Troubleshooting

Here we’ll list some common issues and how they can be fixed. Hope this helps!

my MI estimate is nan

The most likely reason is that you are using mean of pointwise estimates rather than nanmean. If you get a warning that there are NaNs in the embedding, then there is some deeper problem – first check for NaNs in the input data, then consider playing with some parameters like N_dims or lr.

my MI estimate is high because of a few extremely high pMI contributions

If you have duplicated (x, y) pairs in the data you can get an extremely high pMI estimate as an artifact. This is a known artifact of the KSG estimator, which is why you can’t bootstrap KSG estimates (as discussed by Holmes and Nemenman).