Constrained Linear Regression

constrainedlr is a drop-in replacement for scikit-learn's linear_model.LinearRegression with the extended capability to apply constraints on the model's coefficients, such as signs and lower/upper bounds.

Installation

pip install constrainedlr

Example Usage

Coefficients sign constraints

from constrainedlr import ConstrainedLinearRegression

model = ConstrainedLinearRegression()

model.fit(
    X_train,
    y_train,
    coefficients_sign_constraints={0: "positive", 2: "negative"},
    intercept_sign_constraint="positive",
)

y_pred = model.predict(X_test)

print(model.coef_, model.intercept_)

Coefficients range constraints

from constrainedlr import ConstrainedLinearRegression

model = ConstrainedLinearRegression()

model.fit(
    X_train,
    y_train,
    coefficients_range_constraints={
        0: {"lower": 2},  # 1st coefficient must be 2 or higher
        2: {"upper": 10},  # 3rd coefficient must be smaller than 10
        3: {"lower": 0.1, "upper": 0.5},  # 4th coefficient must be between 0.1 and 0.5
    },
)

y_pred = model.predict(X_test)

print(model.coef_)

See more in the documentation

Licence

MIT