Custom Scatter

This section provides an example showing how to define and use a custom scatter operator with ICS, using the Scatter class.

Suppose you want to apply ICS with the Minimum Covariance Determinant (MCD) scatter, and you did not realize that it is already implemented in ICSpyLab.

In this example, we use the implementation provided by scikit-learn. The code below illustrates how to compute the MCD scatter on the Iris dataset.

from sklearn.datasets import load_iris
from sklearn.covariance import MinCovDet

iris = load_iris()
X = iris.data

mcd_fit = MinCovDet().fit(X)
mcd_loc = mcd_fit.location_
mcd_cov = mcd_fit.covariance_

print("location: ", mcd_loc)
print("scatter: ", mcd_cov)

location:  [5.76788321 3.0540146  3.62992701 1.15693431]
scatter:  [[ 0.62359585 -0.06870339  1.2013055   0.50690727]
           [-0.06870339  0.19486945 -0.39176586 -0.14266417]
           [ 1.2013055  -0.39176586  3.11591145  1.33218733]
           [ 0.50690727 -0.14266417  1.33218733  0.60010943]]

We can now use this scatter to apply ICS. Recall that the scatter parameters of an ICS instance (S1 and S2) must be callables returning a Scatter object. Each Scatter object provides three attributes: location, scatter, and label.

Below, we define a function computing the MCD scatter, which we will use as S1:

from icspylab import Scatter

def mcd_scatter(X, **kwargs):
    mcd_fit = MinCovDet(**kwargs).fit(X)
    mcd_loc = mcd_fit.location_
    mcd_cov = mcd_fit.covariance_

    return Scatter(location=mcd_loc, scatter=mcd_cov, label="MCD")

mcd_scatter(X)

<icspylab.scatter.Scatter at 0x7c2a89188d10>

from icspylab import ICS

ics = ICS(S1=mcd_scatter, S2="cov")

# Fit and transform the ICS model
X_new = ics.fit_transform(X)

This tutorial demonstrates how ICSpyLab can be extended through user-defined scatter operators, enabling advanced customization of the ICS workflow.