svm_sklearn.ipynb

{
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  "nbformat_minor": 0,
  "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": "3.7.4"
    },
    "colab": {
      "name": "svm_sklearn.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mashyko/deep-learning/blob/master/svm_sklearn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FES1Ebgw6b9Y",
        "colab_type": "text"
      },
      "source": [
        "Scikit-Learn パッケージの利用例\n",
        "\n",
        "　1. アヤメの分類\n",
        "\n",
        "　2.　MNISTデータセットの分類\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UZXKuEEg6FU0",
        "colab_type": "text"
      },
      "source": [
        "1.1 Sklearn SVM によるアヤメの分類"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKz-zqaJ6FU3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c47f831a-0810-4d97-fa6c-b99a61da07a5"
      },
      "source": [
        "from sklearn import datasets\n",
        "import numpy as np\n",
        "\n",
        "iris = datasets.load_iris()\n",
        "X = iris.data[:, :2]\n",
        "y = iris.target\n",
        "\n",
        "print('Class labels:', np.unique(y))\n",
        "\n",
        "\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Class labels: [0 1 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JvKO7l8I6FU7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#from sklearn.cross_validation import train_test_split\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.3, random_state=0)\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc = StandardScaler()\n",
        "sc.fit(X_train)\n",
        "X_train_std = sc.transform(X_train)\n",
        "X_test_std = sc.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lKz-2LlG6FU9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):\n",
        "\n",
        "    # setup marker generator and color map\n",
        "    markers = ('s', 'x', 'o', '^', 'v')\n",
        "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
        "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
        "\n",
        "    # plot the decision surface\n",
        "    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
        "    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
        "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
        "                           np.arange(x2_min, x2_max, resolution))\n",
        "    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
        "    Z = Z.reshape(xx1.shape)\n",
        "    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)\n",
        "    plt.xlim(xx1.min(), xx1.max())\n",
        "    plt.ylim(xx2.min(), xx2.max())\n",
        "\n",
        "    for idx, cl in enumerate(np.unique(y)):\n",
        "        plt.scatter(x=X[y == cl, 0], \n",
        "                    y=X[y == cl, 1],\n",
        "                    alpha=0.6, \n",
        "                    c=cmap(idx),\n",
        "                    edgecolor='black',\n",
        "                    marker=markers[idx], \n",
        "                    label=cl)\n",
        "\n",
        "    # highlight test samples\n",
        "    if test_idx:\n",
        "        # plot all samples\n",
        "        X_test, y_test = X[test_idx, :], y[test_idx]\n",
        "        plt.scatter(X_test[:, 0],\n",
        "                    X_test[:, 1],\n",
        "                    c='',\n",
        "                    alpha=1.0,\n",
        "                    edgecolor='black',\n",
        "                    linewidths=1,\n",
        "                    marker='o',\n",
        "                    s=55, label='test set')\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6pjgil896FU_",
        "colab_type": "text"
      },
      "source": [
        "  1.2 線形サポートベクトルマシンの採用"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UjiK1XEv6FVA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "bc8804f7-0124-489e-f464-fd58193512a7"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "# 分類用にサポートベクトルマシンを用意\n",
        "svm = SVC(C=1.0, kernel='linear', random_state=0)\n",
        "\n",
        "svm.fit(X_train_std, y_train)\n",
        "\n",
        "X_combined_std = np.vstack((X_train_std, X_test_std))\n",
        "y_combined = np.hstack((y_train, y_test))\n",
        "\n",
        "plot_decision_regions(X=X_combined_std, y=y_combined,\n",
        "                      classifier=svm, test_idx=range(105, 150))\n",
        "plt.xlabel('sepal length [standardized]')\n",
        "plt.ylabel('sepal width [standardized]')\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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2AJ9KKddUKAuXUs42VhBCCH+gN7DXWHVWxMXJiYTERKSUCKG7fJGfn09aRobR/PullLzy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\nnfk3zCWIqvhnaWkHZs++j3/6p6tZterXDBlyklm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0Rr5jioqk6yRtlVQrqc1nYEi6QtIb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\nHYBzzhWp/waz7hsUrtFvsAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C1j7x-uL6FVD",
        "colab_type": "text"
      },
      "source": [
        "1.3 カーネルSVMの採用：gamma = 0.5"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZGkqy7Zp6FVD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "33d5bd5e-4e0a-46f4-bcb4-825013a31b97"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "svm = SVC(kernel='rbf', gamma=0.5, random_state=0, C=1.0)\n",
        "\n",
        "svm.fit(X_train_std, y_train)\n",
        "\n",
        "X_combined_std = np.vstack((X_train_std, X_test_std))\n",
        "y_combined = np.hstack((y_train, y_test))\n",
        "\n",
        "plot_decision_regions(X=X_combined_std, y=y_combined,\n",
        "                      classifier=svm, test_idx=range(105, 150))\n",
        "plt.xlabel('sepal length [standardized]')\n",
        "plt.ylabel('sepal width [standardized]')\n",
        "plt.legend(loc='upper left')\n",
        "plt.show()\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XlVEmJQFAN2fnGv3k63QcLy1ldEAAYW5uCCG4UFjI9pQURgcH8+czzzTILmMC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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aKVSmZ-j6FVF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "c2e52513-68c2-4538-81a3-6280d897b198"
      },
      "source": [
        "iris = datasets.load_iris()\n",
        "\n",
        "# ４つの特徴量のセットを変数Xに、ターゲット（教師ラベル）を変数yに格納\n",
        "X = iris.data\n",
        "y = iris.target\n",
        "\n",
        "svm = SVC(C=1.0, kernel='linear', random_state=0)\n",
        "\n",
        "svm.fit(X, y)\n",
        "\n",
        "result = svm.predict(X)\n",
        "\n",
        "print('ターゲット（教師ラベル）')\n",
        "print(y)\n",
        "print('機械学習による予測')\n",
        "print(result)\n",
        "\n",
        "total = len(X)\n",
        "success = sum(result==y)\n",
        "\n",
        "# 正解率をパーセント表示\n",
        "print('正解率：％表示')\n",
        "print(100.0*success/total)\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ターゲット（教師ラベル）\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2]\n",
            "機械学習による予測\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2]\n",
            "正解率：％表示\n",
            "99.33333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "oy65aAxR6FVH",
        "colab_type": "text"
      },
      "source": [
        "2.1 MNISTデータセットの手書き数字の分類：MNIST data-set with SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0jEmbInK6FVI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6669fa4e-6359-4d7c-daad-dd8ed5e8330f"
      },
      "source": [
        "from sklearn import datasets, svm\n",
        "\n",
        "digits = datasets.load_digits()\n",
        "\n",
        "X = digits.data\n",
        "y = digits.target\n",
        "\n",
        "svm = svm.SVC(C=1.0, kernel='linear', random_state=0)\n",
        "svm.fit(X, y)\n",
        "\n",
        "y_pred = svm.predict(X)\n",
        "acc = sum(y_pred==y)/len(X)\n",
        "\n",
        "print('training accuracy = % .2f%%' % (acc * 100))\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training accuracy =  100.00%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gweGyo8x6FVK",
        "colab_type": "text"
      },
      "source": [
        "minibatch and test data accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rXrkDxNY6FVK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "19cac7d9-2153-44b8-8b99-87faa247428f"
      },
      "source": [
        "from sklearn import datasets, svm\n",
        "\n",
        "digits = datasets.load_digits()\n",
        "\n",
        "X = digits.data\n",
        "y = digits.target\n",
        "\n",
        "\n",
        "#from sklearn.cross_validation import train_test_split\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.3, random_state=0)\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "svm = SVC(C=1.0, kernel='linear', random_state=0)\n",
        "\n",
        "svm.fit(X_train, y_train)\n",
        "\n",
        "y_test_pred = svm.predict(X_test)\n",
        "acc = sum(y_test_pred==y_test)/len(X_test)\n",
        "\n",
        "print('Test data accuracy = % .2f%%' % (acc * 100))\n",
        "\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test data accuracy =  97.41%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "colab_type": "text",
        "id": "wu_KSj4M8E-_"
      },
      "source": [
        "2.2 MNISTデータセットの手書き数字の分類：MNIST data-set with SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5uqPbVFB6FVN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "outputId": "cc385cfa-a21d-4659-ebf4-4a960b93b957"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(nrows=2, ncols=5, sharex=True, sharey=True,)\n",
        "ax = ax.flatten()\n",
        "for i in range(10):\n",
        "    img = X_train[y_train == i][0].reshape(8, 8)\n",
        "    ax[i].imshow(img, cmap='Greys', interpolation='nearest')\n",
        "\n",
        "ax[0].set_xticks([])\n",
        "ax[0].set_yticks([])\n",
        "#plt.tight_layout()\n",
        "# plt.savefig('./figures/mnist_all.png', dpi=300)\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAADCCAYAAAB3whgdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAALrklEQVR4nO3dT0hUbRvH8XvenkiKKQslyiDRSqLa\nSfRnUaCE5UJbiFqbxo30Z1WLFukqg4JylS41glJxkS2yPyRRlEI5qxYmNpJggWhmi8gWMe/64fW6\nbs/pzFzj2/ezfH5z5pzu5vyeQa/uE0un0w4AkH3/sb4AAPhbUcAAYIQCBgAjFDAAGKGAAcAIBQwA\nRv4J8uKCgoJ0cXFx4JN8+/ZNzefn58WspKREzGKxWOBr8fn06ZObm5tb9huHXZPZ2Vk1z8vLE7N4\nPB74fH8qmUzOpdPpwuW8VluT379/i8dNTEyo7/vjxw8xy8/PF7PS0lL1fcMKsibOZe7+mZycFDNt\nXTJxb2Xr/vHRPmfv378Xs6KiIjErLFz2X/X/kD4rgQq4uLjYjY6OBj55X1+fmvf29opZT0+PmGkl\nFVZ5eXmg14ddk87OTjUvKysTs4qKisDn+1OxWGxqua/V1mRhYUE8rrq6Wn3f4eFhMTt69KiYPXjw\nQH3fsIKsiXOZu38aGhrETFuXTNxb2bp/fLTPmfY/nsuXL4vZuXPnQl+P9FnhRxAAYIQCBgAjFDAA\nGKGAAcAIBQwARgJNQYR19uxZNdfGbLZu3SpmIyMjYqZNEeQC3/U1NzeL2cePH6O+nKy5ffu2mI2N\njanHXrx4Ucza29vF7PPnz2KmjR3lCm3KwTnnEomEmHV3d4tZV1eXmP3Jb/yzYXFxUc21SYfdu3eL\nWU1NTehrCoNvwABghAIGACMUMAAYoYABwAgFDABGKGAAMBLZGJq2+YVvN6erV6+GOvbGjRtipo3Y\nrASpVErMVvJYVVVVlZjt3LlTPVbb1UwbQ1vppqen1VwbNdPk+qim5ubNm2qujZoNDQ2JWSY2+NLw\nDRgAjFDAAGCEAgYAIxQwABihgAHACAUMAEYoYAAwEtkccDKZFLPa2lr12JaWllDnPHnypJhps7LO\n2c/LDg4Ohj5Wm/sMu5bZoj200fdAx0uXLomZNiNs/Xf9p3zX//jx41DvW1dXJ2ZfvnxRj83GvKy2\n5aRv7ru/v1/MtGvXHhDqe5BumH97wDdgADBCAQOAEQoYAIxQwABghAIGACMUMAAYiWwMbXx8PKq3\nWrYLFy6I2cOHD9VjrZ/6eu3aNTU/ceKEmFVWVoqZtib5+fn+CzOkjQA5p48e9fb2Rn05OcP3BOBH\njx6JmfYEbe1py76RqmzcP9o9vGnTJvXYiooKMdPW89ixY2LW1tamnjMMvgEDgBEKGACMUMAAYIQC\nBgAjFDAAGKGAAcBIZGNo2hNW7927F9Vp/mVkZETMfGMq1ny7SWljNIcOHRKzp0+fill9fb3/wgxp\nY1E+NTU1YqbtjPf69evQ7xslbQTPty7aqJm2w9yZM2fEzLerXjbG0L5+/Spm8/Pz6rHayJg2zqg9\nhT0TnwW+AQOAEQoYAIxQwABghAIGACMUMAAYoYABwEhkY2iHDx8Ws+Hh4dDHjo2NiZk2MjI9Pa2e\n05pv97ihoSExm5mZEbMrV66ImTbW45w++hYVbSQslUqpx2oPd9X+3NrYUSKRUM+ZrTG0eDwuZr51\n6evrE7OCggIx0x7mmQtjnE1NTWLm2zmvtbU11DkvXrwoZpl4uCvfgAHACAUMAEYoYAAwQgEDgBEK\nGACMUMAAYIQCBgAjkc0Ba9srPn/+XD22rq5OzLS5PG2GMxMze1Hq7+9X87BzjJrz58+rubbNZVS0\nvxff+QcGBkIdqz0x2Tfn69s2NCradq6+Jz7/yTaekg8fPkT+nkFpa+97arM2+11SUiJmjY2N/guL\nEN+AAcAIBQwARihgADBCAQOAEQoYAIxQwABgJJZOp5f/4lhs1jk3lbnLyQnb0+l04XJf/JesiXMB\n1oU1Wdpfsi6sydKWXJdABQwAiA4/ggAAIxQwABihgAHACAUMAEYoYAAwQgEDgBEKGACMUMAAYIQC\nBgAjFDAAGKGAAcAIBQwARihgADBCAQOAEQoYAIxQwABghAIGACMUMAAYoYABwAgFDABGKGAAMEIB\nA4ARChgAjFDAAGCEAgYAIxQwABihgAHACAUMAEYoYAAwQgEDgBEKGACMUMAAYIQCBgAjFDAAGKGA\nAcAIBQwARihgADBCAQOAEQoYAIxQwABghAIGACMUMAAYoYABwAgFDABGKGAAMEIBA4ARChgAjFDA\nAGCEAgYAIxQwABihgAHACAUMAEYoYAAwQgEDgBEKGACMUMAAYIQCBgAjFDAAGKGAAcDIP0FeXFBQ\nkC4uLg58ksXFRTX/9OmTmP348UPM1qxZI2Z79+71Xpd0LXNzc7Hlvj7smvjMzs6K2efPn8Vsz549\nYrZ69erQ15NMJufS6XThcl4bdk2+ffum5pOTk4Hf0znnVq1aJWa7du1Sj127dq2YBVkT51bW/bN7\n9271nNKaroT759evX2K2sLAgZmVlZeo5tftL+qwEKuDi4mI3Ojoa5BDnnHPj4+Nq3tTUJGbDw8Ni\ntm3bNjELc53OOVdeXh7o9WHXxKezs1PMWlpaxGxwcFDMioqKQl9PLBabWu5rw65JX1+fmjc0NAR+\nT+ecW79+vZjdvXtXPVb7PARZE+dW1v3z4sUL9Zz5+flL/veVcP+kUikxe/jwoZhp95Zz+v0lfVb4\nEQQAGKGAAcAIBQwARihgADAS6JdwGu23h9XV1eqx2g/Fa2trxez69ev+C8tRvt9sa79o09Zk3bp1\noa/J2sTEhJprf+6XL1+KmTZdoX32nAv+S6WwtPvn4MGD6rHPnj0Ts3g8LmbafTkzM6OeU/olXJS0\ne6SioiL0+x4/flzM9u3bJ2baL+icc+7cuXOBr4VvwABghAIGACMUMAAYoYABwAgFDABGKGAAMBLZ\nGJo20uIb9Xn37p2YZWsMKNt8YzTa6JT279zz8vJCX5M1bfTOp62tTcza29vFrKamJvQ5o3T//v3Q\nx37//l3MtE2p5ufnxWz79u2hrycqb968ETNtjwvnnOvo6Ah1zoGBATHLxNgr34ABwAgFDABGKGAA\nMEIBA4ARChgAjFDAAGCEAgYAI5HNAWsPrPPN7PX09IiZNseY6zOv2sMzfWtSWloqZrn+5w7L90y4\n3t5eMdO2o9R0dXWpeZgtBsPQ5sLPnz+vHltZWSlmhw4dErNEIiFmufAZ27BhQ+hjfWsmuXr1qpj5\nHsoZBt+AAcAIBQwARihgADBCAQOAEQoYAIxQwABgJLIxNG1cR9vizTl9u8DGxkYx+3/dqtI5favA\n0dFRMcv1NdGedNvQ0JCRc2qjWPv378/IOYPSRpx+/vypHnvz5k0xa21tFbMDBw74L8yQ9lnWtrB1\nzrm3b9+KmdY3yWTSf2ER4hswABihgAHACAUMAEYoYAAwQgEDgBEKGACMRDaGpo2MTE5OqseWlJSI\n2ZMnT0KdM9dt3LhRzcM+FfnVq1didu3aNfWc9fX1ah4FbZetdDqtHrtjxw4x055ufOvWLf+F5bCp\nqSk1v3PnjphNT0+L2ZEjR8TsxIkT6jl9T/XONN+9v2XLFjHTdkrTdtzLBL4BA4ARChgAjFDAAGCE\nAgYAIxQwABihgAHASGRjaJqPHz9m4zQ5p6ioSMxqa2vVY7u7u8Xs9OnTobK6ujr1nNkYQ9Nou7w5\n51wqlRIzbdevla6/v1/NtRE8zebNm8VsfHxcPdZ6DM1n3759YtbR0SFm2R5t5RswABihgAHACAUM\nAEYoYAAwQgEDgBEKGACMUMAAYCQrc8DxeFzNta0Xq6qqor6cnNDV1aXm2naVzc3NYqbNP7a1tfkv\nzJD2JFvnnCstLRUzbZvLlS6RSKi59neuPQFYe9+mpib/hRnSnq7tnL7V5qlTp6K+nND4BgwARihg\nADBCAQOAEQoYAIxQwABghAIGACMx35No//XiWGzWOac/onXl255OpwuX++K/ZE2cC7AurMnS/pJ1\nYU2WtuS6BCpgAEB0+BEEABihgAHACAUMAEYoYAAwQgEDgBEKGACMUMAAYIQCBgAjFDAAGPkvQVUf\na4CaW3UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-3MwKSh6FVP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 816
        },
        "outputId": "4bfdaca8-0c7d-453b-a1b1-76228fcc877b"
      },
      "source": [
        "print(__doc__)\n",
        "\n",
        "# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>\n",
        "# License: BSD 3 clause\n",
        "\n",
        "# Standard scientific Python imports\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Import datasets, classifiers and performance metrics\n",
        "from sklearn import datasets, svm, metrics\n",
        "\n",
        "# The digits dataset\n",
        "digits = datasets.load_digits()\n",
        "\n",
        "# The data that we are interested in is made of 8x8 images of digits, let's\n",
        "# have a look at the first 4 images, stored in the `images` attribute of the\n",
        "# dataset.  If we were working from image files, we could load them using\n",
        "# matplotlib.pyplot.imread.  Note that each image must have the same size. For these\n",
        "# images, we know which digit they represent: it is given in the 'target' of\n",
        "# the dataset.\n",
        "images_and_labels = list(zip(digits.images, digits.target))\n",
        "for index, (image, label) in enumerate(images_and_labels[:4]):\n",
        "    plt.subplot(2, 4, index + 1)\n",
        "    plt.axis('off')\n",
        "    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')\n",
        "    plt.title('Training: %i' % label)\n",
        "\n",
        "# To apply a classifier on this data, we need to flatten the image, to\n",
        "# turn the data in a (samples, feature) matrix:\n",
        "n_samples = len(digits.images)\n",
        "data = digits.images.reshape((n_samples, -1))\n",
        "\n",
        "# Create a classifier: a support vector classifier\n",
        "classifier = svm.SVC(gamma=0.001)\n",
        "\n",
        "# We learn the digits on the first half of the digits\n",
        "classifier.fit(data[:n_samples // 2], digits.target[:n_samples // 2])\n",
        "\n",
        "# Now predict the value of the digit on the second half:\n",
        "expected = digits.target[n_samples // 2:]\n",
        "predicted = classifier.predict(data[n_samples // 2:])\n",
        "\n",
        "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
        "      % (classifier, metrics.classification_report(expected, predicted)))\n",
        "print(\"Confusion matrix:\\n%s\" % metrics.confusion_matrix(expected, predicted))\n",
        "\n",
        "images_and_predictions = list(zip(digits.images[n_samples // 2:], predicted))\n",
        "for index, (image, prediction) in enumerate(images_and_predictions[:4]):\n",
        "    plt.subplot(2, 4, index + 5)\n",
        "    plt.axis('off')\n",
        "    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')\n",
        "    plt.title('Prediction: %i' % prediction)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Automatically created module for IPython interactive environment\n",
            "Classification report for classifier SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
            "    decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n",
            "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "    tol=0.001, verbose=False):\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.99      0.99        88\n",
            "           1       0.99      0.97      0.98        91\n",
            "           2       0.99      0.99      0.99        86\n",
            "           3       0.98      0.87      0.92        91\n",
            "           4       0.99      0.96      0.97        92\n",
            "           5       0.95      0.97      0.96        91\n",
            "           6       0.99      0.99      0.99        91\n",
            "           7       0.96      0.99      0.97        89\n",
            "           8       0.94      1.00      0.97        88\n",
            "           9       0.93      0.98      0.95        92\n",
            "\n",
            "    accuracy                           0.97       899\n",
            "   macro avg       0.97      0.97      0.97       899\n",
            "weighted avg       0.97      0.97      0.97       899\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[87  0  0  0  1  0  0  0  0  0]\n",
            " [ 0 88  1  0  0  0  0  0  1  1]\n",
            " [ 0  0 85  1  0  0  0  0  0  0]\n",
            " [ 0  0  0 79  0  3  0  4  5  0]\n",
            " [ 0  0  0  0 88  0  0  0  0  4]\n",
            " [ 0  0  0  0  0 88  1  0  0  2]\n",
            " [ 0  1  0  0  0  0 90  0  0  0]\n",
            " [ 0  0  0  0  0  1  0 88  0  0]\n",
            " [ 0  0  0  0  0  0  0  0 88  0]\n",
            " [ 0  0  0  1  0  1  0  0  0 90]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T8VJxruZK24O2P3GMp1+m4nzPkypO9K9t8PHox9j+gu0vjLBPz6v4Deaf1l5bn6TvSrom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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aA32ARtX6FVQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}