regular_modelling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d9617abe-f773-432e-9a62-07659d0f3ca9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9424e376-3155-4d51-bd20-33981c92e496",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('cleaned_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9bf62121-119e-45e2-bae3-f36407e798cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['order_load_date','order_unload_date', 'order_create_date']] = df[['order_load_date','order_unload_date', 'order_create_date']].apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bc044f1f-3066-46ba-bae7-a03c168cf2f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 233016 entries, 0 to 233015\n",
      "Data columns (total 22 columns):\n",
      " #   Column              Non-Null Count   Dtype         \n",
      "---  ------              --------------   -----         \n",
      " 0   debtor              233016 non-null  int64         \n",
      " 1   ordernr             233016 non-null  int64         \n",
      " 2   volgnr              233016 non-null  int64         \n",
      " 3   order_load_date     233016 non-null  datetime64[ns]\n",
      " 4   order_unload_date   233016 non-null  datetime64[ns]\n",
      " 5   order_create_date   233016 non-null  datetime64[ns]\n",
      " 6   order_create_time   233016 non-null  object        \n",
      " 7   ritsoort            233016 non-null  object        \n",
      " 8   order_kind          233016 non-null  object        \n",
      " 9   order_dep           233016 non-null  object        \n",
      " 10  order_size          233016 non-null  float64       \n",
      " 11  order_weight        233016 non-null  float64       \n",
      " 12  order_class         233016 non-null  object        \n",
      " 13  order_class_size    233016 non-null  object        \n",
      " 14  distance            233016 non-null  int64         \n",
      " 15  ritten              233016 non-null  int64         \n",
      " 16  uitbesteed          233013 non-null  object        \n",
      " 17  order_load_year     233016 non-null  int64         \n",
      " 18  order_load_week     233016 non-null  int64         \n",
      " 19  order_load_day      233016 non-null  int64         \n",
      " 20  order_load_quarter  233016 non-null  int64         \n",
      " 21  order_load_month    233016 non-null  int64         \n",
      "dtypes: datetime64[ns](3), float64(2), int64(10), object(7)\n",
      "memory usage: 39.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "39a3717c-9869-47d0-bbe9-3033c6301105",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['order_load_date','order_size']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d6a27288-b2c4-4363-99a9-8e69ebc491b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58135b55-9331-4426-94f6-57fbc2c471a7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eb2e9134-c955-45fa-8dc3-5e071b62dd39",
   "metadata": {},
   "outputs": [],
   "source": [
    "def date_features(df):\n",
    "    #Date features\n",
    "    df['year'] = df.order_load_date.dt.year\n",
    "    df['month'] = df.order_load_date.dt.month\n",
    "    df['day'] = df.order_load_date.dt.day\n",
    "    df['day_of_year'] = df.order_load_date.dt.dayofyear\n",
    "    df['day_of_week'] = df.order_load_date.dt.isocalendar().day\n",
    "    df['week_of_year'] = df.order_load_date.dt.isocalendar().week\n",
    "    \n",
    "    # Additionnal Data Features\n",
    "    df['day^year'] = np.log((np.log(df['day_of_year'] + 1)) ** (df['year'] - 2000))\n",
    "    \n",
    "    #Drop transfered date column\n",
    "    df = df.drop('order_load_date', axis=1)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9ecf1ba2-fb55-41ed-b3db-97f53dc40c07",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform data with new features\n",
    "df_1 = date_features(df_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2f220c37-f0e9-4405-898c-314c98136781",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Daily Average, Monthly Average for train\n",
    "df_1['daily_avg'] = df_1.groupby('day_of_week')['order_size'].transform('mean')\n",
    "df_1['monthly_avg'] = df_1.groupby('month')['order_size'].transform('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fe3c3233-b5d9-4564-9805-07314956d1ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           NaN\n",
       "1           NaN\n",
       "2           NaN\n",
       "3           NaN\n",
       "4           NaN\n",
       "          ...  \n",
       "233011    0.455\n",
       "233012    0.515\n",
       "233013    0.485\n",
       "233014    0.445\n",
       "233015    0.465\n",
       "Name: order_size, Length: 233016, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rolling average over 10 days\n",
    "df_1['order_size'].rolling(10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f88e4567-06e7-49a7-b1d5-d30921b96b32",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create correlation\n",
    "corr = df_1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "01a7f6bf-bea6-475e-b123-62c6f8810f8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create heatmap\n",
    "f, ax = plt.subplots(figsize=(12, 10))\n",
    "\n",
    "#masking top half\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "\n",
    "# Configure a custom diverging colormap\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "\n",
    "# create heatmap\n",
    "sns.heatmap(corr, annot=True, mask = mask, cmap=cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f3e390be-519b-4aa5-b60a-fd56b4ff5c1b",
   "metadata": {},
   "outputs": [
    {
     "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>order_size</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>week_of_year</th>\n",
       "      <th>day^year</th>\n",
       "      <th>daily_avg</th>\n",
       "      <th>monthly_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.4</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>26.012973</td>\n",
       "      <td>0.665582</td>\n",
       "      <td>0.678177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>51</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>27.480608</td>\n",
       "      <td>0.670471</td>\n",
       "      <td>0.678177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.5</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>51</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>27.480608</td>\n",
       "      <td>0.670471</td>\n",
       "      <td>0.678177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>18.928349</td>\n",
       "      <td>0.659266</td>\n",
       "      <td>0.667206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.6</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>214</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "      <td>33.618934</td>\n",
       "      <td>0.663887</td>\n",
       "      <td>0.676901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_size  year  month  day  day_of_year  day_of_week  week_of_year  \\\n",
       "0         0.4  2019      2   19           50            2             8   \n",
       "1         0.2  2020      2   20           51            4             8   \n",
       "2         0.5  2020      2   20           51            4             8   \n",
       "3         0.2  2019      1   14           14            1             3   \n",
       "4         0.6  2020      8    1          214            6            31   \n",
       "\n",
       "    day^year  daily_avg  monthly_avg  \n",
       "0  26.012973   0.665582     0.678177  \n",
       "1  27.480608   0.670471     0.678177  \n",
       "2  27.480608   0.670471     0.678177  \n",
       "3  18.928349   0.659266     0.667206  \n",
       "4  33.618934   0.663887     0.676901  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8b75ea26-eaa4-45c0-871b-0d5ff843d3fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = df.groupby('order_load_date').sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6f385390-e678-4e81-b182-bc5c7b83777a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform data with new features\n",
    "df_2 = date_features(df_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cb3d7ccb-bb11-485b-95a7-115288dc8697",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_size</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>day_of_week</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45.37500</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>365</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31.957174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>221.35570</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.786909</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>254.63450</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6.206051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>208.07718</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>9.041815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>226.09680</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>11.080764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>263.63500</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>271</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>36.199845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>286.02800</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>272</td>\n",
       "      <td>3</td>\n",
       "      <td>39</td>\n",
       "      <td>36.213588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>254.22700</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>273</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>36.227271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>233.87700</td>\n",
       "      <td>2021</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>275</td>\n",
       "      <td>6</td>\n",
       "      <td>39</td>\n",
       "      <td>36.254463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>314.31060</td>\n",
       "      <td>2021</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>276</td>\n",
       "      <td>7</td>\n",
       "      <td>39</td>\n",
       "      <td>36.267972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>780 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     order_size  year  month  day  day_of_year  day_of_week  week_of_year  \\\n",
       "0      45.37500  2018     12   31          365            1             1   \n",
       "1     221.35570  2019      1    2            2            3             1   \n",
       "2     254.63450  2019      1    3            3            4             1   \n",
       "3     208.07718  2019      1    4            4            5             1   \n",
       "4     226.09680  2019      1    5            5            6             1   \n",
       "..          ...   ...    ...  ...          ...          ...           ...   \n",
       "775   263.63500  2021      9   28          271            2            39   \n",
       "776   286.02800  2021      9   29          272            3            39   \n",
       "777   254.22700  2021      9   30          273            4            39   \n",
       "778   233.87700  2021     10    2          275            6            39   \n",
       "779   314.31060  2021     10    3          276            7            39   \n",
       "\n",
       "      day^year  \n",
       "0    31.957174  \n",
       "1     1.786909  \n",
       "2     6.206051  \n",
       "3     9.041815  \n",
       "4    11.080764  \n",
       "..         ...  \n",
       "775  36.199845  \n",
       "776  36.213588  \n",
       "777  36.227271  \n",
       "778  36.254463  \n",
       "779  36.267972  \n",
       "\n",
       "[780 rows x 8 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c38e2a85-dfef-4425-9f1b-68b9dc8770ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = df_2.rename(columns={'order_size':'total'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e5e5b749-c710-4b76-800a-22057c7fe7a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Daily Average, Monthly Average for train\n",
    "df_2['daily_avg'] = df_2.groupby('day_of_week')['total'].transform('mean')\n",
    "df_2['monthly_avg'] = df_2.groupby('month')['total'].transform('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "79fcf4f1-126a-4647-8b90-90bbdb1f24e2",
   "metadata": {},
   "outputs": [
    {
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       "      <th>24</th>\n",
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       "      <td>211.288535</td>\n",
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       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>127.9120</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>200.2300</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
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       "      <td>209.620139</td>\n",
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       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>254.2270</td>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>273</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>36.227271</td>\n",
       "      <td>218.783329</td>\n",
       "      <td>208.633194</td>\n",
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       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>241.0610</td>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>31.636242</td>\n",
       "      <td>225.923188</td>\n",
       "      <td>218.417103</td>\n",
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       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>280.7485</td>\n",
       "      <td>2021</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>33.898256</td>\n",
       "      <td>211.253393</td>\n",
       "      <td>204.152380</td>\n",
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       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>4.7000</td>\n",
       "      <td>2021</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>212</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>35.263305</td>\n",
       "      <td>111.336368</td>\n",
       "      <td>181.955145</td>\n",
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       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>232.6375</td>\n",
       "      <td>2021</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>243</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>35.788895</td>\n",
       "      <td>214.363986</td>\n",
       "      <td>180.046535</td>\n",
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      "text/plain": [
       "        total  year  month  day  day_of_year  day_of_week  week_of_year  \\\n",
       "0     45.3750  2018     12   31          365            1             1   \n",
       "24   163.5965  2019      2    1           32            5             5   \n",
       "45   127.9120  2019      3    1           60            5             9   \n",
       "66   200.2300  2019      4    1           91            1            14   \n",
       "90     0.0000  2019      5    1          121            3            18   \n",
       "..        ...   ...    ...  ...          ...          ...           ...   \n",
       "777  254.2270  2021      9   30          273            4            39   \n",
       "635  241.0610  2021      3   31           90            3            13   \n",
       "680  280.7485  2021      5   31          151            1            22   \n",
       "728    4.7000  2021      7   31          212            6            30   \n",
       "752  232.6375  2021      8   31          243            2            35   \n",
       "\n",
       "      day^year   daily_avg  monthly_avg  \n",
       "0    31.957174  211.253393   145.575734  \n",
       "24   23.783528  210.604226   211.288535  \n",
       "45   26.859077  210.604226   218.417103  \n",
       "66   28.669245  211.253393   209.620139  \n",
       "90   29.819612  225.923188   204.152380  \n",
       "..         ...         ...          ...  \n",
       "777  36.227271  218.783329   208.633194  \n",
       "635  31.636242  225.923188   218.417103  \n",
       "680  33.898256  211.253393   204.152380  \n",
       "728  35.263305  111.336368   181.955145  \n",
       "752  35.788895  214.363986   180.046535  \n",
       "\n",
       "[780 rows x 10 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.sort_values(by=['year','day'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5dd90268-c90c-4cff-987a-23e4b73d4a9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2['shift_7'] = df_2['total'].shift(7)\n",
    "df_2 = df_2.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bbdd046-d028-48e5-9c17-c7957e1f55de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "976c5583-a1af-478e-85ec-766422963aa6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.079351</td>\n",
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       "      <th>month</th>\n",
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       "      <td>-0.054714</td>\n",
       "      <td>-0.548807</td>\n",
       "      <td>-0.014783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>-0.055992</td>\n",
       "      <td>-0.028051</td>\n",
       "      <td>-0.002787</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.085349</td>\n",
       "      <td>-0.136141</td>\n",
       "      <td>0.057728</td>\n",
       "      <td>0.106449</td>\n",
       "      <td>0.160736</td>\n",
       "      <td>-0.013678</td>\n",
       "      <td>0.029944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_year</th>\n",
       "      <td>-0.122797</td>\n",
       "      <td>-0.175179</td>\n",
       "      <td>0.996080</td>\n",
       "      <td>0.085349</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014174</td>\n",
       "      <td>0.965698</td>\n",
       "      <td>0.765187</td>\n",
       "      <td>-0.040289</td>\n",
       "      <td>-0.547489</td>\n",
       "      <td>-0.012885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_week</th>\n",
       "      <td>-0.291555</td>\n",
       "      <td>-0.001627</td>\n",
       "      <td>0.026094</td>\n",
       "      <td>-0.136141</td>\n",
       "      <td>0.014174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021867</td>\n",
       "      <td>-0.025538</td>\n",
       "      <td>-0.656202</td>\n",
       "      <td>-0.014945</td>\n",
       "      <td>0.106827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week_of_year</th>\n",
       "      <td>-0.096205</td>\n",
       "      <td>-0.164758</td>\n",
       "      <td>0.963952</td>\n",
       "      <td>0.057728</td>\n",
       "      <td>0.965698</td>\n",
       "      <td>0.021867</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.698365</td>\n",
       "      <td>-0.050301</td>\n",
       "      <td>-0.514001</td>\n",
       "      <td>-0.022348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day^year</th>\n",
       "      <td>-0.084229</td>\n",
       "      <td>0.140160</td>\n",
       "      <td>0.760385</td>\n",
       "      <td>0.106449</td>\n",
       "      <td>0.765187</td>\n",
       "      <td>-0.025538</td>\n",
       "      <td>0.698365</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011759</td>\n",
       "      <td>-0.357235</td>\n",
       "      <td>0.064005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daily_avg</th>\n",
       "      <td>0.439090</td>\n",
       "      <td>-0.003213</td>\n",
       "      <td>-0.054714</td>\n",
       "      <td>0.160736</td>\n",
       "      <td>-0.040289</td>\n",
       "      <td>-0.656202</td>\n",
       "      <td>-0.050301</td>\n",
       "      <td>0.011759</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.042728</td>\n",
       "      <td>0.021855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly_avg</th>\n",
       "      <td>0.215795</td>\n",
       "      <td>0.079351</td>\n",
       "      <td>-0.548807</td>\n",
       "      <td>-0.013678</td>\n",
       "      <td>-0.547489</td>\n",
       "      <td>-0.014945</td>\n",
       "      <td>-0.514001</td>\n",
       "      <td>-0.357235</td>\n",
       "      <td>0.042728</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.115324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shift_7</th>\n",
       "      <td>0.080261</td>\n",
       "      <td>-0.113106</td>\n",
       "      <td>-0.014783</td>\n",
       "      <td>0.029944</td>\n",
       "      <td>-0.012885</td>\n",
       "      <td>0.106827</td>\n",
       "      <td>-0.022348</td>\n",
       "      <td>0.064005</td>\n",
       "      <td>0.021855</td>\n",
       "      <td>0.115324</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 total      year     month       day  day_of_year  \\\n",
       "total         1.000000 -0.071262 -0.118029 -0.055992    -0.122797   \n",
       "year         -0.071262  1.000000 -0.173577 -0.028051    -0.175179   \n",
       "month        -0.118029 -0.173577  1.000000 -0.002787     0.996080   \n",
       "day          -0.055992 -0.028051 -0.002787  1.000000     0.085349   \n",
       "day_of_year  -0.122797 -0.175179  0.996080  0.085349     1.000000   \n",
       "day_of_week  -0.291555 -0.001627  0.026094 -0.136141     0.014174   \n",
       "week_of_year -0.096205 -0.164758  0.963952  0.057728     0.965698   \n",
       "day^year     -0.084229  0.140160  0.760385  0.106449     0.765187   \n",
       "daily_avg     0.439090 -0.003213 -0.054714  0.160736    -0.040289   \n",
       "monthly_avg   0.215795  0.079351 -0.548807 -0.013678    -0.547489   \n",
       "shift_7       0.080261 -0.113106 -0.014783  0.029944    -0.012885   \n",
       "\n",
       "              day_of_week  week_of_year  day^year  daily_avg  monthly_avg  \\\n",
       "total           -0.291555     -0.096205 -0.084229   0.439090     0.215795   \n",
       "year            -0.001627     -0.164758  0.140160  -0.003213     0.079351   \n",
       "month            0.026094      0.963952  0.760385  -0.054714    -0.548807   \n",
       "day             -0.136141      0.057728  0.106449   0.160736    -0.013678   \n",
       "day_of_year      0.014174      0.965698  0.765187  -0.040289    -0.547489   \n",
       "day_of_week      1.000000      0.021867 -0.025538  -0.656202    -0.014945   \n",
       "week_of_year     0.021867      1.000000  0.698365  -0.050301    -0.514001   \n",
       "day^year        -0.025538      0.698365  1.000000   0.011759    -0.357235   \n",
       "daily_avg       -0.656202     -0.050301  0.011759   1.000000     0.042728   \n",
       "monthly_avg     -0.014945     -0.514001 -0.357235   0.042728     1.000000   \n",
       "shift_7          0.106827     -0.022348  0.064005   0.021855     0.115324   \n",
       "\n",
       "               shift_7  \n",
       "total         0.080261  \n",
       "year         -0.113106  \n",
       "month        -0.014783  \n",
       "day           0.029944  \n",
       "day_of_year  -0.012885  \n",
       "day_of_week   0.106827  \n",
       "week_of_year -0.022348  \n",
       "day^year      0.064005  \n",
       "daily_avg     0.021855  \n",
       "monthly_avg   0.115324  \n",
       "shift_7       1.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create correlation\n",
    "corr_2 = df_2.corr()\n",
    "corr_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "eaaa5e87-f794-40e3-87cc-105ee7ba8b03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create heatmap\n",
    "f, ax = plt.subplots(figsize=(12, 10))\n",
    "\n",
    "#masking top half\n",
    "mask = np.triu(np.ones_like(corr_2, dtype=bool))\n",
    "\n",
    "# Configure a custom diverging colormap\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "\n",
    "# create heatmap\n",
    "sns.heatmap(corr_2, annot=True, mask = mask, cmap=cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10fb2a32-6951-4074-9845-406c04709df3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "20eaa145-b30e-4c54-874e-eabe1b70a91d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='month', ylabel='total'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='month', y='total', data=df_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "44385106-5af7-4de8-b86d-aeb2a81f2b43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='month', ylabel='order_size'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='month', y='order_size', data=df_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1d1ee7b9-1000-4012-b3dc-f817190a9ff9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = df_2.drop(['day_of_year', 'month'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "431e8a4b-8390-4484-9de1-22e5bfe35904",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import train_test_split and StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4dc2e0b0-6aae-49d2-a69a-2dd5802ebd0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the scaler\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ab2f95c8-14b6-43a8-adbf-bbd60d07e5dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split dataset in train and test\n",
    "X = df_2.drop('total',axis=1)\n",
    "y = df_2['total']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fcd8a2d3-6394-4fce-9d5a-beecb45cd063",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2['day_of_week'] = df_2['day_of_week'].astype('int64')\n",
    "df_2['week_of_year'] = df_2['week_of_year'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "01668844-e44c-4e62-9fef-3cce62903f6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3154d40f-9a87-477a-9ff2-83ad8bf2f9c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(155, 8)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3398bf1a-9ece-4956-9b4b-2858b2986e6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import XGB model\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "33f794b1-4201-4f89-a997-f2f06caa9d2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set XGB Model\n",
    "#matrix_train = xgb.DMatrix(X_train, label = y_train)\n",
    "#matrix_test = xgb.DMatrix(X_test, label = y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "120f0899-2643-4658-b334-78ef454a3633",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model = xgb.train(params={'objective':'reg:squarederror','eval_metric':'mae'},\n",
    "#            dtrain = matrix_train, \n",
    " #           num_boost_round = 500, \n",
    "  #          early_stopping_rounds = 20, \n",
    "   #         evals = [(matrix_test,'test')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "a07510d3-09be-4225-9f89-7780c1c34e75",
   "metadata": {},
   "outputs": [],
   "source": [
    "#xg_reg = xgb.XGBRegressor(objective ='reg:squarederror', colsample_bytree = 0.3, learning_rate = 0.1,\n",
    "  #              max_depth = 10, alpha = 10, n_estimators = 10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "3cada62d-fdb1-4b19-948a-edbf1a82cf22",
   "metadata": {},
   "outputs": [],
   "source": [
    "#xg_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a98f097e-eea8-4837-b8b2-641aed9dca1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "9ee38c21-90ff-46c5-8dee-060944001b77",
   "metadata": {},
   "outputs": [],
   "source": [
    "#kfold = KFold(n_splits=10, random_state=7, shuffle=True)\n",
    "#results = cross_val_score(xg_reg, X_train, y_train, cv=kfold)\n",
    "#y_test_pred = xg_reg.predict(X_test)\n",
    "\n",
    "#mse = mean_squared_error(y_test_pred, y_test)\n",
    "\n",
    "#y_pred = xg_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "379d9de4-b406-40a9-800b-fe766c700a11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create custom scorer for the model part I\n",
    "from sklearn.metrics import make_scorer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6aeb5477-61c0-499c-883a-fa02bd0af756",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create custom scorer for the model part II\n",
    "def rmse(actual, predict):\n",
    "    predict = np.array(predict)\n",
    "    actual = np.array(actual)\n",
    "    \n",
    "    distance = predict - actual\n",
    "    \n",
    "    square_distance = distance ** 2\n",
    "    \n",
    "    mean_square_distance = square_distance.mean()\n",
    "    \n",
    "    score = np.sqrt(mean_square_distance)\n",
    "    \n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "79234f4e-c56f-4ea9-afc9-86f34d6be8ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create custom scorer for the model part III\n",
    "rmse_score = make_scorer(rmse, greater_is_better=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f7d4e4f9-7a4d-49fa-9846-9dc32b520a8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# performing Grid Search\n",
    "model = xgb.XGBRegressor()\n",
    "param_search = {\n",
    "    'eta': [0.1, 0.2, 0.3],\n",
    "    'objective' :['reg:squarederror'],    \n",
    "    'max_depth':[i for i in range(1,6)],\n",
    "    'num_class':[i for i in range (1,4)]\n",
    "}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "94b0ab63-6ccb-495c-81d4-fac48e207b93",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "531404b7-b115-4a84-a1c2-9a28d93edba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_model = GridSearchCV(estimator=model, \n",
    "                       cv=5, \n",
    "                       param_grid = param_search, \n",
    "                       scoring = rmse_score,\n",
    "                         verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9c449b0c-cbfc-44e9-9fd7-c4a5c9c8077e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n",
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:610: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 593, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 422, in inner_f\n",
      "    return f(**kwargs)\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 578, in fit\n",
      "    train_dmatrix, evals = self._wrap_evaluation_matrices(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py\", line 265, in _wrap_evaluation_matrices\n",
      "    train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/core.py\", line 500, in __init__\n",
      "    handle, feature_names, feature_types = dispatch_data_backend(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 539, in dispatch_data_backend\n",
      "    return _from_pandas_df(data, enable_categorical, missing, threads,\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 242, in _from_pandas_df\n",
      "    data, feature_names, feature_types = _transform_pandas_df(\n",
      "  File \"/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py\", line 207, in _transform_pandas_df\n",
      "    raise ValueError(msg + ', '.join(bad_fields))\n",
      "ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When\n",
      "                categorical type is supplied, DMatrix parameter\n",
      "                `enable_categorical` must be set to `True`.day_of_week, week_of_year\n",
      "\n",
      "  warnings.warn(\"Estimator fit failed. The score on this train-test\"\n"
     ]
    },
    {
     "ename": "NotFittedError",
     "evalue": "All estimators failed to fit",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotFittedError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-44-098a9345b705>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrid_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    839\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    840\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 841\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    842\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    843\u001b[0m             \u001b[0;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1286\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1287\u001b[0m         \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1288\u001b[0;31m         \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1289\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1290\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[1;32m    825\u001b[0m                 \u001b[0;31m# of out will be done in `_insert_error_scores`.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    826\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscoring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 827\u001b[0;31m                     \u001b[0m_insert_error_scores\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    828\u001b[0m                 \u001b[0mall_candidate_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcandidate_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    829\u001b[0m                 \u001b[0mall_out\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_insert_error_scores\u001b[0;34m(results, error_score)\u001b[0m\n\u001b[1;32m    295\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msuccessful_score\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mNotFittedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"All estimators failed to fit\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msuccessful_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNotFittedError\u001b[0m: All estimators failed to fit"
     ]
    }
   ],
   "source": [
    "grid_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "64f86313-4efa-4b14-91ef-0267edc10e0b",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'GridSearchCV' object has no attribute 'best_params_'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-45-caf2e10e1312>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrid_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'best_params_'"
     ]
    }
   ],
   "source": [
    "grid_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "15a08515-8e99-47f9-a869-f402b61703a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = grid_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "da1a44fb-1542-495d-8d3a-360f7939fb10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145.04036812957614"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c01b1379-2782-4044-8cab-f792f1e26ccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.metrics as metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d7ba66e3-59e3-49a6-9a87-61d9895a1dac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def regression_results(y_true, y_pred):\n",
    "    #regression metrics\n",
    "    explained_variance = metrics.explained_variance_score(y_true, y_pred)\n",
    "    mean_absolute_error = metrics.mean_absolute_error(y_true, y_pred)\n",
    "    mse = metrics.mean_squared_error(y_true, y_pred)\n",
    "    mean_squared_log_error = metrics.mean_squared_log_error(y_true, y_pred)\n",
    "    median_absolute_error = metrics.median_absolute_error(y_true, y_pred)\n",
    "    r2 = metrics.r2_score(y_true, y_pred)\n",
    "    \n",
    "    print('explained variance :',round(explained_variance,4))\n",
    "    print('mean squared log error :', round(mean_squared_log_error,4))\n",
    "    print('r2 :',round(r2,4))\n",
    "    print('MAE :',round(mean_absolute_error,4))\n",
    "    print('MSE :',round(mse,4))\n",
    "    print('RMSE :',round(np.sqrt(mse),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "756519b7-84ea-4c69-95ff-4f24048a67e3",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_pred' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-48-3968640dcd7c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mregression_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'y_pred' is not defined"
     ]
    }
   ],
   "source": [
    "regression_results(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2e98494a-9135-4a5b-a475-b3a051bf1e61",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2.to_csv('featured_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3520db9c-73d8-4e78-a654-6133438374c4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ab766a6-871e-4f7a-bfa2-69166e1005d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2055a8c1-58b1-483e-851f-3ce3674907ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f1738896-620b-44b8-b8ad-2c2f3043dcc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model3 = xgb.XGBRegressor(n_estimators=500, \n",
    "                        learning_rate = 0.01,\n",
    "                         eval_metric='mae')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ffad2f23-620c-40a1-a8a1-2a731ab4f94c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_3 = df_2.drop('total',axis=1)\n",
    "y_3 = df_2['total']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "41889ca1-1c70-4dad-81fb-5bb050823523",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train3, X_test3, y_train3, y_test3 = train_test_split(X_3, y_3, test_size=0.2, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "96dd3e2c-539e-4120-9e62-a531b1393f37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, eval_metric='mae', gamma=0,\n",
       "             gpu_id=-1, importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.01, max_delta_step=0, max_depth=6,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=500, n_jobs=8, num_parallel_tree=1, random_state=0,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.fit(X_train3, y_train3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6b545e7c-37e1-407b-a593-94351d4938d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds3 = model3.predict(X_test3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "79e137dc-2746-4a55-a4ca-8416936b5830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6516542847641094"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test3, y_preds3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "02851c1f-6c8f-4893-a8a0-a22140665c00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.66651890837735"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_absolute_error(y_test3, y_preds3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7f87c38-cf29-4d99-a312-094a213379a2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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