"""
Nuisance Regression
===================
"""
# %%
#
# .. image:: ../../Tutorial/figure/nuisance_cover.png
#
# %%
# Introduction
# ------------
# Nuisance regression refers to best subset selection with some prior information that some variables are required to stay in the active set.
# For example, if we are interested in a certain gene and want to find out what other genes are associated with the response when this particular gene shows effect.
# The nuisance selection can be achieved by solving:
#
# .. math::
# \min_{(\beta, \gamma) \in \mathbb{R}^p} \frac{1}{2n} ||y-X (\beta^\top, \gamma^\top)^\top||_2^2,\; \textup{s.t.} \ ||\beta||_{0}\leq s .
#
# Note that, the sparsity constraint restricts on :math:`\beta` and not on :math:`\gamma`. The effect of :math:`\gamma` corresponds to
# variables that stay in the active set.
#
# Using: nuisance regression
# --------------------------
# In the ``LinearRegression()`` (or other methods), the argument ``always_select`` is designed to realize this goal.
# Users can pass a list containing the indexes of the target variables to
# ``always_select``.
#
# Here is an example demonstrating the advantage of nuisance selection.
# We generate a high-dimensional dataset whose predictors are highly correlated (pairwise correlation: 0.6)
# and the effect of predictors on response is weaker than noise.
#
import numpy as np
from abess.datasets import make_glm_data
# generate dataset
np.random.seed(12345)
data = make_glm_data(n=100, p=500, k=3, rho=0.6, family='gaussian', snr=0.5)
print('True effective subset: ', np.nonzero(data.coef_))
# %%
# We use the standard ``abess`` to tackle this dataset:
#
from abess.linear import LinearRegression
model = LinearRegression(support_size=range(10))
model.fit(data.x, data.y)
print('Estimated subset:', np.nonzero(model.coef_))
# %%
# The result from ``model`` omits the 87-th predictor belonging to the true effective set.
# But if we suppose that the 87-th predictor are worthy to be
# included in the model, we can call:
model = LinearRegression(support_size=range(10), always_select=[87])
model.fit(data.x, data.y)
print('Estimated subset:', np.nonzero(model.coef_))
# %%
# Now the estimated subset is the same as the true effective set.
# The comparison between nuisance selection and standard ABESS suggests that
# reasonably leveraging prior information promotes the quality of subset selection.
#
# The ``abess`` R package also supports nuisance regression.
# For R tutorial, please view
# https://abess-team.github.io/abess/articles/v07-advancedFeatures.html.
#
# sphinx_gallery_thumbnail_path = 'Tutorial/figure/nuisance_cover.png'
#