sklearnを用いた主成分分析：基礎編

sklearnによる主成分分析.ipynb

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  "metadata": {
    "colab": {
      "name": "sklearnによる主成分分析.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "uIt0wlAfuy4A",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# はじめに\n",
        "本Notebookではsklearnを用いてPCAによる次元圧縮の例を示したもの。"
      ]
    },
    {
      "metadata": {
        "id": "tACYTt7CGsEj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 共通関数"
      ]
    },
    {
      "metadata": {
        "id": "xxK5-EZ5Gx79",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_scatter(X, plot_range,  pca=None):\n",
        "  fig = plt.figure()\n",
        "  axes = fig.add_subplot(111,aspect='equal')\n",
        "  axes.scatter(X[:,0],X[:,1])\n",
        "  axes.set_xlim(plot_range)\n",
        "  axes.set_ylim(plot_range)\n",
        "  axes.set_xlabel('0-th')\n",
        "  axes.set_ylabel('1-th')\n",
        "  if pca:\n",
        "    axes.quiver(pca.mean_[0], pca.mean_[1], pca.components_[0,0],pca.components_[0,1], color='red', width=0.01, scale=3)\n",
        "    axes.quiver(pca.mean_[0], pca.mean_[1], pca.components_[1,0],pca.components_[1,1], color='blue', width=0.01, scale=3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4v2iSdCDGlUg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# サンプルデータの生成"
      ]
    },
    {
      "metadata": {
        "id": "l3Xk0buOF0Bx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def create_sample_dataset(min_x, max_x, slope, intercept, n_data):\n",
        "  x = np.linspace(min_x, max_x, n_data)\n",
        "  y = slope * x + intercept + np.random.randn(n_data) * 0.1\n",
        "  X = np.vstack([x, y]).T\n",
        "  np.random.shuffle(X)\n",
        "  return X\n",
        "\n",
        "X_train = create_sample_dataset(0.1, 1.0, 0.7, 0.0, 100)\n",
        "X_test = create_sample_dataset(0.1, 1.0, 0.7, 0.0, 100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "V-kgEFyRjyYU",
        "colab_type": "code",
        "outputId": "50b9dde1-8c87-41da-c1c4-67725416a810",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 713
        }
      },
      "cell_type": "code",
      "source": [
        "plot_scatter(X_train, [-0., 1.2])\n",
        "plot_scatter(X_test, [-0., 1.2])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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40dYpe+1EWyf+/b5cuBwT/8mL1o9kpasfA4PD6L4QQsEUp+zfK1VnAxfR1Ru7LM/uyIF3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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YxgkUafqXlgUZakI83Ag32hOj1WB4EmnDmrGOxtd91Uz/irQgI9zuZlqnlnHW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\nHwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "fmPh9PO5wuXe",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 主成分分析\n",
        "skleanのPCAモジュールを利用して主成分分析を実際に行っていく。\n",
        "* コンストラクタのパラメータ、`n_components`は残す主成分の数。指定しない場合は元データの次元数となり、次元圧縮しない動きになる。\n",
        "* 主成分ベクトルはPCAインスタンスの`components_ `属性に格納される。\n",
        "  * 寄与率の大きさによってソートされている。\n",
        "  * ベクトルは正規化済み。\n",
        "* 各主成分の寄与率は`explained_variance_ratio_`に格納される。\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "7xBv10G4GD37",
        "colab_type": "code",
        "outputId": "8d0c56e1-037a-43dc-fe54-bf2daf314ee1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components=2)\n",
        "pca.fit(X_train)\n",
        "\n",
        "def explain_n_th_principal_component(n):\n",
        "  pc_vector = pca.components_[n-1,:]\n",
        "  print(\"== 第\", n, \"主成分 ==\")\n",
        "  print(\"ベクトル成分:\", pc_vector)\n",
        "  print('L2ノルム: ', np.linalg.norm(pc_vector, ord=2))\n",
        "  print('寄与率', pca.explained_variance_ratio_[n-1])\n",
        "\n",
        "explain_n_th_principal_component(1)\n",
        "explain_n_th_principal_component(2)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "== 第 1 主成分 ==\n",
            "ベクトル成分: [0.79483315 0.60682803]\n",
            "L2ノルム:  1.0\n",
            "寄与率 0.9369477796111689\n",
            "== 第 2 主成分 ==\n",
            "ベクトル成分: [-0.60682803  0.79483315]\n",
            "L2ノルム:  1.0\n",
            "寄与率 0.06305222038883108\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Ig9gF-Xw4uw1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "※PCAは共分散行列の固有ベクトルを求める問題と同じなので、興味があれば`np.cov`や`linalg.eig`を使って求めたものと上記が一致するかを確認してもよい。\n"
      ]
    },
    {
      "metadata": {
        "id": "fYaGk16y7rZ1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "主成分ベクトルをプロットしてみる"
      ]
    },
    {
      "metadata": {
        "id": "Nzl48bqpGKtQ",
        "colab_type": "code",
        "outputId": "1a04158d-3054-4ce6-fd1f-8190c1388a32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "cell_type": "code",
      "source": [
        "plot_scatter(X_train, [0., 1.2], pca=pca)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mM9KS/K/UkKuwyP35IFw5/gy/qiG8VWywqsIzlSRFzpfS2trmcDeh19LTk4Tsh8kEXHtt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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "MOaxy6zy8jUS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "データを主成分ベクトルへの射影は、`pca.transform()`関数を使用。\n",
        "元の学習データだけでなく、同じ射影をテストデータにも施すことが可能。"
      ]
    },
    {
      "metadata": {
        "id": "qtDorxVpmSbm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Xd_train = pca.transform(X_train)\n",
        "Xd_test = pca.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PWzynS5SA6b3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "射影結果をプロット（各軸はもとの特徴量ではなく、各主成分ベクトル方向成分であることに注意）\n",
        "\n",
        "テストデータも同じ射影ができているね。"
      ]
    },
    {
      "metadata": {
        "id": "uNKmXjkhMn9C",
        "colab_type": "code",
        "outputId": "f0c387cf-c755-4b47-b191-4c7ead65d217",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 713
        }
      },
      "cell_type": "code",
      "source": [
        "plot_scatter(Xd_train, [-0.8, 0.8])\n",
        "plot_scatter(Xd_test, [-0.8, 0.8])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uXpS/dSzkdpraXBGf8wgVrk8ty+mzdIAcLb9XgwVDWiXBxpOGO7OeZjah9WZX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\nExEJjCFNRCSw/w9V9vFwDkPYOwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QJrOQUl+X7C7s/NMZeP7+mgv24RXstdb3A7D/h3+ggAbCaxRE+/yTP4Z0FCLp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\nxpAmIhIYQ5qISGAMaSIigf1/loPHKnVrq4gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "u59wrHg8CzsY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 次元圧縮"
      ]
    },
    {
      "metadata": {
        "id": "JMO-3i7kC4CW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "もし２つめの主成分の寄与率が低く無視できるなら次元圧縮してもよい。\n",
        "\n",
        "その場合はたとえば射影後のデータの第二主成分部分をゼロに置換する。もしくはPCAのインスタンスをパラメータ`n_component`を指定してもよい。\n",
        "\n",
        "今回は前者のやり方で。"
      ]
    },
    {
      "metadata": {
        "id": "wdJMaCgbEEtU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Xd_train[:, 1] = 0.0\n",
        "Xd_test[:, 1] = 0.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ja9fZoc9Eyks",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "もとの空間に戻す（逆射影）\n",
        "\n",
        "射影の時と同様、テストデータについても可能。"
      ]
    },
    {
      "metadata": {
        "id": "1_dzxbrFE6mu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train_reduce = pca.inverse_transform(Xd_train)\n",
        "X_test_reduce = pca.inverse_transform(Xd_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "I5LdwxHUFQzd",
        "colab_type": "code",
        "outputId": "637f1e92-5b74-482d-da4f-23c003bf7794",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 713
        }
      },
      "cell_type": "code",
      "source": [
        "plot_scatter(X_train_reduce, [-0.2, 1.2])\n",
        "plot_scatter(X_test_reduce, [-0.2, 1.2])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zq2oc/OG8p4yjtRHInDIO+XfeiFlT4rH3aN9V7WZN4e4pRKHGkg4DxXtr+pRw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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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i1/ffHORERHQtWNJhoPfiSPF67YCLI6lVl4Y+jCN1yEgdzce7iRSAJa1gV94g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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "nbi3R6AiFbH-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "第２主成分が抜けたデータが出来上がる。"
      ]
    },
    {
      "metadata": {
        "id": "zCK2Kzh_vI9c",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 参考\n",
        "\n",
        "\n",
        "*   [PythonでPCAを行う方法@Qiita](https://qiita.com/supersaiakujin/items/138c0d8e6511735f1f45)"
      ]
    },
    {
      "metadata": {
        "id": "udDfAknbvXf0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}