Fix zero percentage replacement in get_binned_data Function and update parameter name

[boemer00]

This pull request introduces changes that address the issue: The fixed value for feel_zeroes in get_binned_data may lead to deviation in some case. #334

• Dynamic Fill Value Calculation: The fill value used to replace zero percentages is now calculated dynamically based on the actual data, rather than using a fixed value.

• Ensuring Correct Fill Value: The fill value is guaranteed to be smaller than the minimum non-zero percentage in both the reference and current datasets. This adjustment ensures that the data distribution remains accurate.

• Maintaining Data Distribution: These changes help maintain the correct distribution of data, which is crucial for accurate statistical tests, including Kullback-Leibler divergence drift score calculations.

• Parameter Name Update: The parameter name has been updated from "feel_zeroes" to "fill_zeroes" for clarity and consistency.

Hope it helps
boemer00

[boemer00]

Enhance get_binned_data Function with Flexible Fill Methods and Dynamic Scaling

This pull request introduces enhancements to my previous PR regarding the get_binned_data function. These changes address issues with handling very small percentages in data drift calculations while providing some flexibility and maintaining its original purpose.

Changes Made:

1. New Parameters:

   • fill_method (str, default='auto'): Allows users to select the method for calculating the fill value for zero percentages.
   • dynamic_scale (bool, default=False): Enables dynamic scaling of the fill value based on the data distribution.

2. Fill Methods: Three fill methods are now supported:

   • auto: Calculates a fill value that is the minimum of 1/10 and 1/2 of the smallest non-zero percentage.
   • min: Uses the minimum non-zero percentage as the fill value.
   • mean: Uses the average of the minimum non-zero percentages from reference and current data.

3. Dynamic Scaling: When enabled, applies an additional scaling factor to the fill value:

   scale_factor = min(min_non_zero_ref, min_non_zero_cur) / max(min_non_zero_ref, min_non_zero_cur)

This is beneficial in cases where there is a significant difference between the minimum non-zero percentages in the reference and current data.

4. Normalisation: After applying the fill value, the percentages are normalised to ensure they always sum to 1:

reference_percents = reference_percents / np.sum(reference_percents)
current_percents = current_percents / np.sum(current_percents)

5. Error Handling: Added a ValueError for invalid fill_method values to prevent unexpected behaviour.

Lastly, I am happy to write a test for the new parameters but would appreciate direction on where this should be implemented.

Thanks