sklearn_quiz.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wqlw5ktcykfi"
      },
      "source": [
        "# sklearnの確認問題"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "YBUtM5k_y5Md"
      },
      "source": [
        "## 必要モジュールのインポート\n",
        "\n",
        "この問題で使うモジュールをインポートします。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "UCgaMzcYFTtW"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dp2rHHDHzZd0"
      },
      "source": [
        "## 前準備\n",
        "\n",
        "\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "2j_gL_RC1_8_"
      },
      "source": [
        "### データの読み取り\n",
        "scikit-learnから提供される`iris`というデータセットを読み込みます。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "YKJY4Q9R0aB7"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "iris = load_iris()\n",
        "\n",
        "#説明変数の読み取り\n",
        "data = iris.data\n",
        "feature_names = iris.feature_names\n",
        "df_data = pd.DataFrame(data=data, columns=feature_names)\n",
        "\n",
        "#目的変数の読み取り\n",
        "target = iris.target\n",
        "target_names = iris.target_names\n",
        "df_target = pd.DataFrame(data=target, columns=['target'])"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "hfBraFA414qg"
      },
      "source": [
        "### 問1. 説明変数の把握"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "1.1. `df_data` の先頭五行を表示してください。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "T7StoNXU2iXm",
        "outputId": "0fedad15-b679-44d2-b9c7-d3bf12198c40"
      },
      "outputs": [
        {
          "data": {
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              "<div>\n",
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              "    }\n",
              "\n",
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              "    }\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>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
              "0                5.1               3.5                1.4               0.2\n",
              "1                4.9               3.0                1.4               0.2\n",
              "2                4.7               3.2                1.3               0.2\n",
              "3                4.6               3.1                1.5               0.2\n",
              "4                5.0               3.6                1.4               0.2"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_data.head()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "1.2. `df_data` の行数と列数を表示しましょう。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zdx6hnA026SC",
        "outputId": "bfd5bff8-4a4d-4a8d-f8a4-a5dae9e2e82f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(150, 4)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_data.shape"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "lJ-3Jg-k6QSx"
      },
      "source": [
        "1.3. `df_data` の各列の `mean`, `count`, `std` などの基本特徴量を表示しましょう。\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "mDKbvknP6arO",
        "outputId": "9b0dc5de-a88d-49e4-b2ba-b325ef698519"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.057333</td>\n",
              "      <td>3.758000</td>\n",
              "      <td>1.199333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.435866</td>\n",
              "      <td>1.765298</td>\n",
              "      <td>0.762238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
              "count         150.000000        150.000000         150.000000   \n",
              "mean            5.843333          3.057333           3.758000   \n",
              "std             0.828066          0.435866           1.765298   \n",
              "min             4.300000          2.000000           1.000000   \n",
              "25%             5.100000          2.800000           1.600000   \n",
              "50%             5.800000          3.000000           4.350000   \n",
              "75%             6.400000          3.300000           5.100000   \n",
              "max             7.900000          4.400000           6.900000   \n",
              "\n",
              "       petal width (cm)  \n",
              "count        150.000000  \n",
              "mean           1.199333  \n",
              "std            0.762238  \n",
              "min            0.100000  \n",
              "25%            0.300000  \n",
              "50%            1.300000  \n",
              "75%            1.800000  \n",
              "max            2.500000  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_data.describe()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "1.4. `df_data` に欠損値がないかを確認しましょう。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DqRq3xhB61Sx",
        "outputId": "21e8de9b-478b-4a88-c17e-b2d335c98fc6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "sepal length (cm)    0\n",
              "sepal width (cm)     0\n",
              "petal length (cm)    0\n",
              "petal width (cm)     0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_data.isnull().sum()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "R3ZD__9G7FeJ"
      },
      "source": [
        "### 問2. 目的変数の把握\n",
        "\n",
        "2.1. `df_target` のうち、ランダムに 10 行表示しましょう。\n",
        "\n",
        "目的変数の数字はそれぞれ、\n",
        "\n",
        "- 0 が `setosa`（ヒオウギアヤメ）\n",
        "- 1 が `versicolor`（ブルーフラッグ）\n",
        "- 2 が `virginica`（バージニカ）\n",
        "\n",
        "を指しています。\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_CDs0X1Y8YX8",
        "outputId": "d359aa9d-1044-4eb9-b31c-85f9b79ab99b"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     target\n",
              "94        1\n",
              "112       2\n",
              "34        0\n",
              "81        1\n",
              "139       2\n",
              "69        1\n",
              "75        1\n",
              "16        0\n",
              "22        0\n",
              "99        1"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_target.sample(n=10)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "XPik8k4I2_Zz"
      },
      "source": [
        "### 問3. データの可視化（Extra）\n",
        "\n",
        "3.1. x 軸を petal length (cm)、y 軸を petal width (cm)にして散布図を作成しましょう。 ただし、目的変数に応じて点の色と形を変更してください。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "TnUb1cRZ5ajf",
        "outputId": "ba5a4fb4-15c2-4dc3-a5d4-a3907bff2a51"
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      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "merge_data = pd.merge(df_data, df_target, left_index=True, right_index=True)\n",
        "\n",
        "setosa = merge_data[merge_data[\"target\"] == 0]\n",
        "versicolor = merge_data[merge_data[\"target\"] == 1]\n",
        "virginica = merge_data[merge_data[\"target\"] == 2]\n",
        "\n",
        "plt.scatter(setosa[\"petal length (cm)\"], setosa[\"petal width (cm)\"], label = \"setosa\", color = \"red\")\n",
        "plt.scatter(versicolor[\"petal length (cm)\"], versicolor[\"petal width (cm)\"], label = \"versicolor\", color = \"green\", marker = \"+\")\n",
        "plt.scatter(virginica[\"petal length (cm)\"], virginica[\"petal width (cm)\"], label = \"virginica\", color = \"blue\", marker = \"x\")\n",
        "\n",
        "plt.title(\"Iris Plats Database\")\n",
        "plt.xlabel(\"petal length(cm)\")\n",
        "plt.ylabel(\"petal width(cm)\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "sjDOl12S86VJ"
      },
      "source": [
        "## 機械学習\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "OZYFySxQ9BrG"
      },
      "source": [
        "### 問4. データの分割\n",
        "\n",
        "4.1. iris データを 4:1 に分割して、80% を学習データとして 20% をテストデータとしてください。\n",
        "\n",
        "ただし、分割した後の変数名は以下の通りにしてください。\n",
        "\n",
        "| 変数名  |      内容      |\n",
        "| :-----: | :------------: |\n",
        "| x_train | 学習用の説明変数 |\n",
        "| x_test | テスト用の説明変数 |\n",
        "| y_train | 学習用の目的変数 |\n",
        "| y_test | テスト用の目的変数 |\n",
        "\n",
        "学習データでモデルの学習を行い、テストデータでモデルの評価を行います。\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "KZfVWvll9T-X"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(df_data, df_target, test_size=0.2,random_state=0 )"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "Ce6m5w8K-Zui"
      },
      "source": [
        "4.2. 本当に4:1に分割できているか確認しましょう。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X4dBgVxV-dz9",
        "outputId": "c74c6df3-1d92-4ef8-b7ca-53e779e61ad5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "x_train :  (120, 4)\n",
            "x_test :  (30, 4)\n",
            "y_train :  (120, 1)\n",
            "y_test :  (30, 1)\n"
          ]
        }
      ],
      "source": [
        "print(\"x_train : \" ,str(x_train.shape))\n",
        "print(\"x_test : \" , str(x_test.shape))\n",
        "print(\"y_train : \" , str(y_train.shape))\n",
        "print(\"y_test : \" , str(y_test.shape))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "psKYYN9_-jTI"
      },
      "source": [
        "### 問5. 学習\n",
        "\n",
        "5.1. RandamForest の学習モデルのインスタンスを生成しましょう。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "tQGdKQsh_HHe"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "study_model = RandomForestClassifier()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "OseolTqIGfsc"
      },
      "source": [
        "5.2. `x_train` と `y_train` を用いて、モデルを学習させましょう。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "id": "x7ePCmqnJ6Ic",
        "outputId": "cfad6048-c06f-40f2-846c-cafc73b7168c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/kanato/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return fit_method(estimator, *args, **kwargs)\n"
          ]
        },
        {
          "data": {
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            ],
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "study_model.fit(x_train, y_train)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "5cKU0YfKIUTQ"
      },
      "source": [
        "### 問6. 推論\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "iHjsUA3K3MeI"
      },
      "source": [
        "6.1. 学習したモデルに `x_test` を入力して予測をしましょう。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "RUgEb9q-Idbb"
      },
      "outputs": [],
      "source": [
        "predict = study_model.predict(x_test)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "c1ZE3GGx3Tal"
      },
      "source": [
        "6.2. 予測結果と `y_test` に対して、`accuracy` を計算することでモデルを評価しましょう。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TTI2DqDlIjhP",
        "outputId": "9b0c755f-652e-4017-fd29-382369aec2b3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test, predict)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "実は、iris データセットはかなり簡単な分布をしているので、正解率はほぼ 100% になったと思います。"
      ]
    }
  ],
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