v1.4.9

Change Log

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.