[Bug]: - deel.influenciae.plots not functioning due to incompatibility with numpy 1.24 onwards (none of the google collabs are functioning)

[CA-Fernandes]

Module

None

Contact Details

f.clyde21@gmail.com

Current Behavior

Tried different google collabs to test influencia. Cannot import deel.influencia.plots due to issues with numpy compatibility. Seems to be an issue with the use of np.float in the library which has been depricated since numpy 1.24

Expected Behavior

the function would be imported with no error

Version

v0.1.0

Environment

- OS:Google Collab
- Python version:3.11
- Tensorflow version: 2.18
- Packages used version:
 - numpy: 1.26.4

Relevant log output

from deel.influenciae.utils import ORDER
from deel.influenciae.plots import plot_datacentric_explanations, plot_most_influential_images

---------------------------------------------------------------------------

AttributeError                            Traceback (most recent call last)

<ipython-input-19-7e886f2fedc4> in <cell line: 0>()
      1 from deel.influenciae.utils import ORDER
----> 2 from deel.influenciae.plots import plot_datacentric_explanations, plot_most_influential_images

4 frames

/usr/local/lib/python3.11/dist-packages/numpy/__init__.py in __getattr__(attr)
    322 
    323         if attr in __former_attrs__:
--> 324             raise AttributeError(__former_attrs__[attr])
    325 
    326         if attr == 'testing':

AttributeError: module 'numpy' has no attribute 'float'.
`np.float` was a deprecated alias for the builtin `float`. To avoid this error in existing code, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

To Reproduce

In Tracin.ipynb run the cells
from deel.influenciae.utils import ORDER
from deel.influenciae.plots import plot_datacentric_explanations, plot_most_influential_images

From heading "Now we can use it to compute influence values!"