Customizing measures arguments

In this example we will show you how to custorize the measures.

# Load a dataset
from sklearn.datasets import load_iris
from pymfe.mfe import MFE

data = load_iris()
y = data.target
X = data.data

Custom Arguments

It is possible to pass custom arguments to every meta-feature using PyMFE extract method kwargs. The keywords must be the target meta-feature name, and the value must be a dictionary in the format {argument: value}, i.e., each key in the dictionary is a target argument with its respective value. In the example below, the extraction of metafeatures min and max happens as usual, but the meta-features sd, nr_norm and nr_cor_attr will receive user custom argument values, which will interfere in each metafeature result.

# Extract measures with custom user arguments
mfe = MFE(features=["sd", "nr_norm", "nr_cor_attr", "min", "max"])
mfe.fit(X, y)
ft = mfe.extract(
    sd={"ddof": 0},
    nr_norm={"method": "all", "failure": "hard", "threshold": 0.025},
    nr_cor_attr={"threshold": 0.6},
)
print("\n".join("{:50} {:30}".format(x, y) for x, y in zip(ft[0], ft[1])))

max.mean                                                        5.425000000000001
max.sd                                                         2.1158627082114756
min.mean                                                       1.8499999999999999
min.sd                                                         1.5660459763365826
nr_cor_attr                                                                   0.5
nr_norm                                                                       1.0
sd.mean                                                        0.9447022382995245
sd.sd                                                          0.4931078458294242