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
1 maintainer
Metadata
1 maintainer
Release files for constrainedlr 0.2.0
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| File | Size | Uploaded | Provenance | Actions |
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| File | Interpreter | ABI | Platform | Actions Reset |
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Total release size: 19.0 kB
Release files / constrainedlr-0.2.0.tar.gz
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|---|---|
| Size | 7.8 kB |
| Tags | Python 3 |
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