[scikit-learn] mutual information for continuous variables with scikit-learn

Sole Galli solegalli at protonmail.com
Wed Feb 1 09:14:55 EST 2023

```Hey,

My understanding is that with sklearn you can compare 2 continuous variables like this:

mutual_info_regression(data["var1"].to_frame(), data["var"], discrete_features=[False])

Where var1 and var are continuous.

You can also compare multiple continuous variables against one continuous variables like this:

mutual_info_regression(data[["var1", "var_2", "var_3"]], data["var"],

discrete_features=[False, False, False])

I understand Sklearn uses nonparametric methods based on entropy estimation from k-nearest neighbors as explained in Nearest-neighbor approach to estimate the MI. Taken from Ross, 2014, PLoS ONE 9(2): e87357.

More details here: https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.mutual_info_regression.html

And I've got a blog post about Mutual info with Python here: https://www.blog.trainindata.com/mutual-information-with-python/

Cheers
Sole

https://www.trainindata.com/

Sent with [Proton Mail](https://proton.me/) secure email.

------- Original Message -------
On Wednesday, February 1st, 2023 at 10:32 AM, m m <mhfh.kvd5 at gmail.com> wrote:

> Hello,
>
> I have two continuous variables (heart rate samples over a period of time), and would like to compute mutual information between them as a measure of similarity.
>
> I've read some posts suggesting to use the mutual_info_score from scikit-learn but will this work for continuous variables? One stackoverflow answer suggested converting the data into probabilities with np.histogram2d() and passing the contingency table to the mutual_info_score.
>
> from sklearn.metrics import mutual_info_score
>
> def calc_MI(x, y, bins):
> c_xy = np.histogram2d(x, y, bins)[0]
> mi = mutual_info_score(None, None, contingency=c_xy)
> return mi
>
> # generate data
> L = np.linalg.cholesky( [[1.0, 0.60], [0.60, 1.0]])
> uncorrelated = np.random.standard_normal((2, 300))
> correlated = np.dot(L, uncorrelated)
> A = correlated[0]
> B = correlated[1]
> x = (A - np.mean(A)) / np.std(A)
> y = (B - np.mean(B)) / np.std(B)
>
> # calculate MI
> mi = calc_MI(x, y, 50)
>
> Is calc_MI a valid approach? I'm asking because I also read that when variables are continuous, then the sums in the formula for discrete data become integrals, but I'm not sure if this procedure is implemented in scikit-learn?
>
> Thanks!
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <https://mail.python.org/pipermail/scikit-learn/attachments/20230201/918a6b71/attachment.html>
```