36-3.1 Support Vector Machine SVM (scikit).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "36-3.1 Support Vector Machine SVM (scikit)",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOu98vzkwoptVOH6DLzmGeO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Gressling/examples/blob/master/36-3.1%20Support%20Vector%20Machine%20SVM%20(scikit).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lQVWsnPIgakl"
      },
      "source": [
        "# Support Vector Machine SVM (scikit)\n",
        "# author: Gressling, T\n",
        "# license: MIT License # code: github.com/gressling/examples\n",
        "# activity: single example # index: 36-3"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6yy82daCjdLS"
      },
      "source": [
        "from sklearn import svm"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DXtOBfngjg4z",
        "outputId": "66b6d56e-cf90-4c4a-97f1-8e5629591e31"
      },
      "source": [
        "X = [[0, 0], [1, 1]]\n",
        "y = [0, 1]\n",
        "clf = svm.SVC()\n",
        "clf.fit(X, y)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GX29633Djjm6",
        "outputId": "098c5661-dfe2-4e91-d9f6-932b9404b93f"
      },
      "source": [
        "clf.predict([[2., 2.]])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hdFSBgYOkDOB",
        "outputId": "14ded107-9a5d-49c6-f192-96310dede788"
      },
      "source": [
        "# Get support vector indices\n",
        "support_vector_indices = clf.support_\n",
        "print(support_vector_indices)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yRDWrtSOkHQQ",
        "outputId": "a025df6b-c5a2-48c9-bb75-c05252512e91"
      },
      "source": [
        "# Get number of support vectors per class\n",
        "support_vectors_per_class = clf.n_support_\n",
        "print(support_vectors_per_class)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WR8A8g8RkUnJ",
        "outputId": "5bdd9713-62e9-4f1c-bfb1-a92143e6ddd6"
      },
      "source": [
        "# Get support vectors themselves\n",
        "support_vectors = clf.support_vectors_\n",
        "print(support_vectors)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0. 0.]\n",
            " [1. 1.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l30H827akw4H"
      },
      "source": [
        "from sklearn.metrics import plot_confusion_matrix\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "hlsZblJGkMaA",
        "outputId": "6ae9d023-4c16-417f-dde7-a857bf07c718"
      },
      "source": [
        "plt.scatter(support_vectors[:,0], support_vectors[:,1], color='red')\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}