How to transform the normal SecInt data into np format?

I tried to improve the performance using the code in np_*.py. I have encountered a format issue TypeError: can't multiply sequence by non-int of type 'ArraySecInt42'.

T = secint.array(np.ones(n, dtype='O'))
S = [secint.array(np.array([[t[i] == j for t in transactions] for j in attr_ranges[i]]))
         for i in range(d)]
S_A = [[mpc.in_prod([S[k][j][l] for k in RR], ii) for l in range(len(S[0][0]))] for j in range(ell)]
T_SA = T * S_A  # mpc.schur_prod

S_A is a list of normal SecInt data, while T is np format. S_A is 2D list. I tried to use np.concatenate etc. to convert S_A but failed.

The normal version without using np has been tested successfully. If you want to test the np version to check the error, I can provide the code

[DylanWangWQF]

Sure, I will first put the initial result for the binary case here. (avg 5 times here)

Yeah, great, maybe you can also report the timings that you get for both versions here?

Yes, as you said, it’s not hard to add. I will do it later since I have some deadlines recently.

And it would be nice to cover the nonbinary case for the class attribute. I don’t think that’s hard to add. Then all datasets in demos/data/id3 can be used as is. For that also the case that the class attribute is not the last one needs to be handled, as the above code simply appends a 0 to the unit vector k.

[lschoe]

Yeah, great, maybe you can also report the timings that you get for both versions here?

And it would be nice to cover the nonbinary case for the class attribute. I don’t think that’s hard to add. Then all datasets in demos/data/id3 can be used as is. For that also the case that the class attribute is not the last one needs to be handled, as the above code simply appends a 0 to the unit vector k.