bilzard · April 10, 2021 02:18
diff --git a/solafune_083.ipynb b/solafune_083.ipynb
 {
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "sorafune_083.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.8.5"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/bilzard/2ad73a835f6e31fb2b3a0c22e827b388/sorafune_083.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iZUjhQoeHbOB"
      },
      "source": [
        "# 環境セットアップ"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ivHauT6Q4Oom",
        "outputId": "15185aa0-52e8-4629-c52d-7be1975e172a"
      },
      "source": [
        "from google.colab import drive\n",
        "drive = drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qncEMhQJ1k1T",
        "outputId": "049e138a-870d-4e1e-aa07-53180f38e4c1"
      },
      "source": [
        "! pip install catboost\n",
        "! pip install optuna\n",
        "! pip install xfeat"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting catboost\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1e/21/d1718eb4c93d6bacdd540b3792187f32ccb1ad9c51b9c4f10875d63ec176/catboost-0.25-cp37-none-manylinux1_x86_64.whl (67.3MB)\n",
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            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n",
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            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.10.0)\n",
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            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (2.4.7)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost) (1.3.3)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-0.25\n",
            "Collecting optuna\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/2b/21/d13081805e1e1afc71f5bb743ece324c8bd576237c51b899ecb38a717502/optuna-2.7.0-py3-none-any.whl (293kB)\n",
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            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from optuna) (20.9)\n",
            "Collecting alembic\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/72/a4/97eb6273839655cac14947986fa7a5935350fcfd4fff872e9654264c82d8/alembic-1.5.8-py2.py3-none-any.whl (159kB)\n",
            "\u001b[K     |████████████████████████████████| 163kB 6.9MB/s \n",
            "\u001b[?25hCollecting cliff\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a2/d6/7d9acb68a77acd140be7fececb7f2701b2a29d2da9c54184cb8f93509590/cliff-3.7.0-py3-none-any.whl (80kB)\n",
            "\u001b[K     |████████████████████████████████| 81kB 5.6MB/s \n",
            "\u001b[?25hCollecting colorlog\n",
            "  Downloading https://files.pythonhosted.org/packages/51/62/61449c6bb74c2a3953c415b2cdb488e4f0518ac67b35e2b03a6d543035ca/colorlog-4.8.0-py2.py3-none-any.whl\n",
            "Collecting cmaes>=0.8.2\n",
            "  Downloading https://files.pythonhosted.org/packages/01/1f/43b01223a0366171f474320c6e966c39a11587287f098a5f09809b45e05f/cmaes-0.8.2-py3-none-any.whl\n",
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            "Collecting python-editor>=0.3\n",
            "  Downloading https://files.pythonhosted.org/packages/c6/d3/201fc3abe391bbae6606e6f1d598c15d367033332bd54352b12f35513717/python_editor-1.0.4-py3-none-any.whl\n",
            "Collecting Mako\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f3/54/dbc07fbb20865d3b78fdb7cf7fa713e2cba4f87f71100074ef2dc9f9d1f7/Mako-1.1.4-py2.py3-none-any.whl (75kB)\n",
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            "\u001b[?25hCollecting pbr!=2.1.0,>=2.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fb/48/69046506f6ac61c1eaa9a0d42d22d54673b69e176d30ca98e3f61513e980/pbr-5.5.1-py2.py3-none-any.whl (106kB)\n",
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            "Collecting stevedore>=2.0.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d4/49/b602307aeac3df3384ff1fcd05da9c0376c622a6c48bb5325f28ab165b57/stevedore-3.3.0-py3-none-any.whl (49kB)\n",
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            "Collecting cmd2>=1.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/15/8b/15061b32332bb35ea2a2f6263d0f616779d576e82739ec8e7fcf3c94abf5/cmd2-1.5.0-py3-none-any.whl (133kB)\n",
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            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->sqlalchemy>=1.1.0->optuna) (3.4.1)\n",
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            "Collecting pyperclip>=1.6\n",
            "  Downloading https://files.pythonhosted.org/packages/a7/2c/4c64579f847bd5d539803c8b909e54ba087a79d01bb3aba433a95879a6c5/pyperclip-1.8.2.tar.gz\n",
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            "Collecting colorama>=0.3.7\n",
            "  Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n",
            "Building wheels for collected packages: pyperclip\n",
            "  Building wheel for pyperclip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyperclip: filename=pyperclip-1.8.2-cp37-none-any.whl size=11107 sha256=35d136ecd1e897322ea8266c20d262daea520c815b93b48ba87548a4bb77debc\n",
            "  Stored in directory: /root/.cache/pip/wheels/25/af/b8/3407109267803f4015e1ee2ff23be0c8c19ce4008665931ee1\n",
            "Successfully built pyperclip\n",
            "Installing collected packages: python-editor, Mako, alembic, pbr, stevedore, pyperclip, colorama, cmd2, cliff, colorlog, cmaes, optuna\n",
            "Successfully installed Mako-1.1.4 alembic-1.5.8 cliff-3.7.0 cmaes-0.8.2 cmd2-1.5.0 colorama-0.4.4 colorlog-4.8.0 optuna-2.7.0 pbr-5.5.1 pyperclip-1.8.2 python-editor-1.0.4 stevedore-3.3.0\n",
            "Collecting xfeat\n",
            "  Downloading https://files.pythonhosted.org/packages/25/9c/25be4530fe3d2b8af8271c0be9a0a9af44ca05c865211ba3b04fcdba5190/xfeat-0.1.1-py3-none-any.whl\n",
            "Requirement already satisfied: optuna>=1.3.0 in /usr/local/lib/python3.7/dist-packages (from xfeat) (2.7.0)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from xfeat) (3.13)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from xfeat) (0.22.2.post1)\n",
            "Requirement already satisfied: lightgbm in /usr/local/lib/python3.7/dist-packages (from xfeat) (2.2.3)\n",
            "Requirement already satisfied: pyarrow in /usr/local/lib/python3.7/dist-packages (from xfeat) (3.0.0)\n",
            "Collecting ml-metrics\n",
            "  Downloading https://files.pythonhosted.org/packages/c1/e7/c31a2dd37045a0c904bee31c2dbed903d4f125a6ce980b91bae0c961abb8/ml_metrics-0.1.4.tar.gz\n",
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            "Building wheels for collected packages: ml-metrics\n",
            "  Building wheel for ml-metrics (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ml-metrics: filename=ml_metrics-0.1.4-cp37-none-any.whl size=7850 sha256=f2715a9e8da1cbc5a345ec2d4327ec63bb2a16814bfa5513ea516638e0d2f8b5\n",
            "  Stored in directory: /root/.cache/pip/wheels/b3/61/2d/776be7b8a4f14c5db48c8e5451451cabc58dc6aa7ee3801163\n",
            "Successfully built ml-metrics\n",
            "Installing collected packages: ml-metrics, xfeat\n",
            "Successfully installed ml-metrics-0.1.4 xfeat-0.1.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s22UX4hA4SoI"
      },
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set_theme(style=\"ticks\")\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6oTmOnQnBR1"
      },
      "source": [
        "import lightgbm as lgb\n",
        "import xgboost as xgb\n",
        "from catboost import CatBoost\n",
        "from catboost import Pool\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.linear_model import Ridge"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XVZItOQuYDXC"
      },
      "source": [
        "# 履歴"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2nh2u8g94Sq9"
      },
      "source": [
        "class Cachable:\n",
        "  def __init__(self, f):\n",
        "    self.f = f\n",
        "    self.memo = {}\n",
        "  def __call__(self, *args):\n",
        "    if not args in self.memo:\n",
        "      print('run')\n",
        "      self.memo[args] = self.f(*args)\n",
        "    return self.memo[args]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tqKkwXJi4Swh"
      },
      "source": [
        "PROJECT_DIR = os.path.join(os.sep, 'content', 'drive', 'MyDrive', 'sorafune', 'NightLight')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUQeMl764Szx"
      },
      "source": [
        "@Cachable\n",
        "def load_data(name: str):\n",
        "  return pd.read_csv(os.path.join(PROJECT_DIR, f'{name}.csv'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dTXtRD2_fyGr",
        "outputId": "32d32867-00b0-4091-ff23-c753ae8f2cac"
      },
      "source": [
        "def plotHistory(input_df):\n",
        "  data = input_df.copy()\n",
        "  fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
        "  data['mean(LB, CV)'] = (data['LB'] + data['CV']) / 2\n",
        "  sns.lineplot(x='id', y='CV', data=data, color='r', label='CV', ax=ax)\n",
        "  sns.lineplot(x='id', y='LB', data=data, color='g', label='LB', ax=ax)\n",
        "  sns.lineplot(x='id', y='mean(LB, CV)', data=data, color='b', label='mean(LB, CV)', ax=ax)\n",
        "  ax.set_ylim([0.46, 0.6])\n",
        "\n",
        "\n",
        "history = load_data('history').set_index('id')\n",
        "history.sort_index(ascending=False)\n",
        "\n",
        "history.loc[22] = [225, 0.5194, 0.510447, 'remove missing values']\n",
        "history.loc[23] = [225, 0.5312, 0.488052, 'Back/Forward Fill(<=30%), using model prediction']\n",
        "history.loc[24] = [225, 0.5301, 0.480227, 'Back/Forward Fill(<=50%), using model prediction']\n",
        "history.loc[25] = [225, 0.5301, 0.480227, 'Back/Forward Fill(<=70%), using model prediction']\n",
        "history.loc[26] = [225, 0.5194, 0.480273, 'Back/Forward Fill(<=50%), not using model prediction']\n",
        "history.loc[27] = [218, 0.5432, 0.479738, 'KNN(whole)']\n",
        "history.loc[28] = [218, 0.5278, 0.479943, 'KNN(<=3０%)']\n",
        "history.loc[29] = [218, 0.5429, 0.477013, 'KNN(<=5０%)']\n",
        "history.loc[30] = [218, 0.5395, 0.478465, 'StratifiedGroupKFoldFold; KNN(<=5０%)']\n",
        "history.loc[31] = [218, 0.5250, 0.484093, 'StratifiedGroupKFoldFold; KNN(<=3０%)']\n",
        "history.loc[32] = [218, 0.5337, 0.480518, 'modified GroupKFold; KNN(<=3０%)']\n",
        "history.loc[33] = [218, 0.5367, np.NaN, 'modified GroupKFold; KNN(<=5０%); Log:MeanLight']\n",
        "history.loc[34] = [218, 0.5374, np.NaN, 'modified GroupKFold; KNN(<=5０%)']\n",
        "history.loc[35] = [218, 0.5719, np.NaN, 'modified GroupKFold; KNN(<=5０%); MovingMean']\n",
        "history.loc[36] = [218, 0.5383, np.NaN, 'modified GroupKFold; KNN(<=5０%); add p25']\n",
        "history.loc[37] = [198, 0.5324, np.NaN, 'modified GroupKFold; KNN(<=5０%); KNN MeanLight(<=30%)']\n",
        "history.loc[38] = [187, 0.5375, np.NaN, 'Add SumLight & reduce feat.']\n",
        "history.loc[39] = [119, 0.5327, 0.475288, 'Remove SumLight']\n",
        "history.loc[40] = [119, 0.7455, np.NaN, 'KNN Regression']\n",
        "history.loc[42] = [143, 0.5241, np.NaN, 'modified GroupKFold; KNN(<=5０%); Fold=10']\n",
        "history.loc[43] = [143, 0.5243, np.NaN, 'StratifiedGroupKFold; KNN(<=5０%); Fold=5']\n",
        "history.loc[44] = [143, 0.5221, 0.480211, 'StratifiedGroupKFold(AverageLandPrice, bins=20); KNN(<=5０%); Fold=10']\n",
        "history.loc[45] = [143, 0.5224, np.NaN, 'StratifiedGroupKFold(AverageLandPrice, bins=5); KNN(<=5０%); Fold=10']\n",
        "history.loc[46] = [143, 0.5315, np.NaN, 'StratifiedGroupKFold(PopulationDensity, bins=5); KNN(<=5０%); Fold=5']\n",
        "history.loc[47] = [143, 0.5250, 0.48033, 'StratifiedGroupKFold(PopulationDensity, bins=5); KNN(<=5０%); Fold=20']\n",
        "history.loc[48] = [143, 0.5298, np.NaN, 'StratifiedGroupKFold(Year, bins=22); KNN(<=5０%); Fold=20']\n",
        "history.loc[49] = [143, 0.5330, 0.478645, 'StratifiedGroupKFold(Year, bins=22); KNN(whole); Fold=5']\n",
        "history.loc[50] = [143, 0.5237, np.NaN, 'modified GroupKFold; KNN(whole); Fold=5; drop outlier']\n",
        "history.loc[51] = [143, 0.5445, np.NaN, 'modified GroupKFold; KNN(whole); Fold=5; drop outlier; select KNN train set']\n",
        "history.loc[52] = [143, 0.5171, 0.480925, 'modified GroupKFold; KNN(whole); Fold=5, random_state=10; drop outlier']\n",
        "history.loc[53] = [143, 0.5249, 0.487633, 'modified GroupKFold; KNN(whole); stacking(lgbm, catboost); early stop round 20, 30; drop outlier']\n",
        "history.loc[54] = [143, 0.5222, 0.481983, 'modified GroupKFold; KNN(whole); stacking(lgbm, catboost); early stop round:100, 100; drop outlier']\n",
        "history.loc[56] = [143, 0.5175, np.NaN, 'feature select(top 40); add SumLight; modified GroupKFold; KNN(whole); stacking(lgbm, catboost); early stop round:100, 100; drop outlier']\n",
        "history.loc[57] = [143, 0.5176, 0.486523, 'stack xgb prediction; modified GroupKFold; KNN(whole); stacking(lgbm, catboost)']\n",
        "history.loc[58] = [47, 0.5120, 0.485286, 'pseudo labeling; modified GroupKFold; KNN(whole); stacking(lgbm, catboost)']\n",
        "history.loc[59] = [143, 0.5207, 0.476822, 'correct top-coded MeanLight; modified GroupKFold; KNN(whole); stacking(lgbm, catboost)']\n",
        "history.loc[60] = [47, 0.5329, 0.480938, 'correct top-coded MeanLight; modified GroupKFold; KNN(whole); stacking(lgbm, catboost)']\n",
        "history.loc[61] = [47, 0.5191, 0.492641, 'correct top-coded MeanLight(bug-fixed); modified GroupKFold; KNN(whole); stacking(lgbm, catboost)']\n",
        "history.loc[62] = [143, 0.5235, np.NaN, 'naiive censored data imputation; modified GroupKFold; KNN(whole); stacking(lgbm, catboost)']\n",
        "history.loc[63] = [143, 0.5222, np.NaN, 'no censored data imputation; modified GroupKFold; KNN(whole); stacking(lgbm, catboost)']\n",
        "history.loc[65] = [116, 0.5178, np.NaN, 'feature transform; modified GroupKFold; KNN(whole); stacking(lgbm, catboost)']\n",
        "history.loc[66] = [116, 0.5145, np.NaN, 'use tuned lgbm param: feature transform; modified GroupKFold; KNN(whole); stacking(lgbm, catboost)']\n",
        "history.loc[67] = [93, 0.5434, np.NaN, 'drop features except MeanLight: feature transform; modified GroupKFold; KNN(whole); stacking(lgbm, catboost)']\n",
        "history.loc[69] = [110, 0.5167, 0.488087, 'fold=5, seeds=1: feature transform; modified GroupKFold; KNN(whole); stacking(lgbm, catboost)']\n",
        "history.loc[70] = [110, 0.5197, 0.486083, 'fold=2, seeds=3: feature transform; modified GroupKFold; KNN(whole); stacking(lgbm, catboost)']\n",
        "history.loc[72] = [138, 0.5253, 0.49615, '?; Stratified GroupKFold(Area); KNN(whole); stacking(lgbm, catboost)']\n",
        "history.loc[74] = [138, 0.5232, 0.49492, '?; Stratified GroupKFold(Area); KNN(whole); stacking(lgbm, catboost)']\n",
        "history.loc[76] = [138, 0.5222, 0.49333, '?; Stratified GroupKFold(Area); KNN(whole); stacking(lgbm, catboost)']\n",
        "history.loc[77] = [138, 0.5297, np.NaN, '?; Stratified GroupKFold(Area); KNN(whole); stacking(lgbm, catboost)']\n",
        "history.loc[78] = [138, 0.5385, 0.487841, 'Stratified GroupKFold(Area); impute=likelihood(0.5); stacking(lgbm, catboost)']\n",
        "history.loc[79] = [135, 0.5272, 0.514091, 'add likelihood (leaky); Stratified GroupKFold(Area); impute=likelihood(0.5); stacking(lgbm, catboost)']\n",
        "history.loc[81] = [135, 0.5361, 0.488124, 'Stratified GroupKFold(Area); impute=likelihood(0.7); stacking(lgbm, catboost)']\n",
        "\n",
        "\n",
        "plotHistory(history[15:].reset_index())\n",
        "history.to_csv(os.path.join(PROJECT_DIR, 'history.csv'))\n",
        "history.tail(20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "run\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>nFeat</th>\n",
              "      <th>CV</th>\n",
              "      <th>LB</th>\n",
              "      <th>description</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>143</td>\n",
              "      <td>0.5176</td>\n",
              "      <td>0.486523</td>\n",
              "      <td>stack xgb prediction; modified GroupKFold; KNN...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>47</td>\n",
              "      <td>0.5120</td>\n",
              "      <td>0.485286</td>\n",
              "      <td>pseudo labeling; modified GroupKFold; KNN(whol...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>143</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.476822</td>\n",
              "      <td>correct top-coded MeanLight; modified GroupKFo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>47</td>\n",
              "      <td>0.5329</td>\n",
              "      <td>0.480938</td>\n",
              "      <td>correct top-coded MeanLight; modified GroupKFo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>47</td>\n",
              "      <td>0.5191</td>\n",
              "      <td>0.492641</td>\n",
              "      <td>correct top-coded MeanLight(bug-fixed); modifi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>143</td>\n",
              "      <td>0.5235</td>\n",
              "      <td>NaN</td>\n",
              "      <td>naiive censored data imputation; modified Grou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>143</td>\n",
              "      <td>0.5222</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no censored data imputation; modified GroupKFo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>116</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>NaN</td>\n",
              "      <td>feature transform; modified GroupKFold; KNN(wh...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>116</td>\n",
              "      <td>0.5145</td>\n",
              "      <td>NaN</td>\n",
              "      <td>use tuned lgbm param: feature transform; modif...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>93</td>\n",
              "      <td>0.5434</td>\n",
              "      <td>NaN</td>\n",
              "      <td>drop features except MeanLight: feature transf...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>93</td>\n",
              "      <td>0.5253</td>\n",
              "      <td>0.553330</td>\n",
              "      <td>learn from others: feature transform; modified...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>110</td>\n",
              "      <td>0.5167</td>\n",
              "      <td>0.488087</td>\n",
              "      <td>fold=5, seeds=1: feature transform; modified G...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>110</td>\n",
              "      <td>0.5197</td>\n",
              "      <td>0.486083</td>\n",
              "      <td>fold=2, seeds=3: feature transform; modified G...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>138</td>\n",
              "      <td>0.5253</td>\n",
              "      <td>0.496150</td>\n",
              "      <td>?; Stratified GroupKFold(Area); KNN(whole); st...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>138</td>\n",
              "      <td>0.5232</td>\n",
              "      <td>0.494920</td>\n",
              "      <td>?; Stratified GroupKFold(Area); KNN(whole); st...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>138</td>\n",
              "      <td>0.5222</td>\n",
              "      <td>0.493330</td>\n",
              "      <td>?; Stratified GroupKFold(Area); KNN(whole); st...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>138</td>\n",
              "      <td>0.5297</td>\n",
              "      <td>NaN</td>\n",
              "      <td>?; Stratified GroupKFold(Area); KNN(whole); st...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>138</td>\n",
              "      <td>0.5385</td>\n",
              "      <td>0.487841</td>\n",
              "      <td>Stratified GroupKFold(Area); impute=likelihood...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>135</td>\n",
              "      <td>0.5272</td>\n",
              "      <td>0.514091</td>\n",
              "      <td>add likelihood (leaky); Stratified GroupKFold(...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>135</td>\n",
              "      <td>0.5361</td>\n",
              "      <td>0.488124</td>\n",
              "      <td>Stratified GroupKFold(Area); impute=likelihood...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    nFeat      CV        LB                                        description\n",
              "id                                                                            \n",
              "57    143  0.5176  0.486523  stack xgb prediction; modified GroupKFold; KNN...\n",
              "58     47  0.5120  0.485286  pseudo labeling; modified GroupKFold; KNN(whol...\n",
              "59    143  0.5207  0.476822  correct top-coded MeanLight; modified GroupKFo...\n",
              "60     47  0.5329  0.480938  correct top-coded MeanLight; modified GroupKFo...\n",
              "61     47  0.5191  0.492641  correct top-coded MeanLight(bug-fixed); modifi...\n",
              "62    143  0.5235       NaN  naiive censored data imputation; modified Grou...\n",
              "63    143  0.5222       NaN  no censored data imputation; modified GroupKFo...\n",
              "65    116  0.5178       NaN  feature transform; modified GroupKFold; KNN(wh...\n",
              "66    116  0.5145       NaN  use tuned lgbm param: feature transform; modif...\n",
              "67     93  0.5434       NaN  drop features except MeanLight: feature transf...\n",
              "68     93  0.5253  0.553330  learn from others: feature transform; modified...\n",
              "69    110  0.5167  0.488087  fold=5, seeds=1: feature transform; modified G...\n",
              "70    110  0.5197  0.486083  fold=2, seeds=3: feature transform; modified G...\n",
              "72    138  0.5253  0.496150  ?; Stratified GroupKFold(Area); KNN(whole); st...\n",
              "74    138  0.5232  0.494920  ?; Stratified GroupKFold(Area); KNN(whole); st...\n",
              "76    138  0.5222  0.493330  ?; Stratified GroupKFold(Area); KNN(whole); st...\n",
              "77    138  0.5297       NaN  ?; Stratified GroupKFold(Area); KNN(whole); st...\n",
              "78    138  0.5385  0.487841  Stratified GroupKFold(Area); impute=likelihood...\n",
              "79    135  0.5272  0.514091  add likelihood (leaky); Stratified GroupKFold(...\n",
              "81    135  0.5361  0.488124  Stratified GroupKFold(Area); impute=likelihood..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MrbfXtKtEBox"
      },
      "source": [
        "# このnotebook でやっていること"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RttcN0Z88Xtj"
      },
      "source": [
        "## 欠損値をKNNで補完\n",
        "\n",
        "* PlaceID ごとに、　AverageLandPrice を年ごとの推移をベクトルとみなし、KNNでフィット（似た特徴を持った土地の価格は近い）"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n0KEmZ0a1iPP"
      },
      "source": [
        "## 良いCVを作る\n",
        "\n",
        "CVとLBの差が大きくてあまりCVが役に立ってない。\n",
        "CVとLBが近い値になるようにFoldの切り方を工夫する\n",
        "\n",
        "具体的には、\n",
        "* testになくてtrainにのみ存在する外れ値を除外する\n",
        "* 1つのPlaceIDに紐づくデータが少なすぎると、予測が困難なので、1 PlaceID ごとに　　Full Set 必ず含まれるようにする\n",
        "* 特徴空間において特定のセグメントの値が含まれていないと予測は困難なので、必ず　１ fold に各セグメントの訓練データが含まれているようにする\n",
        "* 補完したデータが訓練データに含まれる割合をコントロールする\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q5TrMhIIfjPd"
      },
      "source": [
        "## AverageLandPrice の時系列的特徴について\n",
        "\n",
        "MeanLight も時系列だが、AverageLandPriceも時系列である。\n",
        "予測モデルは目的変数が時系列である、という制約を（明示的には）与えていないので、なんらかの特徴が存在すれば精度が向上するかもしれない"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YgUea9t0YMpH"
      },
      "source": [
        "## 平均への回帰\n",
        "\n",
        "Fold　ごとにボラがおおきいので、できるだけ多様な cv のホールドを切って、その効果を平均化することを考える。\n",
        "\n",
        "数をかせぐかわりに learnig rate は大きめでOK?\n",
        "数を増やすほど精度がよくなるかどうかを確認する。\n",
        "\n",
        "randomize: 10\n",
        "fold: 5\n",
        "\n",
        "oof の予測値の平均が、サンプルを増やすにつれて小さくなるかどうかを確認する\n",
        "\n",
        "memo: \n",
        "\n",
        "* oof もファイルに保存しておくと、notebook個別で学習してあとでマージしたりできる"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cC_Q5Vs-B6j8"
      },
      "source": [
        "## 補完をもすこし丁寧に\n",
        "\n",
        "* AveragaLandPrice の補完を丁寧におこなう -> 効果はほぼなかった。むしろ MeanLight や SumLight の補完の効果が大きかったよう。\n",
        "* KNNでどのくらいノイズがのっているか？\n",
        "* 補完したデータの数は何％か？"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMVgjXetK1dk"
      },
      "source": [
        "## 最終調整\n",
        "\n",
        "* 60sub以降からCVもLBも上がってきている -> 過学習が原因？\n",
        "* 過去に試したことのうち、何がCV, LBに一番寄与したかを分析する\n",
        "* adversarial みたいなことをやる（テストデータに近いデータを率先してvalidation setとして選択）\n",
        "* 過去に作ったモデルのうち、分布の形が異なるどうしでブレンディング"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "XXJ8migJvYYs",
        "outputId": "a35e99bd-80f4-461e-876f-1b5522b50c1f"
      },
      "source": [
        "history_analysis_df = load_data('history_analysis')\n",
        "history_analysis_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "run\n"
          ],
          "name": "stdout"
        },
        {
          "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>id</th>\n",
              "      <th>nFeat</th>\n",
              "      <th>CV</th>\n",
              "      <th>LB</th>\n",
              "      <th>description</th>\n",
              "      <th>imputation</th>\n",
              "      <th>threshold</th>\n",
              "      <th>nFeat.1</th>\n",
              "      <th>Fold</th>\n",
              "      <th>drop Outlier</th>\n",
              "      <th>model</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23</td>\n",
              "      <td>225</td>\n",
              "      <td>0.5312</td>\n",
              "      <td>0.488052</td>\n",
              "      <td>Back/Forward Fill(&lt;=30%), using model prediction</td>\n",
              "      <td>BF</td>\n",
              "      <td>0.3</td>\n",
              "      <td>225</td>\n",
              "      <td>GroupK</td>\n",
              "      <td>0</td>\n",
              "      <td>lgbm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>24</td>\n",
              "      <td>225</td>\n",
              "      <td>0.5301</td>\n",
              "      <td>0.480227</td>\n",
              "      <td>Back/Forward Fill(&lt;=50%), using model prediction</td>\n",
              "      <td>BF</td>\n",
              "      <td>0.5</td>\n",
              "      <td>225</td>\n",
              "      <td>GroupK</td>\n",
              "      <td>0</td>\n",
              "      <td>lgbm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>25</td>\n",
              "      <td>225</td>\n",
              "      <td>0.5301</td>\n",
              "      <td>0.480227</td>\n",
              "      <td>Back/Forward Fill(&lt;=70%), using model prediction</td>\n",
              "      <td>BF</td>\n",
              "      <td>0.7</td>\n",
              "      <td>225</td>\n",
              "      <td>GroupK</td>\n",
              "      <td>0</td>\n",
              "      <td>lgbm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26</td>\n",
              "      <td>225</td>\n",
              "      <td>0.5194</td>\n",
              "      <td>0.480273</td>\n",
              "      <td>Back/Forward Fill(&lt;=50%), not using model pred...</td>\n",
              "      <td>BF</td>\n",
              "      <td>0.5</td>\n",
              "      <td>225</td>\n",
              "      <td>GroupK</td>\n",
              "      <td>0</td>\n",
              "      <td>lgbm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27</td>\n",
              "      <td>218</td>\n",
              "      <td>0.5432</td>\n",
              "      <td>0.479738</td>\n",
              "      <td>KNN(whole)</td>\n",
              "      <td>KNN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>218</td>\n",
              "      <td>GroupK</td>\n",
              "      <td>0</td>\n",
              "      <td>lgbm</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  nFeat      CV        LB  ... nFeat.1    Fold  drop Outlier  model\n",
              "0  23    225  0.5312  0.488052  ...     225  GroupK             0   lgbm\n",
              "1  24    225  0.5301  0.480227  ...     225  GroupK             0   lgbm\n",
              "2  25    225  0.5301  0.480227  ...     225  GroupK             0   lgbm\n",
              "3  26    225  0.5194  0.480273  ...     225  GroupK             0   lgbm\n",
              "4  27    218  0.5432  0.479738  ...     218  GroupK             0   lgbm\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "mOhmw-gWvd_s",
        "outputId": "b3404aa6-4d25-4e3d-86d7-1ce859521097"
      },
      "source": [
        "history_analysis_df.groupby('Fold').mean()[['CV', 'LB']].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f675c595590>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "s-pPJhWH6MDF",
        "outputId": "f130a5a6-6a67-46e2-b717-bb764f1dc987"
      },
      "source": [
        "history_analysis_df.groupby(['imputation', 'threshold']).mean()[['CV', 'LB']].plot()\n",
        "plt.xticks(rotation=90)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([-1.,  0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.]),\n",
              " <a list of 10 Text major ticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "OSsdb-w9voX5",
        "outputId": "88998821-4359-4520-c6c1-69edcbe724ce"
      },
      "source": [
        "history_analysis_df.groupby('nFeat').mean()[['CV', 'LB']].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f65e6148d50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "YoZr5rEBwFb0",
        "outputId": "06dcdeca-fb83-420b-f84a-87b3fa669468"
      },
      "source": [
        "history_analysis_df.groupby('imputation').mean()[['CV', 'LB']].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f65e600fe90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "D18tFQZowQKA",
        "outputId": "a277f62b-8012-4397-92b0-34a1f265ffee"
      },
      "source": [
        "history_analysis_df.groupby('threshold').mean()[['CV', 'LB']].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f65e605fe90>"
            ]
          },
          "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": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "pTzq4hi7wVLK",
        "outputId": "3910c922-9574-4010-e506-c5fdb6e65875"
      },
      "source": [
        "history_analysis_df.groupby('drop Outlier').mean()[['CV', 'LB']].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f65e5f629d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "mkaFwKZrwiVL",
        "outputId": "a15cf8cf-11c3-42d7-be12-55e7259970ab"
      },
      "source": [
        "history_analysis_df.groupby('model').mean()[['CV', 'LB']].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f65e5ee9490>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IB_ZBVspxZom"
      },
      "source": [
        "単純にLBが良いパラメータは、\n",
        "* Fold: GroupK\n",
        "* nFeat: 125\n",
        "* Fill: Back/Forward\n",
        "* threshold: 0.3-0.7\n",
        "* drop outlier: false\n",
        "* model: LGBM"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YEofUKq0gd7y"
      },
      "source": [
        "## スタッキング\n",
        "\n",
        "* [1] https://qiita.com/tubo/items/f83a97f2488cc1f40088\n",
        "* [2] http://highschoolstudent.hatenablog.com/entry/2015/12/28/141445\n",
        "* [3] https://mlwave.com/kaggle-ensembling-guide/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJSTQV_p7d97"
      },
      "source": [
        "## 特徴量の変換\n",
        "\n",
        "* log1p(MeanLight) / log1p(Std:MeanLight) という変換が AverageLandPrace に対して線形だったので、 MeanLight のかわりにこの値をベースとしてほかのさまざまな特徴量を作成する\n",
        "\n",
        "![](https://drive.google.com/uc?export=view&id=1-ECBP_vREYm5FWTYMZSbmtAV0vjaxwjj)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vJDqtIXHUxlZ"
      },
      "source": [
        "# Backlog\n",
        "\n",
        "1. 地価の時系列を求めるのでなく、地価の平均値をバイアスなく求められれば十分なのでは？\n",
        "  - PlaceID をキーとした特徴で y の平均値を学習させる\n",
        "2. 1の発展型として、平均値＋回帰直線の傾きを学習させる\n",
        "3. 1, 2の一般系として、地価の時系列を関数フィットさせたパラメータを学習させる\n",
        "\n",
        "平均値より中央値の方が抵抗性が強い\n",
        "beansplot\n",
        "誤差が有効\n",
        "移動中央値"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CEHlSqthIKeI"
      },
      "source": [
        "## Pseudo Labeling\n",
        "\n",
        "[1] https://yukoishizaki.hatenablog.com/entry/2019/11/14/115814"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mOf7SLEkzpf"
      },
      "source": [
        "# 共通関数"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2OuaGtjBk2MD"
      },
      "source": [
        "## EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QjBCBHURk49b"
      },
      "source": [
        "def plot2DFeats(input_df: pd.DataFrame, feats: list, target: str, cols=4):\n",
        "  rows = (len(feats) - 1) // cols + 1\n",
        "\n",
        "  fig, axes = plt.subplots(rows, cols, figsize=(6 * cols, 4 * rows), sharey=True)\n",
        "  axes = axes.ravel()\n",
        "\n",
        "  for i, (feat, hue) in enumerate(feats):\n",
        "    sns.scatterplot(x=feat, y=target, data=input_df, ax=axes[i], hue=hue)\n",
        "    axes[i].title.set_text(f'{target}-{feat} Plot')\n",
        "\n",
        "  plt.tight_layout()\n",
        "\n",
        "\n",
        "def plotCompareTrainTest(df1: pd.DataFrame, df2: pd.DataFrame, pairs, cols=4):\n",
        "  rows = (len(pairs) - 1) // cols + 1\n",
        "\n",
        "  fig, axes = plt.subplots(rows, cols, figsize=(6 * cols, 4 * rows))\n",
        "  axes = axes.ravel()\n",
        "\n",
        "  for i, (feat, target) in enumerate(pairs):\n",
        "    sns.scatterplot(x=feat, y=target, data=df1, ax=axes[i], color='r', label='train')\n",
        "    sns.scatterplot(x=feat, y=target, data=df2, ax=axes[i], color='g', label='test')\n",
        "\n",
        "  plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SwisFWI1YlKO"
      },
      "source": [
        "## 前処理"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ntc8Ys7HYdxh"
      },
      "source": [
        "from sklearn.impute import KNNImputer\n",
        "\n",
        "\n",
        "def dropNaNRow(input_df):\n",
        "  out_df = input_df.copy()\n",
        "  out_df.dropna(axis=0, how='all', inplace=True)\n",
        "  out_df = out_df.reset_index().drop('index', axis=1) # 後続の処理で番号がオリジナルとずれるので注意\n",
        "  return out_df\n",
        "\n",
        "\n",
        "def makePerPlacePivotTable(input_df: pd.DataFrame):\n",
        "  '''Yearを名目変数のように用いて place ごとの 年のMean, Sum の特徴をつくる（経年変化っぽい特徴量を作る）'''\n",
        "  columns = input_df.drop('PlaceID', axis=1).columns\n",
        "  return pd.pivot_table(input_df, index='PlaceID', columns='Year', values=columns)\n",
        "\n",
        "\n",
        "def dropTooMuchNaNValues(input_df, threshold=0.7):\n",
        "  '''NaNがあまりに多いデータを除外する'''\n",
        "  def _select_with_threshold(df, col):\n",
        "    return df[col].notna().sum(axis=1) >= len(df[col].columns) * threshold\n",
        "\n",
        "  idx = (_select_with_threshold(input_df, 'AverageLandPrice')) \\\n",
        "      & (_select_with_threshold(input_df, 'MeanLight')) \\\n",
        "      & (_select_with_threshold(input_df, 'SumLight'))\n",
        "\n",
        "  return input_df[idx]\n",
        "\n",
        "\n",
        "def convertTargetLog1p(input_df):\n",
        "  '''目的変数を対数化する'''\n",
        "  out_df = input_df.copy()\n",
        "  out_df['AverageLandPrice'] = np.log1p(input_df['AverageLandPrice'])\n",
        "  return out_df\n",
        "\n",
        "\n",
        "def _createStatisticFeature(input_df):\n",
        "  '''PlaceID ごとの分位点の特徴をつくる'''\n",
        "  features = [\n",
        "    'MeanLight',\n",
        "  ]\n",
        "  feature_df = pd.DataFrame(columns={'PlaceID': input_df['PlaceID'].unique()}).set_index('PlaceID')\n",
        "  group = input_df.groupby('PlaceID')\n",
        "\n",
        "  df_list = [\n",
        "             (np.log1p(group.median()) - np.log1p((group.quantile(0.75) - group.quantile(0.25))))[features].add_prefix('Median_div_IQR:'),\n",
        "  ]\n",
        "  feature_df = pd.concat(df_list, axis=1)\n",
        "  return feature_df\n",
        "\n",
        "\n",
        "def _create_likelihood(input_df):\n",
        "  return _createStatisticFeature(recoverToOriginalTable(input_df))['Median_div_IQR:MeanLight']\n",
        "\n",
        "\n",
        "def _createDiffLandPrice(input_df, diff=-1):\n",
        "  likelihood = _create_likelihood(input_df)\n",
        "  out_df = pd.DataFrame(index=input_df.index, columns=input_df['AverageLandPrice'].columns)\n",
        "  out_df = input_df['AverageLandPrice'].diff(-1, axis=1)\n",
        "  out_df[2013] = likelihood\n",
        "\n",
        "  return out_df\n",
        "\n",
        "\n",
        "def _createLandPriceLikelihood(input_df):\n",
        "  likelihood = _create_likelihood(input_df)\n",
        "  land_price_df = _createDiffLandPrice(input_df)\n",
        "  out_df = pd.DataFrame(index=input_df.index)\n",
        "\n",
        "  for year in range(1992, 2014):\n",
        "    assert len(land_price_df) == len(likelihood)\n",
        "    cut = pd.cut(likelihood, 20, labels=list(range(20)))\n",
        "    out_df['bin_id'] = cut\n",
        "    likelihood_df = pd.DataFrame(land_price_df[year].groupby(cut).median().rename(year))\n",
        "    likelihood_df.index = likelihood_df.index.rename('bin_id')\n",
        "    out_df = pd.merge(out_df, likelihood_df, left_on='bin_id', right_index=True, how='left')\n",
        "  return out_df.drop('bin_id', axis=1)\n",
        "\n",
        "\n",
        "def _addPriceDiff(input_df):\n",
        "  '''1年後との差分の尤度をカラムに加える'''\n",
        "  out_df = pd.DataFrame(index=input_df.index)\n",
        "\n",
        "  out_df = _createLandPriceLikelihood(input_df)\n",
        "\n",
        "  out_df.columns = pd.MultiIndex.from_product([['PriceDiff'], out_df.columns])\n",
        "  return pd.merge(input_df, out_df, left_index=True, right_index=True, how='left')\n",
        "\n",
        "\n",
        "def imputeByLikelihood(input_df):\n",
        "  '''尤度によってimputeする'''\n",
        "  out_df = _addPriceDiff(input_df)\n",
        "\n",
        "  for year in range(2013, 1991, -1):\n",
        "    if out_df[('AverageLandPrice', year)].isna().sum() > 0:\n",
        "      idx = out_df[('AverageLandPrice', year)].isna()\n",
        "\n",
        "      out_df.loc[idx, ('AverageLandPrice', year)] = out_df.loc[idx, ('AverageLandPrice', year + 1)] + out_df.loc[idx, ('PriceDiff', year)]\n",
        "  return out_df.drop('PriceDiff', axis=1, level=0)\n",
        "\n",
        "\n",
        "def imputeBackForwardFill(input_df: pd.DataFrame):\n",
        "  '''欠測値を挿入する(ffill & bfill)'''\n",
        "  out_df = input_df.copy()\n",
        "  columns = [\n",
        "             'MeanLight',\n",
        "             'SumLight',\n",
        "             'AverageLandPrice',\n",
        "             ]\n",
        "  for column in columns:\n",
        "    out_df[column] = out_df[column].fillna(method='bfill', axis=1)\n",
        "    out_df[column] = out_df[column].fillna(method='ffill', axis=1)\n",
        "  return out_df\n",
        "\n",
        "\n",
        "def imputeKNN(input_df, neighbors=5):\n",
        "  out_df = input_df.copy()\n",
        "  columns = [\n",
        "             'MeanLight',\n",
        "             'SumLight',\n",
        "             'AverageLandPrice',\n",
        "  ]\n",
        "  for column in columns:\n",
        "    X = input_df[column].values\n",
        "    imputer = KNNImputer(n_neighbors=neighbors)\n",
        "    X_inputed = imputer.fit_transform(X)\n",
        "    out_df[column] = X_inputed\n",
        "\n",
        "  return out_df\n",
        "\n",
        "\n",
        "def addAreaFeature(input_df):\n",
        "  out_df = input_df.copy()\n",
        "\n",
        "  # 0 が最頻値にならないようにする\n",
        "  estimated_area = np.log1p((input_df['SumLight'] / input_df['MeanLight']).replace(0.0, np.NaN).mode(axis=1)[0])\n",
        "  # 面積が定義できないカラムを0で補完\n",
        "  estimated_area.fillna(0.0, inplace=True)\n",
        "  assert estimated_area.isna().sum() == 0\n",
        "\n",
        "  years = range(1992, 2014)\n",
        "  for year in years:\n",
        "    out_df['Area', year] = estimated_area\n",
        "\n",
        "  return out_df\n",
        "\n",
        "\n",
        "def addPopulationFeature(input_df):\n",
        "  out_df = input_df.copy()\n",
        "  years = range(1992, 2014)\n",
        "  for year in years:\n",
        "    out_df['PopulationDensity', year] = np.log1p(input_df['MeanLight', year] / input_df['Area', 1992])\n",
        "\n",
        "    # 人口密度が定義できないカラムを0で補完\n",
        "    out_df['PopulationDensity', year].fillna(0.0, inplace=True)\n",
        "    assert out_df['PopulationDensity', year].isna().sum() == 0\n",
        "\n",
        "  return out_df\n",
        "\n",
        "\n",
        "def recoverToOriginalTable(input_df):\n",
        "  return input_df.stack().reset_index()\n",
        "\n",
        "\n",
        "def orderAsOriginalData(input_df, original_df):\n",
        "  keys = ['PlaceID', 'Year']\n",
        "  return pd.merge(original_df[keys], input_df, on=keys, how='left', sort=False)\n",
        "\n",
        "\n",
        "def selectFeatures(input_df):\n",
        "  '''後続で使う特徴量のみを選択する'''\n",
        "  columns = [\n",
        "    'PlaceID',\n",
        "    'Year',\n",
        "    'AverageLandPrice',\n",
        "    'MeanLight',\n",
        "    'SumLight',\n",
        "    'PopulationDensity',\n",
        "    'Area',\n",
        "#    'Rolling(7):MeanLight',\n",
        "#    'Rolling(7):SumLight',\n",
        "#    'Residual(7):MeanLight',\n",
        "#    'Residual(7):SumLight',\n",
        "  ]\n",
        "  return input_df[columns]\n",
        "\n",
        "\n",
        "def applyRollingMedianForPlaceFeatures(place_df, win=7, std=3):\n",
        "  '''特定の特徴に年ごとの移動平均を適用する'''\n",
        "  features = ['MeanLight', 'SumLight', 'PopulationDensity']\n",
        "  for feature in features:\n",
        "    for year in range(1992, 2014):\n",
        "      place_df[f'Rolling({win}):{feature}', year] = place_df[feature].rolling(win, axis=1, center=True, min_periods=1).median()[year]\n",
        "      place_df[f'Residual({win}):{feature}', year] = place_df[feature, year] - place_df[f'Rolling({win}):{feature}', year]\n",
        "\n",
        "  return place_df\n",
        "\n",
        "\n",
        "# whole set\n",
        "def preprocess(input_df):\n",
        "  return dropNaNRow(\n",
        "            selectFeatures(\n",
        "              recoverToOriginalTable( # ここまで\n",
        "                addPopulationFeature(\n",
        "                  imputeBackForwardFill(\n",
        "                    imputeKNN(\n",
        "                      dropTooMuchNaNValues(\n",
        "                        addAreaFeature(\n",
        "                          makePerPlacePivotTable( # ここから pivot table\n",
        "                            convertTargetLog1p(input_df))), threshold=0.5)))))))\n",
        "\n",
        "\n",
        "def addTargetFeature(input_df):\n",
        "  '''やや強引だが、test データに preprocess を適用できないため、ダミーで Target の特徴を追加する'''\n",
        "  out_df = input_df.copy()\n",
        "  out_df['AverageLandPrice'] = 0\n",
        "  return out_df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oKZc837nmZGM"
      },
      "source": [
        "## 特徴量エンジニアリング"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kBRY8pIVstUr"
      },
      "source": [
        "# https://github.com/nyk510/vivid/blob/master/vivid/utils.py\n",
        "from contextlib import contextmanager\n",
        "from time import time\n",
        "\n",
        "\n",
        "class Timer:\n",
        "    def __init__(self, logger=None, format_str='{:.3f}[s]', prefix=None, suffix=None, sep=' '):\n",
        "\n",
        "        if prefix: format_str = str(prefix) + sep + format_str\n",
        "        if suffix: format_str = format_str + sep + str(suffix)\n",
        "        self.format_str = format_str\n",
        "        self.logger = logger\n",
        "        self.start = None\n",
        "        self.end = None\n",
        "\n",
        "    @property\n",
        "    def duration(self):\n",
        "        if self.end is None:\n",
        "            return 0\n",
        "        return self.end - self.start\n",
        "\n",
        "    def __enter__(self):\n",
        "        self.start = time()\n",
        "\n",
        "    def __exit__(self, exc_type, exc_val, exc_tb):\n",
        "        self.end = time()\n",
        "        out_str = self.format_str.format(self.duration)\n",
        "        if self.logger:\n",
        "            self.logger.info(out_str)\n",
        "        else:\n",
        "            print(out_str)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V-h6C1H0mdTE"
      },
      "source": [
        "from itertools import chain\n",
        "from tqdm import tqdm\n",
        "\n",
        "\n",
        "def createRawFeature(input_df):\n",
        "  out_df = pd.DataFrame()\n",
        "  features = [\n",
        "    'AverageLandPrice',\n",
        "    'MeanLight',\n",
        "    'SumLight',\n",
        "    'PopulationDensity',\n",
        "    'PlaceID',\n",
        "    'Year',\n",
        "    'Area',\n",
        "#    'Rolling(7):MeanLight',\n",
        "#    'Rolling(7):SumLight',\n",
        "#    'Residual(7):MeanLight',\n",
        "#    'Residual(7):SumLight',\n",
        "  ]\n",
        "  for feature in features:\n",
        "    out_df[feature] = input_df[feature]\n",
        "  return out_df\n",
        "\n",
        "\n",
        "def createBoostFeature(input_df):\n",
        "  '''Boostモデルが処理しやすくなると思われる特徴量を追加する'''\n",
        "  epsiron = 0.01\n",
        "\n",
        "  out_df = pd.DataFrame()\n",
        "\n",
        "  # 実は　特徴重要度をみるとあまりつかわれてない\n",
        "#  out_df['MeanLight eq 63'] = (abs(input_df['MeanLight'] - 63.0) < epsiron).astype(int)\n",
        "#  out_df['MeanLight lt 12'] = (input_df['MeanLight'] < 12.0).astype(int)\n",
        "#  out_df['MeanLight lt 30'] = (input_df['MeanLight'] < 27.0).astype(int)\n",
        "#  out_df['MeanLight gt 57'] = (input_df['MeanLight'] > 50.0).astype(int)\n",
        "#  out_df['PopulationDensity lt 2'] = (input_df['PopulationDensity'] < 2.0).astype(int)\n",
        "#  out_df['PopulationDensity gt 3'] = (input_df['PopulationDensity'] > 3.0).astype(int)\n",
        "\n",
        "  return out_df\n",
        "\n",
        "\n",
        "def makeCovarianceFeature(input_df: pd.DataFrame, feat1: str, feat2: str, index: str):\n",
        "  '''feature どうしの共分散の特徴量を作る'''\n",
        "  feature_df = pd.DataFrame(columns={index: input_df[index].unique()}).set_index(index)\n",
        "\n",
        "  place_df = input_df[[index, feat1, feat2]]\n",
        "  group = place_df.groupby(index)\n",
        "  cov_df = group.cov().reset_index()\n",
        "\n",
        "  idx = cov_df['level_1'] == feat1\n",
        "  x_df = cov_df[idx].drop('level_1', axis=1).set_index(index).add_prefix(f'Cov_{feat2}:')\n",
        "\n",
        "  idx = cov_df['level_1'] == feat2\n",
        "  y_df = cov_df[idx].drop('level_1', axis=1).set_index(index).add_prefix(f'Cov_{feat1}:')\n",
        "\n",
        "  # 重複するカラムを削除\n",
        "  y_df = y_df.drop(f'Cov_{feat1}:{feat2}', axis=1)\n",
        "\n",
        "  for feature in x_df.columns:\n",
        "    feature_df[feature] = x_df[feature]\n",
        "\n",
        "  for feature in y_df.columns:\n",
        "    feature_df[feature] = y_df[feature]\n",
        "\n",
        "  return feature_df\n",
        "\n",
        "\n",
        "def createCovarianceFeature(input_df):\n",
        "  '''feature どうしの 共分散の特徴を作る'''\n",
        "  columns = [\n",
        "             'Year',\n",
        "             'MeanLight',\n",
        "             'SumLight',\n",
        "  ]\n",
        "\n",
        "  pairs = []\n",
        "  for i in range(len(columns)):\n",
        "    for j in range(i + 1, len(columns)):\n",
        "      pairs.append([columns[i], columns[j]])\n",
        "\n",
        "  df_list = []\n",
        "  for feat1, feat2 in pairs:\n",
        "    df_list.append(makeCovarianceFeature(input_df, feat1, feat2, 'PlaceID'))\n",
        "\n",
        "  return pd.concat(df_list, axis=1)\n",
        "\n",
        "\n",
        "def createStatisticFeature(input_df):\n",
        "  '''PlaceID ごとの分位点の特徴をつくる'''\n",
        "  features = [\n",
        "    'MeanLight',\n",
        "    'SumLight',\n",
        "    'PopulationDensity',\n",
        "  ]\n",
        "  feature_df = pd.DataFrame(columns={'PlaceID': input_df['PlaceID'].unique()}).set_index('PlaceID')\n",
        "  group = input_df.groupby('PlaceID')\n",
        "\n",
        "  df_list = [\n",
        "             group.mean()[features].add_prefix('Mean:'),\n",
        "             group.std()[features].add_prefix('Std:'),\n",
        "             group.min()[features].add_prefix('Min:'),\n",
        "             (group.max() - group.min())[features].add_prefix('Max-Min:'),\n",
        "             group.max()[features].add_prefix('Max:'),\n",
        "             group.median()[features].add_prefix('Median:'),\n",
        "             group.quantile(0.1)[features].add_prefix('p10:'),\n",
        "             group.quantile(0.25)[features].add_prefix('p25:'),\n",
        "             group.quantile(0.75)[features].add_prefix('p75:'),\n",
        "             group.quantile(0.90)[features].add_prefix('p90:'),\n",
        "             (group.quantile(0.75) - group.quantile(0.25))[features].add_prefix('I4QR:'),\n",
        "             (group.quantile(0.9) - group.quantile(0.1))[features].add_prefix('I9QR:'),\n",
        "             (((group.quantile(0.75) - group.median()) - (group.median() - group.quantile(0.25))) / \\\n",
        "                (group.quantile(0.75) - group.quantile(0.25)))[features].add_prefix('Scue:'),\n",
        "             ((group.quantile(0.75) - group.quantile(0.25)) / \\\n",
        "                (group.quantile(0.9) - group.quantile(0.1)))[features].add_prefix('Cum:'),\n",
        "             (np.log1p(group.median()) - np.log1p((group.quantile(0.75) - group.quantile(0.25))))[features].add_prefix('Median_div_IQR:'),\n",
        "  ]\n",
        "  feature_df = pd.concat(df_list, axis=1)\n",
        "  return feature_df\n",
        "\n",
        "\n",
        "def createMedianTarget(input_df):\n",
        "  target = 'AverageLandPrice'\n",
        "  feature_df = pd.DataFrame(columns={'PlaceID': input_df['PlaceID'].unique()}).set_index('PlaceID')\n",
        "  group = input_df.groupby('PlaceID')\n",
        "\n",
        "  out_df = group.median()[[target]].add_prefix('Median:')\n",
        "  return out_df\n",
        "\n",
        "\n",
        "def createPlaceYearFeature(input_df: pd.DataFrame):\n",
        "  '''Yearを名目変数のように用いて place ごとの 年のMean, Sum の特徴をつくる（経年変化っぽい特徴量を作る）'''\n",
        "  return pd.pivot_table(input_df, index='PlaceID', columns='Year', values='MeanLight').add_prefix(\"MeanLight=\")\n",
        "\n",
        "\n",
        "def createPlaceYearLagFeature(input_df: pd.DataFrame, column: str, lag: int):\n",
        "  '''土地のYearごとのLag'''\n",
        "  out_df = pd.pivot_table(input_df, index='PlaceID', columns='Year', values=column).diff(lag, axis=1).add_prefix(f'Lag({lag}):{column}=')\n",
        "\n",
        "  return out_df\n",
        "\n",
        "\n",
        "def _reverse_columnwise_cumsum(input_df):\n",
        "  return input_df.T[::-1].cumsum().fillna(0)[::-1].T\n",
        "\n",
        "\n",
        "def createLikelihoodPlaceFeature(input_df):\n",
        "  '''尤度を追加; leaky'''\n",
        "  out_df = _reverse_columnwise_cumsum(\n",
        "    _createLandPriceLikelihood(\n",
        "        makePerPlacePivotTable(input_df)))\n",
        "  out_df.columns = pd.MultiIndex.from_product([['Likelihood'], out_df.columns])\n",
        "  out_df.columns = out_df.columns.rename('Year', level=1)\n",
        "\n",
        "  return orderAsOriginalData(recoverToOriginalTable(out_df), input_df)[['Likelihood']]\n",
        "\n",
        "\n",
        "def createPlaceFeature(input_df):\n",
        "  lag = 5\n",
        "\n",
        "  df_list = [\n",
        "    createStatisticFeature(input_df),\n",
        "    createPlaceYearFeature(input_df),\n",
        "    *([createPlaceYearLagFeature(input_df, 'MeanLight', i) for i in range(-lag, 0)]),\n",
        "#    *([createPlaceYearLagFeature(input_df, 'SumLight', i) for i in range(-lag, 0)]),\n",
        "#    *([createPlaceYearLagFeature(input_df, 'PopulationDensity', i) for i in range(-lag, 0)]),\n",
        "  ]\n",
        "  place_feature = pd.concat(df_list, axis=1)\n",
        "\n",
        "  return pd.merge(input_df[['PlaceID']], place_feature.reset_index(), on='PlaceID').drop('PlaceID', axis=1)\n",
        "\n",
        "\n",
        "def createLagFeature(input_df, columns, lag):\n",
        "  '''Lag 特徴量。PlaceIDごとに Year でソートされていることが条件'''\n",
        "  output_df = input_df.groupby('PlaceID')[columns].diff(lag)\n",
        "  return output_df.add_prefix(f'Lag({lag}):')\n",
        "\n",
        "\n",
        "def createShiftFeature(input_df, columns, lag):\n",
        "  '''Shift 特徴量。PlaceIDごとに Year でソートされていることが条件'''\n",
        "  output_df = input_df.groupby('PlaceID')[columns].shift(lag)\n",
        "  return output_df.add_prefix(f'Shift({lag}):')\n",
        "\n",
        "\n",
        "def createTimeSeriesFeature(input_df):\n",
        "  '''時系列に関する特徴量を作成する'''\n",
        "  lag = 15\n",
        "  shift = 5\n",
        "\n",
        "  df_list = [\n",
        "    *([createLagFeature(input_df, ['MeanLight'], i) for i in chain(range(-lag, 0), range(1, lag + 1))]),\n",
        "    *([createShiftFeature(input_df, ['MeanLight'], i) for i in chain(range(-shift, 0), range(1, shift + 1))]),\n",
        "  ]\n",
        "  return pd.concat(df_list, axis=1)\n",
        "\n",
        "\n",
        "def to_feature(input_df):\n",
        "  \"\"\"input_df を特徴量行列に変換した新しいデータフレームを返す.\n",
        "  \"\"\"\n",
        "  processors = [\n",
        "    createRawFeature,\n",
        "#    createBoostFeature,\n",
        "    createPlaceFeature,\n",
        "#    createTimeSeriesFeature,\n",
        "#    createLikelihoodPlaceFeature, # leaky\n",
        "  ]\n",
        "\n",
        "  out_df = pd.DataFrame()\n",
        "\n",
        "  for func in tqdm(processors, total=len(processors)):\n",
        "    with Timer(prefix='create' + func.__name__ + ' '):\n",
        "      _df = func(input_df)\n",
        "    \n",
        "    # 長さが等しいことをチェック (ずれている場合, func の実装がおかしい)\n",
        "    assert len(_df) == len(input_df), func.__name__\n",
        "    out_df = pd.concat([out_df, _df], axis=1)\n",
        "      \n",
        "  return out_df\n",
        "\n",
        "\n",
        "def convertResidual(input_df):\n",
        "  '''残差の特徴量に変換する'''\n",
        "  out_df = input_df.copy()\n",
        "  features = [\n",
        "              'AverageLandPrice',\n",
        "              'MeanLight',\n",
        "              'SumLight',\n",
        "              'PopulationDensity',\n",
        "  ]\n",
        "  bias = 'Median'\n",
        "  for feature in features:\n",
        "    out_df[f'Residual:{feature}'] = out_df[feature] - out_df[f'{bias}:{feature}']\n",
        "\n",
        "  return out_df.drop(features, axis=1)\n",
        "\n",
        "\n",
        "def postprocess(input_df):\n",
        "  out_df = input_df.copy()\n",
        "  out_df = out_df.dropna(axis=1, how='all')\n",
        "  return out_df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vACAb5VPqsL8"
      },
      "source": [
        "## 交差検証"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G-VGpk5xqwjN"
      },
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "\n",
        "def create_bins(series, num):\n",
        "  '''\n",
        "  目的変数をbinning\n",
        "  '''\n",
        "  bins = pd.cut(\n",
        "    series,\n",
        "    bins=num,\n",
        "    labels=False\n",
        "  )\n",
        "  return bins\n",
        "\n",
        "\n",
        "class FullSetPlaceFeatureKFold:\n",
        "  def __init__(self, n_splits, shuffle=False, random_state=None):\n",
        "    self.n_splits = n_splits\n",
        "    self.random_state = random_state\n",
        "    self.skf_ = StratifiedKFold(n_splits=n_splits, shuffle=shuffle, random_state=random_state)\n",
        "\n",
        "  def split(self, input_df: pd.DataFrame, target, bins=16):\n",
        "    '''KFold の wrapper. train/feature で PlaceID の Fullset が含まれるように分割する'''\n",
        "    def _get_original_index(df: pd.DataFrame):\n",
        "      df_ = df.stack().reset_index().rename(columns={'level_0': 'PlaceID'})\n",
        "\n",
        "      keys = ['PlaceID', 'Year']\n",
        "      return pd.merge(input_df[keys].reset_index(), df_, on=keys, how='inner', sort=False)['index'].values\n",
        "\n",
        "\n",
        "    place_df = makePerPlacePivotTable(input_df)\n",
        "    place_ids = place_df.index.values\n",
        "    X = place_df.values\n",
        "    y = place_df[target].values\n",
        "    y = create_bins(place_df[target].median(axis=1), bins)\n",
        "    \n",
        "    idx_train = []\n",
        "    idx_valid = []\n",
        "    for train_idx, valid_idx in self.skf_.split(X, y):\n",
        "      # 一度 PlaceID ごとの pivot table にして Fold を切ってもとに戻す\n",
        "      train_df = pd.DataFrame(\n",
        "        data=X[train_idx],\n",
        "        index=place_ids[train_idx],\n",
        "        columns=place_df.columns)\n",
        "      valid_df = pd.DataFrame(\n",
        "        data=X[valid_idx],\n",
        "        index=place_ids[valid_idx],\n",
        "        columns=place_df.columns)\n",
        "      out_train_idx = _get_original_index(train_df)\n",
        "      out_valid_idx = _get_original_index(valid_df)\n",
        "      assert len(out_train_idx) + len(out_valid_idx) == len(input_df)\n",
        "      assert set(out_train_idx) & set(out_valid_idx) == set()\n",
        "      idx_train.append(out_train_idx)\n",
        "      idx_valid.append(out_valid_idx)\n",
        "\n",
        "    return zip(idx_train, idx_valid)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xjyDV8Hsh_Q9"
      },
      "source": [
        "## 予測モデル"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7gEzAT6iB_z"
      },
      "source": [
        "from abc import abstractmethod\n",
        "\n",
        "\n",
        "class Base_Model(object):\n",
        "    @abstractmethod\n",
        "    def fit(self, x_train, y_train, x_valid, y_valid):\n",
        "        raise NotImplementedError\n",
        "\n",
        "    @abstractmethod\n",
        "    def predict(self, model, features):\n",
        "        raise NotImplementedError\n",
        "\n",
        "    def cv(self, y_train, train_features, test_features, fold_ids):\n",
        "        test_preds = np.zeros(len(test_features))\n",
        "        oof_preds = np.zeros(len(train_features))\n",
        "\n",
        "        for i_fold, (trn_idx, val_idx) in enumerate(fold_ids):\n",
        "            x_trn = train_features.iloc[trn_idx]\n",
        "            y_trn = y_train[trn_idx]\n",
        "            x_val = train_features.iloc[val_idx]\n",
        "            y_val = y_train[val_idx]\n",
        "\n",
        "            with Timer(prefix=f'fit fold={i_fold}'):\n",
        "                model = self.fit(x_trn, y_trn, x_val, y_val)\n",
        "\n",
        "            oof_preds[val_idx] = self.predict(model, x_val)\n",
        "            oof_score = np.sqrt(mean_squared_error(y_val, oof_preds[val_idx]))\n",
        "            print(f'Fold {i_fold} RMSLE: {oof_score:.4f}')\n",
        "            test_preds += self.predict(model, test_features) / len(fold_ids)\n",
        "\n",
        "        oof_score = np.sqrt(mean_squared_error(y_train, oof_preds))\n",
        "        print('-' * 50)\n",
        "        print(f'FINISHED | Whole RMSLE: {oof_score:.4f}')\n",
        "\n",
        "        evals_results = {\"evals_result\": {\n",
        "            \"oof_score\": oof_score,\n",
        "            \"n_data\": len(train_features),\n",
        "            \"n_features\": len(train_features.columns),\n",
        "        }}\n",
        "\n",
        "        return oof_preds, test_preds, evals_results\n",
        "\n",
        "\n",
        "cat_col = []\n",
        "class Lgbm(Base_Model):\n",
        "    def __init__(self, model_params):\n",
        "        self.model_params = model_params\n",
        "        self.models = []\n",
        "        self.feature_cols = None\n",
        "\n",
        "    def fit(self, x_train, y_train, x_valid, y_valid):\n",
        "        lgb_train = lgb.Dataset(x_train, y_train)\n",
        "        lgb_valid = lgb.Dataset(x_valid, y_valid)\n",
        "\n",
        "        model = lgb.train(self.model_params,\n",
        "            train_set=lgb_train,\n",
        "            valid_sets=[lgb_valid],\n",
        "            valid_names=['valid'],\n",
        "#            categorical_feature=cat_col,\n",
        "            early_stopping_rounds=100,\n",
        "            num_boost_round=10000,\n",
        "            verbose_eval=100)\n",
        "        self.models.append(model)\n",
        "        return model\n",
        "\n",
        "    def predict(self, model, features):\n",
        "        self.feature_cols = features.columns\n",
        "        return model.predict(features)\n",
        "\n",
        "    def visualize_importance(self, top_n=50):\n",
        "        feature_importance_df = pd.DataFrame()\n",
        "\n",
        "        for i,model in enumerate(self.models):\n",
        "            _df = pd.DataFrame()\n",
        "            _df['feature_importance'] = model.feature_importance(importance_type='gain')\n",
        "            _df['column'] = self.feature_cols\n",
        "            _df['fold'] = i + 1\n",
        "            feature_importance_df = pd.concat([feature_importance_df,_df],axis=0,ignore_index=True)\n",
        "\n",
        "        order = feature_importance_df.groupby('column').sum()[['feature_importance']].sort_values('feature_importance', ascending=False).index[:top_n]\n",
        "\n",
        "        fig, ax = plt.subplots(figsize=(8, max(6, len(order) * .25)))\n",
        "        sns.boxenplot(data=feature_importance_df,\n",
        "                      x='feature_importance', \n",
        "                      y='column', \n",
        "                      order=order, \n",
        "                      ax=ax, \n",
        "                      palette='viridis', \n",
        "                      orient='h')\n",
        "        ax.tick_params(axis='x', rotation=90)\n",
        "        ax.tick_params(axis='x', rotation=90)\n",
        "        ax.grid()\n",
        "        fig.tight_layout()\n",
        "        return fig, ax, order\n",
        "\n",
        "\n",
        "class Cat(Base_Model):\n",
        "    def __init__(self,model_params):\n",
        "        self.model_params = model_params\n",
        "    def fit(self,x_train,y_train,x_valid,y_valid):\n",
        "        train_pool = Pool(x_train,\n",
        "                          label=y_train,\n",
        "                          cat_features=cat_col)\n",
        "        valid_pool = Pool(x_valid,\n",
        "                          label=y_valid,\n",
        "                          cat_features=cat_col)\n",
        "\n",
        "        model = CatBoost(self.model_params)\n",
        "        model.fit(train_pool,\n",
        "                  early_stopping_rounds=50,\n",
        "                 plot=False,\n",
        "                 use_best_model=True,\n",
        "                 eval_set=[valid_pool],\n",
        "                  verbose=False)\n",
        "\n",
        "        return model\n",
        "\n",
        "    def predict(self,model,features):\n",
        "      pred = model.predict(features)\n",
        "      return pred\n",
        "\n",
        "\n",
        "class Xgb(Base_Model):\n",
        "    def __init__(self,model_params):\n",
        "        self.model_params = model_params\n",
        "\n",
        "    def fit(self,x_train,y_train,x_valid,y_valid):\n",
        "        xgb_train = xgb.DMatrix(x_train, label=y_train)\n",
        "        xgb_valid = xgb.DMatrix(x_valid, label=y_valid)\n",
        "\n",
        "        evals = [(xgb_train, 'train'), (xgb_valid, 'eval')]\n",
        "\n",
        "        model = xgb.train(self.model_params,\n",
        "                         xgb_train,\n",
        "                         num_boost_round=10000,\n",
        "                         early_stopping_rounds=50,\n",
        "                         evals=evals,\n",
        "                         verbose_eval=False)\n",
        "\n",
        "        return model\n",
        "\n",
        "    def predict(self, model,features):\n",
        "        return model.predict(xgb.DMatrix(features))\n",
        "\n",
        "\n",
        "class Rid(Base_Model):\n",
        "    def __init__(self):\n",
        "      self.model = None\n",
        "    def fit(self, x_train, y_train, x_valid, y_valid):\n",
        "        model = Ridge(\n",
        "            alpha=1, #L2係数\n",
        "            max_iter=1000,\n",
        "            random_state=10,\n",
        "                              )\n",
        "        model.fit(x_train, y_train)\n",
        "        return model\n",
        "\n",
        "    def predict(self, model, features):\n",
        "      return model.predict(features)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iDuvtMxTZOQ5"
      },
      "source": [
        "## 誤差分析"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1jzgCG76ZMUr"
      },
      "source": [
        "def addOutOfFoldPrediction(input_df: pd.DataFrame, out_of_fold_pred: np.ndarray):\n",
        "  out_df = input_df.copy()\n",
        "  out_df['CV:AverageLandPrice'] = out_of_fold_pred\n",
        "  return out_df\n",
        "\n",
        "\n",
        "def addSquareError(input_df):\n",
        "  out_df = input_df.copy()\n",
        "  out_df['CV:SquareError'] = (input_df['CV:AverageLandPrice'] - input_df['AverageLandPrice']) ** 2\n",
        "  return out_df\n",
        "\n",
        "\n",
        "def addError(input_df):\n",
        "  out_df = input_df.copy()\n",
        "  out_df['CV:Error'] = input_df['CV:AverageLandPrice'] - input_df['AverageLandPrice']\n",
        "  return out_df\n",
        "\n",
        "\n",
        "def plot2D(input_df: pd.DataFrame, feat: str, target: str, hue='Year'):\n",
        "  fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n",
        "\n",
        "  sns.scatterplot(x=feat, y=target, data=input_df, ax=ax, hue=hue)\n",
        "  ax.title.set_text(f'{target}-{feat} Plot')\n",
        "\n",
        "\n",
        "def plotCompare(df, feat, target1, target2, labels=('original', 'pred')):\n",
        "  fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n",
        "\n",
        "  sns.scatterplot(x=feat, y=target1, data=df, ax=ax, color='r', label=labels[0])\n",
        "  sns.scatterplot(x=feat, y=target2, data=df, ax=ax, color='g', label=labels[1])\n",
        "\n",
        "\n",
        "def plotCompareFeats(input_df: pd.DataFrame, feats: list, target1:str, target2: str, cols=4):\n",
        "  rows = (len(feats) - 1) // cols + 1\n",
        "\n",
        "  fig, axes = plt.subplots(rows, cols, figsize=(6 * cols, 4 * rows), sharey=True)\n",
        "  axes = axes.ravel()\n",
        "\n",
        "  for i, feat in enumerate(feats):\n",
        "    sns.scatterplot(x=feat, y=target1, data=input_df, ax=axes[i], color='r', label='original')\n",
        "    sns.scatterplot(x=feat, y=target2, data=input_df, ax=axes[i], color='g', label='pred')\n",
        "\n",
        "  plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fTx0iDBaZSIK"
      },
      "source": [
        "# データのロード"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0vfGM784Y-S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52a33b00-9533-4718-c552-54900c673527"
      },
      "source": [
        "train_df = load_data('TrainDataSet')\n",
        "test_df = load_data('EvaluationData')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "run\n",
            "run\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "AuYKDVseylDD",
        "outputId": "ee54c67b-63b2-4080-e9b9-998421e114aa"
      },
      "source": [
        "df_ = makePerPlacePivotTable(convertTargetLog1p(train_df))\n",
        "\n",
        "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
        "idx = df_['AverageLandPrice'].isnull().sum(axis=1) <= 22 * 0.3\n",
        "sns.heatmap(df_.loc[idx, 'AverageLandPrice'].isna(), ax=ax, cbar=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6759c26b50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fd5jr9i_OBY6"
      },
      "source": [
        "# データのない部分の地価を推定する\n",
        "\n",
        "* 増加率の中央値で逆算する\n",
        "* boost model を作る"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XhlOHNynkz4j"
      },
      "source": [
        "## 光量の大きさと、前年と比べて増減しやすいかどうかの指標"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S9usZSF4sI-n"
      },
      "source": [
        "### 補完をしなかった場合"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "vh5L5SqimncE",
        "outputId": "046eae44-341f-4ca5-f236-3f8194efa440"
      },
      "source": [
        "input_df = makePerPlacePivotTable(convertTargetLog1p(train_df))\n",
        "df_ = _reverse_columnwise_cumsum(\n",
        "    _createLandPriceLikelihood(input_df))\n",
        "df__ = pd.concat([recoverToOriginalTable(input_df)[['AverageLandPrice', 'Year']], recoverToOriginalTable(df_)[0].rename('Likelihood')], axis=1)\n",
        "sns.scatterplot('Likelihood', 'AverageLandPrice', data=df__, hue='Year')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6758e09a10>"
            ]
          },
          "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": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWdErUAHs_JN"
      },
      "source": [
        "### 不完全なデータを捨てた場合"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "yuiLY7g8sSrb",
        "outputId": "7d34a5f7-5c2e-47a6-8a28-df1ba210684a"
      },
      "source": [
        "input_df = dropTooMuchNaNValues(makePerPlacePivotTable(convertTargetLog1p(train_df)), threshold=1.0)\n",
        "df_ = _reverse_columnwise_cumsum(\n",
        "    _createLandPriceLikelihood(input_df))\n",
        "df__ = pd.concat([recoverToOriginalTable(input_df)[['AverageLandPrice', 'Year']], recoverToOriginalTable(df_)[0].rename('Likelihood')], axis=1)\n",
        "sns.scatterplot('Likelihood', 'AverageLandPrice', data=df__, hue='Year')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f674f44e450>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5MTNG1vsthEf"
      },
      "source": [
        "## NaN が30%未満のデータを補完"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "KuIh6rdytO6c",
        "outputId": "518662b7-ad6f-4530-e17b-8e1a883cb1e0"
      },
      "source": [
        "input_df = imputeKNN(dropTooMuchNaNValues(addAreaFeature(makePerPlacePivotTable(convertTargetLog1p(train_df))), threshold=.7))\n",
        "df_ = _reverse_columnwise_cumsum(\n",
        "    _createLandPriceLikelihood(input_df))\n",
        "df__ = pd.concat([recoverToOriginalTable(input_df)[['AverageLandPrice', 'Year']], recoverToOriginalTable(df_)[0].rename('Likelihood')], axis=1)\n",
        "sns.scatterplot('Likelihood', 'AverageLandPrice', data=df__, hue='Year')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb05ed3b10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vfTWzagtsN3k"
      },
      "source": [
        "### 補完あり"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "G42i3DH7pdDu",
        "outputId": "bc7bb6e2-0800-4343-c076-f5e9f922550f"
      },
      "source": [
        "input_df = makePerPlacePivotTable(preprocess(train_df))\n",
        "df_ = _reverse_columnwise_cumsum(\n",
        "    _createLandPriceLikelihood(input_df))\n",
        "df__ = pd.concat([recoverToOriginalTable(input_df)[['AverageLandPrice', 'Year']], recoverToOriginalTable(df_)[0].rename('Likelihood')], axis=1)\n",
        "sns.scatterplot('Likelihood', 'AverageLandPrice', data=df__, hue='Year')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb078f8850>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O-7WDNe41uU7"
      },
      "source": [
        "# 前処理"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GnHj5vkMuL4H"
      },
      "source": [
        "preprocessed_train_feat_df = preprocess(train_df)\n",
        "preprocessed_test_feat_df = orderAsOriginalData(\n",
        "  preprocess(\n",
        "      addTargetFeature(test_df)\n",
        "  ), test_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_mnoi1zKmp2F",
        "outputId": "caff02e0-b009-4bf2-81d9-349854543e73"
      },
      "source": [
        "print(preprocessed_train_feat_df.shape)\n",
        "print(preprocessed_test_feat_df.shape)\n",
        "\n",
        "print(preprocessed_train_feat_df.isna().sum())\n",
        "assert preprocessed_train_feat_df.isna().sum().sum() == 0\n",
        "assert preprocessed_test_feat_df.isna().sum().sum() == 0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(22066, 7)\n",
            "(13860, 7)\n",
            "PlaceID              0\n",
            "Year                 0\n",
            "AverageLandPrice     0\n",
            "MeanLight            0\n",
            "SumLight             0\n",
            "PopulationDensity    0\n",
            "Area                 0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eM19FXtW2G3E"
      },
      "source": [
        "# 特徴量の作成"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z5GCv63f4wGN",
        "outputId": "f1ff027f-cb32-4b39-97eb-0c4e70acad46"
      },
      "source": [
        "train_feat_df = postprocess(to_feature(preprocessed_train_feat_df))\n",
        "test_feat_df = postprocess(to_feature(preprocessed_test_feat_df))\n",
        "assert len(train_feat_df) == len(preprocessed_train_feat_df)\n",
        "assert len(test_feat_df) == len(preprocessed_test_feat_df)\n",
        "print(train_feat_df.shape)\n",
        "print(test_feat_df.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/2 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "createcreateRawFeature  0.010[s]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 2/2 [00:00<00:00,  3.82it/s]\n",
            "  0%|          | 0/2 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "createcreatePlaceFeature  0.499[s]\n",
            "createcreateRawFeature  0.009[s]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 2/2 [00:00<00:00,  5.39it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "createcreatePlaceFeature  0.352[s]\n",
            "(22066, 169)\n",
            "(13860, 169)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pNLnENIU98ma"
      },
      "source": [
        "# モデルの学習と交差検証\n",
        "\n",
        "* Group K Fold\n",
        "\n",
        "同じ PlaceID だと予測がしやすいが、学習データとテストデータに PlaceID の重複が少ないため、過剰適合になる。test と train で同じ PlaceID が現れないようにするために、 GroupKFold を使う。\n",
        "\n",
        "ただし、普通にGroupKFoldしたのでは、\n",
        "\n",
        "[1] https://qiita.com/cha_kabu/items/679ff02f6386f8e3034a\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gbJ-BhEk8gkk"
      },
      "source": [
        "def fit_lgbm(X, y, train_df, test_df, n_splits, random_state, features):\n",
        "  lgbm_params = {\n",
        "      # 目的関数. これの意味で最小となるようなパラメータを探します. \n",
        "      'objective': 'rmse', \n",
        "\n",
        "      # 学習率. 小さいほどなめらかな決定境界が作られて性能向上に繋がる場合が多いです、\n",
        "      # がそれだけ木を作るため学習に時間がかかります\n",
        "      'learning_rate': .01,\n",
        "      \n",
        "      # L2 Reguralization\n",
        "      'reg_lambda': 1.,\n",
        "      # こちらは L1 \n",
        "      'reg_alpha': .1,\n",
        "      \n",
        "      # 木の深さ. 深い木を許容するほどより複雑な交互作用を考慮するようになります\n",
        "      'max_depth': 10,\n",
        "      \n",
        "      # 木の最大数. early_stopping という枠組みで木の数は制御されるようにしていますのでとても大きい値を指定しておきます.\n",
        "      'n_estimators': 20000,\n",
        "      \n",
        "      # 木を作る際に考慮する特徴量の割合. 1以下を指定すると特徴をランダムに欠落させます。小さくすることで, まんべんなく特徴を使うという効果があります.\n",
        "      'colsample_bytree': .5, \n",
        "      \n",
        "      # 最小分割でのデータ数. 小さいとより細かい粒度の分割方法を許容します.\n",
        "      'min_child_samples': 5,\n",
        "      \n",
        "      # bagging の頻度と割合\n",
        "      'subsample_freq': 3,\n",
        "      'subsample': .9,\n",
        "      \n",
        "      # 特徴重要度計算のロジック(後述)\n",
        "      'importance_type': 'gain', \n",
        "      'random_state': 71,\n",
        "  }\n",
        "  print('-' * 50)\n",
        "  print(f'random_state={random_state}')\n",
        "  cv = split_cv(train_df, n_splits=n_splits, random_state=random_state)\n",
        "\n",
        "  model = Lgbm(lgbm_params)\n",
        "  oof_pred, test_pred, eval_result = model.cv(\n",
        "    y, train_df[features], test_df[features], cv)\n",
        "\n",
        "  return model, oof_pred, test_pred, eval_result\n",
        "\n",
        "\n",
        "def fit_catboost(X, y, train_df, test_df, n_splits, random_state, features):\n",
        "  cat_col =[]\n",
        "  cat_params = {\n",
        "      'loss_function': 'RMSE',\n",
        "      'num_boost_round': 10000,\n",
        "      'depth': 7\n",
        "      }\n",
        "\n",
        "  print('-' * 50)\n",
        "  print(f'random_state={random_state}')\n",
        "  cv = split_cv(train_df, n_splits=n_splits, random_state=random_state)\n",
        "\n",
        "  model = Cat(cat_params)\n",
        "  oof_pred, test_pred, eval_result = model.cv(\n",
        "      y, train_df[features], test_df[features], cv)\n",
        "\n",
        "  return model, oof_pred, test_pred, eval_result\n",
        "\n",
        "\n",
        "def fit_xgb(X, y, train_df, test_df, n_splits, random_state, features):\n",
        "  xgb_params = {\n",
        "      'max_depth': 6,\n",
        "      'lambda': 10, \n",
        "      'objective': 'reg:squarederror',\n",
        "      'eval_metric': 'rmse',\n",
        "      'tree_method': 'hist',\n",
        "      'learning_rate': 0.01}\n",
        "\n",
        "  print('-' * 50)\n",
        "  print(f'random_state={random_state}')\n",
        "  cv = split_cv(train_df, n_splits=n_splits, random_state=random_state)\n",
        "\n",
        "  model = Xgb(xgb_params)\n",
        "  oof_pred, test_pred, eval_result = model.cv(\n",
        "    y, train_df[features], test_df[features], cv)\n",
        "\n",
        "  return model, oof_pred, test_pred, eval_result\n",
        "\n",
        "\n",
        "def fit_ridge(y, train_df, test_df, n_splits, random_state, features):\n",
        "  print('-' * 50)\n",
        "  print(f'random_state={random_state}')\n",
        "  cv = split_cv(train_df, n_splits=n_splits, random_state=random_state)\n",
        "\n",
        "  model = Rid()\n",
        "  oof_pred, test_pred, eval_result = model.cv(\n",
        "      y, stack_train_df, stack_test_df, cv)\n",
        "\n",
        "  return model, oof_pred, test_pred, eval_result"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XrJ-N7508VVE"
      },
      "source": [
        "def split_cv(feature_df, random_state, n_splits=5):\n",
        "  y = feature_df['AverageLandPrice'].values\n",
        "  X = feature_df.drop(['AverageLandPrice', 'PlaceID'], axis=1).values\n",
        "\n",
        "  kf = FullSetPlaceFeatureKFold(n_splits=n_splits, shuffle=True, random_state=random_state)\n",
        "  cv = list(kf.split(feature_df, target='Area', bins=8))\n",
        "\n",
        "  return cv"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M45u8lrSTHRY"
      },
      "source": [
        "## Feature Impotance の確認"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ja8ez0w19Rgu"
      },
      "source": [
        "n_splits=5\n",
        "random_states=[17]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXeU4MoFTmNI"
      },
      "source": [
        "X = train_feat_df.drop(['AverageLandPrice', 'PlaceID'], axis=1).values\n",
        "y = train_feat_df['AverageLandPrice'].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zeZGBtJ78bkj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9701b85f-00e8-475a-8dee-6ac1238a7115"
      },
      "source": [
        "features = train_feat_df.drop(['AverageLandPrice', 'PlaceID'], axis=1).columns\n",
        "lgbm_model, lgbm_oof_pred, lgbm_test_pred, lgbm_eval_result = \\\n",
        "  fit_lgbm(X, y, train_feat_df, test_feat_df, n_splits=n_splits, random_state=random_states[0], features=features)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--------------------------------------------------\n",
            "random_state=17\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:667: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5.\n",
            "  % (min_groups, self.n_splits)), UserWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:118: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
            "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training until validation scores don't improve for 100 rounds.\n",
            "[100]\tvalid's rmse: 0.699993\n",
            "[200]\tvalid's rmse: 0.595504\n",
            "[300]\tvalid's rmse: 0.568813\n",
            "[400]\tvalid's rmse: 0.557388\n",
            "[500]\tvalid's rmse: 0.553386\n",
            "[600]\tvalid's rmse: 0.551338\n",
            "[700]\tvalid's rmse: 0.550093\n",
            "[800]\tvalid's rmse: 0.550043\n",
            "[900]\tvalid's rmse: 0.549642\n",
            "[1000]\tvalid's rmse: 0.549238\n",
            "[1100]\tvalid's rmse: 0.548792\n",
            "[1200]\tvalid's rmse: 0.54852\n",
            "[1300]\tvalid's rmse: 0.548402\n",
            "[1400]\tvalid's rmse: 0.548224\n",
            "[1500]\tvalid's rmse: 0.548118\n",
            "Early stopping, best iteration is:\n",
            "[1462]\tvalid's rmse: 0.548099\n",
            "fit fold=0 22.570[s]\n",
            "Fold 0 RMSLE: 0.5481\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:118: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
            "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training until validation scores don't improve for 100 rounds.\n",
            "[100]\tvalid's rmse: 0.645894\n",
            "[200]\tvalid's rmse: 0.539665\n",
            "[300]\tvalid's rmse: 0.509137\n",
            "[400]\tvalid's rmse: 0.497764\n",
            "[500]\tvalid's rmse: 0.493491\n",
            "[600]\tvalid's rmse: 0.491089\n",
            "[700]\tvalid's rmse: 0.489141\n",
            "[800]\tvalid's rmse: 0.488436\n",
            "[900]\tvalid's rmse: 0.487746\n",
            "[1000]\tvalid's rmse: 0.487525\n",
            "[1100]\tvalid's rmse: 0.487252\n",
            "[1200]\tvalid's rmse: 0.487144\n",
            "[1300]\tvalid's rmse: 0.487015\n",
            "Early stopping, best iteration is:\n",
            "[1275]\tvalid's rmse: 0.486915\n",
            "fit fold=1 19.796[s]\n",
            "Fold 1 RMSLE: 0.4869\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:118: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
            "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training until validation scores don't improve for 100 rounds.\n",
            "[100]\tvalid's rmse: 0.71307\n",
            "[200]\tvalid's rmse: 0.601094\n",
            "[300]\tvalid's rmse: 0.575604\n",
            "[400]\tvalid's rmse: 0.566487\n",
            "[500]\tvalid's rmse: 0.563075\n",
            "[600]\tvalid's rmse: 0.561363\n",
            "[700]\tvalid's rmse: 0.560875\n",
            "[800]\tvalid's rmse: 0.560568\n",
            "[900]\tvalid's rmse: 0.560124\n",
            "Early stopping, best iteration is:\n",
            "[876]\tvalid's rmse: 0.560006\n",
            "fit fold=2 14.796[s]\n",
            "Fold 2 RMSLE: 0.5600\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:118: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
            "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training until validation scores don't improve for 100 rounds.\n",
            "[100]\tvalid's rmse: 0.676389\n",
            "[200]\tvalid's rmse: 0.553646\n",
            "[300]\tvalid's rmse: 0.514629\n",
            "[400]\tvalid's rmse: 0.499988\n",
            "[500]\tvalid's rmse: 0.493617\n",
            "[600]\tvalid's rmse: 0.49189\n",
            "[700]\tvalid's rmse: 0.490153\n",
            "[800]\tvalid's rmse: 0.488718\n",
            "[900]\tvalid's rmse: 0.487833\n",
            "[1000]\tvalid's rmse: 0.487071\n",
            "[1100]\tvalid's rmse: 0.486275\n",
            "[1200]\tvalid's rmse: 0.485723\n",
            "[1300]\tvalid's rmse: 0.485277\n",
            "[1400]\tvalid's rmse: 0.484875\n",
            "[1500]\tvalid's rmse: 0.484775\n",
            "[1600]\tvalid's rmse: 0.484646\n",
            "[1700]\tvalid's rmse: 0.484582\n",
            "[1800]\tvalid's rmse: 0.484427\n",
            "[1900]\tvalid's rmse: 0.484386\n",
            "[2000]\tvalid's rmse: 0.484362\n",
            "Early stopping, best iteration is:\n",
            "[1938]\tvalid's rmse: 0.484306\n",
            "fit fold=3 28.237[s]\n",
            "Fold 3 RMSLE: 0.4843\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/lightgbm/engine.py:118: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
            "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training until validation scores don't improve for 100 rounds.\n",
            "[100]\tvalid's rmse: 0.674793\n",
            "[200]\tvalid's rmse: 0.588342\n",
            "[300]\tvalid's rmse: 0.566423\n",
            "[400]\tvalid's rmse: 0.556895\n",
            "[500]\tvalid's rmse: 0.550839\n",
            "[600]\tvalid's rmse: 0.548122\n",
            "[700]\tvalid's rmse: 0.546763\n",
            "[800]\tvalid's rmse: 0.545516\n",
            "[900]\tvalid's rmse: 0.544762\n",
            "[1000]\tvalid's rmse: 0.544133\n",
            "[1100]\tvalid's rmse: 0.543439\n",
            "[1200]\tvalid's rmse: 0.543095\n",
            "[1300]\tvalid's rmse: 0.542578\n",
            "[1400]\tvalid's rmse: 0.542286\n",
            "[1500]\tvalid's rmse: 0.541982\n",
            "[1600]\tvalid's rmse: 0.541814\n",
            "[1700]\tvalid's rmse: 0.541663\n",
            "[1800]\tvalid's rmse: 0.541376\n",
            "[1900]\tvalid's rmse: 0.541149\n",
            "[2000]\tvalid's rmse: 0.541012\n",
            "[2100]\tvalid's rmse: 0.540892\n",
            "[2200]\tvalid's rmse: 0.54087\n",
            "[2300]\tvalid's rmse: 0.54082\n",
            "[2400]\tvalid's rmse: 0.540732\n",
            "[2500]\tvalid's rmse: 0.540687\n",
            "[2600]\tvalid's rmse: 0.540599\n",
            "[2700]\tvalid's rmse: 0.540527\n",
            "[2800]\tvalid's rmse: 0.540466\n",
            "Early stopping, best iteration is:\n",
            "[2790]\tvalid's rmse: 0.540433\n",
            "fit fold=4 38.361[s]\n",
            "Fold 4 RMSLE: 0.5404\n",
            "--------------------------------------------------\n",
            "FINISHED | Whole RMSLE: 0.5249\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 909
        },
        "id": "NekrrTUWVghD",
        "outputId": "d742f4ef-0286-44b3-e2cd-eee081e41037"
      },
      "source": [
        "fig, ax, order = lgbm_model.visualize_importance()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x900 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "wjMT-WS6ZQsn",
        "outputId": "f89f9d46-ec71-4799-b05d-cb2129d22708"
      },
      "source": [
        "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
        "sns.scatterplot(x=y, y=lgbm_oof_pred, ax=ax, label='oof', color='orange')\n",
        "ax.set_xlabel('OOF Grand Truth')\n",
        "ax.set_ylabel('OOF Pred')\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f674bf0b790>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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