set_default_parameter('epsilon', 1e-5)
set_default_parameter('epochs', 25)
To change the number epochs for a single iteration of PFS, use the `epochs` argument of the `fit` method of your `PFS` object. The `fit` method now also has a `seed` parameter you may use to make the PFS implementation deterministic.
Example:
Python
from kxy.pfs import PFS
selector = PFS()
selector.fit(x, y, epochs=25, seed=123)
Alternatively, you may also use the `kxy.misc.tf.set_seed` method to make PFS deterministic.
v.1.4.6 Changes
Minor PFS improvements.
* Adding more (robust) mutual information loss functions.
* Exposing the learned total mutual information between principal features and target as an attribute of PFS.
* Exposing the number of epochs as a parameter of PFS' fit.