Pushing the docs to dev/ for branch: main, commit fb9f0b95988bf4a9a1e04fa0667daedbec82a3af

Author: sklearn-ci

dev/.buildinfo

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 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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+config: 7c588470fefc9f086d133514a7c5fa6f
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Lasso, Lasso-LARS, and Elastic Net paths\n\nThis example shows how to compute the \"paths\" of coefficients along the Lasso,\nLasso-LARS, and Elastic Net regularization paths. In other words, it shows the\nrelationship between the regularization parameter (alpha) and the coefficients.\n\nLasso and Lasso-LARS impose a sparsity constraint on the coefficients,\nencouraging some of them to be zero. Elastic Net is a generalization of\nLasso that adds an L2 penalty term to the L1 penalty term. This allows for\nsome coefficients to be non-zero while still encouraging sparsity.\n\nLasso and Elastic Net use a coordinate descent method to compute the paths, while\nLasso-LARS uses the LARS algorithm to compute the paths.\n\nThe paths are computed using :func:`~sklearn.linear_model.lasso_path`,\n:func:`~sklearn.linear_model.lars_path`, and :func:`~sklearn.linear_model.enet_path`.\n\nThe results show different comparison plots:\n\n- Compare Lasso and Lasso-LARS\n- Compare Lasso and Elastic Net\n- Compare Lasso with positive Lasso\n- Compare LARS and Positive LARS\n- Compare Elastic Net and positive Elastic Net\n\nEach plot shows how the model coefficients vary as the regularization strength changes,\noffering insight into the behavior of these models\nunder different constraints.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom itertools import cycle\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.linear_model import enet_path, lars_path, lasso_path\n\nX, y = load_diabetes(return_X_y=True)\nX /= X.std(axis=0)  # Standardize data (easier to set the l1_ratio parameter)\n\n# Compute paths\n\neps = 5e-3  # the smaller it is the longer is the path\n\nprint(\"Computing regularization path using the lasso...\")\nalphas_lasso, coefs_lasso, _ = lasso_path(X, y, eps=eps)\n\nprint(\"Computing regularization path using the positive lasso...\")\nalphas_positive_lasso, coefs_positive_lasso, _ = lasso_path(\n    X, y, eps=eps, positive=True\n)\n\nprint(\"Computing regularization path using the LARS...\")\nalphas_lars, _, coefs_lars = lars_path(X, y, method=\"lasso\")\n\nprint(\"Computing regularization path using the positive LARS...\")\nalphas_positive_lars, _, coefs_positive_lars = lars_path(\n    X, y, method=\"lasso\", positive=True\n)\n\nprint(\"Computing regularization path using the elastic net...\")\nalphas_enet, coefs_enet, _ = enet_path(X, y, eps=eps, l1_ratio=0.8)\n\nprint(\"Computing regularization path using the positive elastic net...\")\nalphas_positive_enet, coefs_positive_enet, _ = enet_path(\n    X, y, eps=eps, l1_ratio=0.8, positive=True\n)\n\n# Display results\n\nplt.figure(1)\ncolors = cycle([\"b\", \"r\", \"g\", \"c\", \"k\"])\nfor coef_lasso, coef_lars, c in zip(coefs_lasso, coefs_lars, colors):\n    l1 = plt.semilogx(alphas_lasso, coef_lasso, c=c)\n    l2 = plt.semilogx(alphas_lars, coef_lars, linestyle=\"--\", c=c)\n\nplt.xlabel(\"alpha\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Lasso and LARS Paths\")\nplt.legend((l1[-1], l2[-1]), (\"Lasso\", \"LARS\"), loc=\"lower right\")\nplt.axis(\"tight\")\n\nplt.figure(2)\ncolors = cycle([\"b\", \"r\", \"g\", \"c\", \"k\"])\nfor coef_l, coef_e, c in zip(coefs_lasso, coefs_enet, colors):\n    l1 = plt.semilogx(alphas_lasso, coef_l, c=c)\n    l2 = plt.semilogx(alphas_enet, coef_e, linestyle=\"--\", c=c)\n\nplt.xlabel(\"alpha\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Lasso and Elastic-Net Paths\")\nplt.legend((l1[-1], l2[-1]), (\"Lasso\", \"Elastic-Net\"), loc=\"lower right\")\nplt.axis(\"tight\")\n\n\nplt.figure(3)\nfor coef_l, coef_pl, c in zip(coefs_lasso, coefs_positive_lasso, colors):\n    l1 = plt.semilogy(alphas_lasso, coef_l, c=c)\n    l2 = plt.semilogy(alphas_positive_lasso, coef_pl, linestyle=\"--\", c=c)\n\nplt.xlabel(\"alpha\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Lasso and positive Lasso\")\nplt.legend((l1[-1], l2[-1]), (\"Lasso\", \"positive Lasso\"), loc=\"lower right\")\nplt.axis(\"tight\")\n\n\nplt.figure(4)\ncolors = cycle([\"b\", \"r\", \"g\", \"c\", \"k\"])\nfor coef_lars, coef_positive_lars, c in zip(coefs_lars, coefs_positive_lars, colors):\n    l1 = plt.semilogx(alphas_lars, coef_lars, c=c)\n    l2 = plt.semilogx(alphas_positive_lars, coef_positive_lars, linestyle=\"--\", c=c)\n\nplt.xlabel(\"alpha\")\nplt.ylabel(\"coefficients\")\nplt.title(\"LARS and Positive LARS\")\nplt.legend((l1[-1], l2[-1]), (\"LARS\", \"Positive LARS\"), loc=\"lower right\")\nplt.axis(\"tight\")\n\nplt.figure(5)\nfor coef_e, coef_pe, c in zip(coefs_enet, coefs_positive_enet, colors):\n    l1 = plt.semilogx(alphas_enet, coef_e, c=c)\n    l2 = plt.semilogx(alphas_positive_enet, coef_pe, linestyle=\"--\", c=c)\n\nplt.xlabel(\"alpha\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Elastic-Net and positive Elastic-Net\")\nplt.legend((l1[-1], l2[-1]), (\"Elastic-Net\", \"positive Elastic-Net\"), loc=\"lower right\")\nplt.axis(\"tight\")\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.20"
+    }
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+  "nbformat": 4,
+  "nbformat_minor": 0
+}

dev/_downloads/0358b1921962fd7c6f5a94c5abc91476/plot_hashing_vs_dict_vectorizer.zip

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+"""
+========================================
+Lasso, Lasso-LARS, and Elastic Net paths
+========================================
+
+This example shows how to compute the "paths" of coefficients along the Lasso,
+Lasso-LARS, and Elastic Net regularization paths. In other words, it shows the
+relationship between the regularization parameter (alpha) and the coefficients.
+
+Lasso and Lasso-LARS impose a sparsity constraint on the coefficients,
+encouraging some of them to be zero. Elastic Net is a generalization of
+Lasso that adds an L2 penalty term to the L1 penalty term. This allows for
+some coefficients to be non-zero while still encouraging sparsity.
+
+Lasso and Elastic Net use a coordinate descent method to compute the paths, while
+Lasso-LARS uses the LARS algorithm to compute the paths.
+
+The paths are computed using :func:`~sklearn.linear_model.lasso_path`,
+:func:`~sklearn.linear_model.lars_path`, and :func:`~sklearn.linear_model.enet_path`.
+
+The results show different comparison plots:
+
+- Compare Lasso and Lasso-LARS
+- Compare Lasso and Elastic Net
+- Compare Lasso with positive Lasso
+- Compare LARS and Positive LARS
+- Compare Elastic Net and positive Elastic Net
+
+Each plot shows how the model coefficients vary as the regularization strength changes,
+offering insight into the behavior of these models
+under different constraints.
+"""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from itertools import cycle
+
+import matplotlib.pyplot as plt
+
+from sklearn.datasets import load_diabetes
+from sklearn.linear_model import enet_path, lars_path, lasso_path
+
+X, y = 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 LARS...")
+alphas_lars, _, coefs_lars = lars_path(X, y, method="lasso")
+
+print("Computing regularization path using the positive LARS...")
+alphas_positive_lars, _, coefs_positive_lars = lars_path(
+    X, y, method="lasso", 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"])
+for coef_lasso, coef_lars, c in zip(coefs_lasso, coefs_lars, colors):
+    l1 = plt.semilogx(alphas_lasso, coef_lasso, c=c)
+    l2 = plt.semilogx(alphas_lars, coef_lars, linestyle="--", c=c)
+
+plt.xlabel("alpha")
+plt.ylabel("coefficients")
+plt.title("Lasso and LARS Paths")
+plt.legend((l1[-1], l2[-1]), ("Lasso", "LARS"), loc="lower right")
+plt.axis("tight")
+
+plt.figure(2)
+colors = cycle(["b", "r", "g", "c", "k"])
+for coef_l, coef_e, c in zip(coefs_lasso, coefs_enet, colors):
+    l1 = plt.semilogx(alphas_lasso, coef_l, c=c)
+    l2 = plt.semilogx(alphas_enet, coef_e, linestyle="--", c=c)
+
+plt.xlabel("alpha")
+plt.ylabel("coefficients")
+plt.title("Lasso and Elastic-Net Paths")
+plt.legend((l1[-1], l2[-1]), ("Lasso", "Elastic-Net"), loc="lower right")
+plt.axis("tight")
+
+
+plt.figure(3)
+for coef_l, coef_pl, c in zip(coefs_lasso, coefs_positive_lasso, colors):
+    l1 = plt.semilogy(alphas_lasso, coef_l, c=c)
+    l2 = plt.semilogy(alphas_positive_lasso, coef_pl, linestyle="--", c=c)
+
+plt.xlabel("alpha")
+plt.ylabel("coefficients")
+plt.title("Lasso and positive Lasso")
+plt.legend((l1[-1], l2[-1]), ("Lasso", "positive Lasso"), loc="lower right")
+plt.axis("tight")
+
+
+plt.figure(4)
+colors = cycle(["b", "r", "g", "c", "k"])
+for coef_lars, coef_positive_lars, c in zip(coefs_lars, coefs_positive_lars, colors):
+    l1 = plt.semilogx(alphas_lars, coef_lars, c=c)
+    l2 = plt.semilogx(alphas_positive_lars, coef_positive_lars, linestyle="--", c=c)
+
+plt.xlabel("alpha")
+plt.ylabel("coefficients")
+plt.title("LARS and Positive LARS")
+plt.legend((l1[-1], l2[-1]), ("LARS", "Positive LARS"), loc="lower right")
+plt.axis("tight")
+
+plt.figure(5)
+for coef_e, coef_pe, c in zip(coefs_enet, coefs_positive_enet, colors):
+    l1 = plt.semilogx(alphas_enet, coef_e, c=c)
+    l2 = plt.semilogx(alphas_positive_enet, coef_pe, linestyle="--", c=c)
+
+plt.xlabel("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 right")
+plt.axis("tight")
+plt.show()