s4e2_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20758, 18), (13840, 17), (2111, 17))"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train_df = pd.read_csv(\"/kaggle/input/playground-series-s4e2/train.csv\")\n",
    "# test_df = pd.read_csv(\"/kaggle/input/playground-series-s4e2/test.csv\")\n",
    "train_df = pd.read_csv(\"data/s4e2/train.csv\")\n",
    "test_df = pd.read_csv(\"data/s4e2/test.csv\")\n",
    "origin_df = pd.read_csv(\"data/s4e2/ObesityDataSet.csv\")\n",
    "(train_df.shape, test_df.shape, origin_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 计算 BIM 特征\n",
    "# train_df['BIM'] = train_df['Weight'] / ((train_df['Height']) ** 2)\n",
    "# test_df['BIM'] = test_df['Weight'] / ((test_df['Height']) ** 2)\n",
    "# origin_df['BIM'] = origin_df['Weight'] / ((origin_df['Height']) ** 2)\n",
    "# (train_df.shape, test_df.shape, origin_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20758 entries, 0 to 20757\n",
      "Data columns (total 18 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   id                              20758 non-null  int64  \n",
      " 1   Gender                          20758 non-null  object \n",
      " 2   Age                             20758 non-null  float64\n",
      " 3   Height                          20758 non-null  float64\n",
      " 4   Weight                          20758 non-null  float64\n",
      " 5   family_history_with_overweight  20758 non-null  object \n",
      " 6   FAVC                            20758 non-null  object \n",
      " 7   FCVC                            20758 non-null  float64\n",
      " 8   NCP                             20758 non-null  float64\n",
      " 9   CAEC                            20758 non-null  object \n",
      " 10  SMOKE                           20758 non-null  object \n",
      " 11  CH2O                            20758 non-null  float64\n",
      " 12  SCC                             20758 non-null  object \n",
      " 13  FAF                             20758 non-null  float64\n",
      " 14  TUE                             20758 non-null  float64\n",
      " 15  CALC                            20758 non-null  object \n",
      " 16  MTRANS                          20758 non-null  object \n",
      " 17  NObeyesdad                      20758 non-null  object \n",
      "dtypes: float64(8), int64(1), object(9)\n",
      "memory usage: 2.9+ MB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_cols = ['Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CH2O', 'FAF', 'TUE', 'BIM']\n",
    "category_cols = ['Gender', 'family_history_with_overweight', 'FAVC', 'CAEC', 'SMOKE', 'SCC', 'CALC', 'MTRANS', 'NObeyesdad']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将category_cols中的object转为数字类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_object_cols(se):\n",
    "    value = se.unique().tolist()\n",
    "    value.sort()\n",
    "    return se.map(pd.Series(range(len(value)), index = value)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22845, 16)"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.concat([train_df, origin_df]).drop_duplicates()  # (22869, 18) -->  (22845, 18)\n",
    "\n",
    "train_df['NObeyesdad'] = change_object_cols(train_df['NObeyesdad'])\n",
    "\n",
    "train_target = train_df['NObeyesdad']\n",
    "\n",
    "train_df = train_df.drop(columns=['NObeyesdad'])\n",
    "train_df = train_df.drop(columns=['id'])\n",
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>family_history_with_overweight</th>\n",
       "      <th>FAVC</th>\n",
       "      <th>FCVC</th>\n",
       "      <th>NCP</th>\n",
       "      <th>CAEC</th>\n",
       "      <th>SMOKE</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>SCC</th>\n",
       "      <th>FAF</th>\n",
       "      <th>TUE</th>\n",
       "      <th>CALC</th>\n",
       "      <th>MTRANS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>24.443011</td>\n",
       "      <td>1.699998</td>\n",
       "      <td>81.669950</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.983297</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.763573</td>\n",
       "      <td>no</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.976473</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>no</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>no</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>no</td>\n",
       "      <td>Automobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.711460</td>\n",
       "      <td>50.165754</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.880534</td>\n",
       "      <td>1.411685</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>1.910378</td>\n",
       "      <td>no</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>1.673584</td>\n",
       "      <td>no</td>\n",
       "      <td>Public_Transportation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>20.952737</td>\n",
       "      <td>1.710730</td>\n",
       "      <td>131.274851</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>1.674061</td>\n",
       "      <td>no</td>\n",
       "      <td>1.467863</td>\n",
       "      <td>0.780199</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>31.641081</td>\n",
       "      <td>1.914186</td>\n",
       "      <td>93.798055</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>2.679664</td>\n",
       "      <td>1.971472</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>1.979848</td>\n",
       "      <td>no</td>\n",
       "      <td>1.967973</td>\n",
       "      <td>0.931721</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender        Age    Height      Weight family_history_with_overweight  \\\n",
       "0    Male  24.443011  1.699998   81.669950                            yes   \n",
       "1  Female  18.000000  1.560000   57.000000                            yes   \n",
       "2  Female  18.000000  1.711460   50.165754                            yes   \n",
       "3  Female  20.952737  1.710730  131.274851                            yes   \n",
       "4    Male  31.641081  1.914186   93.798055                            yes   \n",
       "\n",
       "  FAVC      FCVC       NCP        CAEC SMOKE      CH2O SCC       FAF  \\\n",
       "0  yes  2.000000  2.983297   Sometimes    no  2.763573  no  0.000000   \n",
       "1  yes  2.000000  3.000000  Frequently    no  2.000000  no  1.000000   \n",
       "2  yes  1.880534  1.411685   Sometimes    no  1.910378  no  0.866045   \n",
       "3  yes  3.000000  3.000000   Sometimes    no  1.674061  no  1.467863   \n",
       "4  yes  2.679664  1.971472   Sometimes    no  1.979848  no  1.967973   \n",
       "\n",
       "        TUE       CALC                 MTRANS  \n",
       "0  0.976473  Sometimes  Public_Transportation  \n",
       "1  1.000000         no             Automobile  \n",
       "2  1.673584         no  Public_Transportation  \n",
       "3  0.780199  Sometimes  Public_Transportation  \n",
       "4  0.931721  Sometimes  Public_Transportation  "
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [],
   "source": [
    "def featuring_processing(data):\n",
    "    for col in category_cols[:-1]:\n",
    "        data[col] = change_object_cols(data[col])\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def generate_poly_feature(data):\n",
    "# 初始化 PolynomialFeatures\n",
    "    poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "    # 生成所有特征的组合特征\n",
    "    poly_features = poly.fit_transform(data)\n",
    "    # 创建列名\n",
    "    column_names = poly.get_feature_names_out(input_features=data.columns)\n",
    "    # 将生成的特征转换为 DataFrame\n",
    "    poly_df = pd.DataFrame(poly_features, columns=column_names)\n",
    "    return poly_df\n",
    "\n",
    "\n",
    "\n",
    "def scaled_processing(data):\n",
    "    scaler = StandardScaler()\n",
    "    scaled_data = scaler.fit_transform(data)\n",
    "    scaled_data = pd.DataFrame(scaled_data, columns = data.columns)\n",
    "    return scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>family_history_with_overweight</th>\n",
       "      <th>FAVC</th>\n",
       "      <th>FCVC</th>\n",
       "      <th>NCP</th>\n",
       "      <th>CAEC</th>\n",
       "      <th>SMOKE</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>SCC</th>\n",
       "      <th>FAF</th>\n",
       "      <th>TUE</th>\n",
       "      <th>CALC</th>\n",
       "      <th>MTRANS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>24.443011</td>\n",
       "      <td>1.699998</td>\n",
       "      <td>81.669950</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.983297</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.763573</td>\n",
       "      <td>no</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.976473</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>no</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>no</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>no</td>\n",
       "      <td>Automobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.711460</td>\n",
       "      <td>50.165754</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.880534</td>\n",
       "      <td>1.411685</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>1.910378</td>\n",
       "      <td>no</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>1.673584</td>\n",
       "      <td>no</td>\n",
       "      <td>Public_Transportation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>20.952737</td>\n",
       "      <td>1.710730</td>\n",
       "      <td>131.274851</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>1.674061</td>\n",
       "      <td>no</td>\n",
       "      <td>1.467863</td>\n",
       "      <td>0.780199</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>31.641081</td>\n",
       "      <td>1.914186</td>\n",
       "      <td>93.798055</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>2.679664</td>\n",
       "      <td>1.971472</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>1.979848</td>\n",
       "      <td>no</td>\n",
       "      <td>1.967973</td>\n",
       "      <td>0.931721</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender        Age    Height      Weight family_history_with_overweight  \\\n",
       "0    Male  24.443011  1.699998   81.669950                            yes   \n",
       "1  Female  18.000000  1.560000   57.000000                            yes   \n",
       "2  Female  18.000000  1.711460   50.165754                            yes   \n",
       "3  Female  20.952737  1.710730  131.274851                            yes   \n",
       "4    Male  31.641081  1.914186   93.798055                            yes   \n",
       "\n",
       "  FAVC      FCVC       NCP        CAEC SMOKE      CH2O SCC       FAF  \\\n",
       "0  yes  2.000000  2.983297   Sometimes    no  2.763573  no  0.000000   \n",
       "1  yes  2.000000  3.000000  Frequently    no  2.000000  no  1.000000   \n",
       "2  yes  1.880534  1.411685   Sometimes    no  1.910378  no  0.866045   \n",
       "3  yes  3.000000  3.000000   Sometimes    no  1.674061  no  1.467863   \n",
       "4  yes  2.679664  1.971472   Sometimes    no  1.979848  no  1.967973   \n",
       "\n",
       "        TUE       CALC                 MTRANS  \n",
       "0  0.976473  Sometimes  Public_Transportation  \n",
       "1  1.000000         no             Automobile  \n",
       "2  1.673584         no  Public_Transportation  \n",
       "3  0.780199  Sometimes  Public_Transportation  \n",
       "4  0.931721  Sometimes  Public_Transportation  "
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>FCVC</th>\n",
       "      <th>NCP</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>FAF</th>\n",
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       "      <th>SCC_yes</th>\n",
       "      <th>CALC_Always</th>\n",
       "      <th>CALC_Frequently</th>\n",
       "      <th>CALC_Sometimes</th>\n",
       "      <th>CALC_no</th>\n",
       "      <th>MTRANS_Automobile</th>\n",
       "      <th>MTRANS_Bike</th>\n",
       "      <th>MTRANS_Motorbike</th>\n",
       "      <th>MTRANS_Public_Transportation</th>\n",
       "      <th>MTRANS_Walking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.443011</td>\n",
       "      <td>1.699998</td>\n",
       "      <td>81.669950</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.983297</td>\n",
       "      <td>2.763573</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.976473</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.711460</td>\n",
       "      <td>50.165754</td>\n",
       "      <td>1.880534</td>\n",
       "      <td>1.411685</td>\n",
       "      <td>1.910378</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>1.673584</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.952737</td>\n",
       "      <td>1.710730</td>\n",
       "      <td>131.274851</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.674061</td>\n",
       "      <td>1.467863</td>\n",
       "      <td>0.780199</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31.641081</td>\n",
       "      <td>1.914186</td>\n",
       "      <td>93.798055</td>\n",
       "      <td>2.679664</td>\n",
       "      <td>1.971472</td>\n",
       "      <td>1.979848</td>\n",
       "      <td>1.967973</td>\n",
       "      <td>0.931721</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Age    Height      Weight      FCVC       NCP      CH2O       FAF  \\\n",
       "0  24.443011  1.699998   81.669950  2.000000  2.983297  2.763573  0.000000   \n",
       "1  18.000000  1.560000   57.000000  2.000000  3.000000  2.000000  1.000000   \n",
       "2  18.000000  1.711460   50.165754  1.880534  1.411685  1.910378  0.866045   \n",
       "3  20.952737  1.710730  131.274851  3.000000  3.000000  1.674061  1.467863   \n",
       "4  31.641081  1.914186   93.798055  2.679664  1.971472  1.979848  1.967973   \n",
       "\n",
       "        TUE  Gender_Female  Gender_Male  ...  SCC_yes  CALC_Always  \\\n",
       "0  0.976473          False         True  ...    False        False   \n",
       "1  1.000000           True        False  ...    False        False   \n",
       "2  1.673584           True        False  ...    False        False   \n",
       "3  0.780199           True        False  ...    False        False   \n",
       "4  0.931721          False         True  ...    False        False   \n",
       "\n",
       "   CALC_Frequently  CALC_Sometimes  CALC_no  MTRANS_Automobile  MTRANS_Bike  \\\n",
       "0            False            True    False              False        False   \n",
       "1            False           False     True               True        False   \n",
       "2            False           False     True              False        False   \n",
       "3            False            True    False              False        False   \n",
       "4            False            True    False              False        False   \n",
       "\n",
       "   MTRANS_Motorbike  MTRANS_Public_Transportation  MTRANS_Walking  \n",
       "0             False                          True           False  \n",
       "1             False                         False           False  \n",
       "2             False                          True           False  \n",
       "3             False                          True           False  \n",
       "4             False                          True           False  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.get_dummies(train_df, columns=category_cols[:-1])\n",
    "test_df = pd.get_dummies(test_df, columns=category_cols[:-1])\n",
    "train_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "         Age    Height      Weight      FCVC       NCP      CH2O       FAF  \\\n",
       "0  24.443011  1.699998   81.669950  2.000000  2.983297  2.763573  0.000000   \n",
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       "3  20.952737  1.710730  131.274851  3.000000  3.000000  1.674061  1.467863   \n",
       "4  31.641081  1.914186   93.798055  2.679664  1.971472  1.979848  1.967973   \n",
       "\n",
       "        TUE  Gender_Female  Gender_Male  ...  SCC_yes  CALC_Always  \\\n",
       "0  0.976473              0            1  ...        0            0   \n",
       "1  1.000000              1            0  ...        0            0   \n",
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       "3  0.780199              1            0  ...        0            0   \n",
       "4  0.931721              0            1  ...        0            0   \n",
       "\n",
       "   CALC_Frequently  CALC_Sometimes  CALC_no  MTRANS_Automobile  MTRANS_Bike  \\\n",
       "0                0               1        0                  0            0   \n",
       "1                0               0        1                  1            0   \n",
       "2                0               0        1                  0            0   \n",
       "3                0               1        0                  0            0   \n",
       "4                0               1        0                  0            0   \n",
       "\n",
       "   MTRANS_Motorbike  MTRANS_Public_Transportation  MTRANS_Walking  \n",
       "0                 0                             1               0  \n",
       "1                 0                             0               0  \n",
       "2                 0                             1               0  \n",
       "3                 0                             1               0  \n",
       "4                 0                             1               0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def bool_to_int(x):\n",
    "    if isinstance(x, bool):\n",
    "        return int(x)\n",
    "    else:\n",
    "        return x\n",
    "\n",
    "train_df = train_df.map(bool_to_int)\n",
    "test_df = test_df.map(bool_to_int)\n",
    "\n",
    "\n",
    "train_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.116808</td>\n",
       "      <td>1.649336</td>\n",
       "      <td>1.043017</td>\n",
       "      <td>0.343326</td>\n",
       "      <td>-0.580319</td>\n",
       "      <td>0.575530</td>\n",
       "      <td>0.264131</td>\n",
       "      <td>0.996984</td>\n",
       "      <td>-0.996984</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.041353</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.046363</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.502119</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.152918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.347052</td>\n",
       "      <td>2.432398</td>\n",
       "      <td>0.227756</td>\n",
       "      <td>0.442440</td>\n",
       "      <td>-1.102923</td>\n",
       "      <td>-0.077764</td>\n",
       "      <td>1.171105</td>\n",
       "      <td>0.515499</td>\n",
       "      <td>-1.003025</td>\n",
       "      <td>1.003025</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.041353</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.046363</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.502119</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.152918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 527 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age    Height    Weight      FCVC       NCP      CH2O       FAF  \\\n",
       "0  0.096347 -0.005336 -0.232290 -0.831819  0.319839  1.210272 -1.172529   \n",
       "1 -1.023162 -1.598693 -1.168077 -0.831819  0.343326 -0.044645  0.018358   \n",
       "2 -1.023162  0.125116 -1.427315 -1.055798 -1.890059 -0.191936 -0.141167   \n",
       "3 -0.510107  0.116808  1.649336  1.043017  0.343326 -0.580319  0.575530   \n",
       "4  1.347052  2.432398  0.227756  0.442440 -1.102923 -0.077764  1.171105   \n",
       "\n",
       "        TUE  Gender_Female  Gender_Male  ...  MTRANS_Bike^2  \\\n",
       "0  0.589741      -1.003025     1.003025  ...      -0.041353   \n",
       "1  0.628771       0.996984    -0.996984  ...      -0.041353   \n",
       "2  1.746217       0.996984    -0.996984  ...      -0.041353   \n",
       "3  0.264131       0.996984    -0.996984  ...      -0.041353   \n",
       "4  0.515499      -1.003025     1.003025  ...      -0.041353   \n",
       "\n",
       "   MTRANS_Bike MTRANS_Motorbike  MTRANS_Bike MTRANS_Public_Transportation  \\\n",
       "0                           0.0                                       0.0   \n",
       "1                           0.0                                       0.0   \n",
       "2                           0.0                                       0.0   \n",
       "3                           0.0                                       0.0   \n",
       "4                           0.0                                       0.0   \n",
       "\n",
       "   MTRANS_Bike MTRANS_Walking  MTRANS_Motorbike^2  \\\n",
       "0                         0.0           -0.046363   \n",
       "1                         0.0           -0.046363   \n",
       "2                         0.0           -0.046363   \n",
       "3                         0.0           -0.046363   \n",
       "4                         0.0           -0.046363   \n",
       "\n",
       "   MTRANS_Motorbike MTRANS_Public_Transportation  \\\n",
       "0                                            0.0   \n",
       "1                                            0.0   \n",
       "2                                            0.0   \n",
       "3                                            0.0   \n",
       "4                                            0.0   \n",
       "\n",
       "   MTRANS_Motorbike MTRANS_Walking  MTRANS_Public_Transportation^2  \\\n",
       "0                              0.0                        0.502119   \n",
       "1                              0.0                       -1.991558   \n",
       "2                              0.0                        0.502119   \n",
       "3                              0.0                        0.502119   \n",
       "4                              0.0                        0.502119   \n",
       "\n",
       "   MTRANS_Public_Transportation MTRANS_Walking  MTRANS_Walking^2  \n",
       "0                                          0.0         -0.152918  \n",
       "1                                          0.0         -0.152918  \n",
       "2                                          0.0         -0.152918  \n",
       "3                                          0.0         -0.152918  \n",
       "4                                          0.0         -0.152918  \n",
       "\n",
       "[5 rows x 527 columns]"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train_df = featuring_processing(train_df)\n",
    "train_df = generate_poly_feature(train_df)\n",
    "train_df = scaled_processing(train_df)\n",
    "train_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(train_df, train_target, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 逻辑回归模型0.8725"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# # 初始化逻辑回归模型\n",
    "# logistic_regression = LogisticRegression(random_state=42)\n",
    "# # 在训练数据上训练模型\n",
    "# logistic_regression.fit(X_train, y_train)\n",
    "\n",
    "# # 在测试数据上进行预测\n",
    "# y_pred = logistic_regression.predict(X_test)\n",
    "# print(\"Accuracy: \", accuracy_score(y_pred, y_test))\n",
    "# # 0.87475915"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 支持向量机0.8798"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.svm import SVC\n",
    "\n",
    "# # 初始化支持向量机模型\n",
    "# svm = SVC(kernel='linear', random_state=42)\n",
    "\n",
    "# # 在训练数据上训练模型\n",
    "# svm.fit(X_train, y_train)\n",
    "\n",
    "# # 在测试数据上进行预测\n",
    "# print(\"Accuracy: \", svm.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决策树0.8330"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# # 初始化决策树分类器\n",
    "# decision_tree = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "# # 在训练数据上训练模型\n",
    "# decision_tree.fit(X_train, y_train)\n",
    "\n",
    "# # 在测试数据上进行预测\n",
    "# print(\"Accuracy:\", decision_tree.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN分类器0.7567"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# # 初始化KNN分类器\n",
    "# knn = KNeighborsClassifier()\n",
    "\n",
    "# # 在训练数据上训练模型\n",
    "# knn.fit(X_train, y_train)\n",
    "\n",
    "# # 在测试数据上进行预测\n",
    "# print(\"Accuracy:\",  knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 朴素贝叶斯0.7333"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# # 初始化朴素贝叶斯分类器\n",
    "# naive_bayes = GaussianNB()\n",
    "\n",
    "# # 在训练数据上训练模型\n",
    "# naive_bayes.fit(X_train, y_train)\n",
    "\n",
    "# # 在测试数据上进行预测\n",
    "# print(\"Accuracy:\",  naive_bayes.score(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 极端随机树0.89041"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "# # 初始化Extra Trees分类器\n",
    "# extra_trees = ExtraTreesClassifier(random_state=42)\n",
    "\n",
    "# # 在训练数据上训练模型\n",
    "# extra_trees.fit(X_train, y_train)\n",
    "\n",
    "# # 在测试数据上进行预测\n",
    "# print(\"Accuracy:\",  extra_trees.score(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 梯度提升树 0.897398  6min12s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "# # 初始化梯度提升树分类器\n",
    "# gradient_boosting = GradientBoostingClassifier(random_state=42)\n",
    "\n",
    "# # 在训练数据上训练模型\n",
    "# gradient_boosting.fit(X_train, y_train)\n",
    "\n",
    "# # 在测试数据上进行预测\n",
    "# print(\"Accuracy:\",  gradient_boosting.score(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 平衡随机森林0.897880"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from imblearn.ensemble import BalancedRandomForestClassifier\n",
    "\n",
    "# # 初始化平衡随机森林分类器\n",
    "# balanced_random_forest = BalancedRandomForestClassifier(random_state=42)\n",
    "\n",
    "# # 在训练数据上训练模型\n",
    "# balanced_random_forest.fit(X_train, y_train)\n",
    "\n",
    "# # 在测试数据上进行预测\n",
    "# print(\"Accuracy:\",  balanced_random_forest.score(X_test, y_test))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机森林0.8993"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rf_classifier = RandomForestClassifier(\n",
    "#     n_estimators = 550,\n",
    "#     # min_samples_leaf = 9,\n",
    "#     random_state=42\n",
    "# )\n",
    "# rf_classifier.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "# # 5. 模型预测\n",
    "# y_pred = rf_classifier.predict(X_test)\n",
    "# # 6. 模型评估\n",
    "# print(\"Accuracy:\", rf_classifier.score(X_test, y_test))\n",
    "# # 0.8952  # 没有特征融合：0.900529  # 0.8954\n",
    "# # 0.8966  170特征  0.89643\n",
    "# # 0.89547  {'min_samples_leaf': 9, 'n_estimators': 550}  0.895472"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型Voting融合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from scipy.stats import mode\n",
    "\n",
    "# voting_predictions = mode([y_pred, balanced_random_forest.predict(X_test)], axis=0)[0]\n",
    "\n",
    "# # 最终的预测结果\n",
    "# final_predictions = voting_predictions.ravel()\n",
    "# print(final_predictions)\n",
    "\n",
    "# print(accuracy_score(final_predictions, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from xgboost import XGBClassifier\n",
    "\n",
    "# xgb_2 = XGBClassifier(\n",
    "#     learning_rate =0.01,\n",
    "#     n_estimators=2000,\n",
    "#     early_stopping_rounds=500,\n",
    "#     max_depth=4,\n",
    "#     min_child_weight=0.5,\n",
    "#     gamma=0.2,\n",
    "#     colsample_bytree=0.7,\n",
    "#     subsample=0.7,\n",
    "#     reg_alpha=1,\n",
    "#     objective= 'multi:softmax', # softmax， softprob\n",
    "#     nthread=-1,\n",
    "#     random_state=42\n",
    "# )\n",
    "# xgb_2.fit(X_train, y_train, eval_set=[(X_test, y_test)])\n",
    "# acc_train = accuracy_score(xgb_2.predict(X_train),y_train)\n",
    "# acc_val = accuracy_score(xgb_2.predict(X_test),y_test)\n",
    "# print(f\"model: xgb_2\\ntrain_accuracy: {acc_train:.4f}\\ntest_accuracy: {acc_val:.4f}\")\n",
    "# # train_accuracy: 0.9893 test_accuracy: 0.9053  5000\n",
    "# # train_accuracy: 0.9353 test_accuracy: 0.9082  2000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGboost 0.906310"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.9065441015539505\n",
      "Train Accuracy: 0.9960604070912672\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "# 初始化XGBoost分类器\n",
    "xgb_classifier = XGBClassifier(objective='multi:softmax', num_class=7, random_state=42)\n",
    "\n",
    "# 在训练数据上训练模型\n",
    "xgb_classifier.fit(X_train, y_train)\n",
    "\n",
    "# 在测试数据上进行预测\n",
    "print(\"Test Accuracy:\", xgb_classifier.score(X_test, y_test))\n",
    "print(\"Train Accuracy:\", xgb_classifier.score(X_train, y_train))\n",
    "# 0.912672357189757  du + 01\n",
    "# 0.908513898008317 + 通用特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LightGBM 0.903901"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.9139855548260013\n"
     ]
    }
   ],
   "source": [
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "# Best parameters obtained from Optuna optimization process\n",
    "\n",
    "best_params = {\n",
    "    \"objective\": \"multiclass\",          # Objective function for the model\n",
    "    \"metric\": \"multi_logloss\",          # Evaluation metric\n",
    "    \"verbosity\": -1,                    # Verbosity level (-1 for silent)\n",
    "    \"boosting_type\": \"gbdt\",            # Gradient boosting type\n",
    "    \"random_state\": 42,       # Random state for reproducibility\n",
    "    \"num_class\": 7,                     # Number of classes in the dataset\n",
    "    'learning_rate': 0.030962211546832760,  # Learning rate for gradient boosting\n",
    "    'n_estimators': 500,                # Number of boosting iterations\n",
    "    'lambda_l1': 0.009667446568254372,  # L1 regularization term\n",
    "    'lambda_l2': 0.04018641437301800,   # L2 regularization term\n",
    "    'max_depth': 10,                    # Maximum depth of the trees\n",
    "    'colsample_bytree': 0.40977129346872643,  # Fraction of features to consider for each tree\n",
    "    'subsample': 0.9535797422450176,    # Fraction of samples to consider for each boosting iteration\n",
    "    'min_child_samples': 26             # Minimum number of data needed in a leaf\n",
    "}\n",
    "\n",
    "# 初始化LightGBM分类器\n",
    "# lgbm_classifier = LGBMClassifier(objective='multiclass', num_class=7, random_state=42)\n",
    "lgbm_classifier = LGBMClassifier(**best_params)\n",
    "\n",
    "# 在训练数据上训练模型\n",
    "lgbm_classifier.fit(X_train, y_train)\n",
    "\n",
    "# 在测试数据上进行预测\n",
    "print(\"Test Accuracy:\", lgbm_classifier.score(X_test, y_test))\n",
    "# print(\"Train Accuracy:\", lgbm_classifier.score(X_train, y_train))\n",
    "# Test Accuracy: 0.912015758371635 Train Accuracy: 0.9818888159334647\n",
    "# Test Accuracy: 0.9168308163711972 Train Accuracy: 0.9808492011381046\n",
    "# 0.9131100897351718"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 5 4 ... 6 3 3]\n",
      "0.912015758371635\n"
     ]
    }
   ],
   "source": [
    "# from scipy.stats import mode\n",
    "\n",
    "# voting_predictions = mode([xgb_classifier.predict(X_test), lgbm_classifier.predict(X_test)], axis=0)[0]\n",
    "\n",
    "# # 最终的预测结果\n",
    "# final_predictions = voting_predictions.ravel()\n",
    "# print(final_predictions)\n",
    "\n",
    "# print(accuracy_score(final_predictions, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 4. 模型训练\n",
    "# # 使用网格搜索来调整模型参数\n",
    "# param_grid = {\n",
    "#     'n_estimators': [90, 100, 110],  # 尝试不同数量的树\n",
    "# #     'max_depth': [None],  # 尝试不同深度的树\n",
    "#     'min_samples_leaf': [9, 10],\n",
    "# #     'min_samples_split': [2, 4],\n",
    "# }  # {'max_depth': None, 'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 200}\n",
    "\n",
    "# rf_classifier = RandomForestClassifier(random_state=42)\n",
    "# grid_search = GridSearchCV(rf_classifier, param_grid, cv=5, scoring='accuracy')\n",
    "# grid_search.fit(X_train, y_train)\n",
    "# print(grid_search.best_params_)\n",
    "# best_rf_classifier = grid_search.best_estimator_\n",
    "\n",
    "# # 5. 模型预测\n",
    "# y_pred = best_rf_classifier.predict(X_test)\n",
    "\n",
    "# # 6. 模型评估\n",
    "# accuracy = accuracy_score(y_test, y_pred)\n",
    "# print(\"Accuracy:\", accuracy)  \n",
    "# # {'min_samples_leaf': 9, 'n_estimators': 400} Accuracy: 0.894990366088632\n",
    "# # {'min_samples_leaf': 9, 'n_estimators': 390} Accuracy: 0.894990366088632\n",
    "# # {'min_samples_leaf': 9, 'n_estimators': 380} Accuracy: 0.8945086705202312\n",
    "# {'min_samples_leaf': 9, 'n_estimators': 100} Accuracy: 0.8971579961464354"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>FCVC</th>\n",
       "      <th>NCP</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>FAF</th>\n",
       "      <th>TUE</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>...</th>\n",
       "      <th>SCC_yes</th>\n",
       "      <th>CALC_Always</th>\n",
       "      <th>CALC_Frequently</th>\n",
       "      <th>CALC_Sometimes</th>\n",
       "      <th>CALC_no</th>\n",
       "      <th>MTRANS_Automobile</th>\n",
       "      <th>MTRANS_Bike</th>\n",
       "      <th>MTRANS_Motorbike</th>\n",
       "      <th>MTRANS_Public_Transportation</th>\n",
       "      <th>MTRANS_Walking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26.899886</td>\n",
       "      <td>1.848294</td>\n",
       "      <td>120.644178</td>\n",
       "      <td>2.938616</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.825629</td>\n",
       "      <td>0.855400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>1.643355</td>\n",
       "      <td>111.600553</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.621877</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250502</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.979254</td>\n",
       "      <td>1.553127</td>\n",
       "      <td>103.669116</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.977909</td>\n",
       "      <td>2.786417</td>\n",
       "      <td>0.094851</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>1.627396</td>\n",
       "      <td>104.835346</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.653531</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.741069</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Age    Height      Weight      FCVC       NCP      CH2O       FAF  \\\n",
       "0  26.899886  1.848294  120.644178  2.938616  3.000000  2.825629  0.855400   \n",
       "1  21.000000  1.600000   66.000000  2.000000  1.000000  3.000000  1.000000   \n",
       "2  26.000000  1.643355  111.600553  3.000000  3.000000  2.621877  0.000000   \n",
       "3  20.979254  1.553127  103.669116  2.000000  2.977909  2.786417  0.094851   \n",
       "4  26.000000  1.627396  104.835346  3.000000  3.000000  2.653531  0.000000   \n",
       "\n",
       "        TUE  Gender_Female  Gender_Male  ...  SCC_yes  CALC_Always  \\\n",
       "0  0.000000          False         True  ...    False        False   \n",
       "1  0.000000           True        False  ...    False        False   \n",
       "2  0.250502           True        False  ...    False        False   \n",
       "3  0.000000          False         True  ...    False        False   \n",
       "4  0.741069           True        False  ...    False        False   \n",
       "\n",
       "   CALC_Frequently  CALC_Sometimes  CALC_no  MTRANS_Automobile  MTRANS_Bike  \\\n",
       "0            False            True    False              False        False   \n",
       "1            False            True    False              False        False   \n",
       "2            False            True    False              False        False   \n",
       "3            False            True    False              False        False   \n",
       "4            False            True    False              False        False   \n",
       "\n",
       "   MTRANS_Motorbike  MTRANS_Public_Transportation  MTRANS_Walking  \n",
       "0             False                          True           False  \n",
       "1             False                          True           False  \n",
       "2             False                          True           False  \n",
       "3             False                          True           False  \n",
       "4             False                          True           False  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_ids = test_df['id']\n",
    "test_df = test_df.drop(columns=['id'])\n",
    "\n",
    "# test_df = featuring_processing(test_df)\n",
    "# test_df = generate_poly_feature(test_df)\n",
    "# test_df = scaled_processing(test_df)\n",
    "test_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "# voting_predictions = mode([xgb_classifier.predict(test_df), lgbm_classifier.predict(test_df)], axis=0)[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 5, 4, ..., 0, 1, 3], dtype=int64)"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# 最终的预测结果\n",
    "# pred = voting_predictions.ravel()\n",
    "# pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Obesity_Type_II', 'Overweight_Level_I', 'Obesity_Type_III', ...,\n",
       "       'Insufficient_Weight', 'Normal_Weight', 'Obesity_Type_II'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = lgbm_classifier.predict(test_df)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Obesity_Type_II' 'Overweight_Level_I' 'Obesity_Type_III' ...\n",
      " 'Insufficient_Weight' 'Normal_Weight' 'Obesity_Type_II']\n"
     ]
    }
   ],
   "source": [
    "mapping = {\n",
    "    0: 'Insufficient_Weight',\n",
    "    1: 'Normal_Weight',\n",
    "    2: 'Obesity_Type_I',\n",
    "    3: 'Obesity_Type_II',\n",
    "    4: 'Obesity_Type_III',\n",
    "    5: 'Overweight_Level_I',\n",
    "    6: 'Overweight_Level_II'\n",
    "}\n",
    "\n",
    "# 使用列表推导式将数组中的数字映射为字符串\n",
    "sub_target = np.array([mapping[num] for num in pred])\n",
    "\n",
    "print(sub_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          id           NObeyesdad\n",
      "0      20758      Obesity_Type_II\n",
      "1      20759   Overweight_Level_I\n",
      "2      20760     Obesity_Type_III\n",
      "3      20761       Obesity_Type_I\n",
      "4      20762     Obesity_Type_III\n",
      "...      ...                  ...\n",
      "13835  34593  Overweight_Level_II\n",
      "13836  34594   Overweight_Level_I\n",
      "13837  34595  Insufficient_Weight\n",
      "13838  34596        Normal_Weight\n",
      "13839  34597      Obesity_Type_II\n",
      "\n",
      "[13840 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "sub = pd.DataFrame({'id': test_ids, 'NObeyesdad': pred})\n",
    "\n",
    "sub.to_csv('data/s4e2/submission_lgbm.csv', index=False)\n",
    "\n",
    "print(sub)"
   ]
  }
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