from sklearn.feature_selection import SelectKBest
from minepy import MINE

# Since the design of MINE is not functional, the mic method is defined as a functional one, returning a binary group, and the second item of the binary group is set to a fixed P value of 0.5.
def mic(x, y):
     m = MINE()
     m.compute_score(x, y)
     return (m.mic(), 0.5)
#Select K best features, return the data after feature selection
SelectKBest(lambda X, Y: array(map(lambda x:mic(x, Y), X.T)).T, k=2).fit_transform(iris.data, iris.target)