.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/linear_model/plot_lasso_coordinate_descent_path.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_linear_model_plot_lasso_coordinate_descent_path.py: ===================== Lasso and Elastic Net ===================== Lasso and elastic net (L1 and L2 penalisation) implemented using a coordinate descent. The coefficients can be forced to be positive. .. GENERATED FROM PYTHON SOURCE LINES 12-90 .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_lasso_coordinate_descent_path_001.png :alt: Lasso and Elastic-Net Paths :srcset: /auto_examples/linear_model/images/sphx_glr_plot_lasso_coordinate_descent_path_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_lasso_coordinate_descent_path_002.png :alt: Lasso and positive Lasso :srcset: /auto_examples/linear_model/images/sphx_glr_plot_lasso_coordinate_descent_path_002.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_lasso_coordinate_descent_path_003.png :alt: Elastic-Net and positive Elastic-Net :srcset: /auto_examples/linear_model/images/sphx_glr_plot_lasso_coordinate_descent_path_003.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out .. code-block:: none Computing regularization path using the lasso... Computing regularization path using the positive lasso... Computing regularization path using the elastic net... Computing regularization path using the positive elastic net... | .. code-block:: Python # Author: Alexandre Gramfort # License: BSD 3 clause from itertools import cycle import matplotlib.pyplot as plt import numpy as np from sklearn import datasets from sklearn.linear_model import enet_path, lasso_path X, y = datasets.load_diabetes(return_X_y=True) X /= X.std(axis=0) # Standardize data (easier to set the l1_ratio parameter) # Compute paths eps = 5e-3 # the smaller it is the longer is the path print("Computing regularization path using the lasso...") alphas_lasso, coefs_lasso, _ = lasso_path(X, y, eps=eps) print("Computing regularization path using the positive lasso...") alphas_positive_lasso, coefs_positive_lasso, _ = lasso_path( X, y, eps=eps, positive=True ) print("Computing regularization path using the elastic net...") alphas_enet, coefs_enet, _ = enet_path(X, y, eps=eps, l1_ratio=0.8) print("Computing regularization path using the positive elastic net...") alphas_positive_enet, coefs_positive_enet, _ = enet_path( X, y, eps=eps, l1_ratio=0.8, positive=True ) # Display results plt.figure(1) colors = cycle(["b", "r", "g", "c", "k"]) neg_log_alphas_lasso = -np.log10(alphas_lasso) neg_log_alphas_enet = -np.log10(alphas_enet) for coef_l, coef_e, c in zip(coefs_lasso, coefs_enet, colors): l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c) l2 = plt.plot(neg_log_alphas_enet, coef_e, linestyle="--", c=c) plt.xlabel("-Log(alpha)") plt.ylabel("coefficients") plt.title("Lasso and Elastic-Net Paths") plt.legend((l1[-1], l2[-1]), ("Lasso", "Elastic-Net"), loc="lower left") plt.axis("tight") plt.figure(2) neg_log_alphas_positive_lasso = -np.log10(alphas_positive_lasso) for coef_l, coef_pl, c in zip(coefs_lasso, coefs_positive_lasso, colors): l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c) l2 = plt.plot(neg_log_alphas_positive_lasso, coef_pl, linestyle="--", c=c) plt.xlabel("-Log(alpha)") plt.ylabel("coefficients") plt.title("Lasso and positive Lasso") plt.legend((l1[-1], l2[-1]), ("Lasso", "positive Lasso"), loc="lower left") plt.axis("tight") plt.figure(3) neg_log_alphas_positive_enet = -np.log10(alphas_positive_enet) for coef_e, coef_pe, c in zip(coefs_enet, coefs_positive_enet, colors): l1 = plt.plot(neg_log_alphas_enet, coef_e, c=c) l2 = plt.plot(neg_log_alphas_positive_enet, coef_pe, linestyle="--", c=c) plt.xlabel("-Log(alpha)") plt.ylabel("coefficients") plt.title("Elastic-Net and positive Elastic-Net") plt.legend((l1[-1], l2[-1]), ("Elastic-Net", "positive Elastic-Net"), loc="lower left") plt.axis("tight") plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.298 seconds) .. _sphx_glr_download_auto_examples_linear_model_plot_lasso_coordinate_descent_path.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.4.X?urlpath=lab/tree/notebooks/auto_examples/linear_model/plot_lasso_coordinate_descent_path.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/linear_model/plot_lasso_coordinate_descent_path.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_lasso_coordinate_descent_path.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_lasso_coordinate_descent_path.py ` .. include:: plot_lasso_coordinate_descent_path.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_