Hi @Morgan-Sell

Thanks for kicking this off.

I reckon this one is not ready for review, right?

It would be great to have some tests with the expected result for the transformer.

Thank you!

hola @solegalli,

Correct, it's not ready for review. I'm still working on it. And, yes, I'll create tests.

@solegalli,

Should we avoid using dataframes and instead use dictionaries and numpy arrays? I suspect iterating through dataframes increases computational costs.

I'm going to keep the question, but I think the answer is "yes". Dictionaries and numpy arrays simplify the merging of frequency distributions ;)

hola @solegalli,

I think I need an extra set of eyes. I'm struggling to identify what is causing the error for test_chi_merge(). I believe the root cause is in _calc_chi_square(); however, I cannot identify where.

In test_chi_merge(), the expected results are [4.1, 2.4, 8.6, 2.9, 1.7, 1.8, 2.2, 4.8, 4.1, 3.2, 1.5, 3.6]. These are the results stated in the Chi-Merge paper.

The transformer returns the following results: [5.9, 5.2, 8.6, 2.9, 1.8, 1.8, 3.1, 4.8, 4.1, 3.2, 1.8, 13.0].

The above values are the results from the chi-square tests of the consecutive distributions. 5 of the 12 expected results are incorrect.

Indices of the values that don't reconcile: 0, 1, 6, 10, and 11.

Do you see the bug?

hola @solegalli,

Did you have a chance to look at this bug? I'm stumped.

hi @solegalli,

Are you still getting around to reviewing this discretizer? I think it's super cool!

I know you're quite busy. I'm trying to organize myself.

Still pending. I send you an email? would that work?