Speed up classification_report

### Describe the workflow you want to enable

I'm concerned with slow execution speed of the classification_report  procedure which makes it barely suitable for production-grade workloads.
On a 8M sample it already takes 15 seconds whereas simple POC numba implementation takes 20 to 40 **milli**seconds. I understand that sklearn code is well covered with tests, has wide functionality, follows style guildlines and best practices, but there should be no excuse for the performace leap with simple POC of magnitude that big (x1000).

```python
import numpy as np
from sklearn.metrics import classification_report

y_true = np.random.randint(0, 2, size=2 ** 23)
y_pred = y_true.copy()
np.random.shuffle(y_pred[2 ** 20 : 2 ** 21])

print(classification_report(y_true=y_true, y_pred=y_pred, digits=10, output_dict=False, zero_division=0,))
```

>               precision    recall  f1-score   support
> 
>            0  0.9373906697 0.9373906697 0.9373906697   4192570
>            1  0.9374424159 0.9374424159 0.9374424159   4196038
> 
>     accuracy                      0.9374165535   8388608
>     macro avg  0.9374165428 0.9374165428 0.9374165428   8388608
>     weighted avg  0.9374165535 0.9374165535 0.9374165535   8388608
> 
> time: 18.6 s (started: 2023-07-08 16:10:08 +03:00)
> 


`print(own_classification_report(y_true=y_true, y_pred=y_pred, zero_division=0))`


> (3930076, 3933544, 262494, 262494, 0.9374165534973145, array([4192570, 4196038], dtype=int64), array([0.93739067, 0.93744242]), array([0.93739067, 0.93744242]), array([0.93739067, 0.93744242]))
> time: 16 ms (started: 2023-07-08 16:11:18 +03:00)

### Describe your proposed solution

```python
import numpy as np
from numba import njit


@njit()
def own_classification_report(y_true: np.ndarray, y_pred: np.ndarray, nclasses: int = 2, zero_division: int = 0):

    correct = np.zeros(nclasses, dtype=np.int64)
    wrong = np.zeros(nclasses, dtype=np.int64)
    for truth, pred in zip(y_true, y_pred):
        if pred == truth:
            correct[pred] += 1
        else:
            wrong[pred] += 1
    tn, tp = correct
    fn, fp = wrong
    accuracy = (tn + tp) / len(y_true)

    groups = np.array([(tn + fn), (tp + fp)])
    supports = np.array([(tn + fp), (tp + fn)])

    precisions = np.array([tn, tp]) / groups
    recalls = np.array([tn, tp]) / supports
    f1s = 2 * (precisions * recalls) / (precisions + recalls)

    for arr in (precisions, recalls, f1s):
        np.nan_to_num(arr, copy=False, nan=zero_division)

    return tn, tp, fn, fp, accuracy, supports, precisions, recalls, f1s
```

### Describe alternatives you've considered, if relevant

Can someone from original classification_report authors take a look please why it's taking so long?

### Additional context

I have the latest versions of scikit-learn and numba to this date, an Intel CPU with AVX.

COMMENT:
Wow that's weird 'cause when I apply np.unique to either y_true or y_pred, it's never above 200 milliseconds on my 8M elements array. 

Is it calling np. unique multiple times within one classification_report call, why would it do that? 
Where are random floats coming from, we only have integer  inputs, no?

What's making it even more sad is that I need to fit hundreds and thousands of models and then estimate their quality on a bunch of subsets like that, and I know there are only 2 classes possible 'cause it's a binary classification. Is there an option to pass nclasses directly to save on recomputing them each time?

Labels: Performance, Needs Investigation