Enabling higher orders feature importance for F filter and LR filter

[zhenyuz0500]

Proposed changes

Enabling higher orders of feature importance for F filter and LR filter.

Previously, the F filter and LR filter can only detect uplift feature importance based on linear pattern.
This diff adds 2nd and 3rd orders of feature transformations into the feature selection model.
An argument 'order' is added to F filter and LR filter methods to control the orders of feature to be added to the evaluation, which takes value in 1, 2, 3.

The example notebook for feature selection is updated to show how to use this new feature.

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[paullo0106]

@zhenyuz0500 can you remind me why is the r_matrix configured in this way? Maybe we can add comment here too

[zhenyuz0500]

The r_matrix assumes the linear combination of each row times the coefficients equals 0.
For example, when order=2, then the linear model will be: a + b1 * I_treatment + b2 * x + b3 * x * I_treatment + b4 * x^2 + b5 * x^2 * I_treatment
we want to test H0: b3==0 and b5 ==0 vs H1: b3!=0 or b5 !=0
then it translates to test H0: [0, 0, 0, 1, 0, 0] * [a, b1, b2, b3, b4,b5]' = 0 and [0, 0, 0, 0, 0, 1] * [a, b1, b2, b3, b4, b5]' = 0

reference: https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.f_test.html

[paullo0106]

I see, thanks!

[zhenyuz0500]

thanks for the review!

[paullo0106]

LGTM with one question, thanks Zhenyu!