spencebeecher

3 ms·

1.
by spencebeecher 10y ago
Thanks for the feedback - happy hacking =)
2.
by spencebeecher 10y ago
Yep! You are right. I don't think it is a huge disparity but I would like to implement the pivotal/empirical bootstrap instead. The change is just a few lines of code.
3.
by spencebeecher 10y ago
Completely agree it is easy. Doing it quickly (for Python) is what this is optimized for. We would love a contribution if you have a method for resampling + percentiles that beats numpy.
4.
by spencebeecher 10y ago
John, you are a true wizard. I admire you & will work to incorporate your feedback (gathered offline) into the library =) Thanks for the feedback!
5.
by spencebeecher 10y ago
I agree with you! Pandas is only used in the power analysis code (which also has matplotlib for plotting). The best thing would be to pair this down. We would gladly take contributions - i think the path forward on this feedback is clear bu
6.
by spencebeecher 10y ago
<3
7.
by spencebeecher 10y ago
Excellent q - we intend to add in other options as we go. Pivotal being one. Id also like to add in permutation tests. If you have ideas we welcome diffs =)
8.
by spencebeecher 10y ago
Thanks for the feedback! numpy is used to give a speed improvement when generating the bootstrap samples - this would be very slow in a Python for loop. Pandas is only used in the power analysis code. Ill make that more clear. Would love mo
9.
by spencebeecher 10y ago
That uses the BCa method which in some situations is better. This library gives you a/b test functionality and should be faster on large input datasets.
10.
by spencebeecher 10y ago
Thanks for the feedback Petters! I agree in principle. I am familiar with that method. The use case for this is for situations where you have large initial sample counts (so the correction should be less important, we do throw warnings when
11.
PySparNN: Approximate Nearest Neighbor Search for Sparse Data in Python(github.com)
11 points by spencebeecher 10y ago|0 comments