Change Log
v.1.4.10 Changes
- Added a function to construct features derived from PFS mutual information estimation that should be expected to be linearly related to the target.
- Fixed a global name conflict in
kxy.learning.base_learners.
v.1.4.9 Changes
- Change the activation function used by PFS from ReLU to switch/SILU.
- Leaving it to the user to set the logging level.
v.1.4.8 Changes
- Froze the versions of all python packages in the docker file.
v.1.4.7 Changes
Changes related to optimizing Principal Feature Selection.
- Made it easy to change PFS' default learning parameters.
- Changed PFS' default learning parameters (learning rate is now 0.005 and epsilon 1e-04)
- Adding a seed parameter to PFS' fit for reproducibility.
To globally change the learning rate to 0.003, change Adam's epsilon to 1e-5, and the number of epochs to 25, do
from kxy.misc.tf import set_default_parameter
set_default_parameter('lr', 0.003)
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:
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.