{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# SVM: Separating hyperplane for unbalanced classes\n\nFind the optimal separating hyperplane using an SVC for classes that\nare unbalanced.\n\nWe first find the separating plane with a plain SVC and then plot\n(dashed) the separating hyperplane with automatically correction for\nunbalanced classes.\n\n.. currentmodule:: sklearn.linear_model\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>This example will also work by replacing ``SVC(kernel=\"linear\")``\n    with ``SGDClassifier(loss=\"hinge\")``. Setting the ``loss`` parameter\n    of the :class:`SGDClassifier` equal to ``hinge`` will yield behaviour\n    such as that of a SVC with a linear kernel.\n\n    For example try instead of the ``SVC``::\n\n        clf = SGDClassifier(n_iter=100, alpha=0.01)</p></div>\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\nimport matplotlib.lines as mlines\nimport matplotlib.pyplot as plt\n\nfrom sklearn import svm\nfrom sklearn.datasets import make_blobs\nfrom sklearn.inspection import DecisionBoundaryDisplay\n\n# we create two clusters of random points\nn_samples_1 = 1000\nn_samples_2 = 100\ncenters = [[0.0, 0.0], [2.0, 2.0]]\nclusters_std = [1.5, 0.5]\nX, y = make_blobs(\n    n_samples=[n_samples_1, n_samples_2],\n    centers=centers,\n    cluster_std=clusters_std,\n    random_state=0,\n    shuffle=False,\n)\n\n# fit the model and get the separating hyperplane\nclf = svm.SVC(kernel=\"linear\", C=1.0)\nclf.fit(X, y)\n\n# fit the model and get the separating hyperplane using weighted classes\nwclf = svm.SVC(kernel=\"linear\", class_weight={1: 10})\nwclf.fit(X, y)\n\n# plot the samples\nplt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired, edgecolors=\"k\")\n\n# plot the decision functions for both classifiers\nax = plt.gca()\ndisp = DecisionBoundaryDisplay.from_estimator(\n    clf,\n    X,\n    plot_method=\"contour\",\n    colors=\"k\",\n    levels=[0],\n    alpha=0.5,\n    linestyles=[\"-\"],\n    ax=ax,\n)\n\n# plot decision boundary and margins for weighted classes\nwdisp = DecisionBoundaryDisplay.from_estimator(\n    wclf,\n    X,\n    plot_method=\"contour\",\n    colors=\"r\",\n    levels=[0],\n    alpha=0.5,\n    linestyles=[\"-\"],\n    ax=ax,\n)\n\nplt.legend(\n    [\n        mlines.Line2D([], [], color=\"k\", label=\"non weighted\"),\n        mlines.Line2D([], [], color=\"r\", label=\"weighted\"),\n    ],\n    [\"non weighted\", \"weighted\"],\n    loc=\"upper right\",\n)\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": "3.9.21"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}