historoid/gist_example.ipynb Secret

## gist_example.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Gist_example.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNPoiGPTb4Q6iMdaCNFBEhO",
      "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/gist/historoid/8bc36422b8b5b630b4175a9b1ae8c718/gist_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eIGms9mx8pJr",
        "colab_type": "text"
      },
      "source": [
        "# Gistの一例\n",
        "このGistでは、線形回帰学習のノートブックを使ってGistの機能を示します。  \n",
        "\n",
        "以下の内容は、単なる線形回帰の勉強会で使った内容です。  \n",
        "\n",
        "Gist自体の説明は、https://bdarc.net/github-gist/ で説明しています。そちらを参照してください。\n",
        "\n",
        "## GistによるMarkdownの表示\n",
        "この文章は、Google Colaboratory上でMarkdown形式で書いたものです。  \n",
        "\n",
        "Gistで見ている方は、Markdownそのもののテキストを見ているわけではなく、きちんとHTMLに変換された文章を読んでいます。見出しは大きく表示されていますよね?それがHTMLに変換されていることの証左です。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MpkqAAexGOW8",
        "colab_type": "text"
      },
      "source": [
        "## コードの表示\n",
        "では早速、Gistでコードやグラフがどのように表示されているかを見ていきましょう。\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kV3gIn1zHbQK",
        "colab_type": "text"
      },
      "source": [
        "### 線形回帰\n",
        "今回の学習内容は線形回帰です。\n",
        "\n",
        "scikit-learnを使って、線形回帰の機械学習モデルを実装してみましょう。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "szqpZMgT9gRW",
        "colab_type": "text"
      },
      "source": [
        "#### 線形回帰とは\n",
        "線形回帰は、特徴量とターゲットベクトルの間におおよそ線形の関係があることを仮定する。つまり、特徴量のターゲットベクトルに与える影響（係数（coefficient）、重み（weight）、パラメータとも呼ばれる）が、一定であると仮定する。ここでは、説明をわかりやすくするため、2つだけ特徴量を持つモデルを訓練している。この場合線形モデルは次のようになる。  \n",
        "\n",
        "$$\n",
        "    \\hat{y} = \\hat{\\beta_0} + \\hat{\\beta_1}x_1+\\hat{\\beta_2}x_2+\\varepsilon\n",
        "$$\n",
        "\n",
        "ここで$\\hat{y}$はターゲット、$x_i$は個々の特徴量、$\\hat{\\beta_1}$と$\\hat{\\beta_2}$は、モデルを訓練することで得られる係数だ。$\\varepsilon$はエラー（誤差）を表す。モデルを訓練し終わったら、結果のパラメータを見ることができる。例えば、$\\hat{\\beta_0}$は、バイアス（bias）もしくは切片（intercept）とも呼ばれる値だが、これは`intercept_`で見ることができる。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zye_uCCC8X8U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ライブラリをロード\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.datasets import load_boston"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CD0vbOZT8ojG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# データをロードし、特徴量を2つに制限\n",
        "boston = load_boston()\n",
        "features = boston.data[:,0:2]\n",
        "target = boston.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJS4E9yx9VwV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 線形回帰器を作成\n",
        "regression = LinearRegression()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3WwkM1ya9ZEL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 線形回帰器を訓練\n",
        "model = regression.fit(features, target)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jOE8BdMH9cRp",
        "colab_type": "code",
        "outputId": "ecd2c1e6-da11-4ff8-fd17-670e90f89fd2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# 切片(intercept)を表示\n",
        "model.intercept_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22.485628113468223"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZwQXVFECIEGu",
        "colab_type": "text"
      },
      "source": [
        "上記の`model.intercept_`の返り値を見ることはできていますか?  \n",
        "Gistでは、このような実行結果もきちんと表示されているはずです。\n",
        "\n",
        "一方で、.pyファイルや.ipynbファイルをのまま相手に送っても、この返り値を見ることはできません。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jim99gz8ssxN",
        "colab_type": "text"
      },
      "source": [
        "## グラフの表示\n",
        "Gistでのコードの表示を確認したので、次はテーブルとグラフを確認してみましょう。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yJHLK_LUqhZg",
        "colab_type": "code",
        "outputId": "ab0d9fdc-e8da-4223-a309-506900b01edb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "# Pandasデータフレームに変換\n",
        "import pandas as pd\n",
        "df = pd.DataFrame(boston.data, columns=boston.feature_names)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>501</th>\n",
              "      <td>0.06263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.593</td>\n",
              "      <td>69.1</td>\n",
              "      <td>2.4786</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>391.99</td>\n",
              "      <td>9.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>0.04527</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.120</td>\n",
              "      <td>76.7</td>\n",
              "      <td>2.2875</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>503</th>\n",
              "      <td>0.06076</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.976</td>\n",
              "      <td>91.0</td>\n",
              "      <td>2.1675</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>504</th>\n",
              "      <td>0.10959</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.794</td>\n",
              "      <td>89.3</td>\n",
              "      <td>2.3889</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>393.45</td>\n",
              "      <td>6.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>505</th>\n",
              "      <td>0.04741</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.030</td>\n",
              "      <td>80.8</td>\n",
              "      <td>2.5050</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>7.88</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>506 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        CRIM    ZN  INDUS  CHAS    NOX  ...  RAD    TAX  PTRATIO       B  LSTAT\n",
              "0    0.00632  18.0   2.31   0.0  0.538  ...  1.0  296.0     15.3  396.90   4.98\n",
              "1    0.02731   0.0   7.07   0.0  0.469  ...  2.0  242.0     17.8  396.90   9.14\n",
              "2    0.02729   0.0   7.07   0.0  0.469  ...  2.0  242.0     17.8  392.83   4.03\n",
              "3    0.03237   0.0   2.18   0.0  0.458  ...  3.0  222.0     18.7  394.63   2.94\n",
              "4    0.06905   0.0   2.18   0.0  0.458  ...  3.0  222.0     18.7  396.90   5.33\n",
              "..       ...   ...    ...   ...    ...  ...  ...    ...      ...     ...    ...\n",
              "501  0.06263   0.0  11.93   0.0  0.573  ...  1.0  273.0     21.0  391.99   9.67\n",
              "502  0.04527   0.0  11.93   0.0  0.573  ...  1.0  273.0     21.0  396.90   9.08\n",
              "503  0.06076   0.0  11.93   0.0  0.573  ...  1.0  273.0     21.0  396.90   5.64\n",
              "504  0.10959   0.0  11.93   0.0  0.573  ...  1.0  273.0     21.0  393.45   6.48\n",
              "505  0.04741   0.0  11.93   0.0  0.573  ...  1.0  273.0     21.0  396.90   7.88\n",
              "\n",
              "[506 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZqacXk0lsnHN",
        "colab_type": "text"
      },
      "source": [
        "### データの内容を表形式で表示\n",
        "\n",
        "| 列名 | 説明 |\n",
        "|:----:|:----:|\n",
        "| CRIM | 人口1人あたりの犯罪発生率 |\n",
        "| ZN | 25,000平方フィート以上の居住区画の占める割合 |\n",
        "| INDUS | 小売業以外の商業が占める面積の割合 |\n",
        "| CHAS | チャールズ側によるダミー変数（1:川の周辺、0:それ以外）|\n",
        "| NOX | NOXの濃度 |\n",
        "| RM | 住居の平均部屋数 |\n",
        "| AGE | 1940年より前に建てられた物件の割合 |\n",
        "| DIS | 5つのボストン市の雇用施設からの距離（重み付け済み）|\n",
        "| RAD | 感情高速道路へのアクセスのしやすさ |\n",
        "| TAX | 10,000ドルあたりの不動産税率の総計 |\n",
        "| PTRATIO | 町ごとの児童と教師の比率 |\n",
        "| B | 町ごとの黒人の比率 |\n",
        "| LSTAT | 給与の低い職業に従事する人口の割合 |\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7pdwxPJ_JXA7",
        "colab_type": "text"
      },
      "source": [
        "この段階でPandasのデータフレームと、Markdownによるテーブルが確認できました。\n",
        "\n",
        "Gistでも問題なく表示できていますね。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yz3Eiozjw1TQ",
        "colab_type": "text"
      },
      "source": [
        "### Seabornによる可視化\n",
        "では散布図を描いて、Gistで適切に表示されるかを確認してみましょう。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tcSR1nThwKiE",
        "colab_type": "code",
        "outputId": "ae9ec833-a298-410e-85c8-465f3e7e544d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "source": [
        "# seabornのインポート\n",
        "import seaborn as sns\n",
        "\n",
        "# 可視化\n",
        "sns.jointplot(df['AGE'], boston.target)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7fb194352f98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l7RbgKFRyrlQ",
        "colab_type": "code",
        "outputId": "d3a2c359-8721-48ef-8b70-9cfecbc6a601",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "# 見やすいようにスタイルの変更\n",
        "sns.set_style(\"whitegrid\")\n",
        "# 回帰モデルの可視化\n",
        "sns.regplot(df['RM'], boston.target)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb194a72da0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iMYeR58hJ70s",
        "colab_type": "text"
      },
      "source": [
        "以上です。\n",
        "\n",
        "GistでJupyterノートブックを共有するとどのように見えるかが体験できたと思います。"
      ]
    }
  ]
}