[데이콘]따릉이_대여량_예측.ipynb

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    "colab": {
      "name": "[데이콘]따릉이_대여량_예측.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOxpGxDw7rjs3B1d5Si3iYm",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/inrap8206/Dacon/blob/main/%5B%EB%8D%B0%EC%9D%B4%EC%BD%98%5D%EB%94%B0%EB%A6%89%EC%9D%B4_%EB%8C%80%EC%97%AC%EB%9F%89_%EC%98%88%EC%B8%A1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oPKN44HDnvF2"
      },
      "source": [
        "# Data Load"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "d6AYG8xCRK9N",
        "outputId": "d445b2d4-02c9-4310-d94b-90ab9d9925ad"
      },
      "source": [
        "import pandas as pd\n",
        "from google.colab import drive, files\n",
        "drive.mount('/content/drive')\n",
        "import numpy as np\n",
        "import re\n",
        "import sklearn\n",
        "import xgboost as xgb\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "import plotly.offline as py\n",
        "py.init_notebook_mode(connected=True)\n",
        "import plotly.graph_objs as go\n",
        "import plotly.tools as tls\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import KFold\n",
        "\n",
        "\n",
        "train = pd.read_csv('./drive/My Drive/Colab Notebooks/dacon/따릉이/train.csv', parse_dates=['date_time'])\n",
        "test = pd.read_csv('./drive/My Drive/Colab Notebooks/dacon/따릉이/test.csv', parse_dates=['date_time'])\n",
        "sub = pd.read_csv('./drive/My Drive/Colab Notebooks/dacon/따릉이/sample_submission.csv', parse_dates=['date_time'])\n",
        "\n",
        "\n",
        "for df in [train, test]:\n",
        "\n",
        "  # date_time 전처리\n",
        "  df['year'] = df['date_time'].dt.year\n",
        "  df['month'] = df['date_time'].dt.month\n",
        "  df['day'] = df['date_time'].dt.day\n",
        "  df['weekday'] = df['date_time'].dt.weekday\n",
        "  df.drop('date_time', axis=1, inplace=True)\n",
        "  \n",
        "  # temp 생성 및 불쾌지수 생성\n",
        "  df['temp'] = (df['low_temp'] + df['high_temp'])/2\n",
        "  df['discomfort'] = (5/9)*df['temp']-0.55*((100-df['humidity'])/100)*((5/9)*df['temp']-26)+32                                                                     \n",
        "\n",
        "  # 일교차, 덥고 습한 날씨, 춥고 바람부는 날씨\n",
        "  df['temp_diff'] = df['high_temp'] - df['low_temp']\n",
        "  df['sweat'] = df['high_temp']*df['humidity']\n",
        "  df['cold_windy'] = df['low_temp']*df['wind_speed']\n",
        "\n",
        "  # 강수확률 Binning\n",
        "  df.loc[df['Precipitation_Probability'] <40, 'Precipitation_Probability'] = 0\n",
        "  df.loc[df['Precipitation_Probability'] >=40, 'Precipitation_Probability'] = 1\n",
        "\n",
        "train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {}
        },
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          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>sky_condition</th>\n",
              "      <th>precipitation_form</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>humidity</th>\n",
              "      <th>low_temp</th>\n",
              "      <th>high_temp</th>\n",
              "      <th>Precipitation_Probability</th>\n",
              "      <th>number_of_rentals</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>weekday</th>\n",
              "      <th>temp</th>\n",
              "      <th>discomfort</th>\n",
              "      <th>temp_diff</th>\n",
              "      <th>sweat</th>\n",
              "      <th>cold_windy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>207.500</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.050</td>\n",
              "      <td>75.000</td>\n",
              "      <td>12.600</td>\n",
              "      <td>21.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22994</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>16.8000</td>\n",
              "      <td>43.625000</td>\n",
              "      <td>8.400</td>\n",
              "      <td>1575.000000</td>\n",
              "      <td>38.430000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>208.317</td>\n",
              "      <td>2.950</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3.278</td>\n",
              "      <td>69.833</td>\n",
              "      <td>12.812</td>\n",
              "      <td>19.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28139</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>15.9060</td>\n",
              "      <td>43.684381</td>\n",
              "      <td>6.188</td>\n",
              "      <td>1326.827000</td>\n",
              "      <td>41.997736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>213.516</td>\n",
              "      <td>2.911</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.690</td>\n",
              "      <td>74.879</td>\n",
              "      <td>10.312</td>\n",
              "      <td>15.316</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26817</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>12.8140</td>\n",
              "      <td>41.727607</td>\n",
              "      <td>5.004</td>\n",
              "      <td>1146.846764</td>\n",
              "      <td>27.739280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>143.836</td>\n",
              "      <td>3.692</td>\n",
              "      <td>0.425</td>\n",
              "      <td>3.138</td>\n",
              "      <td>71.849</td>\n",
              "      <td>8.312</td>\n",
              "      <td>12.368</td>\n",
              "      <td>1.0</td>\n",
              "      <td>26034</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>10.3400</td>\n",
              "      <td>40.880622</td>\n",
              "      <td>4.056</td>\n",
              "      <td>888.628432</td>\n",
              "      <td>26.083056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>95.905</td>\n",
              "      <td>4.000</td>\n",
              "      <td>0.723</td>\n",
              "      <td>3.186</td>\n",
              "      <td>73.784</td>\n",
              "      <td>5.875</td>\n",
              "      <td>10.421</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2833</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>8.1480</td>\n",
              "      <td>39.622864</td>\n",
              "      <td>4.546</td>\n",
              "      <td>768.903064</td>\n",
              "      <td>18.717750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>268</th>\n",
              "      <td>228.662</td>\n",
              "      <td>3.980</td>\n",
              "      <td>0.223</td>\n",
              "      <td>2.271</td>\n",
              "      <td>78.378</td>\n",
              "      <td>20.500</td>\n",
              "      <td>27.526</td>\n",
              "      <td>0.0</td>\n",
              "      <td>96150</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>26</td>\n",
              "      <td>4</td>\n",
              "      <td>24.0130</td>\n",
              "      <td>46.846029</td>\n",
              "      <td>7.026</td>\n",
              "      <td>2157.432828</td>\n",
              "      <td>46.555500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>269</th>\n",
              "      <td>207.770</td>\n",
              "      <td>2.865</td>\n",
              "      <td>0.081</td>\n",
              "      <td>1.794</td>\n",
              "      <td>78.412</td>\n",
              "      <td>20.812</td>\n",
              "      <td>28.842</td>\n",
              "      <td>0.0</td>\n",
              "      <td>107001</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>27</td>\n",
              "      <td>5</td>\n",
              "      <td>24.8270</td>\n",
              "      <td>47.242190</td>\n",
              "      <td>8.030</td>\n",
              "      <td>2261.558904</td>\n",
              "      <td>37.336728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270</th>\n",
              "      <td>282.568</td>\n",
              "      <td>1.730</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.820</td>\n",
              "      <td>72.736</td>\n",
              "      <td>21.000</td>\n",
              "      <td>29.053</td>\n",
              "      <td>0.0</td>\n",
              "      <td>98568</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
              "      <td>25.0265</td>\n",
              "      <td>47.717489</td>\n",
              "      <td>8.053</td>\n",
              "      <td>2113.199008</td>\n",
              "      <td>38.220000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271</th>\n",
              "      <td>137.027</td>\n",
              "      <td>2.257</td>\n",
              "      <td>0.088</td>\n",
              "      <td>2.043</td>\n",
              "      <td>70.473</td>\n",
              "      <td>19.625</td>\n",
              "      <td>26.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>70053</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>22.8125</td>\n",
              "      <td>46.837797</td>\n",
              "      <td>6.375</td>\n",
              "      <td>1832.298000</td>\n",
              "      <td>40.093875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>272</th>\n",
              "      <td>120.797</td>\n",
              "      <td>3.622</td>\n",
              "      <td>0.432</td>\n",
              "      <td>5.574</td>\n",
              "      <td>77.061</td>\n",
              "      <td>19.125</td>\n",
              "      <td>26.053</td>\n",
              "      <td>1.0</td>\n",
              "      <td>38086</td>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>22.5890</td>\n",
              "      <td>46.246427</td>\n",
              "      <td>6.928</td>\n",
              "      <td>2007.670233</td>\n",
              "      <td>106.602750</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>273 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     wind_direction  sky_condition  ...        sweat  cold_windy\n",
              "0           207.500          4.000  ...  1575.000000   38.430000\n",
              "1           208.317          2.950  ...  1326.827000   41.997736\n",
              "2           213.516          2.911  ...  1146.846764   27.739280\n",
              "3           143.836          3.692  ...   888.628432   26.083056\n",
              "4            95.905          4.000  ...   768.903064   18.717750\n",
              "..              ...            ...  ...          ...         ...\n",
              "268         228.662          3.980  ...  2157.432828   46.555500\n",
              "269         207.770          2.865  ...  2261.558904   37.336728\n",
              "270         282.568          1.730  ...  2113.199008   38.220000\n",
              "271         137.027          2.257  ...  1832.298000   40.093875\n",
              "272         120.797          3.622  ...  2007.670233  106.602750\n",
              "\n",
              "[273 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "tj90tm5rMWF-",
        "outputId": "57b62019-124f-4e72-e144-81c5c0a2b357"
      },
      "source": [
        "train.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>sky_condition</th>\n",
              "      <th>precipitation_form</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>humidity</th>\n",
              "      <th>low_temp</th>\n",
              "      <th>high_temp</th>\n",
              "      <th>Precipitation_Probability</th>\n",
              "      <th>number_of_rentals</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>weekday</th>\n",
              "      <th>temp</th>\n",
              "      <th>discomfort</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "      <td>273.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>202.750967</td>\n",
              "      <td>2.288256</td>\n",
              "      <td>0.100963</td>\n",
              "      <td>2.480963</td>\n",
              "      <td>56.745491</td>\n",
              "      <td>13.795249</td>\n",
              "      <td>23.384733</td>\n",
              "      <td>0.106227</td>\n",
              "      <td>59574.978022</td>\n",
              "      <td>2019.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>18.589991</td>\n",
              "      <td>46.114569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>56.659232</td>\n",
              "      <td>0.961775</td>\n",
              "      <td>0.203193</td>\n",
              "      <td>0.884397</td>\n",
              "      <td>12.351268</td>\n",
              "      <td>5.107711</td>\n",
              "      <td>5.204605</td>\n",
              "      <td>0.308694</td>\n",
              "      <td>27659.575774</td>\n",
              "      <td>0.817996</td>\n",
              "      <td>0.813489</td>\n",
              "      <td>26.316093</td>\n",
              "      <td>2.003673</td>\n",
              "      <td>5.052679</td>\n",
              "      <td>2.073094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>57.047000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.753000</td>\n",
              "      <td>24.831000</td>\n",
              "      <td>1.938000</td>\n",
              "      <td>9.895000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1037.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.197500</td>\n",
              "      <td>39.303282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>171.541000</td>\n",
              "      <td>1.405000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.820000</td>\n",
              "      <td>47.196000</td>\n",
              "      <td>9.938000</td>\n",
              "      <td>19.842000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>36761.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.083500</td>\n",
              "      <td>44.517314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>209.774000</td>\n",
              "      <td>2.167000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.411000</td>\n",
              "      <td>55.845000</td>\n",
              "      <td>14.375000</td>\n",
              "      <td>24.158000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>63032.000000</td>\n",
              "      <td>2019.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>19.398000</td>\n",
              "      <td>46.443876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>238.412000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.088000</td>\n",
              "      <td>2.924000</td>\n",
              "      <td>66.419000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>27.526000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>81515.000000</td>\n",
              "      <td>2020.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>22.837500</td>\n",
              "      <td>47.618209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>321.622000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.607000</td>\n",
              "      <td>88.885000</td>\n",
              "      <td>22.312000</td>\n",
              "      <td>33.421000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>110377.000000</td>\n",
              "      <td>2020.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>27.523000</td>\n",
              "      <td>49.833182</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       wind_direction  sky_condition  ...        temp  discomfort\n",
              "count      273.000000     273.000000  ...  273.000000  273.000000\n",
              "mean       202.750967       2.288256  ...   18.589991   46.114569\n",
              "std         56.659232       0.961775  ...    5.052679    2.073094\n",
              "min         57.047000       1.000000  ...    6.197500   39.303282\n",
              "25%        171.541000       1.405000  ...   15.083500   44.517314\n",
              "50%        209.774000       2.167000  ...   19.398000   46.443876\n",
              "75%        238.412000       3.000000  ...   22.837500   47.618209\n",
              "max        321.622000       4.000000  ...   27.523000   49.833182\n",
              "\n",
              "[8 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fk752pTXbPOx"
      },
      "source": [
        "# lgbm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "vlUupVVrs1Vc",
        "outputId": "1bde2462-8edb-47bc-8f0e-bfa38dc48d23"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression \n",
        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from xgboost import XGBRegressor\n",
        "from lightgbm import LGBMRegressor\n",
        "#from catboost import CatBoostRegressor\n",
        "from sklearn.model_selection import cross_val_score, KFold, LeaveOneOut\n",
        "from sklearn.metrics import mean_absolute_error, make_scorer\n",
        "\n",
        "\n",
        "vars = ['sky_condition', 'precipitation_form', 'wind_speed',\n",
        "       'humidity', 'low_temp', 'high_temp', 'Precipitation_Probability',\n",
        "       'year', 'month', 'temp','discomfort', 'temp_diff', 'sweat'] # wind_direction,'day','week_day','cold_windy' 삭제\n",
        "\n",
        "X_train = train[vars]  \n",
        "X_test = test[vars] \n",
        "\n",
        "\n",
        "y_train = np.log1p(train['number_of_rentals'])\n",
        "\n",
        "loo = LeaveOneOut()\n",
        "\n",
        "# model = GradientBoostingRegressor(random_state=2021) # val_score : 7343.656513396672\n",
        "lgb_model = LGBMRegressor(random_state=2021,  # val_score : 7553.643677541325\n",
        "                      max_depth=2,\n",
        "                      learning_rate=0.2,\n",
        "                      n_estimators=100,\n",
        "                      min_child_samples = 12,\n",
        "                      )\n",
        "\n",
        "def nmae(true, pred):\n",
        "  true = np.expm1(true)\n",
        "  pred = np.expm1(pred)\n",
        "  score = np.mean((np.abs(true-pred))/true)    \n",
        "  return score\n",
        "\n",
        "score = -1*cross_val_score(lgb_model, X_train, y_train, cv=loo, scoring=make_scorer(nmae,greater_is_better=False)) \n",
        "print(score.mean()) #0.1834527668189701\n",
        "\n",
        "\n",
        "lgb_model.fit(X_train, y_train)\n",
        "lg_pred = np.expm1(lgb_model.predict(X_test))\n",
        "\n",
        "sub.iloc[:,1] = lg_pred\n",
        "sub.to_csv('bicycle_lg.csv', index=False) # 0.405187303\n",
        "files.download('bicycle_lg.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.1834527668189701\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_1e37f558-4ea6-4ea9-8f96-024e931494f0\", \"bicycle_lg.csv\", 2673)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fBaahUvkbw5Q"
      },
      "source": [
        "# gb"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "BOIEEO-KbybA",
        "outputId": "3009b448-e3a2-4e3b-ebc4-9a69c3f5e4b8"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression \n",
        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from xgboost import XGBRegressor\n",
        "from lightgbm import LGBMRegressor\n",
        "#from catboost import CatBoostRegressor\n",
        "from sklearn.model_selection import cross_val_score, KFold, LeaveOneOut\n",
        "from sklearn.metrics import mean_absolute_error, make_scorer\n",
        "\n",
        "\n",
        "vars = ['sky_condition', 'precipitation_form', 'wind_speed',\n",
        "       'humidity', 'low_temp', 'high_temp', 'Precipitation_Probability',\n",
        "       'year', 'month', 'temp','discomfort', 'temp_diff', 'sweat'] # wind_direction,'day','week_day','cold_windy' 삭제\n",
        "\n",
        "X_train = train[vars]  \n",
        "X_test = test[vars] \n",
        "\n",
        "\n",
        "y_train = np.log1p(train['number_of_rentals'])\n",
        "\n",
        "loo = LeaveOneOut()\n",
        "\n",
        "gb_model = GradientBoostingRegressor(random_state=2021,\n",
        "                                     max_depth=2,\n",
        "                                     criterion='mae',\n",
        "                                     n_estimators=400,  \n",
        "                                     ) \n",
        "\n",
        "def nmae(true, pred):\n",
        "  true = np.expm1(true)\n",
        "  pred = np.expm1(pred)\n",
        "  score = np.mean((np.abs(true-pred))/true)    \n",
        "  return score\n",
        "\n",
        "# score = -1*cross_val_score(gb_model, X_train, y_train, cv=loo, scoring=make_scorer(nmae,greater_is_better=False)) \n",
        "# print(score.mean()) #0.18532999634717495\n",
        "\n",
        "\n",
        "gb_model.fit(X_train, y_train)\n",
        "gb_pred = np.expm1(gb_model.predict(X_test))\n",
        "\n",
        "sub.iloc[:,1] = gb_pred\n",
        "sub.to_csv('bicycle_gb.csv', index=False) #\t0.6329383315\n",
        "files.download('bicycle_gb.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_621a37c8-f742-4db5-8b2b-9643ec23833a\", \"bicycle_gb.csv\", 2665)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yy648wgc1RWw"
      },
      "source": [
        "# xgb"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "KHTM9eF6ywnq",
        "outputId": "836e81da-ec85-4133-e532-6bf497a868f3"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression \n",
        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from xgboost import XGBRegressor\n",
        "from lightgbm import LGBMRegressor\n",
        "#from catboost import CatBoostRegressor\n",
        "from sklearn.model_selection import cross_val_score, KFold\n",
        "from sklearn.metrics import mean_absolute_error, make_scorer\n",
        "\n",
        "\n",
        "vars = ['discomfort', 'sky_condition', 'precipitation_form', 'wind_speed',\n",
        "       'humidity', 'low_temp', 'high_temp', 'Precipitation_Probability',\n",
        "       'year', 'month', 'temp'] # wind_direction,'week_day','day', 'temp_diff', 'sweat', 'cold_windy 삭제\n",
        "\n",
        "X_train = train[vars]  \n",
        "X_test = test[vars] \n",
        "\n",
        "\n",
        "y_train = np.log1p(train['number_of_rentals'])\n",
        "\n",
        "loo = LeaveOneOut()\n",
        "\n",
        "\n",
        "xg_model = XGBRegressor(random_state=2021, verbosity=0, \n",
        "                      max_depth=2,\n",
        "                      learning_rate=0.19,\n",
        "                      n_estimators=100,\n",
        "                      )\n",
        "\n",
        "def nmae(true, pred):\n",
        "  true = np.expm1(true)\n",
        "  pred = np.expm1(pred)\n",
        "  score = np.mean((np.abs(true-pred))/true)    \n",
        "  return score\n",
        "\n",
        "score = -1*cross_val_score(xg_model, X_train, y_train, cv=loo, scoring=make_scorer(nmae,greater_is_better=False)) \n",
        "print(score.mean()) #0.19358354140529263\n",
        "\n",
        "\n",
        "xg_model.fit(X_train, y_train)\n",
        "xg_pred = np.expm1(xg_model.predict(X_test))\n",
        "\n",
        "sub.iloc[:,1] = xg_pred\n",
        "sub.to_csv('bicycle_xg.csv', index=False) # 0.5389613461\n",
        "files.download('bicycle_xg.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.19358354140529263\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_4d542e3f-52cd-4b8c-abe6-eca337be8e8a\", \"bicycle_xg.csv\", 1885)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MAZnOkTQlWZ2"
      },
      "source": [
        "# ensemble"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "7_l4cXFUmM3X",
        "outputId": "ca2ea0cf-d38f-4422-d279-cca89e66f38e"
      },
      "source": [
        "sub.iloc[:,1] = lg_pred*0.8 + xg_pred*0.2\n",
        "sub.to_csv('bicycle_lg_xg_ensemble.csv', index=False)\n",
        "files.download('bicycle_lg_xg_ensemble.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_eb55eb71-b9cb-41aa-a4a2-a952922e0b61\", \"bicycle_lg_xg_ensemble.csv\", 2685)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "Cy_d3LeaVE3s",
        "outputId": "dd527246-e1f9-4237-f79a-ba7cd252c414"
      },
      "source": [
        "sub.iloc[:,1] = lg_pred*0.8 + xg_pred*0.13 + gb_pred*0.07\n",
        "sub.to_csv('bicycle_lg_xg_gb_ensemble.csv', index=False)\n",
        "files.download('bicycle_lg_xg_gb_ensemble.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_c01d0ab0-e182-4d3c-ba84-1d9f35a88b52\", \"bicycle_lg_xg_gb_ensemble.csv\", 2683)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qddb_2PxVQ0b"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}