices if these neighbours are equally distanced from a point depending on scikit-learn version. Specifically, this can be observed in 0.24.1 vs 1.1.1.  

### Steps/Code to Reproduce

```
import numpy as np
from sklearn.neighbors import BallTree

np.random.seed(61)

X = np.random.randint(0, 3, size=(10,2))  # generated dataset

tree = BallTree(X, 4, 'hamming')
distances, indices = tree.query(X, 3, return_distance=True, dualtree=False, breadth_first=False, sort_results=True)

for i, (point, distances, indices) in enumerate(zip(X, distances, indices)):
    print(f'index {i}: datapoint {point} distances: {[round(dist,2) for dist in distances]} indices: {indices}')

```


### Expected Results

scikit-learn versions 0.24.1 and 1.1.1 would be expected to consistently show the same nearest neighbour indices whenever multiple neighbours are equally distanced from a data point

### Actual Results

scikit-learn v. 0.24.1:

<img width="549" alt="scikit-0 24 1" src="https://user-images.githubusercontent.com/21366061/174240375-d7215db5-6137-4ed6-a0d3-647d41761e19.png">

scikit-learn v. 1.1.1:

<img width="549" alt="scikit-1 1 1" src="https://user-images.githubusercontent.com/21366061/174240420-e4684662-26b1-4808-ae72-8e4ccff8e751.png">




### Versions

```shell
System:
  python: 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15) [Clang 6.0 (clang-600.0.57)]
executable: /Users/korigo/Downloads/scikit-0.24/bin/python3
  machine: macOS-10.16-x86_64-i386-64bit
Python dependencies:
     pip: 22.0.4
  setuptools: 62.1.0
   sklearn: 0.24.1
    numpy: 1.22.4
    scipy: 1.8.1
    Cython: None
    pandas: None
  matplotlib: None
    joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True

System:
  python: 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15) [Clang 6.0 (clang-600.0.57)]
executable: /Users/korigo/Downloads/scikit-1.1.1/bin/python3
  machine: macOS-10.16-x86_64-i386-64bit
Python dependencies:
   sklearn: 1.1.1
     pip: 22.0.4
  setuptools: 62.1.0
    numpy: 1.22.4
    scipy: 1.8.1
    Cython: None
    pandas: None
  matplotlib: None
    joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True
```

COMMENT:
> #23725 resolves the issue.

I don't mind keeping the current (changed) behaviour and not reverting back to pre-v1.0 logic if the change has no real life implications

Labels: Bug, module:neighbors
BallTree.query returns inconsistent indices between scikit-learn versions 0.24.1 and 1.1.1

### Describe the bug

For the same data, `BallTree.query` can return different nearest neighbour indices if these neighbours are equally distanced from a point depending on scikit-learn version. Specifically, this can be observed in 0.24.1 vs 1.1.1.  

### Steps/Code to Reproduce

```
import numpy as np
from sklearn.neighbors import BallTree

np.random.seed(61)

X = np.random.randint(0, 3, size=(10,2))  # generated dataset

tree = BallTree(X, 4, 'hamming')
distances, indices = tree.query(X, 3, return_distance=True, dualtree=False, breadth_first=False, sort_results=True)

for i, (point, distances, indices) in enumerate(zip(X, distances, indices)):
    print(f'index {i}: datapoint {point} distances: {[round(dist,2) for dist in distances]} indices: {indices}')

```


### Expected Results

scikit-learn versions 0.24.1 and 1.1.1 would be expected to consistently show the same nearest neighbour indices whenever multiple neighbours are equally distanced from a data point

### Actual Results

scikit-learn v. 0.24.1:

<img width="549" alt="scikit-0 24 1" src="https://user-images.githubusercontent.com/21366061/174240375-d7215db5-6137-4ed6-a0d3-647d41761e19.png">

scikit-learn v. 1.1.1:

<img width="549" alt="scikit-1 1 1" src="https://user-images.githubusercontent.com/21366061/174240420-e4684662-26b1-4808-ae72-8e4ccff8e751.png">




### Versions

```shell
System:
  python: 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15) [Clang 6.0 (clang-600.0.57)]
executable: /Users/korigo/Downloads/scikit-0.24/bin/python3
  machine: macOS-10.16-x86_64-i386-64bit
Python dependencies:
     pip: 22.0.4
  setuptools: 62.1.0
   sklearn: 0.24.1
    numpy: 1.22.4
    scipy: 1.8.1
    Cython: None
    pandas: None
  matplotlib: None
    joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True

System:
  python: 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15) [Clang 6.0 (clang-600.0.57)]
executable: /Users/korigo/Downloads/scikit-1.1.1/bin/python3
  machine: macOS-10.16-x86_64-i386-64bit
Python dependencies:
   sklearn: 1.1.1
     pip: 22.0.4
  setuptools: 62.1.0
    numpy: 1.22.4
    scipy: 1.8.1
    Cython: None
    pandas: None
  matplotlib: None
    joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True
```

COMMENT:
Actually, there are some discrepancies in some environment configurations which makes it even cumbersome to fix. IMO, the best is to keep the current behaviour.

Labels: Bug, module:neighbors
BallTree.query returns inconsistent indices between scikit-learn versions 0.24.1 and 1.1.1

### Describe the bug

For the same data, `BallTree.query` can return different nearest neighbour indices if these neighbours are equally distanced from a point depending on scikit-learn version. Specifically, this can be observed in 0.24.1 vs 1.1.1.  

### Steps/Code to Reproduce

```
import numpy as np
from sklearn.neighbors import BallTree

np.random.seed(61)

X = np.random.randint(0, 3, size=(10,2))  # generated dataset

tree = BallTree(X, 4, 'hamming')
distances, indices = tree.query(X, 3, return_distance=True, dualtree=False, breadth_first=False, sort_results=True)

for i, (point, distances, indices) in enumerate(zip(X, distances, indices)):
    print(f'index {i}: datapoint {point} distances: {[round(dist,2) for dist in distances]} indices: {indices}')

```


### Expected Results

scikit-learn versions 0.24.1 and 1.1.1 would be expected to consistently show the same nearest neighbour indices whenever multiple neighbours are equally distanced from a data point

### Actual Results

scikit-learn v. 0.24.1:

<img width="549" alt="scikit-0 24 1" src="https://user-images.githubusercontent.com/21366061/174240375-d7215db5-6137-4ed6-a0d3-647d41761e19.png">

scikit-learn v. 1.1.1:

<img width="549" alt="scikit-1 1 1" src="https://user-images.githubusercontent.com/21366061/174240420-e4684662-26b1-4808-ae72-8e4ccff8e751.png">




### Versions

```shell
System:
  python: 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15) [Clang 6.0 (clang-600.0.57)]
executable: /Users/korigo/Downloads/scikit-0.24/bin/python3
  machine: macOS-10.16-x86_64-i386-64bit
Python dependencies:
     pip: 22.0.4
  setuptools: 62.1.0
   sklearn: 0.24.1
    numpy: 1.22.4
    scipy: 1.8.1
    Cython: None
    pandas: None
  matplotlib: None
    joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True

System:
  python: 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15) [Clang 6.0 (clang-600.0.57)]
executable: /Users/korigo/Downloads/scikit-1.1.1/bin/python3
  machine: macOS-10.16-x86_64-i386-64bit
Python dependencies:
   sklearn: 1.1.1
     pip: 22.0.4
  setuptools: 62.1.0
    numpy: 1.22.4
    scipy: 1.8.1
    Cython: None
    pandas: None
  matplotlib: None
    joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True
```

COMMENT:
> @eeghor do you see any practical advantage to the way of handling ties in 0.24? Both seem arbitrary to me.
> 
> Since this behavior was already released in 1.0 and 1.1, I don't think 1.2 should restore the tie breaking behavior of 0.24 as it seems as arbitrary as the released in 1.0/1.1.

@ogrisel I think there's no apparent advantage and it's probably best to keep things the way they are. What I would suggest is to add a comment to the code or a line to the docs saying that the tie breaking behaviour is subject to change without notice. This could help avoid possible misunderstandings.

Labels: Bug, module:neighbors
BallTree.query returns inconsistent indices between scikit-learn versions 0.24.1 and 1.1.1

### Describe the bug

For the same data, `BallTree.query` can return different nearest neighbour indices if these neighbours are equally distanced from a point depending on scikit-learn version. Specifically, this can be observed in 0.24.1 vs 1.1.1.  

### Steps/Code to Reproduce

```
import numpy as np
from sklearn.neighbors import BallTree

np.random.seed(61)

X = np.random.randint(0, 3, size=(10,2))  # generated dataset

tree = BallTree(X, 4, 'hamming')
distances, indices = tree.query(X, 3, return_distance=True, dualtree=False, breadth_first=False, sort_results=True)

for i, (point, distances, indices) in enumerate(zip(X, distances, indices)):
    print(f'index {i}: datapoint {point} distances: {[round(dist,2) for dist in distances]} indices: {indices}')

```


### Expected Results

scikit-learn versions 0.24.1 and 1.1.1 would be expected to consistently show the same nearest neighbour indices whenever multiple neighbours are equally distanced from a data point

### Actual Results

scikit-learn v. 0.24.1:

<img width="549" alt="scikit-0 24 1" src="https://user-images.githubusercontent.com/21366061/174240375-d7215db5-6137-4ed6-a0d3-647d41761e19.png">

scikit-learn v. 1.1.1:

<img width="549" alt="scikit-1 1 1" src="https://user-images.githubusercontent.com/21366061/174240420-e4684662-26b1-4808-ae72-8e4ccff8e751.png">




### Versions

```shell
System:
  python: 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15) [Clang 6.0 (clang-600.0.57)]
executable: /Users/korigo/Downloads/scikit-0.24/bin/python3
  machine: macOS-10.16-x86_64-i386-64bit
Python dependencies:
     pip: 22.0.4
  setuptools: 62.1.0
   sklearn: 0.24.1
    numpy: 1.22.4
    scipy: 1.8.1
    Cython: None
    pandas: None
  matplotlib: None
    joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True

System:
  python: 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15) [Clang 6.0 (clang-600.0.57)]
executable: /Users/korigo/Downloads/scikit-1.1.1/bin/python3
  machine: macOS-10.16-x86_64-i386-64bit
Python dependencies:
   sklearn: 1.1.1
     pip: 22.0.4
  setuptools: 62.1.0
    numpy: 1.22.4
    scipy: 1.8.1
    Cython: None
    pandas: None
  matplotlib: None
    joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True
```

COMMENT:
Feel free to open a PR and link to this issue in the description.

Labels: Bug, module:neighbors
BallTree.query returns inconsistent indices between scikit-learn versions 0.24.1 and 1.1.1

### Describe the bug

For the same data, `BallTree.query` can return different nearest neighbour indices if these neighbours are equally distanced from a point depending on scikit-learn version. Specifically, this can be observed in 0.24.1 vs 1.1.1.  

### Steps/Code to Reproduce

```
import numpy as np
from sklearn.neighbors import BallTree

np.random.seed(61)

X = np.random.randint(0, 3, size=(10,2))  # generated dataset

tree = BallTree(X, 4, 'hamming')
distances, indices = tree.query(X, 3, return_distance=True, dualtree=False, breadth_first=False, sort_results=True)

for i, (point, distances, indices) in enumerate(zip(X, distances, indices)):
    print(f'index {i}: datapoint {point} distances: {[round(dist,2) for dist in distances]} indices: {indices}')

```


### Expected Results

scikit-learn versions 0.24.1 and 1.1.1 would be expected to consistently show the same nearest neighbour indices whenever multiple neighbours are equally distanced from a data point

### Actual Results

scikit-learn v. 0.24.1:

<img width="549" alt="scikit-0 24 1" src="https://user-images.githubusercontent.com/21366061/174240375-d7215db5-6137-4ed6-a0d3-647d41761e19.png">

scikit-learn v. 1.1.1:

<img width="549" alt="scikit-1 1 1" src="https://user-images.githubusercontent.com/21366061/174240420-e4684662-26b1-4808-ae72-8e4ccff8e751.png">




### Versions

```shell
System:
  python: 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15) [Clang 6.0 (clang-600.0.57)]
executable: /Users/korigo/Downloads/scikit-0.24/bin/python3
  machine: macOS-10.16-x86_64-i386-64bit
Python dependencies:
     pip: 22.0.4
  setuptools: 62.1.0
   sklearn: 0.24.1
    numpy: 1.22.4
    scipy: 1.8.1
    Cython: None
    pandas: None
  matplotlib: None
    joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True

System:
  python: 3.9.7 (v3.9.7:1016ef3790, Aug 30 2021, 16:39:15) [Clang 6.0 (clang-600.0.57)]
executable: /Users/korigo/Downloads/scikit-1.1.1/bin/python3
  machine: macOS-10.16-x86_64-i386-64bit
Python dependencies:
   sklearn: 1.1.1
     pip: 22.0.4
  setuptools: 62.1.0
    numpy: 1.22.4
    scipy: 1.8.1
    Cython: None
    pandas: None
  matplotlib: None
    joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True
```

COMMENT:
Seems this can be closed as 'wontfix'.

Labels: Bug, module:neighbors