Bug: Classification metrics do not support label names containing numbers

[florianbodr]

In Evidently 0.4.19 with Python 3.10, the ClassificationQualityMetric() and ClassificationConfusionMatrix() (these are the one I tested but i suspect other metrics to be impacted) throw an error when some data labels contain numerical values. Even if the dataframe column type is specified as string.
See sample code below:

from evidently.report import Report
from evidently.metrics import *
import pandas as pd

label_target = ['foo', 'bar', 'fun', 'foo', 'fun', 'foo', '101', '102']
label_predict = ['foo', 'bar', 'fun', 'bar', 'fun', 'fun', '101', '101']
data_df = pd.DataFrame({'target': label_target, 'prediction': label_predict}, dtype="string")

report = Report(metrics=[
    ClassificationQualityMetric(),
    ClassificationConfusionMatrix(),
])
report.run(reference_data=None, current_data=data_df)
report

It ends up with the following error:

File ~/anaconda3/envs/python3/lib/python3.10/site-packages/evidently/calculations/classification_performance.py:316, in calculate_matrix(target, prediction, labels)
    315 def calculate_matrix(target: pd.Series, prediction: pd.Series, labels: List[Union[str, int]]) -> ConfusionMatrix:
--> 316     sorted_labels = sorted(labels)
    317     matrix = metrics.confusion_matrix(target, prediction, labels=sorted_labels)
    318     return ConfusionMatrix(labels=sorted_labels, values=[row.tolist() for row in matrix])

TypeError: '<' not supported between instances of 'str' and 'int'

Adding a char (like a dot) at the end of the label name numbers fixes the issue:

label_target = ['foo', 'bar', 'fun', 'foo', 'fun', 'foo', '101.', '102-']
label_predict = ['foo', 'bar', 'fun', 'bar', 'fun', 'fun', '101.', '101.']

But I do not think that this is the expected behavior and that the dataframe column type should be respected all along the metric(s) computation.