Classifying and Predicting Stock Market States Using HMM and XGBoost.ipynb

{
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   "cell_type": "code",
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   "outputs": [],
   "source": [
    "# HMM\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import yfinance as yf\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from hmmlearn.hmm import GaussianHMM\n",
    "# XGBoost\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import f1_score\n",
    "import datetime\n",
    "# Backtest\n",
    "from backtrader.feeds import PandasData\n",
    "import backtrader as bt\n",
    "import backtrader.analyzers as btanalyzers\n",
    "# Warning\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2002-12-31</th>\n",
       "      <td>489.010010</td>\n",
       "      <td>491.059998</td>\n",
       "      <td>483.880005</td>\n",
       "      <td>489.489990</td>\n",
       "      <td>489.489990</td>\n",
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       "    <tr>\n",
       "      <th>2003-01-02</th>\n",
       "      <td>489.489990</td>\n",
       "      <td>505.179993</td>\n",
       "      <td>489.489990</td>\n",
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       "    <tr>\n",
       "      <th>2003-01-03</th>\n",
       "      <td>505.179993</td>\n",
       "      <td>506.230011</td>\n",
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       "    <tr>\n",
       "      <th>2003-01-06</th>\n",
       "      <td>504.769989</td>\n",
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       "    <tr>\n",
       "      <th>2003-01-07</th>\n",
       "      <td>516.039978</td>\n",
       "      <td>516.650024</td>\n",
       "      <td>510.769989</td>\n",
       "      <td>512.450012</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2021-12-23</th>\n",
       "      <td>2750.840088</td>\n",
       "      <td>2776.620117</td>\n",
       "      <td>2750.840088</td>\n",
       "      <td>2768.360107</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2021-12-27</th>\n",
       "      <td>2769.340088</td>\n",
       "      <td>2803.879883</td>\n",
       "      <td>2769.340088</td>\n",
       "      <td>2803.739990</td>\n",
       "      <td>2803.739990</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>2803.899902</td>\n",
       "      <td>2813.590088</td>\n",
       "      <td>2795.709961</td>\n",
       "      <td>2798.350098</td>\n",
       "      <td>2798.350098</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>2798.379883</td>\n",
       "      <td>2807.370117</td>\n",
       "      <td>2791.860107</td>\n",
       "      <td>2801.870117</td>\n",
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       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>2802.909912</td>\n",
       "      <td>2813.250000</td>\n",
       "      <td>2794.189941</td>\n",
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      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2002-12-31   489.010010   491.059998   483.880005   489.489990   489.489990   \n",
       "2003-01-02   489.489990   505.179993   489.489990   505.179993   505.179993   \n",
       "2003-01-03   505.179993   506.230011   502.100006   504.769989   504.769989   \n",
       "2003-01-06   504.769989   517.530029   504.769989   516.039978   516.039978   \n",
       "2003-01-07   516.039978   516.650024   510.769989   512.450012   512.450012   \n",
       "...                 ...          ...          ...          ...          ...   \n",
       "2021-12-23  2750.840088  2776.620117  2750.840088  2768.360107  2768.360107   \n",
       "2021-12-27  2769.340088  2803.879883  2769.340088  2803.739990  2803.739990   \n",
       "2021-12-28  2803.899902  2813.590088  2795.709961  2798.350098  2798.350098   \n",
       "2021-12-29  2798.379883  2807.370117  2791.860107  2801.870117  2801.870117   \n",
       "2021-12-30  2802.909912  2813.250000  2794.189941  2795.909912  2795.909912   \n",
       "\n",
       "            Volume  \n",
       "Date                \n",
       "2002-12-31       0  \n",
       "2003-01-02       0  \n",
       "2003-01-03       0  \n",
       "2003-01-06       0  \n",
       "2003-01-07       0  \n",
       "...            ...  \n",
       "2021-12-23       0  \n",
       "2021-12-27       0  \n",
       "2021-12-28       0  \n",
       "2021-12-29       0  \n",
       "2021-12-30       0  \n",
       "\n",
       "[4755 rows x 6 columns]"
      ]
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     "execution_count": 2,
     "metadata": {},
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   "source": [
    "# Download RUA data from yfinance\n",
    "df = yf.download('^RUA', start='2002-12-31', end='2021-12-31', progress=False)\n",
    "df.index = pd.to_datetime(df.index)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1c5aa35e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Low</th>\n",
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       "      <th>Adj Close</th>\n",
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       "      <th>Return</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-12-31</th>\n",
       "      <td>489.010010</td>\n",
       "      <td>491.059998</td>\n",
       "      <td>483.880005</td>\n",
       "      <td>489.489990</td>\n",
       "      <td>489.489990</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2003-01-02</th>\n",
       "      <td>489.489990</td>\n",
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       "      <td>0.032054</td>\n",
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       "    <tr>\n",
       "      <th>2003-01-03</th>\n",
       "      <td>505.179993</td>\n",
       "      <td>506.230011</td>\n",
       "      <td>502.100006</td>\n",
       "      <td>504.769989</td>\n",
       "      <td>504.769989</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.000812</td>\n",
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       "    <tr>\n",
       "      <th>2003-01-06</th>\n",
       "      <td>504.769989</td>\n",
       "      <td>517.530029</td>\n",
       "      <td>504.769989</td>\n",
       "      <td>516.039978</td>\n",
       "      <td>516.039978</td>\n",
       "      <td>0</td>\n",
       "      <td>0.022327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-07</th>\n",
       "      <td>516.039978</td>\n",
       "      <td>516.650024</td>\n",
       "      <td>510.769989</td>\n",
       "      <td>512.450012</td>\n",
       "      <td>512.450012</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.006957</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-23</th>\n",
       "      <td>2750.840088</td>\n",
       "      <td>2776.620117</td>\n",
       "      <td>2750.840088</td>\n",
       "      <td>2768.360107</td>\n",
       "      <td>2768.360107</td>\n",
       "      <td>0</td>\n",
       "      <td>0.006490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-27</th>\n",
       "      <td>2769.340088</td>\n",
       "      <td>2803.879883</td>\n",
       "      <td>2769.340088</td>\n",
       "      <td>2803.739990</td>\n",
       "      <td>2803.739990</td>\n",
       "      <td>0</td>\n",
       "      <td>0.012780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>2803.899902</td>\n",
       "      <td>2813.590088</td>\n",
       "      <td>2795.709961</td>\n",
       "      <td>2798.350098</td>\n",
       "      <td>2798.350098</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.001922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>2798.379883</td>\n",
       "      <td>2807.370117</td>\n",
       "      <td>2791.860107</td>\n",
       "      <td>2801.870117</td>\n",
       "      <td>2801.870117</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>2802.909912</td>\n",
       "      <td>2813.250000</td>\n",
       "      <td>2794.189941</td>\n",
       "      <td>2795.909912</td>\n",
       "      <td>2795.909912</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.002127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4755 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2002-12-31   489.010010   491.059998   483.880005   489.489990   489.489990   \n",
       "2003-01-02   489.489990   505.179993   489.489990   505.179993   505.179993   \n",
       "2003-01-03   505.179993   506.230011   502.100006   504.769989   504.769989   \n",
       "2003-01-06   504.769989   517.530029   504.769989   516.039978   516.039978   \n",
       "2003-01-07   516.039978   516.650024   510.769989   512.450012   512.450012   \n",
       "...                 ...          ...          ...          ...          ...   \n",
       "2021-12-23  2750.840088  2776.620117  2750.840088  2768.360107  2768.360107   \n",
       "2021-12-27  2769.340088  2803.879883  2769.340088  2803.739990  2803.739990   \n",
       "2021-12-28  2803.899902  2813.590088  2795.709961  2798.350098  2798.350098   \n",
       "2021-12-29  2798.379883  2807.370117  2791.860107  2801.870117  2801.870117   \n",
       "2021-12-30  2802.909912  2813.250000  2794.189941  2795.909912  2795.909912   \n",
       "\n",
       "            Volume    Return  \n",
       "Date                          \n",
       "2002-12-31       0       NaN  \n",
       "2003-01-02       0  0.032054  \n",
       "2003-01-03       0 -0.000812  \n",
       "2003-01-06       0  0.022327  \n",
       "2003-01-07       0 -0.006957  \n",
       "...            ...       ...  \n",
       "2021-12-23       0  0.006490  \n",
       "2021-12-27       0  0.012780  \n",
       "2021-12-28       0 -0.001922  \n",
       "2021-12-29       0  0.001258  \n",
       "2021-12-30       0 -0.002127  \n",
       "\n",
       "[4755 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "# Calculate return by using the percentage of close price \n",
    "df['Return'] = df['Close'].pct_change()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6bdc0cf7",
   "metadata": {},
   "outputs": [
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       "      <th>2003-01-02</th>\n",
       "      <td>489.489990</td>\n",
       "      <td>505.179993</td>\n",
       "      <td>489.489990</td>\n",
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       "      <td>505.179993</td>\n",
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       "      <td>0</td>\n",
       "      <td>-0.000812</td>\n",
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       "    <tr>\n",
       "      <th>2003-01-06</th>\n",
       "      <td>504.769989</td>\n",
       "      <td>517.530029</td>\n",
       "      <td>504.769989</td>\n",
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       "    <tr>\n",
       "      <th>2003-01-07</th>\n",
       "      <td>516.039978</td>\n",
       "      <td>516.650024</td>\n",
       "      <td>510.769989</td>\n",
       "      <td>512.450012</td>\n",
       "      <td>512.450012</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.006957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-08</th>\n",
       "      <td>512.450012</td>\n",
       "      <td>512.450012</td>\n",
       "      <td>504.529999</td>\n",
       "      <td>505.309998</td>\n",
       "      <td>505.309998</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.013933</td>\n",
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       "    <tr>\n",
       "      <th>2021-12-23</th>\n",
       "      <td>2750.840088</td>\n",
       "      <td>2776.620117</td>\n",
       "      <td>2750.840088</td>\n",
       "      <td>2768.360107</td>\n",
       "      <td>2768.360107</td>\n",
       "      <td>0</td>\n",
       "      <td>0.006490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-27</th>\n",
       "      <td>2769.340088</td>\n",
       "      <td>2803.879883</td>\n",
       "      <td>2769.340088</td>\n",
       "      <td>2803.739990</td>\n",
       "      <td>2803.739990</td>\n",
       "      <td>0</td>\n",
       "      <td>0.012780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>2803.899902</td>\n",
       "      <td>2813.590088</td>\n",
       "      <td>2795.709961</td>\n",
       "      <td>2798.350098</td>\n",
       "      <td>2798.350098</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.001922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>2798.379883</td>\n",
       "      <td>2807.370117</td>\n",
       "      <td>2791.860107</td>\n",
       "      <td>2801.870117</td>\n",
       "      <td>2801.870117</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>2802.909912</td>\n",
       "      <td>2813.250000</td>\n",
       "      <td>2794.189941</td>\n",
       "      <td>2795.909912</td>\n",
       "      <td>2795.909912</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.002127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4754 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2003-01-02   489.489990   505.179993   489.489990   505.179993   505.179993   \n",
       "2003-01-03   505.179993   506.230011   502.100006   504.769989   504.769989   \n",
       "2003-01-06   504.769989   517.530029   504.769989   516.039978   516.039978   \n",
       "2003-01-07   516.039978   516.650024   510.769989   512.450012   512.450012   \n",
       "2003-01-08   512.450012   512.450012   504.529999   505.309998   505.309998   \n",
       "...                 ...          ...          ...          ...          ...   \n",
       "2021-12-23  2750.840088  2776.620117  2750.840088  2768.360107  2768.360107   \n",
       "2021-12-27  2769.340088  2803.879883  2769.340088  2803.739990  2803.739990   \n",
       "2021-12-28  2803.899902  2813.590088  2795.709961  2798.350098  2798.350098   \n",
       "2021-12-29  2798.379883  2807.370117  2791.860107  2801.870117  2801.870117   \n",
       "2021-12-30  2802.909912  2813.250000  2794.189941  2795.909912  2795.909912   \n",
       "\n",
       "            Volume    Return  \n",
       "Date                          \n",
       "2003-01-02       0  0.032054  \n",
       "2003-01-03       0 -0.000812  \n",
       "2003-01-06       0  0.022327  \n",
       "2003-01-07       0 -0.006957  \n",
       "2003-01-08       0 -0.013933  \n",
       "...            ...       ...  \n",
       "2021-12-23       0  0.006490  \n",
       "2021-12-27       0  0.012780  \n",
       "2021-12-28       0 -0.001922  \n",
       "2021-12-29       0  0.001258  \n",
       "2021-12-30       0 -0.002127  \n",
       "\n",
       "[4754 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select data from 2003-01-02 to 2021-12-30\n",
    "df = df.loc['2003-01-02':'2021-12-30']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1f94d2e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Close Price')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TIEr2MguQEccuPrwSoxTGGGM4mFe6ew+ns+34s2BhjDEJoqAw8R5s+1iwMMaYBJFfGPi84pIT2sSpJKVZsDDGmATx96kr/NODuzZl4jk94liaQBYsjDEmAahqQDcfRzepFcfSlGbBwhhjEsBuT0uoE9o35E/DusaxNKVZsDDGmATw6IzV/un/G9aFjLTEOj0nVmmMMaaaeuGb9YDzxvbx7RrGtzBBWLAwxpgEkpaSGO9VlGTBwhhjEkCtDGfMiov6JU5zWS8LFsYYE2eFRcrBvEIAWjesGefSBGfBwhhj4mzf4fx4FyEsCxbGGBNnb8/bCMC4IZ3iXJLQLFgYY0ycPT5zDQA7snPjXJLQLFgYY0yc7T3kVEPVr5ke55KEZsHCGGMSxNiBHeJdhJAsWBhjTBxc8OQsBj4wIyCtXo3EvbOwMbiNMSYO5qzfDUC7Wz+Kc0kiY3cWxhhjwrJgYYwxCeDUzk3iXYQyWbAwxpgE0KJ+jXgXoUxRCxYi0lpEZojIMhFZIiI3uekNReQzEVnl/mzg2Wa8iKwWkRUiMsyT3kdEFrnLHpFEGcHcGGMqQLX0WNs5+YVxKEnkonlnUQD8UVWPAfoD14tIN+BWYLqqdgKmu/O4y0YD3YHhwOMikuru6wlgLNDJ/QyPYrmNMSaqcguKgqRV02ChqltU9Qd3OhtYBrQERgEvuau9BJztTo8C3lTVXFVdB6wG+olIc6Cuqs5SJxy/7NnGGGOSTnZOQam0nPzSASSRxOSZhYi0A3oB3wHNVHULOAEFaOqu1hLY6Nlsk5vW0p0umR4sn7EiMldE5u7YsaNSv4MxxkSqsEiZuWJ70OomCN6tR7W9s/ARkdrAO8A4Vd1f1qpB0rSM9NKJqk+ral9V7dukSWK3LDDGVF2nP/wFV7zwPZ8t3RZ0+ajHvi6VVpTYNxbRDRYiko4TKF5T1Xfd5G1u1RLuz+1u+iagtWfzVsBmN71VkHRjjElIa3YcBCAlRFuc/MLS17tFIe5CEkU0W0MJ8BywTFUf8ix6HxjjTo8BJnvSR4tIpoi0x3mQPcetqsoWkf7uPi/3bGOMMQllyuIt/umMtMhPsQkeK6La3cdJwGXAIhGZ76bdBkwC3hKRq4CfgF8DqOoSEXkLWIrTkup6VfVV4l0HvAjUAD5xP8YYk3CuffUH/3R+YYLXLZVD1IKFqn5N8OcNAINDbDMRmBgkfS5wbOWVzhhjoi9YdVOe22x29PGtefP74jY9WRmppdZNJPYGtzHGVJLDeYEtmgqCPLWeu8HpQLB7i7oB6Zee0CZ6BasEEd1ZiEgNoI2qrohyeYwxJmn97rV5AfPBqqEufuY7AOp6uiNfP2lkdAtWCcLeWYjIWcB8YIo731NE3o9yuYwxJunMWBH4fpe3GmrNjgM8+cUa/3xekLe4E1kkdxZ3A/2AmQCqOt99yc4YY0wZCjzB4sKnZrPzQPHLeKu3H+Cd604ksxwtpuIpklIWqOq+qJfEGGOSWE5+ISJw0+BOzJswBAishtqfkx+w/k1DOtGnbQOObVkvpuWsqEjuLBaLyMVAqoh0Am4Evo1usYwxJrm8PW8TqtC+cS3S3bsFb7Ao2TS0ZkZyDVQayZ3F73F6gs0FXgf2AeOiWCZjjEkq+YVFTPjfYgDaNqpJeoovWBRXQxUUJfhbd2GEDRaqekhVb1fV493PBFXNiUXhjDEmGXzvjqcNzp1FWqpzH/GJ523uwqoeLNwBiup75huIyNSolsoYY5LI5c/N8U/Xr5lBWooTLBZuCv6496tbTotJuSpTJNVQjVV1r29GVfdQ3K24McZUe74qpm7NnRftwg3m6X3HIllEEiyKRMT/aqGItCVEF+HGGFPdeIdD/fimUyLapnZmcj3chsiCxe3A1yLyioi8AnwJjI9usYwxJjFt3H2If09fRZF7N7FsizNMz72juke8j9SUsu88ElHY8KaqU0SkN8442gL8QVV3Rr1kxhiTYAoKizjlgRkA9G7bgP98v5GOTWsD0KROZjyLFnUhg4WIdFXV5W6ggOIBh9qISBvf+NrGGFNdPPXlWv/0EzPX8PXq4uvmkzuFHp0z1PCqyaSsO4ubgbHAg0GWKfDLqJTIGGNiZMu+wzSqlRl2kKJNew7Rsn4NXpm1wZ82x9NcFsp+DnHY81yjdcMaFSxtfIX8dqo6VkRSgAmq+k0My2SMMVF3y9sLeGvuJqDsXl9fnrWeOycv4Ve/aMHW/cWvmHk7AhzctewGonsPOV199GhZj/dvOOlIih03ZYZTVS0C/hGjshhjTEwUFBb5AwXAtv2h3zO+c/ISAN5fsDnkOjkFhSGXAbwy27kjGdSlSdhmtYkqktZQn4rIeZKs39AYY0rYsi8wOJQctMhn7Y4D/unjWoXu8M935+B1xYntqJuVRkFhEU/MdLomr5eE71f4RBIsbgb+C+SKyH4RyRaR/VEulzHGVMjWfTn8Y+qKkN1reFs0+azefiDoup8v3+6f9r2N/atftCi1Xo300kOipqUIhUUaMHTqiB7Nw3+BBBVJ31B1VDVFVTNUta47XzfcdsYYEw+3v7eIR2es5sef9gRd/ud3FvmnP/z9yQAcyC0Ium5ukAGK/jSsS+l9ntG1VFpqqpBfpAF3LcGCSrIIGSxEpJOITBaRxSLyuoi0jGXBjDGmIna4Awzl5Acfie6dH4qfVdTJctr4BLsL2Z+Tz9+nlh5JukX94tZM5/Rqydr7R3B8u4al1ktPSaGgsAhvBX6NjCoYLIDngQ+B84AfgX/HpETGGHMEdh/MAyCvMPA5xJx1u2l360f++WNb1iXFPZP/8b8LmLGiuMqpqEg57u5Pg+7f+/b1bSOOISXE29hpqUKRwquzi5vbJsuoeMGUVfI6qvqMqq5Q1b8D7WJUJmOMqRBVZdOewwD85/uNjH56ln8AomtfnRew7uTrTw448V/5wvfM2+C8O/HfeRsD1h15XOCzhvWTRrJ+0sgy39r29Ty7ftchf1oytxMqK1hkiUgvEentvsVdo8S8McYkFO8zhqlLtjF77W4273WCR5+2DfzL3r/hJFJTpFQfTec9MQuAGct3+NM+uOFk/nVhz3KXJS01ee8iginrDe4twEOe+a2eeXuD2xiTcEo2iQX4eNFW9h7K47Ol2/xpjWo7dwShWkztdJ97gFNdVZE7grQk7CywLGW9wZ18o3MYY6q1jbsPlUrLzskP6NMJoFGtDAAKCksHC1Vl7ganJdWZxzUPCBTtGtWMuCwlg8WqiWdEvG0iSr5O1Y0xJoQPgrxl/bj7QpzPb05qT5bbhLVVg9L9NO07XPyC3aMXF9e4L7jzdKff7QiVrIZKT/JqKQsWxpgqYdv+HP47b1PI5Uc3qcXb155IQ/euAiAlRTilU2O+WlXce6wvWFzWv23A9vVqlu/t66pWDZXcoc4YY1z/nLayzOXdW9QLCBQ+Jauizn/Sech9fPvS706UR0oSt3wKJmywEMelInKnO99GRPpFv2jGGBO5N+Y4zV1fuOL4oMu9w596zVq7K2B+R7bzcHv5liPr1Wj2uuL9jupZuouQZBPJncXjwADgInc+G3gsaiUyxpgjcFqI7sJvH3FM0PRbhpfuvgPg0hLVUOX1yaKt/ulk7hPKJ5JgcYKqXg/kAKjqHqD0vZwxxsRBUZFy6bPfAXB6t2ZB13n5N/1o17hW0GW/G9SRH+8Yyqmdi0e6q52ZFtCtR0V4H1mkpyZ/lVQkwSJfRFJx3q1ARJoAwTtd8RCR50Vku4gs9qTdLSI/i8h89zPCs2y8iKwWkRUiMsyT3kdEFrnLHrGu0o0xXkff9rF/eNNxQzoHXeekjo3L3EeDWhn8/pcd/fOhOhYsD+8Lf6kpyf94OJLWUI8A7wFNRWQicD4wIYLtXgQeBV4ukf6wqgYMqCQi3YDRQHegBTBNRDqraiHwBM7wrrOBj4HhwCcR5G+MqeI+X74tYL5bC6dD7H9e2JMGtTLof3RDdh7IK/WmdjBZnh5hBxzd6IjL5s0zvQq0jAobLFT1NRGZBwzGaWV8tqoui2C7L0WkXYTlGAW8qaq5wDoRWQ30E5H1QF1VnQUgIi8DZ2PBwhgDzFzhdMsx4OhG3DOquz/97F7FnWS3jLA6KSu9+Or/sgFH9rwCSt5ZJH+wiKQ1VAdgnao+BiwGhopI/SPI8wYRWehWU/k6a2kJeHvu2uSmtXSnS6aHKutYEZkrInN37NgRajVjTBXx8iynR9c3xvanc7M6R7SvmhnF187N6obuIDBS3qazVaGfqEi+wTtAoYh0BJ4F2gOvVzC/J4AOQE+cvqcedNODhV0tIz0oVX1aVfuqat8mTZqEWs0Yk8TyCorYsu8wK7dlV+p+a2UWB4vKGP7U+1JeVXhBL5JnFkWqWiAi5wL/UtV/i8iPFclMVf0VjCLyDM54GeDcMbT2rNoK2OymtwqSboyppjpPCKyFfvTiXpWy3wzP1f++w0f+gDululVD4bSGugi4nOKTe4XCroh4Gxufg1OtBfA+MFpEMkWkPdAJmKOqW4BsEenvtoK6HJhckbyNMclt4+5DAYMX+Zze7ahK2X+ap3mrasgKjIh5A0RVaMMZyZ3FlcC1wERVXeeezF8Nt5GIvAEMAhqLyCbgLmCQiPTEqUpaD1wDoKpLROQtYClQAFzvtoQCuA6nZVUNnAfb9nDbmGrolAdmBE3PqKTR57xVRSF6Li+XVKladxaRtIZaKiL/B3QWkWOBFao6KYLtLgqS/FwZ608EJgZJnwscGy4/Y0zVsmTzPprWyaJx7QyufPH7oOu8c92JlZaf9xWu3m3qH/H+rjypHXdMXgIEBo5kFTZYiMgg4CWcOwEBWovIGFX9MqolM8ZUayMf+RqAC/q28jeRHda9GbUy0rjzrG6s2n4gYPS7ylKvRnqltF4a6HkjPFgHhskmkmqoB4HTVXUFgIh0Bt4A+kSzYMaY6uub1cVdhr81t7j1/L2jjqVp3SwAjm93ZL3CBvPm2P60D9EtSHl5m876RuZLZpEEi3RfoABQ1ZUicuTtyowxJgRvsPBqHOWTbv9KeHPbJ60K9AflFcm91lwReU5EBrmfZ4B50S6YMab6WrvjIG1LDGH6+R9PDWiOmuiCDdmazCK5s7gOuB64EeeZxZc43ZYbY0ylm7FiO1OWbCU9VVg/aSSvfbeBgZ2a0Lph5ONfJ4L8wrD9rSaVSFpD5QIPuR9jjImKvYfy6PmXz/zz+e6V+SUnHHk/TfFQWc8+EkXIYCEiiyi7a43jolIiY0y15A0UAHNuHxynklSOqjaaQll3FmfGrBTGmGqvd5v6/PDTXgAevvAXNK2TFd8CmQBlBYt0oJmqfuNNFJFTsP6ZjDGV6GBugT9QPH9FX37ZNfiIdyZ+ymoN9U+c8bZLOuwuM8aYClNVfx9M05YVD2JkgSIxlXVn0U5VF5ZMVNW55RjUyBhjSvF2CPjUZX147ut1ANw4uFO8imTCKCtYlFVheGQjmRtjkkpRkbI/Jx9VqJ2VRvoRdIdRsufYa16ZR50s51R089DgY2gns8oYojURlBUsvheR36rqM95EEbkKeynPmCrv29U7UWDxz/v46yfLA5bdMrwLvxvUsdz7zC0oDJqenVPAoC5Vb8CydX8dUWVaRZUVLMYB74nIJRQHh75ABs5YFMaYKuqlb9dz1/tLQi5/YMoKvly5gzfHDgCcdyTq1Ugv88S4cfchZq3ZBcDFJ7Th9e9+Cli+52BeJZQ8sVSVQAFlBAt3VLsTReQ0irsI/0hVP49JyYwxMff58m0czivixW/Xh1139trdAKzdcYBfPvgFAL85qT13ntUtYL29h/LIKygKGI9iWPejuGlwJ25/bzE1MlL5YMFmtu7PqbwvYipdJG9wzwCCjzpijKkSDucV0v+v09l3OD/kOjcP7cxDn60MSMsrKOKql+b655//Zh1XnNiONp5+nUq+bAfQqFYGzepm8eyYvmzdl8MHCzZXyoBDJnoqZ4gpY0xSe2vuxpCB4qbBnVg/aSQ3Du7EL7s2DViWV1jEup0HA9IG/r342jJU77Edm9b2Tzetk8mFfVvz7OV9K1p8EwORdCRojKnigj2faFgrg1uGdWF0vzb+tGcv78vyrdnMWbeLuz9YSl5B6M7yCouUS579LuiyrPRU/3RKivC38633oERnwcKYaiQnv5D9OfnUzEjj2LumAvC7QR2Crjt24NEBgQKcE3u3FnWZv3EvAPd8EPwhuKqyZPO+yiu4iTsLFsZUIze/NZ+PF20NSHt85hoAamakciivuGlrWUOW+rrfnjw/eM8/G3cf5ua3Fvjn5985lLU7D3Lu499WuOwmvuyZhTHVxI8/7SkVKLxOdceM7te+IW/8tn+Zw5bm5Ae+L/H2tQNY+pdh+MYmOpRfwOrtBwBYdPfp1K+ZQe82DaidmUbNjNSSuzNJwO4sjKkmzglzVf+XUcfSqHYGt404hpoZZZ8aUkuMWNerTQNSU4SXftOPy56bQ3ZOgX9ZnaziUZjn3TGkAiU3icDuLIypBryjto3q2SLoOk3qZHLf2T3CBopgfMGjdqaz7b5DTsuqLs3qBKyXmZZKZprdWSQju7Mwpor6eNEWfvfaD6SlCB2aOE1VT+/WjFvP6Mrk+Zs5vl0Dxg3pHLLFUkUUuC9LXP2y8+7Fim3BOq42yciChTFVUFGR8rvXfgCcE7jvpH33r7rTvF4NPrnpFNo0rMkq97mC744gUqG6sWjXKHAo0Zb1rc/RqsKChTFVyCuzN3DH/xaHXN60TiYAxzSvC8BRdZ3OpQ/kFoTcJpgeLesB0LN1fXq3KW411bh2RsB60/94arn2axKXBQtjqghVLTNQzJ0whLQSXYv7gkd5x5Ho174hcycMoXHtzID0kncc3pfvTHKzB9zGVAF5BUX0urd0H0zPjXG60Lh8QNtSJ3ZwXrJbP2lkhcaRCLY/U3VZsDCmCug84RP2Hgrs26l5vSwGdWnKXWd147YRx8SsLD/eMTRmeZnYsWooY5Lcq7M3BE0XnCatV57UPqblaVArg4zUFP/od6ZqsL+mMUluguc5xd1ndeOY5nW58OnZNKtX1sjI0bXw7tPjlreJDgsWxiSpgsIinv5qrX/+jjO7ccVJ7TmYW0DXo+owYWS3MraOLnuwXfVE7ZmFiDwvIttFZLEnraGIfCYiq9yfDTzLxovIahFZISLDPOl9RGSRu+wRqUrjFBpTATn5hfxr2iqmLdvOA1NWAFC/ZjpXnexUN9XKTGPKuIFldgRoTHlF8wH3i8DwEmm3AtNVtRMw3Z1HRLoBo4Hu7jaPi4jv0uQJYCzQyf2U3Kcx1UrXO6bw8LSVXPvqPH9a2xIvwxlT2aIWLFT1S2B3ieRRwEvu9EvA2Z70N1U1V1XXAauBfiLSHKirqrNUVYGXPdsYU20UFilb9h1mR3Zu0OXnhOjvyZjKEutnFs1UdQuAqm4REd8YjS2B2Z71Nrlp+e50yfSgRGQszl0Ibdq0CbWaMUnnV49+zZLN+7msf9ugy3u0qh/bAplqJ1Heswj2HELLSA9KVZ9W1b6q2rdJkyaVVjhj4m3J5v2A050HQNtGNf3LFt8zzJ5PmKiLdbDY5lYt4f7c7qZvAlp71msFbHbTWwVJN6baOJxXWCrt0z8MJC1FePvaAeXuBNCYioh1sHgfGONOjwEme9JHi0imiLTHeZA9x62yyhaR/m4rqMs92xhT5RUVKVe99H2p9My0VFbfP4K+ZYxmZ0xlimbT2TeAWUAXEdkkIlcBk4ChIrIKGOrOo6pLgLeApcAU4HpV9V1OXQc8i/PQew3wSbTKbEwiyS8s4pzHv+HbNbsAuGV4lziXyFRnUbt/VdWLQiwaHGL9icDEIOlzgWMrsWjGJIVj75pKbkHxCHfXDOzAA1NWMHbg0XEslamurLLTHJH8wiK27suhdcOa4Vc2EVu9/UBAoACnn6f1k0bGqUSmurNgYSK2aNM+MtNTOP3hL3nxyuMZ1KUpnW53agWvG9SBPw/vGucSVg37DuUz5KEvADixQyNO7dyE3tbaycRZojSdNQlu5ortnPXo15z+8JcAXPHC9xQVFbdifmLmGnILSrfaMeV3wl+n+ae37c/hmlM7cLw9yDZxZsHChDVvw26ueKF0i5y/fLg0YP6CJ2fx2IzVsSpWlXQor4Cc/OLqp7vO6h7H0hhTzKqhTFjnPTEraPrUJVsD5hds2seCTfvo1rwup3VtGnQbU2zRpn2c/+S3jOjRnPEjutK0ThZ/enshAFee1M4ChUkodmdhgnr2q7X8bcpytu7L8add2r8NLTxjJGxxl40/I/BZxZUvlr4LMaU9+eUacguKeO/Hn+k3cTrrdh7ko4VbAOz5j0k4FixMKS98s477PlrGEzPX0P+v0wF4/JLe3Hd2D74dX7rl86+sE7ugioqUjrd9zI1v/IjTD2axPQfz/IHB57R/zPRP23gQJtFYNZTxe/eHTdz81oKgywYfU1ytlJGaQl6hU69eMyOVpnUCR2RrUiez1PaPzVhNTn4hNw7uRHpq9bhGef6bdRQUKe8v2Mz7Czbz1S2n+ZsY97r3sziXzpjyqR7/tSasZ79aGzJQ3HpGVzLTiq90P/+/U/3T398+hNSUwP4ed2TnsmXfYf/8U1+s4e9TV/Dvz1cz1G0SmkxmrdnFL+75lG37cziUV8CyLfvDbrPrQC73fbQsIG39roP8vPcwV3u67zi5Y2Ne/+0J/vmxA49m2s2nYkyisTsLwzNfrmXix8UntnN6teT60zow5CGnmezV7ghsPq0a1KRT09oc07wutdxO7B67uDfXv/6Df50Bf/2cPw/vym9Pac9fP1nuT1+/61A0v0qlWbPjAKu2ZXNcq/pc9IzTe/4J90/3L7/zzG78psTvxaewSOlz37RS6T9s2Mtlz83xz999ljMMqtf4M7pig0GaRGTBopr7adehgEAxd8IQGtfOpNDzDkVakGqjz0pc/Y48rjkjjxvJ+HcX8cacnwD425TlvD5nQ6ltD+QWJHxPqYMfLPsO6C8fLg0ZLGau2B40/eFpKwPzOKaZf7pT09qs2n7AAoVJWIn9H2sqRU5+IVOXbGVY96NKPTgd+PcZ/ukFd55OvZrpQMW7lvjD0E7+YAGwcbdTHTWwcxO+XLkDgBVb99OnbeK+ZDZ/494j2v6ql+YCcM+vujOoSxPaNqpFu1s/KrWet4uUD35/MvmFRaXWMSZR2DOLKiwnv5BfP/ktXe+Ywk1vzufdH34OWP76d8Un9bX3j/AHiiNRMyP49cdpXZr4m9je/NYC5m3YzcJNe484v8q252AeZz/2Tan0D39/cqm0Q3kFAOQVFPmHO83JL36L/cLjWwcdG7tn6/qsvO+MgLSs9FTqZB3579+YaLFgUcXk5Bfy7FdrefTzVXS9Ywrfr9/jX/bPaSv9J7OPF23htvcWAdC4diYpKZVT/VEjRJPPc3u34upTnN5St+/P5bwnZvGrR7/h40Vbgq4fL947LZ+PbjyZY1vWK5Xuewdl0ifLOX7iNBZs3MuDn64AYHiJu7g/DOnsn37rmgFkpNm/nkkuVg1VhezPyeeip2f7h+AsaXt2Ll3vmFIqfc5tQXuNr5DUFGHCyGNo1aAm1746D4DpfzyVejWKr5oPe66+f/faDwnTk+o3q3eSnePcLTx9WR/W7zrI/R8vp1UDp7romcv78tuX5/LA+cdxy9sL/d1yPP/NOgBGee5IHr6wZ8C+bxrSiZ92H+LkTo0sUJikZMGiinhl1nrumLykVPrNQztzw2kdOfq2j4Nu98MdQyvtrsLHdwfh07BmRpnrf7N6Jyd1bFypZShJVct8eDx77S4uefY7AKaMO4WuR9VFVfnNSe39D/iHdmvG+kkjmeE+wN5zKK/Uy3Y+NTJK32E9eMEvjvRrGBM3FiySkO9h6bDuzZi6ZBvzJgwJGig+uOFkerRyqk/+eWFPxv1nfsDyd64bQMNaZZ/Ij8S0mweyfuchGoTJY/baXVELFqpK+/FOoAx1B7NyWzajn3aax4pA16PqutNCWmrpAJPlvnPiCy4l3XqGddVhqh67H05g63Ye5EBugX8+J7+QJ79Y45+fumQbQECb/mk3n8rcCUNYP2mkP1AAnN2rZcC+f92nVdRbJHVsWoch3ZoFpC2463R6t6nPU5f18ad9tnRb1Mrwn+83+qeXhqie+2RRcYeIXZrVCbvPrPSy/21aN7CBoEzVY3cWCWT7/hz63T+dO8/sxrwNe/ho0Ra6HlWHKeMGUlikQZ83eD1w3nF0bFo75PKvbjmNPYfy6NGyXtza89erkc67vzsJgJb1a/Dz3sMs35odlbxy8gu59d1F/vkpS7bSrUXdgHXW7zwY8P7DmBPbhd1vsH6bMtNSyC0o4rzerRjR46iKF9qYBGV3Fgli98E8+rlvCP/lw6V85LYSWr41m1Me+JwOJZ45zJswhInnBA5NfsHxrcvMo3XDmhzXqn7CvPjVumGNqOw3J7+QwiJlzPNzAtIfmb6q1LqDPJ33TRl3Chf1axN2/8GCxVD3DuqEoxsmzO/XmMpkwSIBFBUpvcvoWM73YhvAub1b8vWfT6NR7UwuOaEt7/7uRADqZCXfTeLgrs3Cr1RO//vxZ7reMYUOt31MjjuG9aRze/iXqyqqyhtzfuK7tbv86asnnuF/VhFOepDnGPef24PbRnTl3BLVfcZUFcl3hkkCN/9nPu/+6LwAd2n/NjSqlckfhnYOuf4sz0nr3lHduWPyElrUy+Lt607kxEmf+5fVzUrjoQt6Bmzbu02DhGl6Wl5Xn9I+oKuRI3U4rzDgIf6CjXu5qF8bGtcu7gW3/fiPaVw7k50HcgO2DdalSSgZQdatm5XO2IEdyl9oY5KEBYtK9tnSbf5AAfDqbOct6X95qkCuOfVoxp9xDPsO5fOLv3zqT586biBdjqrDGT2ak56SQr2a6Xz4+5M5899fA/DJuIEx+haxURnVNarKrx79hhXbsskrKN1dRuuGNejdtkFAWslAsfze4eXKs2ndLP41uicdmtT2/22MqeosWFSy3748N+w6T32xlt+c1D6gF1OAdo2dVjTeK+FjW9bj0z8MpFWDGiG70qiu8guLuOSZ71j0876Q6/y6T+uwzYMrMtDQqJ5OddOTl/Zm8c/huyw3JtnZM4tKtHF3cffbwfoS8ioZKICAMSO8OjerU2UDxYV9W9OsbunBkiLx/NfrmLN+d0DaC1cc73/YDMUDMd1/Tg+CqWjePsOPbc7/DetyRPswJhlYsKgEhUXKo5+v4pQHnH6FZvzfII5tWY/vbhvMkGOa8sKVx/vXXTUxsAO5py7rw6X92/DfawfEtMyJol7NdPYdzq/Qtk99uTZg/olLenNa16b8+6JeAPxrdE//sotPaMMdZ3bzz987qjvgBCtjTHhV83I1hnZk53L8xMCBbto3dnoabVY3i2fHOIHi8Ut6071FXdJTUxhyTDOmLXNeRBvW/SiGda++7fKz0lPJyS8K2x1HSRc8NYvdB/MC0nwvIWalpwZ96H/Vye35dvVOpi/fzmUD2nF+n9ZBu+UwxpRmweIIdLjt44BBggC+ufWXQdcd0aO5f/rRi3sx+unZPHD+cVEtXzJId/ulWr/rkD/IhvPhws3MWVdc/fTh70+mdcOaAZ0VhvLQBT3944dboDAmchYsyiE7J59lW7LZdziftBQJCBT92jfkyhPb0bJ++BfNstJT+d/1J0WzqElj+nKnU77T/jGT58b0DRg9LpQ/v72weHp416Ddh4dSGWN2GFMdWbAohx53fxpy2VvXVM9nDkdqf07x84qFm/aVGSyKipQiVQ7mOV2cTzq3B6MjeOPaGHPkLFhEyNvSyeuifq25KsRYzCY871OKOllpFBUp+UVFpVqGbd57OOAFxfN6t7JAYUwMWbCI0NQlW0ulzR4/mKPqZcWhNFWHbwAhAFU4+W+fs3lfDqsnnuF/q3rltmxOf/jLgO2a2+/dmJiKS7AQkfVANlAIFKhqXxFpCPwHaAesBy5Q1T3u+uOBq9z1b1TVqdEqW05+IUs276eP563fb1bv5L6PnG4pVk08g/TUlHK33jHB+caxBgK6/sjOKfCPg/HmnI2ltju2ZWT9OBljKkc837M4TVV7qmpfd/5WYLqqdgKmu/OISDdgNNAdGA48LiJRa8bS9Y4pnPfEt/z4U/HY1d5BbtLdq10LFJXj2TF9g6av3FbcbXl2Tun3MHwv2xljYiORXsobBbzkTr8EnO1Jf1NVc1V1HbAa6BeNAuQXFleJrNp2gC37DvPXT4qvdt+/wVowVbZQAzBd+PRsXpm9gQUb9/LfeZsAp+XTi+4LjuVpAWWMOXLxemahwKciosBTqvo00ExVtwCo6hYRaequ2xKY7dl2k5tWioiMBcYCtGlT/oef6akpfPmn0xj49xnc8s5Cuh5Vxz8wT+uGNTiuVf1y79NU3D3vL6HA0zz5ukFOr67J2suuMcksXncWJ6lqb+AM4HoRKas71WD1PRokDVV9WlX7qmrfJk2aVKhgbRoVD4npHcFt2s2nVmh/JryHLvhF0PSCEu+xGGPiJy7BQlU3uz+3A+/hVCttE5HmAO7P7e7qmwBvBz6tgM2xKy3888KeITv5M0fu3N6t/NOhxsB+87f9Y1UcY0wQMQ8WIlJLROr4poHTgcXA+8AYd7UxwGR3+n1gtIhkikh7oBMQOF5mJfv+9iEB82fb6Gcx88rV/Xjt6hPoelRg0EhJsQYFxsRTPJ5ZNAPec1sTpQGvq+oUEfkeeEtErgJ+An4NoKpLROQtYClQAFyvqoXRLGCTOplM/+Op/PnthXRsWjuaWZkSmtbJommdrIAqwLvP6lbGFsaYWIh5sFDVtUCpSmpV3QUMDrHNRGBilIsWoEOT2rx93YmxzLJaq5mRyqG84muANg1r8pP71vwl/dvGq1jGGFciNZ011diUmwby1GV9/PNPXlo8nV6O8bGNMdFh3X2YhNCmUc2AlmjWnYcxicUu2UxCqm9diRuTUOzOwiQkEWHswKNDNqU1xsSWBQuTsG4bcUy8i2CMcVk1lDHGmLAsWBhjjAnLgoUxxpiwLFgYY4wJy4KFMcaYsCxYGGOMCcuChTHGmLAsWBhjjAlLVIMOOpf0RGQHsCFO2TcGdsYpb8vf8rf8Lf+K5r8TQFWHl1xQZYNFPInIXFXta/lb/pa/5V9V8rdqKGOMMWFZsDDGGBOWBYvoeNryt/wtf8u/KuVvzyyMMcaEZXcWxhhjwrJgYYwxJiwLFhEQkdYiMkNElonIEhG5yU1vKCKficgq92cDzzbjRWS1iKwQkWFB9vm+iCyOdf4ikiEiT4vIShFZLiLnxTj/i0RkkYgsFJEpItK4svMXkUbu+gdE5NES++rj5r9aRB4REYlV/iJSU0Q+cn/vS0RkUri8K/v7e/YZteMvzO8/6sdfmPxjcfwNFZF5bj7zROSXnn3F4vgLmn9Fjz8/VbVPmA/QHOjtTtcBVgLdgAeAW930W4G/udPdgAVAJtAeWAOkevZ3LvA6sDjW+QP3APe50ylA41jljzMy43Zfnu72d0ch/1rAycC1wKMl9jUHGAAI8AlwRqzyB2oCp7nTGcBXscw/hsdfWb//WBx/oX7/sTr+egEt3OljgZ9jfPwFzb+ix5+/HJGuaJ+AP95kYCiwAmju+YOucKfHA+M9608FBrjTtYGv3T92RP+slZz/RqBWPL4/kA7sANq6/yxPAmMrO3/PeleUOFk0B5Z75i8CnopV/kH28y/gt7HMPxbHX5j8o378lfH3j+nx56YLsAvnwimmx1/J/I/0+LNqqHISkXY4kfs7oJmqbgFwfzZ1V2uJ80/hs8lNA7gXeBA4FOv8RaS+rwwi8oOI/FdEmsUqf1XNB64DFgGbcU5Yz0Uh/1BaumUJKFcM8/fupz5wFjA9xvnH4vgLtW19XxmifPwFFafj7zzgR1XNJT7Hnzd/737qU87jz4JFOYhIbeAdYJyq7i9r1SBpKiI9gY6q+l488se5DW8FfKOqvYFZwD9ilb+IpOP8s/YCWgALce5CKjv/cpUrhvn79pMGvAE8oqprY5V/DI+/UGJ1/IXaPqbHn4h0B/4GXONLCrJa1I6/IPn70it0/FmwiJB7oL0DvKaq77rJ20Skubu8OU59KDhXDK09m7fCuZIZAPQRkfU4VQGdRWRmDPPfhXNF6TtZ/BfoHcP8ewKo6hp17oPfAk6MQv6hbHLLUrJcscrf52lglar+M8L1Kyv/WB1/ocTq+AulJ8Tm+BORVjjf83JVXeMmx+z4C5G/T7mPP7BgERG3xcJzwDJVfciz6H1gjDs9Bqcu0Zc+WkQyRaQ90AmYo6pPqGoLVW2H8wBupaoOimH+CnwA+PIcDCyNVf7Az0A3EWnirjcUWBaF/INyb9WzRaS/u8/Lw21Tmfm7+7oPqAeMC7duZecfw+MvVP6xOv5Cicnx51bxfITz3O4b38qxOv5C5e8uK/fx51fehyvV8YPzj6U4t63z3c8IoBFOnd8q92dDzza347QCWkGQFgdAOyJvjVJp+eM83PvS3dd0oE2M878W5x90Ic6Jo1GU8l8P7AYO4FzRdXPT+wKL3bI9ituLQSzyx7mSVPf7+/ZzdSy/fwyPv1C//1gdf6Hyj/rxB0wADnrWnQ80jdXxFyp/Knj8+T7W3YcxxpiwrBrKGGNMWBYsjDHGhGXBwhhjTFgWLIwxxoRlwcIYY0xYFiyMOULi9HI63/1sFZGf3ekDIvJ4vMtnTGWwprPGVCIRuRs4oKoRd2NhTDKwOwtjokREBonIh+703SLykoh8KiLrReRcEXlAnDEHprjdOfjGO/hCnHEIpvq6czAm3ixYGBM7HYCRwCjgVWCGqvYADgMj3YDxb+B8Ve0DPA9MjFdhjfFKi3cBjKlGPlHVfBFZhDMY1BQ3fRFO9xtdcAar+czpDohUYEscymlMKRYsjImdXABVLRKRfC1+YFiE878owBJVHRCvAhoTilVDGZM4VgBNRGQAON1Su2MSGBN3FiyMSRCqmgecD/xNRBbg9Aoa0XgLxkSbNZ01xhgTlt1ZGGOMCcuChTHGmLAsWBhjjAnLgoUxxpiwLFgYY4wJy4KFMcaYsCxYGGOMCev/AX+bDYyzlfXdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Russell 3000 close price from 2003 to 2021\n",
    "plt.plot(df['Close'])\n",
    "plt.title('Russell 3000 Close Price (2003 - 2021)')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Close Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8965660b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Return')"
      ]
     },
     "execution_count": 6,
     "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": [
    "# Plot Russell 3000 return from 2003 to 2021\n",
    "plt.plot(df['Return'])\n",
    "plt.title('Russell 3000 Return (2003 - 2021)')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Return')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c5b48b9c",
   "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>Return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.032054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.000812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.022327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.006957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.013933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4749</th>\n",
       "      <td>0.006490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4750</th>\n",
       "      <td>0.012780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4751</th>\n",
       "      <td>-0.001922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4752</th>\n",
       "      <td>0.001258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4753</th>\n",
       "      <td>-0.002127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4754 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Return\n",
       "0     0.032054\n",
       "1    -0.000812\n",
       "2     0.022327\n",
       "3    -0.006957\n",
       "4    -0.013933\n",
       "...        ...\n",
       "4749  0.006490\n",
       "4750  0.012780\n",
       "4751 -0.001922\n",
       "4752  0.001258\n",
       "4753 -0.002127\n",
       "\n",
       "[4754 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Regard return as observations\n",
    "df_new = df.reset_index(inplace=True, drop=False)\n",
    "df_new = pd.DataFrame(df['Return'])\n",
    "df_new "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5805debc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change return from dataframe to list\n",
    "stock = list(df_new.itertuples(index=False, name=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9b806665",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.03205378],\n",
       "       [-0.0008116 ],\n",
       "       [ 0.02232698],\n",
       "       ...,\n",
       "       [-0.00192239],\n",
       "       [ 0.00125789],\n",
       "       [-0.00212722]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "return_val = np.array([i[0] for i in stock])\n",
    "X = np.column_stack([return_val])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "40c5dced",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4749</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4750</th>\n",
       "      <td>0</td>\n",
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       "      <th>4751</th>\n",
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       "    <tr>\n",
       "      <th>4752</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4753</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4754 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      state\n",
       "0         1\n",
       "1         1\n",
       "2         1\n",
       "3         1\n",
       "4         1\n",
       "...     ...\n",
       "4749      0\n",
       "4750      0\n",
       "4751      0\n",
       "4752      0\n",
       "4753      0\n",
       "\n",
       "[4754 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assume the distribution of emission probabilities is Gaussian, and use HMM to decode states\n",
    "model = GaussianHMM(n_components=3)\n",
    "model.fit(X)\n",
    "hidden_states = model.predict(X)\n",
    "states = pd.DataFrame(hidden_states)\n",
    "states.columns = ['state']\n",
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "48fe91cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Return</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-01-02</th>\n",
       "      <td>489.489990</td>\n",
       "      <td>505.179993</td>\n",
       "      <td>489.489990</td>\n",
       "      <td>505.179993</td>\n",
       "      <td>505.179993</td>\n",
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       "      <td>0.032054</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-03</th>\n",
       "      <td>505.179993</td>\n",
       "      <td>506.230011</td>\n",
       "      <td>502.100006</td>\n",
       "      <td>504.769989</td>\n",
       "      <td>504.769989</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.000812</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-06</th>\n",
       "      <td>504.769989</td>\n",
       "      <td>517.530029</td>\n",
       "      <td>504.769989</td>\n",
       "      <td>516.039978</td>\n",
       "      <td>516.039978</td>\n",
       "      <td>0</td>\n",
       "      <td>0.022327</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-07</th>\n",
       "      <td>516.039978</td>\n",
       "      <td>516.650024</td>\n",
       "      <td>510.769989</td>\n",
       "      <td>512.450012</td>\n",
       "      <td>512.450012</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.006957</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-08</th>\n",
       "      <td>512.450012</td>\n",
       "      <td>512.450012</td>\n",
       "      <td>504.529999</td>\n",
       "      <td>505.309998</td>\n",
       "      <td>505.309998</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.013933</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-23</th>\n",
       "      <td>2750.840088</td>\n",
       "      <td>2776.620117</td>\n",
       "      <td>2750.840088</td>\n",
       "      <td>2768.360107</td>\n",
       "      <td>2768.360107</td>\n",
       "      <td>0</td>\n",
       "      <td>0.006490</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-27</th>\n",
       "      <td>2769.340088</td>\n",
       "      <td>2803.879883</td>\n",
       "      <td>2769.340088</td>\n",
       "      <td>2803.739990</td>\n",
       "      <td>2803.739990</td>\n",
       "      <td>0</td>\n",
       "      <td>0.012780</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>2803.899902</td>\n",
       "      <td>2813.590088</td>\n",
       "      <td>2795.709961</td>\n",
       "      <td>2798.350098</td>\n",
       "      <td>2798.350098</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.001922</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>2798.379883</td>\n",
       "      <td>2807.370117</td>\n",
       "      <td>2791.860107</td>\n",
       "      <td>2801.870117</td>\n",
       "      <td>2801.870117</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001258</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>2802.909912</td>\n",
       "      <td>2813.250000</td>\n",
       "      <td>2794.189941</td>\n",
       "      <td>2795.909912</td>\n",
       "      <td>2795.909912</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.002127</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4754 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2003-01-02   489.489990   505.179993   489.489990   505.179993   505.179993   \n",
       "2003-01-03   505.179993   506.230011   502.100006   504.769989   504.769989   \n",
       "2003-01-06   504.769989   517.530029   504.769989   516.039978   516.039978   \n",
       "2003-01-07   516.039978   516.650024   510.769989   512.450012   512.450012   \n",
       "2003-01-08   512.450012   512.450012   504.529999   505.309998   505.309998   \n",
       "...                 ...          ...          ...          ...          ...   \n",
       "2021-12-23  2750.840088  2776.620117  2750.840088  2768.360107  2768.360107   \n",
       "2021-12-27  2769.340088  2803.879883  2769.340088  2803.739990  2803.739990   \n",
       "2021-12-28  2803.899902  2813.590088  2795.709961  2798.350098  2798.350098   \n",
       "2021-12-29  2798.379883  2807.370117  2791.860107  2801.870117  2801.870117   \n",
       "2021-12-30  2802.909912  2813.250000  2794.189941  2795.909912  2795.909912   \n",
       "\n",
       "            Volume    Return  state  \n",
       "Date                                 \n",
       "2003-01-02       0  0.032054      1  \n",
       "2003-01-03       0 -0.000812      1  \n",
       "2003-01-06       0  0.022327      1  \n",
       "2003-01-07       0 -0.006957      1  \n",
       "2003-01-08       0 -0.013933      1  \n",
       "...            ...       ...    ...  \n",
       "2021-12-23       0  0.006490      0  \n",
       "2021-12-27       0  0.012780      0  \n",
       "2021-12-28       0 -0.001922      0  \n",
       "2021-12-29       0  0.001258      0  \n",
       "2021-12-30       0 -0.002127      0  \n",
       "\n",
       "[4754 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine decoded states with original dataframe\n",
    "result = pd.concat([df, states], axis=1)\n",
    "result.set_index(['Date'], inplace=True)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "64a2f624",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Close Price')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot decoded states from 2003 to 2021\n",
    "colors = {0:'red', 1:'green', 2:'yellow'}\n",
    "plt.figure(figsize = (20,8))\n",
    "sns.scatterplot(x = result.index, y = result['Close'], palette=colors, hue = result['state'])\n",
    "plt.title('Decoded Stock Market States (2003 - 2021)')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Close Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "38220099",
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Return</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-01-02</th>\n",
       "      <td>489.489990</td>\n",
       "      <td>505.179993</td>\n",
       "      <td>489.489990</td>\n",
       "      <td>505.179993</td>\n",
       "      <td>505.179993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.032054</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-03</th>\n",
       "      <td>505.179993</td>\n",
       "      <td>506.230011</td>\n",
       "      <td>502.100006</td>\n",
       "      <td>504.769989</td>\n",
       "      <td>504.769989</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.000812</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-06</th>\n",
       "      <td>504.769989</td>\n",
       "      <td>517.530029</td>\n",
       "      <td>504.769989</td>\n",
       "      <td>516.039978</td>\n",
       "      <td>516.039978</td>\n",
       "      <td>0</td>\n",
       "      <td>0.022327</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-07</th>\n",
       "      <td>516.039978</td>\n",
       "      <td>516.650024</td>\n",
       "      <td>510.769989</td>\n",
       "      <td>512.450012</td>\n",
       "      <td>512.450012</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.006957</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-08</th>\n",
       "      <td>512.450012</td>\n",
       "      <td>512.450012</td>\n",
       "      <td>504.529999</td>\n",
       "      <td>505.309998</td>\n",
       "      <td>505.309998</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.013933</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-23</th>\n",
       "      <td>2750.840088</td>\n",
       "      <td>2776.620117</td>\n",
       "      <td>2750.840088</td>\n",
       "      <td>2768.360107</td>\n",
       "      <td>2768.360107</td>\n",
       "      <td>0</td>\n",
       "      <td>0.006490</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-27</th>\n",
       "      <td>2769.340088</td>\n",
       "      <td>2803.879883</td>\n",
       "      <td>2769.340088</td>\n",
       "      <td>2803.739990</td>\n",
       "      <td>2803.739990</td>\n",
       "      <td>0</td>\n",
       "      <td>0.012780</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>2803.899902</td>\n",
       "      <td>2813.590088</td>\n",
       "      <td>2795.709961</td>\n",
       "      <td>2798.350098</td>\n",
       "      <td>2798.350098</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.001922</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>2798.379883</td>\n",
       "      <td>2807.370117</td>\n",
       "      <td>2791.860107</td>\n",
       "      <td>2801.870117</td>\n",
       "      <td>2801.870117</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001258</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>2802.909912</td>\n",
       "      <td>2813.250000</td>\n",
       "      <td>2794.189941</td>\n",
       "      <td>2795.909912</td>\n",
       "      <td>2795.909912</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.002127</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4754 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2003-01-02   489.489990   505.179993   489.489990   505.179993   505.179993   \n",
       "2003-01-03   505.179993   506.230011   502.100006   504.769989   504.769989   \n",
       "2003-01-06   504.769989   517.530029   504.769989   516.039978   516.039978   \n",
       "2003-01-07   516.039978   516.650024   510.769989   512.450012   512.450012   \n",
       "2003-01-08   512.450012   512.450012   504.529999   505.309998   505.309998   \n",
       "...                 ...          ...          ...          ...          ...   \n",
       "2021-12-23  2750.840088  2776.620117  2750.840088  2768.360107  2768.360107   \n",
       "2021-12-27  2769.340088  2803.879883  2769.340088  2803.739990  2803.739990   \n",
       "2021-12-28  2803.899902  2813.590088  2795.709961  2798.350098  2798.350098   \n",
       "2021-12-29  2798.379883  2807.370117  2791.860107  2801.870117  2801.870117   \n",
       "2021-12-30  2802.909912  2813.250000  2794.189941  2795.909912  2795.909912   \n",
       "\n",
       "            Volume    Return  state  \n",
       "Date                                 \n",
       "2003-01-02       0  0.032054     -1  \n",
       "2003-01-03       0 -0.000812     -1  \n",
       "2003-01-06       0  0.022327     -1  \n",
       "2003-01-07       0 -0.006957     -1  \n",
       "2003-01-08       0 -0.013933     -1  \n",
       "...            ...       ...    ...  \n",
       "2021-12-23       0  0.006490      1  \n",
       "2021-12-27       0  0.012780      1  \n",
       "2021-12-28       0 -0.001922      1  \n",
       "2021-12-29       0  0.001258      1  \n",
       "2021-12-30       0 -0.002127      1  \n",
       "\n",
       "[4754 rows x 8 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map the states to -1,0,1 based on above plot\n",
    "result['state'] = result['state'].map({0: 1, 1: -1,2: 0})\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ebdf9685",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Close Price')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot decoded states from 2003 to 2021 after mapping\n",
    "colors = {1:'red', -1:'green', 0:'yellow'}\n",
    "plt.figure(figsize = (20,8))\n",
    "sns.scatterplot(x = result.index, y = result['Close'], palette=colors, hue = result['state'])\n",
    "plt.title('Decoded Stock Market States (2003 - 2021)')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Close Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c7010113",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters needed for RandomizedSearch\n",
    "xg_grid = {\n",
    "        'min_child_weight': [1, 5, 10],\n",
    "        'gamma': [0.5, 1, 1.5, 2, 5],\n",
    "        'max_depth': [3, 4, 5]\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aaf0460f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 45 candidates, totalling 225 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "/Users/xiyao/opt/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:36:04] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[21:36:04] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "# Set X to be return and y to be state, the training period is 2003-2017 and testing period is 2018-2021 \n",
    "# Fit XGBoost model\n",
    "X_train, y_train = result.loc['2003-01-02':'2018-01-01',:]['Return'], result.loc['2003-01-02':'2018-01-01']['state']\n",
    "X_test, y_test = result.loc['2018-01-01':'2022-01-01',:]['Return'], result.loc['2018-01-01':'2022-01-01']['state']\n",
    "\n",
    "xg = XGBClassifier()\n",
    "xg_random = RandomizedSearchCV(estimator = xg, param_distributions = xg_grid, n_iter = 100, verbose=2, random_state=42, n_jobs = -1)# Fit the random search model\n",
    "xg_random.fit(X_train, y_train)\n",
    "params = xg_random.best_params_\n",
    "\n",
    "model = XGBClassifier(**params).fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "result.loc['2018-01-01':'2022-01-01',['predict']] = y_pred.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6ebb5cb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Return</th>\n",
       "      <th>state</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
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       "  <tbody>\n",
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       "      <th>2018-01-02</th>\n",
       "      <td>1585.089966</td>\n",
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       "      <th>2018-01-03</th>\n",
       "      <td>1595.839966</td>\n",
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       "      <td>1595.839966</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>1605.699951</td>\n",
       "      <td>1613.839966</td>\n",
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       "      <td>1611.020020</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>2018-01-05</th>\n",
       "      <td>1612.140015</td>\n",
       "      <td>1621.479980</td>\n",
       "      <td>1612.140015</td>\n",
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       "      <th>2018-01-08</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2021-12-23</th>\n",
       "      <td>2750.840088</td>\n",
       "      <td>2776.620117</td>\n",
       "      <td>2750.840088</td>\n",
       "      <td>2768.360107</td>\n",
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       "    <tr>\n",
       "      <th>2021-12-27</th>\n",
       "      <td>2769.340088</td>\n",
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       "      <th>2021-12-28</th>\n",
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       "      <th>2021-12-29</th>\n",
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       "<p>978 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2018-01-02  1585.089966  1595.939941  1585.089966  1595.869995  1595.869995   \n",
       "2018-01-03  1595.839966  1605.890015  1595.839966  1605.189941  1605.189941   \n",
       "2018-01-04  1605.699951  1613.839966  1605.699951  1611.020020  1611.020020   \n",
       "2018-01-05  1612.140015  1621.479980  1612.140015  1621.369995  1621.369995   \n",
       "2018-01-08  1621.280029  1624.780029  1617.640015  1624.349976  1624.349976   \n",
       "...                 ...          ...          ...          ...          ...   \n",
       "2021-12-23  2750.840088  2776.620117  2750.840088  2768.360107  2768.360107   \n",
       "2021-12-27  2769.340088  2803.879883  2769.340088  2803.739990  2803.739990   \n",
       "2021-12-28  2803.899902  2813.590088  2795.709961  2798.350098  2798.350098   \n",
       "2021-12-29  2798.379883  2807.370117  2791.860107  2801.870117  2801.870117   \n",
       "2021-12-30  2802.909912  2813.250000  2794.189941  2795.909912  2795.909912   \n",
       "\n",
       "            Volume    Return  state  predict  \n",
       "Date                                          \n",
       "2018-01-02       0  0.008277      1      1.0  \n",
       "2018-01-03       0  0.005840      1      1.0  \n",
       "2018-01-04       0  0.003632      1      1.0  \n",
       "2018-01-05       0  0.006424      1      1.0  \n",
       "2018-01-08       0  0.001838      1      1.0  \n",
       "...            ...       ...    ...      ...  \n",
       "2021-12-23       0  0.006490      1      1.0  \n",
       "2021-12-27       0  0.012780      1      1.0  \n",
       "2021-12-28       0 -0.001922      1      1.0  \n",
       "2021-12-29       0  0.001258      1      1.0  \n",
       "2021-12-30       0 -0.002127      1      1.0  \n",
       "\n",
       "[978 rows x 9 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the predicted result from 2018 to 2021\n",
    "result = result.loc['2018-01-01':'2022-01-01']\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a80f08d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Close Price')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predicted states from 2018 to 2021\n",
    "colors = {1:'red', -1:'green', 0:'yellow'}\n",
    "plt.figure(figsize = (20,8))\n",
    "sns.scatterplot(x = result.index, y = result['Close'], palette=colors, hue = result['predict'])\n",
    "plt.title('Predicted Stock Market States (2018 - 2021)')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Close Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "01948f02",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the function of checking the accuracy of decoded states and predicted states\n",
    "def function(a, b):\n",
    "    if a == b:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2b4e3bff",
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Return</th>\n",
       "      <th>state</th>\n",
       "      <th>predict</th>\n",
       "      <th>bool</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>1585.089966</td>\n",
       "      <td>1595.939941</td>\n",
       "      <td>1585.089966</td>\n",
       "      <td>1595.869995</td>\n",
       "      <td>1595.869995</td>\n",
       "      <td>0</td>\n",
       "      <td>0.008277</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>1595.839966</td>\n",
       "      <td>1605.890015</td>\n",
       "      <td>1595.839966</td>\n",
       "      <td>1605.189941</td>\n",
       "      <td>1605.189941</td>\n",
       "      <td>0</td>\n",
       "      <td>0.005840</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>1605.699951</td>\n",
       "      <td>1613.839966</td>\n",
       "      <td>1605.699951</td>\n",
       "      <td>1611.020020</td>\n",
       "      <td>1611.020020</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003632</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>1612.140015</td>\n",
       "      <td>1621.479980</td>\n",
       "      <td>1612.140015</td>\n",
       "      <td>1621.369995</td>\n",
       "      <td>1621.369995</td>\n",
       "      <td>0</td>\n",
       "      <td>0.006424</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>1621.280029</td>\n",
       "      <td>1624.780029</td>\n",
       "      <td>1617.640015</td>\n",
       "      <td>1624.349976</td>\n",
       "      <td>1624.349976</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001838</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-23</th>\n",
       "      <td>2750.840088</td>\n",
       "      <td>2776.620117</td>\n",
       "      <td>2750.840088</td>\n",
       "      <td>2768.360107</td>\n",
       "      <td>2768.360107</td>\n",
       "      <td>0</td>\n",
       "      <td>0.006490</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-27</th>\n",
       "      <td>2769.340088</td>\n",
       "      <td>2803.879883</td>\n",
       "      <td>2769.340088</td>\n",
       "      <td>2803.739990</td>\n",
       "      <td>2803.739990</td>\n",
       "      <td>0</td>\n",
       "      <td>0.012780</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>2803.899902</td>\n",
       "      <td>2813.590088</td>\n",
       "      <td>2795.709961</td>\n",
       "      <td>2798.350098</td>\n",
       "      <td>2798.350098</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.001922</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>2798.379883</td>\n",
       "      <td>2807.370117</td>\n",
       "      <td>2791.860107</td>\n",
       "      <td>2801.870117</td>\n",
       "      <td>2801.870117</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001258</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>2802.909912</td>\n",
       "      <td>2813.250000</td>\n",
       "      <td>2794.189941</td>\n",
       "      <td>2795.909912</td>\n",
       "      <td>2795.909912</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.002127</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>978 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2018-01-02  1585.089966  1595.939941  1585.089966  1595.869995  1595.869995   \n",
       "2018-01-03  1595.839966  1605.890015  1595.839966  1605.189941  1605.189941   \n",
       "2018-01-04  1605.699951  1613.839966  1605.699951  1611.020020  1611.020020   \n",
       "2018-01-05  1612.140015  1621.479980  1612.140015  1621.369995  1621.369995   \n",
       "2018-01-08  1621.280029  1624.780029  1617.640015  1624.349976  1624.349976   \n",
       "...                 ...          ...          ...          ...          ...   \n",
       "2021-12-23  2750.840088  2776.620117  2750.840088  2768.360107  2768.360107   \n",
       "2021-12-27  2769.340088  2803.879883  2769.340088  2803.739990  2803.739990   \n",
       "2021-12-28  2803.899902  2813.590088  2795.709961  2798.350098  2798.350098   \n",
       "2021-12-29  2798.379883  2807.370117  2791.860107  2801.870117  2801.870117   \n",
       "2021-12-30  2802.909912  2813.250000  2794.189941  2795.909912  2795.909912   \n",
       "\n",
       "            Volume    Return  state  predict  bool  \n",
       "Date                                                \n",
       "2018-01-02       0  0.008277      1      1.0     1  \n",
       "2018-01-03       0  0.005840      1      1.0     1  \n",
       "2018-01-04       0  0.003632      1      1.0     1  \n",
       "2018-01-05       0  0.006424      1      1.0     1  \n",
       "2018-01-08       0  0.001838      1      1.0     1  \n",
       "...            ...       ...    ...      ...   ...  \n",
       "2021-12-23       0  0.006490      1      1.0     1  \n",
       "2021-12-27       0  0.012780      1      1.0     1  \n",
       "2021-12-28       0 -0.001922      1      1.0     1  \n",
       "2021-12-29       0  0.001258      1      1.0     1  \n",
       "2021-12-30       0 -0.002127      1      1.0     1  \n",
       "\n",
       "[978 rows x 10 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataframe the checking results\n",
    "result['bool'] = result.apply(lambda x : function(x['state'], x['predict']),axis = 1)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "17abda4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Close Price')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the checking results from 2018 to 2021 \n",
    "colors = {1:'red', 0:'yellow'}\n",
    "plt.figure(figsize = (20,8))\n",
    "sns.scatterplot(x = result.index, y = result['Close'], palette=colors, hue = result['bool'])\n",
    "plt.title('Checking Stock Market States (2018 - 2021)')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Close Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b8e84599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8725499916540629"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the model's f1 score\n",
    "f1 = f1_score(y_pred,y_test, average='weighted')\n",
    "f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f7b452ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_child_weight': 1, 'max_depth': 4, 'gamma': 5}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print best params of XGBoost model\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eac274be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set strategy\n",
    "class PandasData(PandasData):\n",
    "\n",
    "    '''\n",
    "    The ``dataname`` parameter inherited from ``feed.DataBase`` is the pandas\n",
    "    DataFrame\n",
    "    '''\n",
    "    lines = ('state',)\n",
    "    params = (\n",
    "        ('datetime', 0),\n",
    "        ('high', 'High'),\n",
    "        ('low', 'Low'),\n",
    "        ('close', 'Close'),\n",
    "        ('volume', None),\n",
    "        ('openinterest', None),\n",
    "        ('state', 8),\n",
    "    )\n",
    "    datafields = PandasData.datafields + (['state'])\n",
    "\n",
    "# Strategy based on market states\n",
    "class MarketStatus(bt.Strategy): \n",
    "\n",
    "\n",
    "    def log(self, txt, dt=None):\n",
    "        dt = dt or self.datas[0].datetime.date(0)\n",
    "\n",
    "    def __init__(self):\n",
    "        self.dataclose = self.datas[0].close\n",
    "\n",
    "        # Order variable will contain ongoing order details/status\n",
    "        self.order = None\n",
    "\n",
    "        # Instantiate market status\n",
    "        self.state = self.datas[0].state\n",
    "\n",
    "    def notify_order(self, order):\n",
    "        if order.status in [order.Submitted, order.Accepted]:\n",
    "            # An active Buy/Sell order has been submitted/accepted - Nothing to do\n",
    "            return\n",
    "\n",
    "        # Check if an order has been completed\n",
    "        # Attention: broker could reject order if not enough cash\n",
    "        if order.status in [order.Completed]:\n",
    "            if order.isbuy():\n",
    "                self.log(f'BUY EXECUTED, {order.executed.price:.2f}')\n",
    "            elif order.issell():\n",
    "                self.log(f'SELL EXECUTED, {order.executed.price:.2f}')\n",
    "            self.bar_executed = len(self)\n",
    "\n",
    "        elif order.status in [order.Canceled, order.Margin, order.Rejected]:\n",
    "            self.log('Order Canceled/Margin/Rejected')\n",
    "\n",
    "        # Reset orders\n",
    "        self.order = None\n",
    "\n",
    "    def next(self):\n",
    "\n",
    "        # Check for open orders\n",
    "        if self.order:\n",
    "            return\n",
    "        \n",
    "        if self.state[0] == 1:\n",
    "            if not self.position:\n",
    "                self.log(f'BUY CREATE {self.dataclose[0]:2f} {self.state[0]:2f}')\n",
    "                self.order = self.buy()\n",
    "            elif self.position.size < 0: # already have a sell order\n",
    "                self.order = self.close()\n",
    "                self.log(f'CLOSE CREATE {self.dataclose[0]:2f}')\n",
    "                self.log(f'BUY CREATE {self.dataclose[0]:2f} {self.state[0]:2f}')\n",
    "                self.order = self.buy()\n",
    "            elif self.position.size > 0: # already have a buy order\n",
    "                # Check the period of current states\n",
    "                if len(self) == (self.bar_executed + 5):\n",
    "                    self.log(f'BUY CREATE {self.dataclose[0]:2f} {self.state[0]:2f}')\n",
    "                    self.order = self.buy()\n",
    "                    self.log('Chase')\n",
    "                elif len(self) >= (self.bar_executed + 15):\n",
    "                    self.order = self.close()\n",
    "                    self.log(f'CLOSE CREATE {self.dataclose[0]:2f}')\n",
    "                else:\n",
    "                    pass\n",
    "            else:\n",
    "                pass\n",
    "        \n",
    "        elif self.state[0] == -1:\n",
    "            if not self.position:           \n",
    "                self.log(f'SELL CREATE {self.dataclose[0]:2f} {self.state[0]:2f}')\n",
    "                self.order = self.sell()\n",
    "            elif self.position.size > 0: # already have a buy order             \n",
    "                self.order = self.close()\n",
    "                self.log(f'CLOSE CREATE {self.dataclose[0]:2f}')\n",
    "                self.log(f'SELL CREATE {self.dataclose[0]:2f} {self.state[0]:2f}')\n",
    "                self.order = self.sell()\n",
    "            elif self.position.size < 0: # already have a sell order\n",
    "                if len(self) == (self.bar_executed + 5):\n",
    "                    self.log(f'SELL CREATE {self.dataclose[0]:2f} {self.state[0]:2f}')\n",
    "                    self.order = self.sell()\n",
    "                elif len(self) >= (self.bar_executed + 15):\n",
    "                    self.order = self.close()\n",
    "                    self.log(f'CLOSE CREATE {self.dataclose[0]:2f}')\n",
    "                else:\n",
    "                    pass\n",
    "\n",
    "            else:\n",
    "                pass\n",
    "        \n",
    "        else:\n",
    "            if not self.position:\n",
    "                pass\n",
    "            else:\n",
    "                self.log(f'Hold {self.dataclose[0]:2f} {self.state[0]:2f}')\n",
    "\n",
    "# Benchmark strategy - BuyAndHold\n",
    "class BuyAndHold(bt.Strategy):\n",
    "\n",
    "    def log(self,txt,dt=None):\n",
    "        dt = dt or self.datas[0].datetime.date(0)\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.dataclose = self.datas[0].close\n",
    "\n",
    "        self.order = None\n",
    "        \n",
    "    def notify_order(self,order):\n",
    "        if order.status in [order.Submitted, order.Accepted]:\n",
    "\t\t\t# An active Buy/Sell order has been submitted/accepted - Nothing to do\n",
    "            return\n",
    "        \n",
    "        # Check if an order has been completed\n",
    "\t\t# Attention: broker could reject order if not enough cash\n",
    "        if order.status in [order.Completed]:\n",
    "            if order.isbuy():\n",
    "                self.log(f'BUY EXECUTED, {order.executed.price:.2f}')\n",
    "            elif order.issell():\n",
    "                self.log(f'SELL EXECUTED, {order.executed.price:.2f}')\n",
    "            self.bar_executed = len(self)\n",
    "\n",
    "        elif order.status in [order.Canceled, order.Margin, order.Rejected]:\n",
    "            self.log('Order Canceled/Margin/Rejected')\n",
    "            \n",
    "    def next(self):\n",
    "        # Check for open orders\n",
    "        if self.order:\n",
    "            return\n",
    "        self.log(f'BUY CREATE {self.dataclose[0]:2f}')\n",
    "        self.order = self.buy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "372de97f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_cerebro(df,strategy):\n",
    "    \n",
    "    df = df.reset_index()\n",
    "    state_column = df.shape[1] - 2\n",
    "    # Instantiate Cerebro engine\n",
    "    cerebro = bt.Cerebro()\n",
    "\n",
    "    data = PandasData(\n",
    "        dataname = df,\n",
    "        fromdate=datetime.datetime(2018, 1, 2),\n",
    "        todate=datetime.datetime(2021, 12, 30),\n",
    "        datetime = 0,\n",
    "        open = 1,\n",
    "        high = 2,\n",
    "        low = 3,\n",
    "        close = 4,\n",
    "        state = state_column,\n",
    "    )\n",
    "\n",
    "    # Add data to Cerebro\n",
    "    cerebro.adddata(data)\n",
    "    # Add strategy to Cerebro\n",
    "    cerebro.addstrategy(strategy)\n",
    "\n",
    "    # Default position size\n",
    "    cerebro.addsizer(bt.sizers.SizerFix, stake=1)\n",
    "\n",
    "    # Add analytics to Cerebro\n",
    "    cerebro.addanalyzer(btanalyzers.SharpeRatio, _name='SharpeRatio')\n",
    "    cerebro.addanalyzer(btanalyzers.AnnualReturn, _name='AnnualReturn')\n",
    "    cerebro.addanalyzer(btanalyzers.DrawDown, _name='DrawDown')\n",
    "    cerebro.addanalyzer(btanalyzers.TimeDrawDown, _name='TimeDrawDown')\n",
    "    cerebro.addanalyzer(btanalyzers.PositionsValue, _name='PositionsValue')\n",
    "    cerebro.addanalyzer(btanalyzers.LogReturnsRolling, _name='LogReturnsRolling')\n",
    "    cerebro.addanalyzer(btanalyzers.PeriodStats, _name='PeriodStats')\n",
    "    cerebro.addanalyzer(btanalyzers.Returns, _name='Returns')\n",
    "    cerebro.addanalyzer(btanalyzers.TradeAnalyzer, _name='TradeAnalyzer')\n",
    "    cerebro.addanalyzer(btanalyzers.Transactions, _name='Transactions')\n",
    "\n",
    "    # Run Cerebro engine\n",
    "    start_portfolio_value = cerebro.broker.getvalue()\n",
    "\n",
    "    # cerebro.run()\n",
    "    thestrats = cerebro.run()\n",
    "    thestrat = thestrats[0]\n",
    "\n",
    "    end_portfolio_value = cerebro.broker.getvalue()\n",
    "    pnl = end_portfolio_value - start_portfolio_value\n",
    "\n",
    "    print(f'Starting Portfolio Value: {start_portfolio_value:2f}')\n",
    "    print(f'Final Portfolio Value: {end_portfolio_value:2f}')\n",
    "    print(f'PnL: {pnl:.2f}\\n')\n",
    "\n",
    "\n",
    "    sharpe = thestrat.analyzers.SharpeRatio.get_analysis()['sharperatio']\n",
    "\n",
    "    return pnl, sharpe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6ecfb9e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value: 10000.000000\n",
      "Final Portfolio Value: 13243.012451\n",
      "PnL: 3243.01\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# PnL and Sharpe Ratio using MarketStatus starategy\n",
    "pnl, sharpe = run_cerebro(result, MarketStatus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "df8350fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3243.012451171875"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "12014212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8678369827654386"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sharpe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "797e291a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Portfolio Value: 10000.000000\n",
      "Final Portfolio Value: 11200.069946\n",
      "PnL: 1200.07\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# PnL and Sharpe Ratio using BuyAndHold starategy\n",
    "pnl, sharpe = run_cerebro(result, BuyAndHold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "51c310a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1200.0699462890625"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e0ef1003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7728328561630216"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:34:39] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=3, min_child_weight=1; total time=   2.2s\n",
      "[21:34:41] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=0.5, max_depth=3, min_child_weight=10; total time=   2.2s\n",
      "[21:34:44] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=4, min_child_weight=5; total time=   2.7s\n",
      "[21:34:46] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=5, min_child_weight=1; total time=   3.5s\n",
      "[21:34:50] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=5, min_child_weight=5; total time=   3.4s\n",
      "[21:34:53] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=3, min_child_weight=1; total time=   2.2s\n",
      "[21:34:55] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=3, min_child_weight=1; total time=   2.3s\n",
      "[21:34:58] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=1, max_depth=3, min_child_weight=10; total time=   2.3s\n",
      "[21:35:00] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=4, min_child_weight=5; total time=   2.9s\n",
      "[21:35:03] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=1, max_depth=4, min_child_weight=10; total time=   2.8s\n",
      "[21:35:06] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=5, min_child_weight=5; total time=   3.4s\n",
      "[21:35:09] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=1, max_depth=5, min_child_weight=10; total time=   3.5s\n",
      "[21:35:13] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=3, min_child_weight=5; total time=   4.1s\n",
      "[21:35:17] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=4, min_child_weight=1; total time=   2.7s\n",
      "[21:35:19] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=1.5, max_depth=4, min_child_weight=10; total time=   2.7s\n",
      "[21:35:22] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=5, min_child_weight=1; total time=   3.4s\n",
      "[21:35:25] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=1.5, max_depth=5, min_child_weight=10; total time=   3.5s\n",
      "[21:35:29] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=3, min_child_weight=5; total time=   2.3s\n",
      "[21:35:31] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=2, max_depth=3, min_child_weight=10; total time=   2.2s\n",
      "[21:35:34] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=4, min_child_weight=5; total time=   2.8s\n",
      "[21:35:36] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=2, max_depth=4, min_child_weight=10; total time=   2.9s\n",
      "[21:35:39] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=5, min_child_weight=5; total time=   3.5s\n",
      "[21:35:43] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=3, min_child_weight=1; total time=   2.2s\n",
      "[21:35:45] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=3, min_child_weight=5; total time=   2.3s\n",
      "[21:35:47] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=5, max_depth=3, min_child_weight=10; total time=   3.7s\n",
      "[21:35:51] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=4, min_child_weight=5; total time=   4.4s\n",
      "[21:35:55] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=5, max_depth=4, min_child_weight=10; total time=   2.6s\n",
      "[21:35:58] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=5, min_child_weight=5; total time=   3.2s\n",
      "[21:34:39] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=3, min_child_weight=1; total time=   2.2s\n",
      "[21:34:41] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=0.5, max_depth=3, min_child_weight=10; total time=   2.2s\n",
      "[21:34:44] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=4, min_child_weight=1; total time=   2.8s\n",
      "[21:34:46] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=0.5, max_depth=4, min_child_weight=10; total time=   2.9s\n",
      "[21:34:49] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=5, min_child_weight=5; total time=   3.4s\n",
      "[21:34:53] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=3, min_child_weight=1; total time=   2.2s\n",
      "[21:34:55] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=3, min_child_weight=1; total time=   2.2s\n",
      "[21:34:57] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=1, max_depth=3, min_child_weight=10; total time=   2.2s\n",
      "[21:34:59] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=4, min_child_weight=1; total time=   2.9s\n",
      "[21:35:02] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=1, max_depth=4, min_child_weight=10; total time=   2.8s\n",
      "[21:35:05] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=5, min_child_weight=5; total time=   3.4s\n",
      "[21:35:08] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=1, max_depth=5, min_child_weight=10; total time=   3.4s\n",
      "[21:35:12] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=3, min_child_weight=5; total time=   3.5s\n",
      "[21:35:15] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=4, min_child_weight=1; total time=   3.3s\n",
      "[21:35:19] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=4, min_child_weight=5; total time=   2.6s\n",
      "[21:35:21] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=5, min_child_weight=1; total time=   3.3s\n",
      "[21:35:25] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=5, min_child_weight=5; total time=   3.5s\n",
      "[21:35:28] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=3, min_child_weight=1; total time=   2.3s\n",
      "[21:35:31] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=2, max_depth=3, min_child_weight=10; total time=   2.3s\n",
      "[21:35:33] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=4, min_child_weight=1; total time=   2.8s\n",
      "[21:35:36] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=2, max_depth=4, min_child_weight=10; total time=   2.9s\n",
      "[21:35:39] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=5, min_child_weight=1; total time=   3.6s\n",
      "[21:35:42] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=2, max_depth=5, min_child_weight=10; total time=   3.4s\n",
      "[21:35:46] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=3, min_child_weight=5; total time=   2.3s\n",
      "[21:35:48] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=5, max_depth=3, min_child_weight=10; total time=   4.1s\n",
      "[21:35:52] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=4, min_child_weight=5; total time=   4.0s\n",
      "[21:35:56] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=5, min_child_weight=1; total time=   3.2s\n",
      "[21:35:59] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=5, min_child_weight=5; total time=   3.2s\n",
      "[21:34:39] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=3, min_child_weight=5; total time=   2.3s\n",
      "[21:34:41] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=4, min_child_weight=1; total time=   2.7s\n",
      "[21:34:44] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=4, min_child_weight=5; total time=   2.9s\n",
      "[21:34:47] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=5, min_child_weight=1; total time=   3.4s\n",
      "[21:34:50] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=5, min_child_weight=5; total time=   3.4s\n",
      "[21:34:54] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=3, min_child_weight=1; total time=   2.2s\n",
      "[21:34:56] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=1, max_depth=3, min_child_weight=10; total time=   2.2s\n",
      "[21:34:58] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=4, min_child_weight=1; total time=   2.8s\n",
      "[21:35:01] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=1, max_depth=4, min_child_weight=10; total time=   2.9s\n",
      "[21:35:04] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=5, min_child_weight=1; total time=   3.4s\n",
      "[21:35:08] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=1, max_depth=5, min_child_weight=10; total time=   3.5s\n",
      "[21:35:11] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=3, min_child_weight=5; total time=   2.7s\n",
      "[21:35:14] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=1.5, max_depth=3, min_child_weight=10; total time=   3.6s\n",
      "[21:35:17] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=4, min_child_weight=5; total time=   2.6s\n",
      "[21:35:20] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=1.5, max_depth=4, min_child_weight=10; total time=   2.7s\n",
      "[21:35:23] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=5, min_child_weight=5; total time=   3.4s\n",
      "[21:35:26] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=3, min_child_weight=1; total time=   2.2s\n",
      "[21:35:28] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=3, min_child_weight=1; total time=   2.3s\n",
      "[21:35:31] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=2, max_depth=3, min_child_weight=10; total time=   2.3s\n",
      "[21:35:33] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=4, min_child_weight=1; total time=   2.9s\n",
      "[21:35:36] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=2, max_depth=4, min_child_weight=10; total time=   2.9s\n",
      "[21:35:39] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=5, min_child_weight=5; total time=   3.6s\n",
      "[21:35:42] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=2, max_depth=5, min_child_weight=10; total time=   3.4s\n",
      "[21:35:46] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=3, min_child_weight=5; total time=   2.4s\n",
      "[21:35:48] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=4, min_child_weight=1; total time=   5.1s\n",
      "[21:35:53] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=4, min_child_weight=5; total time=   3.4s\n",
      "[21:35:57] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=5, min_child_weight=1; total time=   3.2s\n",
      "[21:36:00] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=5, min_child_weight=5; total time=   3.1s\n",
      "[21:34:39] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=3, min_child_weight=5; total time=   2.2s\n",
      "[21:34:41] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=0.5, max_depth=3, min_child_weight=10; total time=   2.2s\n",
      "[21:34:44] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=4, min_child_weight=5; total time=   2.7s\n",
      "[21:34:46] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=0.5, max_depth=4, min_child_weight=10; total time=   2.9s\n",
      "[21:34:49] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=5, min_child_weight=5; total time=   3.4s\n",
      "[21:34:53] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=0.5, max_depth=5, min_child_weight=10; total time=   3.4s\n",
      "[21:34:56] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=3, min_child_weight=5; total time=   2.2s\n",
      "[21:34:58] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=1, max_depth=3, min_child_weight=10; total time=   2.2s\n",
      "[21:35:00] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=4, min_child_weight=5; total time=   2.8s\n",
      "[21:35:03] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=5, min_child_weight=1; total time=   3.4s\n",
      "[21:35:07] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=5, min_child_weight=5; total time=   3.4s\n",
      "[21:35:10] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=3, min_child_weight=1; total time=   2.3s\n",
      "[21:35:12] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=3, min_child_weight=5; total time=   3.8s\n",
      "[21:35:16] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=4, min_child_weight=1; total time=   2.8s\n",
      "[21:35:19] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=4, min_child_weight=5; total time=   2.7s\n",
      "[21:35:22] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=5, min_child_weight=1; total time=   3.4s\n",
      "[21:35:25] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=1.5, max_depth=5, min_child_weight=10; total time=   3.5s\n",
      "[21:35:29] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=3, min_child_weight=1; total time=   2.3s\n",
      "[21:35:31] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=2, max_depth=3, min_child_weight=10; total time=   2.2s\n",
      "[21:35:33] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=4, min_child_weight=5; total time=   2.9s\n",
      "[21:35:36] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=2, max_depth=4, min_child_weight=10; total time=   2.9s\n",
      "[21:35:39] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=5, min_child_weight=5; total time=   3.5s\n",
      "[21:35:43] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=2, max_depth=5, min_child_weight=10; total time=   3.5s\n",
      "[21:35:46] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=3, min_child_weight=5; total time=   2.7s\n",
      "[21:35:49] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=4, min_child_weight=1; total time=   5.1s\n",
      "[21:35:54] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=4, min_child_weight=5; total time=   3.1s\n",
      "[21:35:57] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=5, min_child_weight=1; total time=   3.2s\n",
      "[21:36:00] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=5, max_depth=5, min_child_weight=10; total time=   2.9s\n",
      "[21:34:39] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=3, min_child_weight=1; total time=   2.1s\n",
      "[21:34:41] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=3, min_child_weight=5; total time=   2.2s\n",
      "[21:34:44] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=4, min_child_weight=1; total time=   2.8s\n",
      "[21:34:46] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=0.5, max_depth=4, min_child_weight=10; total time=   2.9s\n",
      "[21:34:49] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=5, min_child_weight=5; total time=   3.4s\n",
      "[21:34:53] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=0.5, max_depth=5, min_child_weight=10; total time=   3.4s\n",
      "[21:34:56] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=3, min_child_weight=5; total time=   2.2s\n",
      "[21:34:58] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=4, min_child_weight=1; total time=   2.8s\n",
      "[21:35:01] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=4, min_child_weight=5; total time=   2.8s\n",
      "[21:35:04] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=5, min_child_weight=1; total time=   3.4s\n",
      "[21:35:07] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=1, max_depth=5, min_child_weight=10; total time=   3.5s\n",
      "[21:35:11] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=3, min_child_weight=1; total time=   2.6s\n",
      "[21:35:13] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=1.5, max_depth=3, min_child_weight=10; total time=   3.8s\n",
      "[21:35:17] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=4, min_child_weight=1; total time=   2.6s\n",
      "[21:35:20] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=1.5, max_depth=4, min_child_weight=10; total time=   2.7s\n",
      "[21:35:22] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=5, min_child_weight=5; total time=   3.4s\n",
      "[21:35:26] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=1.5, max_depth=5, min_child_weight=10; total time=   3.5s\n",
      "[21:35:29] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=3, min_child_weight=5; total time=   2.3s\n",
      "[21:35:32] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=2, max_depth=3, min_child_weight=10; total time=   2.3s\n",
      "[21:35:34] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=4, min_child_weight=5; total time=   2.9s\n",
      "[21:35:37] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=5, min_child_weight=1; total time=   3.6s\n",
      "[21:35:40] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=5, min_child_weight=5; total time=   3.5s\n",
      "[21:35:44] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=3, min_child_weight=1; total time=   2.2s\n",
      "[21:35:46] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=3, min_child_weight=5; total time=   2.8s\n",
      "[21:35:49] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=4, min_child_weight=1; total time=   5.1s\n",
      "[21:35:54] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=5, max_depth=4, min_child_weight=10; total time=   2.9s\n",
      "[21:35:57] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=5, min_child_weight=1; total time=   3.2s\n",
      "[21:36:00] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=5, max_depth=5, min_child_weight=10; total time=   2.9s\n",
      "[21:34:39] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=3, min_child_weight=1; total time=   2.2s\n",
      "[21:34:41] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=0.5, max_depth=3, min_child_weight=10; total time=   2.2s\n",
      "[21:34:44] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=4, min_child_weight=1; total time=   2.8s\n",
      "[21:34:46] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=0.5, max_depth=4, min_child_weight=10; total time=   2.9s\n",
      "[21:34:49] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=5, min_child_weight=1; total time=   3.4s\n",
      "[21:34:53] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=0.5, max_depth=5, min_child_weight=10; total time=   3.4s\n",
      "[21:34:56] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=3, min_child_weight=5; total time=   2.2s\n",
      "[21:34:58] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=4, min_child_weight=1; total time=   2.8s\n",
      "[21:35:01] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=4, min_child_weight=5; total time=   2.8s\n",
      "[21:35:04] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=5, min_child_weight=1; total time=   3.4s\n",
      "[21:35:07] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=5, min_child_weight=5; total time=   3.5s\n",
      "[21:35:11] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=3, min_child_weight=1; total time=   2.5s\n",
      "[21:35:13] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=1.5, max_depth=3, min_child_weight=10; total time=   3.8s\n",
      "[21:35:17] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=4, min_child_weight=1; total time=   2.6s\n",
      "[21:35:20] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=1.5, max_depth=4, min_child_weight=10; total time=   2.7s\n",
      "[21:35:22] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=5, min_child_weight=1; total time=   3.4s\n",
      "[21:35:26] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=1.5, max_depth=5, min_child_weight=10; total time=   3.6s\n",
      "[21:35:29] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=3, min_child_weight=5; total time=   2.3s\n",
      "[21:35:32] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=4, min_child_weight=1; total time=   2.8s\n",
      "[21:35:35] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=4, min_child_weight=5; total time=   2.9s\n",
      "[21:35:38] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=5, min_child_weight=1; total time=   3.6s\n",
      "[21:35:41] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=5, min_child_weight=5; total time=   3.5s\n",
      "[21:35:45] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=3, min_child_weight=1; total time=   2.2s\n",
      "[21:35:47] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=5, max_depth=3, min_child_weight=10; total time=   3.4s\n",
      "[21:35:50] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=4, min_child_weight=1; total time=   4.8s\n",
      "[21:35:55] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=5, max_depth=4, min_child_weight=10; total time=   2.6s\n",
      "[21:35:58] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=5, min_child_weight=1; total time=   3.3s\n",
      "[21:36:01] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=5, max_depth=5, min_child_weight=10; total time=   2.5s\n",
      "[21:34:39] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=3, min_child_weight=1; total time=   2.2s\n",
      "[21:34:41] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=3, min_child_weight=5; total time=   2.2s\n",
      "[21:34:44] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=4, min_child_weight=1; total time=   2.8s\n",
      "[21:34:46] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=4, min_child_weight=5; total time=   2.9s\n",
      "[21:34:49] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=5, min_child_weight=1; total time=   3.4s\n",
      "[21:34:53] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=0.5, max_depth=5, min_child_weight=10; total time=   3.4s\n",
      "[21:34:56] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=3, min_child_weight=5; total time=   2.2s\n",
      "[21:34:58] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=4, min_child_weight=1; total time=   2.9s\n",
      "[21:35:01] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=1, max_depth=4, min_child_weight=10; total time=   2.9s\n",
      "[21:35:04] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=5, min_child_weight=1; total time=   3.4s\n",
      "[21:35:07] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=1, max_depth=5, min_child_weight=10; total time=   3.4s\n",
      "[21:35:11] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=3, min_child_weight=1; total time=   2.6s\n",
      "[21:35:14] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=1.5, max_depth=3, min_child_weight=10; total time=   3.7s\n",
      "[21:35:17] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=4, min_child_weight=5; total time=   2.6s\n",
      "[21:35:20] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=1.5, max_depth=4, min_child_weight=10; total time=   2.7s\n",
      "[21:35:23] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=5, min_child_weight=5; total time=   3.4s\n",
      "[21:35:26] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=1.5, max_depth=5, min_child_weight=10; total time=   3.5s\n",
      "[21:35:30] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=3, min_child_weight=5; total time=   2.3s\n",
      "[21:35:32] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=4, min_child_weight=1; total time=   2.9s\n",
      "[21:35:35] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=4, min_child_weight=5; total time=   2.9s\n",
      "[21:35:38] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=5, min_child_weight=1; total time=   3.6s\n",
      "[21:35:41] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=2, max_depth=5, min_child_weight=10; total time=   3.5s\n",
      "[21:35:45] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=3, min_child_weight=1; total time=   2.2s\n",
      "[21:35:47] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=5, max_depth=3, min_child_weight=10; total time=   3.4s\n",
      "[21:35:50] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=4, min_child_weight=1; total time=   4.8s\n",
      "[21:35:55] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=5, max_depth=4, min_child_weight=10; total time=   2.6s\n",
      "[21:35:58] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=5, min_child_weight=5; total time=   3.2s\n",
      "[21:36:01] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=5, max_depth=5, min_child_weight=10; total time=   2.5s\n",
      "[21:34:39] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=3, min_child_weight=5; total time=   2.2s\n",
      "[21:34:41] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=0.5, max_depth=3, min_child_weight=10; total time=   2.2s\n",
      "[21:34:44] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=4, min_child_weight=5; total time=   2.8s\n",
      "[21:34:46] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=0.5, max_depth=4, min_child_weight=10; total time=   2.8s\n",
      "[21:34:49] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=0.5, max_depth=5, min_child_weight=1; total time=   3.4s\n",
      "[21:34:53] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=0.5, max_depth=5, min_child_weight=10; total time=   3.4s\n",
      "[21:34:56] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=3, min_child_weight=5; total time=   2.2s\n",
      "[21:34:58] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=1, max_depth=3, min_child_weight=10; total time=   2.3s\n",
      "[21:35:00] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=4, min_child_weight=5; total time=   2.8s\n",
      "[21:35:03] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=1, max_depth=4, min_child_weight=10; total time=   2.8s\n",
      "[21:35:06] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=1, max_depth=5, min_child_weight=5; total time=   3.4s\n",
      "[21:35:10] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=3, min_child_weight=1; total time=   2.3s\n",
      "[21:35:12] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=3, min_child_weight=5; total time=   3.4s\n",
      "[21:35:15] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ........gamma=1.5, max_depth=3, min_child_weight=10; total time=   2.8s\n",
      "[21:35:18] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=4, min_child_weight=5; total time=   2.7s\n",
      "[21:35:21] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=5, min_child_weight=1; total time=   3.3s\n",
      "[21:35:24] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END .........gamma=1.5, max_depth=5, min_child_weight=5; total time=   3.5s\n",
      "[21:35:28] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=3, min_child_weight=1; total time=   2.3s\n",
      "[21:35:30] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=3, min_child_weight=5; total time=   2.3s\n",
      "[21:35:32] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=4, min_child_weight=1; total time=   2.8s\n",
      "[21:35:35] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=2, max_depth=4, min_child_weight=10; total time=   2.9s\n",
      "[21:35:38] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=2, max_depth=5, min_child_weight=1; total time=   3.6s\n",
      "[21:35:41] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=2, max_depth=5, min_child_weight=10; total time=   3.4s\n",
      "[21:35:45] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=3, min_child_weight=1; total time=   2.3s\n",
      "[21:35:47] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=5, max_depth=3, min_child_weight=10; total time=   3.6s\n",
      "[21:35:51] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=4, min_child_weight=5; total time=   4.6s\n",
      "[21:35:55] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=5, max_depth=4, min_child_weight=10; total time=   2.7s\n",
      "[21:35:58] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ...........gamma=5, max_depth=5, min_child_weight=5; total time=   3.2s\n",
      "[21:36:01] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[CV] END ..........gamma=5, max_depth=5, min_child_weight=10; total time=   2.4s\n"
     ]
    }
   ],
   "source": [
    "sharpe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c079a2c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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