Update README with examples

README.md

@@ -6,34 +6,55 @@
  <img src="https://img.shields.io/pypi/pyversions/constrainedlr.svg?color=%2334D058" alt="Supported Python versions">
 </a>
 
-constrainedlr is a drop-in replacement for `scikit-learn`'s `linear_model.LinearRegression` with the additional flexibility to define more complex (but linear) constraints on the model's coefficients.
+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.
 
-### Use-cases
-#### SHAP
-The Kernel SHAP algorithm includes the training of a constrainted linear regression model where the sum of its coefficients is equal to the model's prediction
-
-#### Marketing Mix Modeling
-In Marketing Mix Modeling (MMM), the attribution of sales to various marketing channels can be informed by business sense or prior knowledge, by enforcing the contribution of channel variables to be positive or negative.
-
-### Installation
+## Installation
 ```bash
 pip install constrainedlr
 ```
 
-### Example Usage
+## Example Usage
+
+### Coefficients sign constraints
 ```python
 from constrainedlr import ConstrainedLinearRegression
-from sklearn.metrics import mean_squared_error
 
 model = ConstrainedLinearRegression()
-model.fit(X_train, y_train, coefficients_sign_constraints={6: 1, 7: -1}) # 6th and 7th feature (s3 and s4)
+
+model.fit(
+ X_train,
+ y_train,
+ coefficients_sign_constraints={0: "positive", 2: "negative"},
+ intercept_sign_constraint="positive",
+)
 
 y_pred = model.predict(X_test)
-print(mean_squared_error(y_test, y_pred))
+
+print(model.coef_, model.intercept_)
+```
+
+### Coefficients range constraints
+```python
+from constrainedlr import ConstrainedLinearRegression
+
+model = ConstrainedLinearRegression()
+
+model.fit(
+ X_train,
+ y_train,
+ coefficients_sign_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 full example in the [notebook](./notebooks/demo.ipynb)
+See more in the [documentation](https://tsitsimis.github.io/constrainedlr/)
 
 
 ### Licence

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = 'constrainedlr'
-version = '0.1.3'
+version = '0.1.4'
 description = 'Constrained Linear Regression with sklearn-compatible API'
 readme = 'README.md'
 authors = [