3_1_decision_tree.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/minyoung347/python101/blob/main/3_1_decision_tree.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zsdDphhqjmIP"
      },
      "source": [
        "### 결정 트리 모델의 시각화(Decision Tree Visualization)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "_SabIEOUjmIU",
        "outputId": "1113d382-5fb1-417c-dc72-cc7b771521f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=3, random_state=156)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3, random_state=156)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=3, random_state=156)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# DecisionTree Classifier 생성\n",
        "dt_clf = DecisionTreeClassifier(random_state=156, max_depth=3)\n",
        "\n",
        "# 붓꽃 데이터를 로딩하고, 학습과 테스트 데이터 셋으로 분리\n",
        "iris_data = load_iris()\n",
        "X_train , X_test , y_train , y_test = train_test_split(iris_data.data, iris_data.target,\n",
        "                                                       test_size=0.2,  random_state=11)\n",
        "\n",
        "# DecisionTreeClassifer 학습.\n",
        "dt_clf.fit(X_train , y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Mm4NOdixjmIW"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import export_graphviz\n",
        "\n",
        "# export_graphviz()의 호출 결과로 out_file로 지정된 tree.dot 파일을 생성함.\n",
        "export_graphviz(dt_clf, out_file=\"tree.dot\", class_names=iris_data.target_names , \\\n",
        "feature_names = iris_data.feature_names, impurity=True, filled=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "scrolled": false,
        "id": "10tdaSOpjmIX",
        "outputId": "eee30493-548d-4520-e1cf-b63c6d3e8972",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.43.0 (0)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"558pt\" height=\"433pt\"\n viewBox=\"0.00 0.00 558.00 433.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 429)\">\n<title>Tree</title>\n<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-429 554,-429 554,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<polygon fill=\"#fffdfd\" stroke=\"black\" points=\"279.5,-425 111.5,-425 111.5,-342 279.5,-342 279.5,-425\"/>\n<text text-anchor=\"middle\" x=\"195.5\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal length (cm) &lt;= 2.45</text>\n<text text-anchor=\"middle\" x=\"195.5\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.667</text>\n<text text-anchor=\"middle\" x=\"195.5\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 120</text>\n<text text-anchor=\"middle\" x=\"195.5\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [41, 40, 39]</text>\n<text text-anchor=\"middle\" x=\"195.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = setosa</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<polygon fill=\"#e58139\" stroke=\"black\" points=\"175,-298.5 58,-298.5 58,-230.5 175,-230.5 175,-298.5\"/>\n<text text-anchor=\"middle\" x=\"116.5\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"116.5\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 41</text>\n<text text-anchor=\"middle\" x=\"116.5\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [41, 0, 0]</text>\n<text text-anchor=\"middle\" x=\"116.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = setosa</text>\n</g>\n<!-- 0&#45;&gt;1 -->\n<g id=\"edge1\" class=\"edge\">\n<title>0&#45;&gt;1</title>\n<path fill=\"none\" stroke=\"black\" d=\"M168.09,-341.91C160.49,-330.65 152.23,-318.42 144.59,-307.11\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"147.39,-305 138.89,-298.67 141.59,-308.91 147.39,-305\"/>\n<text text-anchor=\"middle\" x=\"134.14\" y=\"-319.51\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">True</text>\n</g>\n<!-- 2 -->\n<g id=\"node3\" class=\"node\">\n<title>2</title>\n<polygon fill=\"#fafefc\" stroke=\"black\" points=\"356,-306 193,-306 193,-223 356,-223 356,-306\"/>\n<text text-anchor=\"middle\" x=\"274.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal width (cm) &lt;= 1.55</text>\n<text text-anchor=\"middle\" x=\"274.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n<text text-anchor=\"middle\" x=\"274.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 79</text>\n<text text-anchor=\"middle\" x=\"274.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 40, 39]</text>\n<text text-anchor=\"middle\" x=\"274.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n</g>\n<!-- 0&#45;&gt;2 -->\n<g id=\"edge2\" class=\"edge\">\n<title>0&#45;&gt;2</title>\n<path fill=\"none\" stroke=\"black\" d=\"M222.91,-341.91C228.91,-333.01 235.33,-323.51 241.53,-314.33\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"244.44,-316.27 247.14,-306.02 238.64,-312.35 244.44,-316.27\"/>\n<text text-anchor=\"middle\" x=\"251.9\" y=\"-326.86\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\n</g>\n<!-- 3 -->\n<g id=\"node4\" class=\"node\">\n<title>3</title>\n<polygon fill=\"#3ee684\" stroke=\"black\" points=\"266.5,-187 98.5,-187 98.5,-104 266.5,-104 266.5,-187\"/>\n<text text-anchor=\"middle\" x=\"182.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal length (cm) &lt;= 5.25</text>\n<text text-anchor=\"middle\" x=\"182.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.051</text>\n<text text-anchor=\"middle\" x=\"182.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 38</text>\n<text text-anchor=\"middle\" x=\"182.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 37, 1]</text>\n<text text-anchor=\"middle\" x=\"182.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n</g>\n<!-- 2&#45;&gt;3 -->\n<g id=\"edge3\" class=\"edge\">\n<title>2&#45;&gt;3</title>\n<path fill=\"none\" stroke=\"black\" d=\"M242.58,-222.91C235.52,-213.92 227.96,-204.32 220.68,-195.05\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"223.3,-192.72 214.36,-187.02 217.79,-197.05 223.3,-192.72\"/>\n</g>\n<!-- 6 -->\n<g id=\"node7\" class=\"node\">\n<title>6</title>\n<polygon fill=\"#8b49e7\" stroke=\"black\" points=\"448,-187 285,-187 285,-104 448,-104 448,-187\"/>\n<text text-anchor=\"middle\" x=\"366.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal width (cm) &lt;= 1.75</text>\n<text text-anchor=\"middle\" x=\"366.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.136</text>\n<text text-anchor=\"middle\" x=\"366.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 41</text>\n<text text-anchor=\"middle\" x=\"366.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 3, 38]</text>\n<text text-anchor=\"middle\" x=\"366.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n</g>\n<!-- 2&#45;&gt;6 -->\n<g id=\"edge6\" class=\"edge\">\n<title>2&#45;&gt;6</title>\n<path fill=\"none\" stroke=\"black\" d=\"M306.42,-222.91C313.48,-213.92 321.04,-204.32 328.32,-195.05\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"331.21,-197.05 334.64,-187.02 325.7,-192.72 331.21,-197.05\"/>\n</g>\n<!-- 4 -->\n<g id=\"node5\" class=\"node\">\n<title>4</title>\n<polygon fill=\"#39e581\" stroke=\"black\" points=\"121,-68 0,-68 0,0 121,0 121,-68\"/>\n<text text-anchor=\"middle\" x=\"60.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"60.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 37</text>\n<text text-anchor=\"middle\" x=\"60.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 37, 0]</text>\n<text text-anchor=\"middle\" x=\"60.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n</g>\n<!-- 3&#45;&gt;4 -->\n<g id=\"edge4\" class=\"edge\">\n<title>3&#45;&gt;4</title>\n<path fill=\"none\" stroke=\"black\" d=\"M137.07,-103.73C126.6,-94.33 115.48,-84.35 105.05,-74.99\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"107.38,-72.37 97.6,-68.3 102.71,-77.58 107.38,-72.37\"/>\n</g>\n<!-- 5 -->\n<g id=\"node6\" class=\"node\">\n<title>5</title>\n<polygon fill=\"#8139e5\" stroke=\"black\" points=\"252,-68 139,-68 139,0 252,0 252,-68\"/>\n<text text-anchor=\"middle\" x=\"195.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"195.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n<text text-anchor=\"middle\" x=\"195.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 1]</text>\n<text text-anchor=\"middle\" x=\"195.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n</g>\n<!-- 3&#45;&gt;5 -->\n<g id=\"edge5\" class=\"edge\">\n<title>3&#45;&gt;5</title>\n<path fill=\"none\" stroke=\"black\" d=\"M187.34,-103.73C188.33,-95.43 189.37,-86.67 190.36,-78.28\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"193.84,-78.64 191.55,-68.3 186.89,-77.82 193.84,-78.64\"/>\n</g>\n<!-- 7 -->\n<g id=\"node8\" class=\"node\">\n<title>7</title>\n<polygon fill=\"#ffffff\" stroke=\"black\" points=\"415,-68 294,-68 294,0 415,0 415,-68\"/>\n<text text-anchor=\"middle\" x=\"354.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n<text text-anchor=\"middle\" x=\"354.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n<text text-anchor=\"middle\" x=\"354.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 2, 2]</text>\n<text text-anchor=\"middle\" x=\"354.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n</g>\n<!-- 6&#45;&gt;7 -->\n<g id=\"edge7\" class=\"edge\">\n<title>6&#45;&gt;7</title>\n<path fill=\"none\" stroke=\"black\" d=\"M362.03,-103.73C361.12,-95.43 360.16,-86.67 359.24,-78.28\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"362.72,-77.86 358.15,-68.3 355.76,-78.62 362.72,-77.86\"/>\n</g>\n<!-- 8 -->\n<g id=\"node9\" class=\"node\">\n<title>8</title>\n<polygon fill=\"#843ee6\" stroke=\"black\" points=\"550,-68 433,-68 433,0 550,0 550,-68\"/>\n<text text-anchor=\"middle\" x=\"491.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.053</text>\n<text text-anchor=\"middle\" x=\"491.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 37</text>\n<text text-anchor=\"middle\" x=\"491.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1, 36]</text>\n<text text-anchor=\"middle\" x=\"491.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n</g>\n<!-- 6&#45;&gt;8 -->\n<g id=\"edge8\" class=\"edge\">\n<title>6&#45;&gt;8</title>\n<path fill=\"none\" stroke=\"black\" d=\"M413.05,-103.73C423.77,-94.33 435.17,-84.35 445.85,-74.99\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"448.27,-77.52 453.49,-68.3 443.66,-72.26 448.27,-77.52\"/>\n</g>\n</g>\n</svg>\n",
            "text/plain": [
              "<graphviz.sources.Source at 0x79bf4041b6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "import graphviz\n",
        "\n",
        "# 위에서 생성된 tree.dot 파일을 Graphviz 읽어서 Jupyter Notebook상에서 시각화\n",
        "with open(\"tree.dot\") as f:\n",
        "    dot_graph = f.read()\n",
        "graphviz.Source(dot_graph)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "erJ1xw00jmIX",
        "outputId": "24887dbc-bf75-4d83-ec45-e09d7288fd6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature importances:\n",
            "[0.025 0.    0.555 0.42 ]\n",
            "sepal length (cm) : 0.025\n",
            "sepal width (cm) : 0.000\n",
            "petal length (cm) : 0.555\n",
            "petal width (cm) : 0.420\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "\n",
        "# feature importance 추출\n",
        "print(\"Feature importances:\\n{0}\".format(np.round(dt_clf.feature_importances_, 3)))\n",
        "\n",
        "# feature별 importance 매핑\n",
        "for name, value in zip(iris_data.feature_names , dt_clf.feature_importances_):\n",
        "    print('{0} : {1:.3f}'.format(name, value))\n",
        "\n",
        "# feature importance를 column 별로 시각화 하기\n",
        "sns.barplot(x=dt_clf.feature_importances_ , y=iris_data.feature_names)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rkW1Y6bFjmIY"
      },
      "source": [
        "### 결정 트리(Decision TREE) 과적합(Overfitting)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "3vFLxsiLjmIY",
        "outputId": "bd9ffab8-f105-4a4c-f0eb-861fd5e4d8dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x79bf08949150>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.datasets import make_classification\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "plt.title(\"3 Class values with 2 Features Sample data creation\")\n",
        "\n",
        "# 2차원 시각화를 위해서 feature는 2개, 결정값 클래스는 3가지 유형의 classification 샘플 데이터 생성.\n",
        "X_features, y_labels = make_classification(n_features=2, n_redundant=0, n_informative=2,\n",
        "                             n_classes=3, n_clusters_per_class=1,random_state=0)\n",
        "\n",
        "# plot 형태로 2개의 feature로 2차원 좌표 시각화, 각 클래스값은 다른 색깔로 표시됨.\n",
        "plt.scatter(X_features[:, 0], X_features[:, 1], marker='o', c=y_labels, s=25, cmap='rainbow', edgecolor='k')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "pT4iktygjmIZ"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "# Classifier의 Decision Boundary를 시각화 하는 함수\n",
        "def visualize_boundary(model, X, y):\n",
        "    fig,ax = plt.subplots()\n",
        "\n",
        "    # 학습 데이타 scatter plot으로 나타내기\n",
        "    ax.scatter(X[:, 0], X[:, 1], c=y, s=25, cmap='rainbow', edgecolor='k',\n",
        "               clim=(y.min(), y.max()), zorder=3)\n",
        "    ax.axis('tight')\n",
        "    ax.axis('off')\n",
        "    xlim_start , xlim_end = ax.get_xlim()\n",
        "    ylim_start , ylim_end = ax.get_ylim()\n",
        "\n",
        "    # 호출 파라미터로 들어온 training 데이타로 model 학습 .\n",
        "    model.fit(X, y)\n",
        "    # meshgrid 형태인 모든 좌표값으로 예측 수행.\n",
        "    xx, yy = np.meshgrid(np.linspace(xlim_start,xlim_end, num=200),np.linspace(ylim_start,ylim_end, num=200))\n",
        "    Z = model.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n",
        "\n",
        "    # contourf() 를 이용하여 class boundary 를 visualization 수행.\n",
        "    n_classes = len(np.unique(y))\n",
        "    contours = ax.contourf(xx, yy, Z, alpha=0.3,\n",
        "                           levels=np.arange(n_classes + 1) - 0.5,\n",
        "                           cmap='rainbow', clim=(y.min(), y.max()),\n",
        "                           zorder=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "ncoG-DuLjmIa",
        "outputId": "3e870b01-7905-4e3a-80ac-aa2be72f4973",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# 특정한 트리 생성 제약없는 결정 트리의 Decsion Boundary 시각화.\n",
        "dt_clf = DecisionTreeClassifier(random_state=156).fit(X_features, y_labels)\n",
        "visualize_boundary(dt_clf, X_features, y_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "scrolled": true,
        "id": "x5XaUcCZjmIa",
        "outputId": "92399417-666f-47df-d6ad-dd00ee69265e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# min_samples_leaf=6 으로 트리 생성 조건을 제약한 Decision Boundary 시각화\n",
        "dt_clf = DecisionTreeClassifier(min_samples_leaf=6, random_state=156).fit(X_features, y_labels)\n",
        "visualize_boundary(dt_clf, X_features, y_labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kGbC_7FqjmIb"
      },
      "source": [
        "### 결정 트리 실습 - Human Activity Recognition"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "6LxpifHhJ4TT"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_new_feature_name_df(old_feature_name_df):\n",
        "    feature_dup_df = pd.DataFrame(data=old_feature_name_df.groupby('column_name').cumcount(),\n",
        "                                  columns=['dup_cnt'])\n",
        "    feature_dup_df = feature_dup_df.reset_index()\n",
        "    new_feature_name_df = pd.merge(old_feature_name_df.reset_index(), feature_dup_df, how='outer')\n",
        "    new_feature_name_df['column_name'] = new_feature_name_df[['column_name', 'dup_cnt']].apply(lambda x : x[0]+'_'+str(x[1])\n",
        "                                                                                         if x[1] >0 else x[0] ,  axis=1)\n",
        "    new_feature_name_df = new_feature_name_df.drop(['index'], axis=1)\n",
        "    return new_feature_name_df"
      ],
      "metadata": {
        "id": "4Ozlx4Y_KD-E"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_human_dataset( ):\n",
        "\n",
        "    # 각 데이터 파일들은 공백으로 분리되어 있으므로 read_csv에서 공백 문자를 sep으로 할당.\n",
        "    url = \"https://drive.google.com/file/d/1DmaNsCHpHyJ3gsv8zL-GkKYTPikabutU/view?usp=drive_link\"\n",
        "    path='https://drive.google.com/uc?id='+url.split('/')[-2]\n",
        "    feature_name_df = pd.read_csv(path,sep='\\s+',\n",
        "                        header=None,names=['column_index','column_name'])\n",
        "\n",
        "    # 중복된 피처명을 수정하는 get_new_feature_name_df()를 이용, 신규 피처명 DataFrame생성.\n",
        "    new_feature_name_df = get_new_feature_name_df(feature_name_df)\n",
        "\n",
        "    # DataFrame에 피처명을 컬럼으로 부여하기 위해 리스트 객체로 다시 변환\n",
        "    feature_name = new_feature_name_df.iloc[:, 1].values.tolist()\n",
        "\n",
        "    # 학습 피처 데이터 셋과 테스트 피처 데이터을 DataFrame으로 로딩. 컬럼명은 feature_name 적용\n",
        "    # X_train = pd.read_csv('./human_activity/train/X_train.txt',sep='\\s+', names=feature_name )\n",
        "    # X_test = pd.read_csv('./human_activity/test/X_test.txt',sep='\\s+', names=feature_name)\n",
        "\n",
        "    url = \"https://drive.google.com/file/d/1MsNy3K0kf89q4pS-cKMDBQIlBtZFQD11/view?usp=drive_link\"\n",
        "    path='https://drive.google.com/uc?id='+url.split('/')[-2]\n",
        "    X_train = pd.read_csv(path ,sep='\\s+', names=feature_name )\n",
        "    url = \"https://drive.google.com/file/d/1S6zfRS0sg2ip46IsSDLOomXYLfyrxGmr/view?usp=drive_link\"\n",
        "    path='https://drive.google.com/uc?id='+url.split('/')[-2]\n",
        "    X_test = pd.read_csv(path,sep='\\s+', names=feature_name)\n",
        "\n",
        "    # 학습 레이블과 테스트 레이블 데이터을 DataFrame으로 로딩하고 컬럼명은 action으로 부여\n",
        "    # y_train = pd.read_csv('./human_activity/train/y_train.txt',sep='\\s+',header=None,names=['action'])\n",
        "    # y_test = pd.read_csv('./human_activity/test/y_test.txt',sep='\\s+',header=None,names=['action'])\n",
        "\n",
        "    url = \"https://drive.google.com/file/d/1y2LRevjtT6lvDcvcMfVKBNRcevqzkK7X/view?usp=drive_link\"\n",
        "    path='https://drive.google.com/uc?id='+url.split('/')[-2]\n",
        "    y_train = pd.read_csv(path,sep='\\s+',header=None,names=['action'])\n",
        "    url = \"https://drive.google.com/file/d/1ywc2mKIJJQfebjP8d3GCh3HDD_egcy2-/view?usp=drive_link\"\n",
        "    path='https://drive.google.com/uc?id='+url.split('/')[-2]\n",
        "    y_test = pd.read_csv(path,sep='\\s+',header=None,names=['action'])\n",
        "\n",
        "    # 로드된 학습/테스트용 DataFrame을 모두 반환\n",
        "    return X_train, X_test, y_train, y_test\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = get_human_dataset()"
      ],
      "metadata": {
        "id": "197ygWAoJ1kz"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "scrolled": true,
        "id": "FL64v7NdjmIb",
        "outputId": "29e12da3-8864-4fba-ec80-9ae0cf1596d9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "전체 피처명에서 10개만 추출: ['tBodyAcc-mean()-X', 'tBodyAcc-mean()-Y', 'tBodyAcc-mean()-Z', 'tBodyAcc-std()-X', 'tBodyAcc-std()-Y', 'tBodyAcc-std()-Z', 'tBodyAcc-mad()-X', 'tBodyAcc-mad()-Y', 'tBodyAcc-mad()-Z', 'tBodyAcc-max()-X']\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# features.txt 파일에는 피처 이름 index와 피처명이 공백으로 분리되어 있음. 이를 DataFrame으로 로드.\n",
        "url=\"https://drive.google.com/file/d/1DmaNsCHpHyJ3gsv8zL-GkKYTPikabutU/view?usp=drive_link\" #구글드라이브 csv파일 이미지 주소\n",
        "path='https://drive.google.com/uc?id='+url.split('/')[-2]\n",
        "feature_name_df = pd.read_csv(path, sep='\\s+', header=None,names=['column_index','column_name'])\n",
        "\n",
        "# 피처명 index를 제거하고, 피처명만 리스트 객체로 생성한 뒤 샘플로 10개만 추출\n",
        "feature_name = feature_name_df.iloc[:, 1].values.tolist()\n",
        "print('전체 피처명에서 10개만 추출:', feature_name[:10])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sijhse3AjmIb"
      },
      "source": [
        "**중복된 피처명을 확인**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dAwjfAzojmIb",
        "outputId": "17093f7e-cd9f-4839-edcc-751051b2d3a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "column_index    42\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              column_index\n",
              "column_name                               \n",
              "fBodyAcc-bandsEnergy()-1,16              3\n",
              "fBodyAcc-bandsEnergy()-1,24              3\n",
              "fBodyAcc-bandsEnergy()-1,8               3\n",
              "fBodyAcc-bandsEnergy()-17,24             3\n",
              "fBodyAcc-bandsEnergy()-17,32             3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-365cacea-9ba2-4c3e-af01-a0bde51a9327\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>column_index</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>column_name</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fBodyAcc-bandsEnergy()-1,16</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fBodyAcc-bandsEnergy()-1,24</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fBodyAcc-bandsEnergy()-1,8</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fBodyAcc-bandsEnergy()-17,24</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fBodyAcc-bandsEnergy()-17,32</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-365cacea-9ba2-4c3e-af01-a0bde51a9327')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-365cacea-9ba2-4c3e-af01-a0bde51a9327 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-365cacea-9ba2-4c3e-af01-a0bde51a9327');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "feature_dup_df = feature_name_df.groupby('column_name').count()\n",
        "print(feature_dup_df[feature_dup_df['column_index'] > 1].count())\n",
        "feature_dup_df[feature_dup_df['column_index'] > 1].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mHSnChG0jmIc"
      },
      "source": [
        "**원본 데이터에 중복된 Feature 명으로 인하여 신규 버전의 Pandas에서 Duplicate name 에러를 발생.**  \n",
        "**중복 feature명에 대해서 원본 feature 명에 '_1(또는2)'를 추가로 부여하는 함수인 get_new_feature_name_df() 생성**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FGI2y7AbjmIc"
      },
      "outputs": [],
      "source": [
        "def get_new_feature_name_df(old_feature_name_df):\n",
        "    feature_dup_df = pd.DataFrame(data=old_feature_name_df.groupby('column_name').cumcount(),\n",
        "                                  columns=['dup_cnt'])\n",
        "    feature_dup_df = feature_dup_df.reset_index()\n",
        "    new_feature_name_df = pd.merge(old_feature_name_df.reset_index(), feature_dup_df, how='outer')\n",
        "    new_feature_name_df['column_name'] = new_feature_name_df[['column_name', 'dup_cnt']].apply(lambda x : x[0]+'_'+str(x[1])\n",
        "                                                                                         if x[1] >0 else x[0] ,  axis=1)\n",
        "    new_feature_name_df = new_feature_name_df.drop(['index'], axis=1)\n",
        "    return new_feature_name_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xrl9RYDsjmIc"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "def get_human_dataset( ):\n",
        "\n",
        "    # 각 데이터 파일들은 공백으로 분리되어 있으므로 read_csv에서 공백 문자를 sep으로 할당.\n",
        "    url = \"https://drive.google.com/file/d/1wfoT_FoAnxRTij1eV6uih6AEVfhkaDca/view?usp=drive_link\"\n",
        "    path='https://drive.google.com/uc?id='+url.split('/')[-2]\n",
        "    feature_name_df = pd.read_csv(path,sep='\\s+',\n",
        "                        header=None,names=['column_index','column_name'])\n",
        "\n",
        "    # 중복된 피처명을 수정하는 get_new_feature_name_df()를 이용, 신규 피처명 DataFrame생성.\n",
        "    new_feature_name_df = get_new_feature_name_df(feature_name_df)\n",
        "\n",
        "    # DataFrame에 피처명을 컬럼으로 부여하기 위해 리스트 객체로 다시 변환\n",
        "    feature_name = new_feature_name_df.iloc[:, 1].values.tolist()\n",
        "\n",
        "    # 학습 피처 데이터 셋과 테스트 피처 데이터을 DataFrame으로 로딩. 컬럼명은 feature_name 적용\n",
        "    # X_train = pd.read_csv('./human_activity/train/X_train.txt',sep='\\s+', names=feature_name )\n",
        "    # X_test = pd.read_csv('./human_activity/test/X_test.txt',sep='\\s+', names=feature_name)\n",
        "\n",
        "    url = \"https://drive.google.com/file/d/1qa1nCMMLQz14AfX0OMHvj4za4W-uFRPH/view?usp=drive_link\"\n",
        "    path='https://drive.google.com/uc?id='+url.split('/')[-2]\n",
        "    X_train = pd.read_csv(path ,sep='\\s+', names=feature_name )\n",
        "    url = \"https://drive.google.com/file/d/1BH7QUKxdOBN8uKLSPH0olb10NSVCjbqA/view?usp=drive_link\"\n",
        "    path='https://drive.google.com/uc?id='+url.split('/')[-2]\n",
        "    X_test = pd.read_csv(path,sep='\\s+', names=feature_name)\n",
        "\n",
        "    # 학습 레이블과 테스트 레이블 데이터을 DataFrame으로 로딩하고 컬럼명은 action으로 부여\n",
        "    # y_train = pd.read_csv('./human_activity/train/y_train.txt',sep='\\s+',header=None,names=['action'])\n",
        "    # y_test = pd.read_csv('./human_activity/test/y_test.txt',sep='\\s+',header=None,names=['action'])\n",
        "\n",
        "    url = \"https://drive.google.com/file/d/1WuUH90HnSQ8UHXgY6cjURhkUZHUNvXDF/view?usp=drive_link\"\n",
        "    path='https://drive.google.com/uc?id='+url.split('/')[-2]\n",
        "    y_train = pd.read_csv(path,sep='\\s+',header=None,names=['action'])\n",
        "    url = \"https://drive.google.com/file/d/15633XhAKo1HUI6O9ijjqRCjYb0Nw3D5h/view?usp=drive_link\"\n",
        "    path='https://drive.google.com/uc?id='+url.split('/')[-2]\n",
        "    y_test = pd.read_csv(path,sep='\\s+',header=None,names=['action'])\n",
        "\n",
        "    # 로드된 학습/테스트용 DataFrame을 모두 반환\n",
        "    return X_train, X_test, y_train, y_test\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = get_human_dataset()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fcHIXmqXjmIc"
      },
      "outputs": [],
      "source": [
        "print('## 학습 피처 데이터셋 info()')\n",
        "print(X_train.info())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "F8R1_7AhjmIc"
      },
      "outputs": [],
      "source": [
        "print(y_train['action'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dU4uQdj4jmId"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# 예제 반복 시 마다 동일한 예측 결과 도출을 위해 random_state 설정\n",
        "dt_clf = DecisionTreeClassifier(random_state=156)\n",
        "dt_clf.fit(X_train , y_train)\n",
        "pred = dt_clf.predict(X_test)\n",
        "accuracy = accuracy_score(y_test , pred)\n",
        "print('결정 트리 예측 정확도: {0:.4f}'.format(accuracy))\n",
        "\n",
        "# DecisionTreeClassifier의 하이퍼 파라미터 추출\n",
        "print('DecisionTreeClassifier 기본 하이퍼 파라미터:\\n', dt_clf.get_params())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KtFH23uejmId"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "params = {\n",
        "    'max_depth' : [ 6, 8 ,10, 12, 16 ,20, 24]\n",
        "}\n",
        "\n",
        "grid_cv = GridSearchCV(dt_clf, param_grid=params, scoring='accuracy', cv=5, verbose=1 )\n",
        "grid_cv.fit(X_train , y_train)\n",
        "print('GridSearchCV 최고 평균 정확도 수치:{0:.4f}'.format(grid_cv.best_score_))\n",
        "print('GridSearchCV 최적 하이퍼 파라미터:', grid_cv.best_params_)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tQ4V-fk-jmId"
      },
      "outputs": [],
      "source": [
        "# GridSearchCV객체의 cv_results_ 속성을 DataFrame으로 생성.\n",
        "cv_results_df = pd.DataFrame(grid_cv.cv_results_)\n",
        "\n",
        "# max_depth 파라미터 값과 그때의 테스트(Evaluation)셋, 학습 데이터 셋의 정확도 수치 추출\n",
        "cv_results_df[['param_max_depth', 'mean_test_score']]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VTQDEEMojmId"
      },
      "outputs": [],
      "source": [
        "max_depths = [ 6, 8 ,10, 12, 16 ,20, 24]\n",
        "# max_depth 값을 변화 시키면서 그때마다 학습과 테스트 셋에서의 예측 성능 측정\n",
        "for depth in max_depths:\n",
        "    dt_clf = DecisionTreeClassifier(max_depth=depth, min_samples_split=16, random_state=156)\n",
        "    dt_clf.fit(X_train , y_train)\n",
        "    pred = dt_clf.predict(X_test)\n",
        "    accuracy = accuracy_score(y_test , pred)\n",
        "    print('max_depth = {0} 정확도: {1:.4f}'.format(depth , accuracy))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qa7I8WnBjmId"
      },
      "outputs": [],
      "source": [
        "params = {\n",
        "    'max_depth' : [ 8 , 12, 16 ,20],\n",
        "    'min_samples_split' : [16, 24],\n",
        "}\n",
        "\n",
        "grid_cv = GridSearchCV(dt_clf, param_grid=params, scoring='accuracy', cv=5, verbose=1 )\n",
        "grid_cv.fit(X_train , y_train)\n",
        "print('GridSearchCV 최고 평균 정확도 수치: {0:.4f}'.format(grid_cv.best_score_))\n",
        "print('GridSearchCV 최적 하이퍼 파라미터:', grid_cv.best_params_)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "heuE4wQ2jmId"
      },
      "outputs": [],
      "source": [
        "best_df_clf = grid_cv.best_estimator_\n",
        "pred1 = best_df_clf.predict(X_test)\n",
        "accuracy = accuracy_score(y_test , pred1)\n",
        "print('결정 트리 예측 정확도:{0:.4f}'.format(accuracy))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MRM5fecjjmIe"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "ftr_importances_values = best_df_clf.feature_importances_\n",
        "# Top 중요도로 정렬을 쉽게 하고, 시본(Seaborn)의 막대그래프로 쉽게 표현하기 위해 Series변환\n",
        "ftr_importances = pd.Series(ftr_importances_values, index=X_train.columns  )\n",
        "# 중요도값 순으로 Series를 정렬\n",
        "ftr_top20 = ftr_importances.sort_values(ascending=False)[:20]\n",
        "plt.figure(figsize=(8,6))\n",
        "plt.title('Feature importances Top 20')\n",
        "sns.barplot(x=ftr_top20 , y = ftr_top20.index)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vVnL556-jmIe"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.9.7"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}