38 lines
992 B
Python
38 lines
992 B
Python
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"""
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=====================
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Lasso path using LARS
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=====================
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Computes Lasso Path along the regularization parameter using the LARS
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algorithm on the diabetes dataset. Each color represents a different
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feature of the coefficient vector, and this is displayed as a function
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of the regularization parameter.
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"""
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# Author: Fabian Pedregosa <fabian.pedregosa@inria.fr>
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# Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# License: BSD 3 clause
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn import datasets, linear_model
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X, y = datasets.load_diabetes(return_X_y=True)
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print("Computing regularization path using the LARS ...")
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_, _, coefs = linear_model.lars_path(X, y, method="lasso", verbose=True)
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xx = np.sum(np.abs(coefs.T), axis=1)
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xx /= xx[-1]
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plt.plot(xx, coefs.T)
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ymin, ymax = plt.ylim()
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plt.vlines(xx, ymin, ymax, linestyle="dashed")
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plt.xlabel("|coef| / max|coef|")
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plt.ylabel("Coefficients")
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plt.title("LASSO Path")
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plt.axis("tight")
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plt.show()
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