5-3.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zp6fW8MP-mrO"
      },
      "source": [
        "# 트리의 앙상블"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pv1IwHmU-mrU"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/5-3.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dIaIAizcRSG-"
      },
      "source": [
        "## 랜덤포레스트"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ioJUlZ0M_uSZ"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "wine = pd.read_csv('https://bit.ly/wine_csv_data')\n",
        "\n",
        "data = wine[['alcohol', 'sugar', 'pH']].to_numpy()\n",
        "target = wine['class'].to_numpy()\n",
        "\n",
        "train_input, test_input, train_target, test_target = train_test_split(data, target, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JDKQudr7_8nu",
        "outputId": "b6625aa7-2118-4543-bdcd-d678e84576e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9973541965122431 0.8905151032797809\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_validate\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf = RandomForestClassifier(n_jobs=-1, random_state=42)\n",
        "scores = cross_validate(rf, train_input, train_target, return_train_score=True, n_jobs=-1)\n",
        "\n",
        "print(np.mean(scores['train_score']), np.mean(scores['test_score']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYDbzXNLG8fK",
        "outputId": "17bddd3a-4de6-41c8-9135-af164b021f55"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.23167441 0.50039841 0.26792718]\n"
          ]
        }
      ],
      "source": [
        "rf.fit(train_input, train_target)\n",
        "print(rf.feature_importances_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oMc06S1Fa_A-",
        "outputId": "dea1c2c9-c2cd-452e-f3d9-a24b678f02d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8934000384837406\n"
          ]
        }
      ],
      "source": [
        "rf = RandomForestClassifier(oob_score=True, n_jobs=-1, random_state=42)\n",
        "\n",
        "rf.fit(train_input, train_target)\n",
        "print(rf.oob_score_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KdrVoeQZRU14"
      },
      "source": [
        "## 엑스트라트리"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "noMLdywdOGrE",
        "outputId": "07e18f2b-bb89-46fe-cec2-bf2f43f111b0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9974503966084433 0.8887848893166506\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "\n",
        "et = ExtraTreesClassifier(n_jobs=-1, random_state=42)\n",
        "scores = cross_validate(et, train_input, train_target, return_train_score=True, n_jobs=-1)\n",
        "\n",
        "print(np.mean(scores['train_score']), np.mean(scores['test_score']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HnB0_mBqfcXL",
        "outputId": "d4fde47e-766d-484a-b1a4-a982db8ccbb0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.20183568 0.52242907 0.27573525]\n"
          ]
        }
      ],
      "source": [
        "et.fit(train_input, train_target)\n",
        "print(et.feature_importances_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "csKxnaxeRX8s"
      },
      "source": [
        "## 그레이디언트 부스팅"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_IlNEFkaNsoG",
        "outputId": "9b4b36e3-5b1c-412e-e6c1-d039c389f3e5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8881086892152563 0.8720430147331015\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "gb = GradientBoostingClassifier(random_state=42)\n",
        "scores = cross_validate(gb, train_input, train_target, return_train_score=True, n_jobs=-1)\n",
        "\n",
        "print(np.mean(scores['train_score']), np.mean(scores['test_score']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pNpeS8EWpeEi",
        "outputId": "69f7971c-5011-4b03-a867-4284cb3336d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9464595437171814 0.8780082549788999\n"
          ]
        }
      ],
      "source": [
        "gb = GradientBoostingClassifier(n_estimators=500, learning_rate=0.2, random_state=42)\n",
        "scores = cross_validate(gb, train_input, train_target, return_train_score=True, n_jobs=-1)\n",
        "\n",
        "print(np.mean(scores['train_score']), np.mean(scores['test_score']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qD6iWVsGqCAE",
        "outputId": "175b9cf4-5c19-45d4-a622-d84f1f8baa40"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.15887763 0.6799705  0.16115187]\n"
          ]
        }
      ],
      "source": [
        "gb.fit(train_input, train_target)\n",
        "print(gb.feature_importances_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BthW_II9RbLa"
      },
      "source": [
        "## 히스토그램 기반 부스팅"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_3Ct_NNWQbdA",
        "outputId": "2bccc04e-09d7-407a-95bb-9314b3b1ae23"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9321723946453317 0.8801241948619236\n"
          ]
        }
      ],
      "source": [
        "# 사이킷런 1.0 버전 아래에서는 다음 라인의 주석을 해제하고 실행하세요.\n",
        "# from sklearn.experimental import enable_hist_gradient_boosting\n",
        "from sklearn.ensemble import HistGradientBoostingClassifier\n",
        "\n",
        "hgb = HistGradientBoostingClassifier(random_state=42)\n",
        "scores = cross_validate(hgb, train_input, train_target, return_train_score=True, n_jobs=-1)\n",
        "\n",
        "print(np.mean(scores['train_score']), np.mean(scores['test_score']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TvlB0GMTS3hn",
        "outputId": "2b446a17-5593-4f19-bc59-4b9a4d908379"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.08876275 0.23438522 0.08027708]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.inspection import permutation_importance\n",
        "\n",
        "hgb.fit(train_input, train_target)\n",
        "result = permutation_importance(hgb, train_input, train_target, n_repeats=10,\n",
        "                                random_state=42, n_jobs=-1)\n",
        "print(result.importances_mean)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S8FfxInn-xBQ",
        "outputId": "cd18c70e-7555-4a4b-80cc-1af876e63304"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.05969231 0.20238462 0.049     ]\n"
          ]
        }
      ],
      "source": [
        "result = permutation_importance(hgb, test_input, test_target, n_repeats=10,\n",
        "                                random_state=42, n_jobs=-1)\n",
        "print(result.importances_mean)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pqplZjh0j2nw",
        "outputId": "3d6d6d84-c29e-4110-849a-a8c2b4c3033f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8723076923076923"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "hgb.score(test_input, test_target)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8fz_FrezBezR"
      },
      "source": [
        "#### XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YBYLvOiV6rga",
        "outputId": "a9ad7740-5d4c-48a8-c714-2d6eacfdb20b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9558403027491312 0.8782000074035686\n"
          ]
        }
      ],
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "xgb = XGBClassifier(tree_method='hist', random_state=42)\n",
        "scores = cross_validate(xgb, train_input, train_target, return_train_score=True, n_jobs=-1)\n",
        "\n",
        "print(np.mean(scores['train_score']), np.mean(scores['test_score']))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zl6nh6DOBd-B"
      },
      "source": [
        "#### LightGBM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "maihlDMP7lmY",
        "outputId": "19176c46-8abe-41d7-d6a2-11e9ade8ae9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/dask/dataframe/__init__.py:42: FutureWarning: \n",
            "Dask dataframe query planning is disabled because dask-expr is not installed.\n",
            "\n",
            "You can install it with `pip install dask[dataframe]` or `conda install dask`.\n",
            "This will raise in a future version.\n",
            "\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.935828414851749 0.8801251203079884\n"
          ]
        }
      ],
      "source": [
        "from lightgbm import LGBMClassifier\n",
        "\n",
        "lgb = LGBMClassifier(random_state=42)\n",
        "scores = cross_validate(lgb, train_input, train_target, return_train_score=True, n_jobs=-1)\n",
        "\n",
        "print(np.mean(scores['train_score']), np.mean(scores['test_score']))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "5-3 트리의 앙상블.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "default:Python",
      "language": "python",
      "name": "conda-env-default-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.10"
    }
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  "nbformat": 4,
  "nbformat_minor": 0
}