constrained-linear-regression 0.0.5

Constrained linear regression in scikit-learn style

constrained-linear-regression

This is a Python implementation of constrained linear regression in scikit-learn style. The current version supports upper and lower bound for each slope coefficient.

It was developed after this question https://stackoverflow.com/questions/50410037

Installation: pip install constrained-linear-regression

You can use this model, for example, if you want all coefficients to be non-negative:

from constrained_linear_regression import ConstrainedLinearRegression
from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression
X, y = load_boston(return_X_y=True)
model = ConstrainedLinearRegression(nonnegative=True)
model.fit(X, y)
print(model.intercept_)
print(model.coef_)

The output will be like

-36.99292986145538
[0.         0.05286515 0.         4.12512386 0.         8.04017956
 0.         0.         0.         0.         0.         0.02273805
 0.        ]

You can also impose arbitrary bounds for any coefficients you choose

model = ConstrainedLinearRegression()
min_coef = np.repeat(-np.inf, X.shape[1])
min_coef[0] = 0
min_coef[4] = -1
max_coef = np.repeat(4, X.shape[1])
max_coef[3] = 2
model.fit(X, y, max_coef=max_coef, min_coef=min_coef)
print(model.intercept_)
print(model.coef_)

The output will be

24.060175576410515
[ 0.          0.04504673 -0.0354073   2.         -1.          4.
 -0.01343263 -1.17231216  0.2183103  -0.01375266 -0.7747823   0.01122374
 -0.56678676]

You can also set coefficients lasso and ridge if you want to apply the corresponding penalties. For lasso, however, the output might not be exactly equal to the result of sklearn.linear_model.Lasso due to the difference in the optimization algorithm.