mirror of
https://github.com/Doctorado-ML/mufs.git
synced 2025-08-17 16:45:53 +00:00
31 lines
876 B
Python
31 lines
876 B
Python
from sklearn.datasets import load_wine
|
|
from mfs import MFS
|
|
from mfs.Metrics import Metrics
|
|
|
|
mfsc = MFS(discrete=False)
|
|
mfsd = MFS(discrete=True)
|
|
X, y = load_wine(return_X_y=True)
|
|
m, n = X.shape
|
|
|
|
print("* Differential entropy in X")
|
|
for i in range(n):
|
|
print(i, Metrics.differential_entropy(X[:, i], k=10))
|
|
|
|
print("* Information Gain")
|
|
print("- Discrete features")
|
|
print(Metrics.information_gain(X, y))
|
|
for i in range(n):
|
|
print(i, Metrics.information_gain(X[:, i], y))
|
|
print("- Continuous features")
|
|
print(Metrics.information_gain_cont(X, y))
|
|
for i in range(n):
|
|
print(i, Metrics.information_gain_cont(X[:, i], y))
|
|
print("CFS Discrete")
|
|
print(mfsd.cfs(X, y).get_results())
|
|
print("CFS continuous")
|
|
print(mfsc.cfs(X, y).get_results())
|
|
print("FCBF Discrete")
|
|
print(mfsd.fcbf(X, y, 1e-7).get_results())
|
|
print("FCBF continuous")
|
|
print(mfsc.fcbf(X, y, 1e-7).get_results())
|