2.预处理与特征选择.ipynb

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   "metadata": {},
   "source": [
    "# 2. 预处理与特征选择"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "DIRECTORY = './data'\n",
    "TRAIN_FILE = 'adult/adult.data'\n",
    "MODEL_FILE = 'model_select.txt'\n",
    "JOBLIB_FILE = 'model_select.joblib'\n",
    "COLS = ['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status',\n",
    "        'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss',\n",
    "        'hours-per-week', 'native-country', 'income']\n",
    "LABEL_COL = 'income'"
   ]
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     "text": [
      "MKL_THREADING_LAYER = GNU\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "if os.environ.get('MKL_THREADING_LAYER') is None:\n",
    "    os.environ['MKL_THREADING_LAYER'] = 'GNU'\n",
    "print(f\"MKL_THREADING_LAYER = {os.environ['MKL_THREADING_LAYER']}\")"
   ]
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   "source": [
    "# coding: utf-8\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn.preprocessing\n",
    "import sklearn.model_selection\n",
    "import sklearn.metrics\n",
    "import lightgbm as lgb\n",
    "import matplotlib.pyplot as plt\n",
    "import joblib\n",
    "\n",
    "import util"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "434ae5c8-5b8a-4423-be2c-254268d8c106",
   "metadata": {},
   "source": [
    "## 一、预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e696e50-6b46-4bcc-ab23-d92e5d977c72",
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     "shell.execute_reply.started": "2024-07-09T10:09:06.442727Z"
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   "outputs": [],
   "source": [
    "csv_file = util.gen_abspath(DIRECTORY, TRAIN_FILE)\n",
    "df = util.read_csv(csv_file, sep=',', header=None)\n",
    "df.columns = COLS"
   ]
  },
  {
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   "execution_count": 5,
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   age             32561 non-null  int64 \n",
      " 1   workclass       32561 non-null  object\n",
      " 2   fnlwgt          32561 non-null  int64 \n",
      " 3   education       32561 non-null  object\n",
      " 4   education-num   32561 non-null  int64 \n",
      " 5   marital-status  32561 non-null  object\n",
      " 6   occupation      32561 non-null  object\n",
      " 7   relationship    32561 non-null  object\n",
      " 8   race            32561 non-null  object\n",
      " 9   sex             32561 non-null  object\n",
      " 10  capital-gain    32561 non-null  int64 \n",
      " 11  capital-loss    32561 non-null  int64 \n",
      " 12  hours-per-week  32561 non-null  int64 \n",
      " 13  native-country  32561 non-null  object\n",
      " 14  income          32561 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
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       "       age          workclass  fnlwgt    education  education-num  \\\n",
       "0       39          State-gov   77516    Bachelors             13   \n",
       "1       50   Self-emp-not-inc   83311    Bachelors             13   \n",
       "2       38            Private  215646      HS-grad              9   \n",
       "3       53            Private  234721         11th              7   \n",
       "4       28            Private  338409    Bachelors             13   \n",
       "...    ...                ...     ...          ...            ...   \n",
       "32556   27            Private  257302   Assoc-acdm             12   \n",
       "32557   40            Private  154374      HS-grad              9   \n",
       "32558   58            Private  151910      HS-grad              9   \n",
       "32559   22            Private  201490      HS-grad              9   \n",
       "32560   52       Self-emp-inc  287927      HS-grad              9   \n",
       "\n",
       "            marital-status          occupation    relationship    race  \\\n",
       "0            Never-married        Adm-clerical   Not-in-family   White   \n",
       "1       Married-civ-spouse     Exec-managerial         Husband   White   \n",
       "2                 Divorced   Handlers-cleaners   Not-in-family   White   \n",
       "3       Married-civ-spouse   Handlers-cleaners         Husband   Black   \n",
       "4       Married-civ-spouse      Prof-specialty            Wife   Black   \n",
       "...                    ...                 ...             ...     ...   \n",
       "32556   Married-civ-spouse        Tech-support            Wife   White   \n",
       "32557   Married-civ-spouse   Machine-op-inspct         Husband   White   \n",
       "32558              Widowed        Adm-clerical       Unmarried   White   \n",
       "32559        Never-married        Adm-clerical       Own-child   White   \n",
       "32560   Married-civ-spouse     Exec-managerial            Wife   White   \n",
       "\n",
       "           sex  capital-gain  capital-loss  hours-per-week  native-country  \\\n",
       "0         Male          2174             0              40   United-States   \n",
       "1         Male             0             0              13   United-States   \n",
       "2         Male             0             0              40   United-States   \n",
       "3         Male             0             0              40   United-States   \n",
       "4       Female             0             0              40            Cuba   \n",
       "...        ...           ...           ...             ...             ...   \n",
       "32556   Female             0             0              38   United-States   \n",
       "32557     Male             0             0              40   United-States   \n",
       "32558   Female             0             0              40   United-States   \n",
       "32559     Male             0             0              20   United-States   \n",
       "32560   Female         15024             0              40   United-States   \n",
       "\n",
       "       income  \n",
       "0       <=50K  \n",
       "1       <=50K  \n",
       "2       <=50K  \n",
       "3       <=50K  \n",
       "4       <=50K  \n",
       "...       ...  \n",
       "32556   <=50K  \n",
       "32557    >50K  \n",
       "32558   <=50K  \n",
       "32559   <=50K  \n",
       "32560    >50K  \n",
       "\n",
       "[32561 rows x 15 columns]"
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>occupation</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>relationship</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>race</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sex</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>capital-gain</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>capital-loss</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>hours-per-week</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>native-country</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>income</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          col_name  val_cnt\n",
       "0              age       73\n",
       "1        workclass        9\n",
       "2           fnlwgt    21648\n",
       "3        education       16\n",
       "4    education-num       16\n",
       "5   marital-status        7\n",
       "6       occupation       15\n",
       "7     relationship        6\n",
       "8             race        5\n",
       "9              sex        2\n",
       "10    capital-gain      119\n",
       "11    capital-loss       92\n",
       "12  hours-per-week       94\n",
       "13  native-country       42\n",
       "14          income        2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "util.value_counts(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b126188-d7a8-4dc5-9f81-4f9f2490e2db",
   "metadata": {},
   "source": [
    "### 1.1 标签编码\n",
    "\n",
    "因为 LightGBM 不接受 String 类型特征作为输入（LightGBM 只接受 float, int, bool 三种数据类型），因此需要用 `LabelEncoder()` 函数将类别变量 (category variable) 编码成整数 (int).\n",
    "\n",
    "举个例子说明 `LabelEncoder()` 的作用。假如地址字段有三个枚举值，分别是：北京、上海、香港。经过 `LabelEncoder()` 编码后，北京被编码为 0，上海被编码为 1，香港被编码为 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d803b1ce-c532-459a-8ccc-2a498486b98e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:06.587906Z",
     "iopub.status.busy": "2024-07-09T10:09:06.587199Z",
     "iopub.status.idle": "2024-07-09T10:09:06.607030Z",
     "shell.execute_reply": "2024-07-09T10:09:06.606119Z",
     "shell.execute_reply.started": "2024-07-09T10:09:06.587867Z"
    }
   },
   "outputs": [],
   "source": [
    "# features\n",
    "X = df.drop(LABEL_COL, axis=1)\n",
    "\n",
    "# label\n",
    "y = df[LABEL_COL].apply(lambda e: 0 if e == ' <=50K' else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "566ec163-38c7-4c06-aa9e-b847544fbebc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:06.608759Z",
     "iopub.status.busy": "2024-07-09T10:09:06.608178Z",
     "iopub.status.idle": "2024-07-09T10:09:06.615294Z",
     "shell.execute_reply": "2024-07-09T10:09:06.614230Z",
     "shell.execute_reply.started": "2024-07-09T10:09:06.608729Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['workclass',\n",
       " 'education',\n",
       " 'marital-status',\n",
       " 'occupation',\n",
       " 'relationship',\n",
       " 'race',\n",
       " 'sex',\n",
       " 'native-country']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 类别特征 category features\n",
    "cat_feats = [col for col in X.columns if X[col].dtypes == np.dtype('object')]\n",
    "cat_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "71be58bb-b9f4-4d2c-a3f6-9d501a7616ad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:06.617065Z",
     "iopub.status.busy": "2024-07-09T10:09:06.616397Z",
     "iopub.status.idle": "2024-07-09T10:09:06.622726Z",
     "shell.execute_reply": "2024-07-09T10:09:06.621490Z",
     "shell.execute_reply.started": "2024-07-09T10:09:06.617025Z"
    }
   },
   "outputs": [],
   "source": [
    "# 将类别特征 (String) 编码成整数 (int)\n",
    "def label_encoder(df):\n",
    "    cat_feats = [col for col in df.columns if df[col].dtypes == np.dtype('object')]\n",
    "    for col in cat_feats:\n",
    "        df[col] = sklearn.preprocessing.LabelEncoder().fit_transform(df[col])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dba39089-7ea8-444f-a205-063dc06576bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:06.624875Z",
     "iopub.status.busy": "2024-07-09T10:09:06.624403Z",
     "iopub.status.idle": "2024-07-09T10:09:06.674914Z",
     "shell.execute_reply": "2024-07-09T10:09:06.673611Z",
     "shell.execute_reply.started": "2024-07-09T10:09:06.624847Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
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       "      <th>3</th>\n",
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       "      <td>10</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>257302</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>154374</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>151910</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>201490</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>39</td>\n",
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       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>287927</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
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       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  workclass  fnlwgt  education  education-num  marital-status  \\\n",
       "0       39          7   77516          9             13               4   \n",
       "1       50          6   83311          9             13               2   \n",
       "2       38          4  215646         11              9               0   \n",
       "3       53          4  234721          1              7               2   \n",
       "4       28          4  338409          9             13               2   \n",
       "...    ...        ...     ...        ...            ...             ...   \n",
       "32556   27          4  257302          7             12               2   \n",
       "32557   40          4  154374         11              9               2   \n",
       "32558   58          4  151910         11              9               6   \n",
       "32559   22          4  201490         11              9               4   \n",
       "32560   52          5  287927         11              9               2   \n",
       "\n",
       "       occupation  relationship  race  sex  capital-gain  capital-loss  \\\n",
       "0               1             1     4    1          2174             0   \n",
       "1               4             0     4    1             0             0   \n",
       "2               6             1     4    1             0             0   \n",
       "3               6             0     2    1             0             0   \n",
       "4              10             5     2    0             0             0   \n",
       "...           ...           ...   ...  ...           ...           ...   \n",
       "32556          13             5     4    0             0             0   \n",
       "32557           7             0     4    1             0             0   \n",
       "32558           1             4     4    0             0             0   \n",
       "32559           1             3     4    1             0             0   \n",
       "32560           4             5     4    0         15024             0   \n",
       "\n",
       "       hours-per-week  native-country  \n",
       "0                  40              39  \n",
       "1                  13              39  \n",
       "2                  40              39  \n",
       "3                  40              39  \n",
       "4                  40               5  \n",
       "...               ...             ...  \n",
       "32556              38              39  \n",
       "32557              40              39  \n",
       "32558              40              39  \n",
       "32559              20              39  \n",
       "32560              40              39  \n",
       "\n",
       "[32561 rows x 14 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = label_encoder(X)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1ade5c6-2c6d-4e25-a0f1-5df2cac23cb9",
   "metadata": {},
   "source": [
    "### 1.2 更好的编码方式？\n",
    "\n",
    "- 文本：直接将文本编码成整数的过程，其实损失了大量的语义信息。举例来说，杭州离上海近，离北京远，如果只是进行存粹标签编码的话，整数标签是随机且离散的，无法表达这种三座城市的临近关系。我们可以使用更好的 Embedding 方法，获得更强的表达能力。<!-- 这个我们放在后续章节优化。 -->\n",
    "- `df[col].value_counts()` 太大的情况：可以对数据本身做处理，比如如果是 ip 字段，可以取 ip 前 3 段代替 ip 作为训练特征。可以减少 `value_counts` 的数量，增强模型的稳定性。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8da4609e-8704-412a-acf8-ac882c04eda1",
   "metadata": {},
   "source": [
    "## 二、初次训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "698d2f57-a35b-4f48-a9b6-03d0bec5a9eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:06.676684Z",
     "iopub.status.busy": "2024-07-09T10:09:06.676072Z",
     "iopub.status.idle": "2024-07-09T10:09:06.686684Z",
     "shell.execute_reply": "2024-07-09T10:09:06.686089Z",
     "shell.execute_reply.started": "2024-07-09T10:09:06.676659Z"
    }
   },
   "outputs": [],
   "source": [
    "# 分割数据集\n",
    "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "382ec6ad-3968-45ee-a197-fc888bfd79f7",
   "metadata": {},
   "source": [
    "### 2.1 使用 `lgb.LGBMClassifier` 进行训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "365c0bbc-f24e-41b7-9a76-e50dc0ceff2a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:06.687822Z",
     "iopub.status.busy": "2024-07-09T10:09:06.687508Z",
     "iopub.status.idle": "2024-07-09T10:09:06.894663Z",
     "shell.execute_reply": "2024-07-09T10:09:06.893797Z",
     "shell.execute_reply.started": "2024-07-09T10:09:06.687800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(colsample_bytree=0.8, learning_rate=0.05,\n",
       "               metric=&#x27;binary_logloss&#x27;, objective=&#x27;binary&#x27;, subgroup=0.8,\n",
       "               subsample=0.8, verbose=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LGBMClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LGBMClassifier(colsample_bytree=0.8, learning_rate=0.05,\n",
       "               metric=&#x27;binary_logloss&#x27;, objective=&#x27;binary&#x27;, subgroup=0.8,\n",
       "               subsample=0.8, verbose=-1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LGBMClassifier(colsample_bytree=0.8, learning_rate=0.05,\n",
       "               metric='binary_logloss', objective='binary', subgroup=0.8,\n",
       "               subsample=0.8, verbose=-1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "    'objective': 'binary',  # 二分类的目标函数\n",
    "    'num_leaves': 31,       # 叶子节点的最大数量\n",
    "    'max_depth': -1,       # 让模型决定树的最大深度\n",
    "    'learning_rate': 0.05,  # 学习率\n",
    "    'n_estimators': 100,    # boosting 阶段的数量\n",
    "    'subsample': 0.8,      # 训练每棵树时的采样比例\n",
    "    'subgroup': 0.8,       # 训练每棵树时的特征采样比例\n",
    "    'colsample_bytree': 0.8,# 构建树时的列采样比例\n",
    "    'min_child_samples': 20,# 分割叶子节点所需的最小样本数\n",
    "    'metric': 'binary_logloss',  # 评估指标\n",
    "    'boosting_type': 'gbdt',  # boosting策略\n",
    "    'verbose': -1\n",
    "}\n",
    "\n",
    "# 创建LGBMClassifier实例\n",
    "clf = lgb.LGBMClassifier(**params)\n",
    "\n",
    "# 训练模型\n",
    "clf.fit(X_train, y_train, eval_set=[(X_test, y_test)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bb55929a-4f95-485b-aad8-7d6d378e31ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:06.896624Z",
     "iopub.status.busy": "2024-07-09T10:09:06.896187Z",
     "iopub.status.idle": "2024-07-09T10:09:06.921272Z",
     "shell.execute_reply": "2024-07-09T10:09:06.920539Z",
     "shell.execute_reply.started": "2024-07-09T10:09:06.896588Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 1, 0, 0])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict\n",
    "y_pred_clf = clf.predict(X_test)\n",
    "y_pred_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a84cf914-30fd-4064-8947-cbbbac7680b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:06.923216Z",
     "iopub.status.busy": "2024-07-09T10:09:06.922558Z",
     "iopub.status.idle": "2024-07-09T10:09:06.950997Z",
     "shell.execute_reply": "2024-07-09T10:09:06.950122Z",
     "shell.execute_reply.started": "2024-07-09T10:09:06.923171Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.87748\n",
      "precision: 0.79754\n",
      "recall: 0.65945\n",
      "f1_score: 0.72195\n",
      "True Positive (TP): 1036\n",
      "True Negative (TN): 4679\n",
      "False Positive (FP): 263\n",
      "False Negative (FN): 535\n",
      "confusion matrix:\n",
      "[[4679  263]\n",
      " [ 535 1036]]\n"
     ]
    }
   ],
   "source": [
    "# evaluate\n",
    "acc = sklearn.metrics.accuracy_score(y_true=y_test, y_pred=y_pred_clf)\n",
    "precision = sklearn.metrics.precision_score(y_true=y_test, y_pred=y_pred_clf)\n",
    "recall = sklearn.metrics.recall_score(y_true=y_test, y_pred=y_pred_clf)\n",
    "f1 = sklearn.metrics.f1_score(y_true=y_test, y_pred=y_pred_clf)\n",
    "cm = sklearn.metrics.confusion_matrix(y_true=y_test, y_pred=y_pred_clf)\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "\n",
    "\n",
    "print(f'accuracy: {acc:.5f}')\n",
    "print(f'precision: {precision:.5f}')\n",
    "print(f'recall: {recall:.5f}')\n",
    "print(f'f1_score: {f1:.5f}')\n",
    "print(f'True Positive (TP): {tp}')\n",
    "print(f'True Negative (TN): {tn}')\n",
    "print(f'False Positive (FP): {fp}')\n",
    "print(f'False Negative (FN): {fn}')\n",
    "print(f'confusion matrix:\\n{cm}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d43fd0b4-ae57-4c26-90a0-2755590839a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:06.953444Z",
     "iopub.status.busy": "2024-07-09T10:09:06.952492Z",
     "iopub.status.idle": "2024-07-09T10:09:06.968609Z",
     "shell.execute_reply": "2024-07-09T10:09:06.967511Z",
     "shell.execute_reply.started": "2024-07-09T10:09:06.953394Z"
    }
   },
   "outputs": [],
   "source": [
    "# 将 lgb.LGBMClassifier 保存成 joblib 文件\n",
    "joblib_path = util.gen_abspath(DIRECTORY, JOBLIB_FILE)\n",
    "_ = joblib.dump(clf, joblib_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f7d9b25-3a63-48f6-9a6f-abd89e5a4443",
   "metadata": {},
   "source": [
    "### 2.2 使用原生 API 进行训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cd00dc2a-03c0-4777-9f03-ec59801469ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:06.969997Z",
     "iopub.status.busy": "2024-07-09T10:09:06.969669Z",
     "iopub.status.idle": "2024-07-09T10:09:06.975628Z",
     "shell.execute_reply": "2024-07-09T10:09:06.974432Z",
     "shell.execute_reply.started": "2024-07-09T10:09:06.969973Z"
    }
   },
   "outputs": [],
   "source": [
    "# prepare dataset\n",
    "lgb_train = lgb.Dataset(X_train,\n",
    "                        y_train,\n",
    "                        categorical_feature=cat_feats, \n",
    "                        free_raw_data=True)\n",
    "lgb_eval = lgb.Dataset(X_test,\n",
    "                    y_test,\n",
    "                    reference=lgb_train,\n",
    "                    categorical_feature=cat_feats, \n",
    "                    free_raw_data=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1f2bacd5-77e3-4156-853b-3384c28deab8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:06.977786Z",
     "iopub.status.busy": "2024-07-09T10:09:06.976990Z",
     "iopub.status.idle": "2024-07-09T10:09:06.982851Z",
     "shell.execute_reply": "2024-07-09T10:09:06.981495Z",
     "shell.execute_reply.started": "2024-07-09T10:09:06.977722Z"
    }
   },
   "outputs": [],
   "source": [
    "evals_result = {}  # to record eval results for plotting\n",
    "params = {\n",
    "    \"objective\": \"binary\",\n",
    "    \"boosting_type\": \"gbdt\",\n",
    "    \"metric\": 'auc',\n",
    "    \"num_leaves\": 31,\n",
    "    \"learning_rate\": 0.05,\n",
    "    \"feature_fraction\": 0.9,\n",
    "    \"bagging_fraction\": 0.8,\n",
    "    \"bagging_freq\": 5,\n",
    "    \"verbose\": 1\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f424f529-c6a8-44f3-a596-29c3d5be4cc4",
   "metadata": {},
   "source": [
    "由上一个 notebook 我们知道样本数据的标号有比较严重的倾斜。\n",
    "\n",
    "```\n",
    "income <= 50K:  24720\n",
    "income > 50K:  7841\n",
    "```\n",
    "\n",
    "可以通过对样本进行 上采样、下采样，或者用 `scale_pos_weight` 参数来修正样本数据倾斜。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5db91ce6-a744-43e5-982d-18bd78b672c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:06.984797Z",
     "iopub.status.busy": "2024-07-09T10:09:06.984181Z",
     "iopub.status.idle": "2024-07-09T10:09:06.998007Z",
     "shell.execute_reply": "2024-07-09T10:09:06.996672Z",
     "shell.execute_reply.started": "2024-07-09T10:09:06.984763Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Sample imbalance, set scale_pos_weight=3.154\n"
     ]
    }
   ],
   "source": [
    "# 处理样本数据倾斜\n",
    "positive_ratio = sum(y_train) / len(y_train)\n",
    "tolerance = 0.1\n",
    "if positive_ratio > 0.5 + tolerance or positive_ratio < 0.5 - tolerance:\n",
    "    # if dataset is highly imbalanced\n",
    "    weight = util.gen_scale_pos_weight(y_train)\n",
    "    params[\"scale_pos_weight\"] = weight\n",
    "    print(f\"Warning: Sample imbalance, set scale_pos_weight={weight:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "35ba6371-b72f-4eac-bc61-33564687a45f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:07.000033Z",
     "iopub.status.busy": "2024-07-09T10:09:06.999372Z",
     "iopub.status.idle": "2024-07-09T10:09:07.297436Z",
     "shell.execute_reply": "2024-07-09T10:09:07.296691Z",
     "shell.execute_reply.started": "2024-07-09T10:09:06.999950Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 6270, number of negative: 19778\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000944 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 695\n",
      "[LightGBM] [Info] Number of data points in the train set: 26048, number of used features: 14\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.240709 -> initscore=-1.148794\n",
      "[LightGBM] [Info] Start training from score -1.148794\n",
      "Training until validation scores don't improve for 5 rounds\n",
      "[10]\tvalid_0's auc: 0.913466\n",
      "[20]\tvalid_0's auc: 0.917998\n",
      "[30]\tvalid_0's auc: 0.920509\n",
      "[40]\tvalid_0's auc: 0.92282\n",
      "[50]\tvalid_0's auc: 0.924088\n",
      "[60]\tvalid_0's auc: 0.925256\n",
      "[70]\tvalid_0's auc: 0.926815\n",
      "[80]\tvalid_0's auc: 0.928022\n",
      "[90]\tvalid_0's auc: 0.928385\n",
      "[100]\tvalid_0's auc: 0.929098\n",
      "[110]\tvalid_0's auc: 0.929604\n",
      "[120]\tvalid_0's auc: 0.929643\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[116]\tvalid_0's auc: 0.929727\n"
     ]
    }
   ],
   "source": [
    "# train\n",
    "gbm = lgb.train(params,\n",
    "                lgb_train,\n",
    "                num_boost_round=120,\n",
    "                valid_sets=lgb_eval,\n",
    "                callbacks=[lgb.early_stopping(stopping_rounds=5),\n",
    "                           lgb.log_evaluation(10),\n",
    "                           lgb.record_evaluation(evals_result)],\n",
    "                categorical_feature=cat_feats\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e15e34aa-e5bc-44ee-b093-7e3c60fca2ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:07.299617Z",
     "iopub.status.busy": "2024-07-09T10:09:07.299170Z",
     "iopub.status.idle": "2024-07-09T10:09:07.555105Z",
     "shell.execute_reply": "2024-07-09T10:09:07.554069Z",
     "shell.execute_reply.started": "2024-07-09T10:09:07.299587Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# AUC 随训练迭代次数变化曲线\n",
    "ax = lgb.plot_metric(evals_result, metric=\"auc\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "355eabab-10dc-46e5-889e-574cd021b8d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:07.556778Z",
     "iopub.status.busy": "2024-07-09T10:09:07.556425Z",
     "iopub.status.idle": "2024-07-09T10:09:07.578945Z",
     "shell.execute_reply": "2024-07-09T10:09:07.578146Z",
     "shell.execute_reply.started": "2024-07-09T10:09:07.556757Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02348815, 0.69584045, 0.84949065, ..., 0.99375852, 0.02165435,\n",
       "       0.00361387])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict\n",
    "y_pred = gbm.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a561b101-85f7-4206-b3c5-fef2746a0911",
   "metadata": {},
   "source": [
    "## 三、简单评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a4925b8e-3cfd-4f67-a0bf-c6a1f5b33f13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:07.580656Z",
     "iopub.status.busy": "2024-07-09T10:09:07.580184Z",
     "iopub.status.idle": "2024-07-09T10:09:07.783507Z",
     "shell.execute_reply": "2024-07-09T10:09:07.782275Z",
     "shell.execute_reply.started": "2024-07-09T10:09:07.580601Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.92973\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# AUC, ROC Curve\n",
    "util.roc_curve(y_true=y_test, y_score=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8a4a3e5f-d46b-4fc9-884c-40794027a507",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:07.793058Z",
     "iopub.status.busy": "2024-07-09T10:09:07.792713Z",
     "iopub.status.idle": "2024-07-09T10:09:07.800920Z",
     "shell.execute_reply": "2024-07-09T10:09:07.799214Z",
     "shell.execute_reply.started": "2024-07-09T10:09:07.793035Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11018695191861091"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MSE\n",
    "sklearn.metrics.mean_squared_error(y_true=y_test, y_pred=y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a4cf9de-8f0c-4f5a-a568-57e71c919d55",
   "metadata": {},
   "source": [
    "## 四、模型存储与导入\n",
    "\n",
    "### 4.1 模型存储\n",
    "\n",
    "将模型文件存储到 `model_path` 路径下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6949760a-5170-4094-9186-46233a1d09d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:07.802461Z",
     "iopub.status.busy": "2024-07-09T10:09:07.802193Z",
     "iopub.status.idle": "2024-07-09T10:09:07.813645Z",
     "shell.execute_reply": "2024-07-09T10:09:07.812726Z",
     "shell.execute_reply.started": "2024-07-09T10:09:07.802439Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lightgbm.basic.Booster at 0x7fc634e0f520>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_path = util.gen_abspath(DIRECTORY, MODEL_FILE)\n",
    "gbm.save_model(model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6a005bf-3376-44e6-b2c5-b97b850772ed",
   "metadata": {},
   "source": [
    "### 4.2 模型导入\n",
    "\n",
    "删除 gbm 后，重新从模型文件载入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c82aff43-13a0-49e4-8218-a2d0f31aeb35",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:07.815351Z",
     "iopub.status.busy": "2024-07-09T10:09:07.814921Z",
     "iopub.status.idle": "2024-07-09T10:09:07.833757Z",
     "shell.execute_reply": "2024-07-09T10:09:07.832892Z",
     "shell.execute_reply.started": "2024-07-09T10:09:07.815317Z"
    }
   },
   "outputs": [],
   "source": [
    "bst = lgb.Booster(model_file=model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "07584469-500c-4ba3-bffd-d04925c1865b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:07.835649Z",
     "iopub.status.busy": "2024-07-09T10:09:07.835252Z",
     "iopub.status.idle": "2024-07-09T10:09:07.864666Z",
     "shell.execute_reply": "2024-07-09T10:09:07.863844Z",
     "shell.execute_reply.started": "2024-07-09T10:09:07.835618Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02348815, 0.69584045, 0.84949065, ..., 0.99375852, 0.02165435,\n",
       "       0.00361387])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = bst.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ecb15d0-303c-4ac8-93af-d9fae11bac7a",
   "metadata": {},
   "source": [
    "## 五、特征选择\n",
    "\n",
    "### 5.1 计算特征的重要程度\n",
    "\n",
    "查看特征的重要程度，可以剔除权重较低的特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "18026903-5e69-49c8-b1ba-8c57a3d14aad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:07.866419Z",
     "iopub.status.busy": "2024-07-09T10:09:07.865905Z",
     "iopub.status.idle": "2024-07-09T10:09:07.909664Z",
     "shell.execute_reply": "2024-07-09T10:09:07.908875Z",
     "shell.execute_reply.started": "2024-07-09T10:09:07.866389Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[rank 1] age: 564\n",
      "[rank 2] capital-gain: 451\n",
      "[rank 3] fnlwgt: 408\n",
      "[rank 4] hours-per-week: 387\n",
      "[rank 5] capital-loss: 370\n",
      "[rank 6] occupation: 343\n",
      "[rank 7] education-num: 247\n",
      "[rank 8] workclass: 136\n",
      "[rank 9] relationship: 127\n",
      "[rank 10] education: 125\n",
      "[rank 11] native-country: 113\n",
      "[rank 12] marital-status: 98\n",
      "[rank 13] sex: 86\n",
      "[rank 14] race: 25\n"
     ]
    }
   ],
   "source": [
    "items = [(k, v) for k, v in zip(gbm.feature_name(), gbm.feature_importance())]\n",
    "sorted_items = sorted(items, key=lambda e: e[1], reverse=True)\n",
    "for i, (k, v) in enumerate(sorted_items):\n",
    "    print(f'[rank {i+1}] {k}: {v}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0f551a08-d06a-471c-8600-50fc9e43e339",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:07.911435Z",
     "iopub.status.busy": "2024-07-09T10:09:07.911105Z",
     "iopub.status.idle": "2024-07-09T10:09:08.129599Z",
     "shell.execute_reply": "2024-07-09T10:09:08.128652Z",
     "shell.execute_reply.started": "2024-07-09T10:09:07.911407Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = lgb.plot_importance(gbm)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a325cbd1-3e12-40c4-8360-bcf788d5588e",
   "metadata": {},
   "source": [
    "### 5.2 多次实验求均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "60f23bb4-e454-4e14-b002-d8decdd9d1da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:08.131606Z",
     "iopub.status.busy": "2024-07-09T10:09:08.130976Z",
     "iopub.status.idle": "2024-07-09T10:09:14.624715Z",
     "shell.execute_reply": "2024-07-09T10:09:14.623846Z",
     "shell.execute_reply.started": "2024-07-09T10:09:08.131581Z"
    }
   },
   "outputs": [],
   "source": [
    "feature_importance_list = []\n",
    "for _ in range(30):\n",
    "    X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size=0.25)\n",
    "    d_train = lgb.Dataset(X_train,\n",
    "                          y_train,\n",
    "                          categorical_feature=cat_feats)\n",
    "    \n",
    "    weight = util.gen_scale_pos_weight(y_train)\n",
    "\n",
    "    params = {\n",
    "        \"objective\": \"binary\",\n",
    "        \"boosting_type\": \"gbdt\",\n",
    "        \"metric\": 'auc',\n",
    "        \"num_leaves\": 31,\n",
    "        \"learning_rate\": 0.05,\n",
    "        \"feature_fraction\": 0.9,\n",
    "        \"bagging_fraction\": 0.8,\n",
    "        \"bagging_freq\": 5,\n",
    "        \"verbose\": -1  # 仅输出 fatal 级别信息\n",
    "    }\n",
    "\n",
    "    gbm = lgb.train(params,\n",
    "                d_train,\n",
    "                num_boost_round=120,\n",
    "                categorical_feature=cat_feats)\n",
    "\n",
    "    feature_importance_list.append(gbm.feature_importance())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9a401743-fe2b-4fb6-9e8d-f5008e1e7ff6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:14.626132Z",
     "iopub.status.busy": "2024-07-09T10:09:14.625852Z",
     "iopub.status.idle": "2024-07-09T10:09:14.631981Z",
     "shell.execute_reply": "2024-07-09T10:09:14.631020Z",
     "shell.execute_reply.started": "2024-07-09T10:09:14.626108Z"
    }
   },
   "outputs": [],
   "source": [
    "items = {k: v for k, v in zip(gbm.feature_name(), np.array(feature_importance_list).T)}\n",
    "df_exp = pd.DataFrame(items)\n",
    "# df_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "aa6cfa9e-f9f5-479a-8d48-f4bc4cd0960e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-09T10:09:14.633940Z",
     "iopub.status.busy": "2024-07-09T10:09:14.633458Z",
     "iopub.status.idle": "2024-07-09T10:09:14.856915Z",
     "shell.execute_reply": "2024-07-09T10:09:14.856156Z",
     "shell.execute_reply.started": "2024-07-09T10:09:14.633902Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制特征重要性曲线，柱体高度为均值，短线为标准差\n",
    "arr = sorted(zip(gbm.feature_name(), df_exp.mean(), df_exp.std()), key=lambda e: e[1], reverse=True)\n",
    "key = [e[0] for e in arr]\n",
    "val = [e[1] for e in arr]\n",
    "std = [e[2] for e in arr]\n",
    "\n",
    "plt.bar(key, val, yerr=std, color=\"g\", align=\"center\")\n",
    "plt.title(\"Feature Importances\")\n",
    "plt.xlabel(\"Features\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c811b54-99dc-43a0-a418-5dee9cf248f9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (py3.10)",
   "language": "python",
   "name": "python_3_10"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}