Ridge regression

ridge_regression.py

def ridge_regression(data, predictors, alpha=None):
    # Fit the model
    if alpha is not None and predictors is not None:
        model = Ridge(alpha=alpha, normalize=True, max_iter=1e8)
    else:
        model = RidgeCV(alphas=np.linspace(1e-4, 1e-3, 1000), fit_intercept=True, normalize=True)
    
    model.fit(data[predictors],data['y'])
    y_pred = model.predict(data[predictors])  
    
    # Plot the results
    data_pred = data
    data_pred['y_pred'] = y_pred
    if alpha is not None:
        title = '{:.2e}'.format(alpha)
    else:
        title = None
    graph = generate_plot(data_pred, title=title)
 
    return graph, model


# Initialize predictors to all 15 powers of x
predictors=['x']
predictors.extend(['x_{}'.format(i) for i in range(2,16)])
 
# Define the alpha values to test
alpha_ridge = [1e-8, 1e-5, 1e-4, 1e-3, 1e-2, 1]
 
# Iterate over the 10 alpha values:
graphs = []
data - get_data()
for i in range(6):
    graph, _ = ridge_regression(data, predictors, alpha_ridge[i])
    graphs.append(graph)
    
grid = gridplot([[graphs[0], graphs[1]], [graphs[2], graphs[3]], [graphs[4], graphs[5]]])
show(grid);