{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "973cdef4",
   "metadata": {},
   "source": [
    "# 初步"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f19790c8",
   "metadata": {},
   "source": [
    "## 目标"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74f1e218",
   "metadata": {},
   "source": [
    "这个notebook，展示了机器学习基本流程：\n",
    "1. 了解数据，任务，提交评价标准\n",
    "2. 简单EDA，探索特征和目标的相关性，做出猜想\n",
    "   - 进行必要的分类特征编码\n",
    "   - 清楚特征中缺失值、异常值，多与少，对目标的影响\n",
    "   - 重要特征之间的相关性\n",
    "3. 特征工程：这是可选的。为了模型的改善\n",
    "   - 通过各种方式，产出新的更加重要特征，筛选特征\n",
    "4. 基线模型：\n",
    "   - 逻辑回归、随机森林这些几乎不需要做处理的简单模型\n",
    "5. 改善模型：\n",
    "   - 使用特征工程等\n",
    "6. 解释模式：\n",
    "   - 这一点往往很难做到，我们尽量"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "145735f8",
   "metadata": {},
   "source": [
    "这个notebook使用了*application_train/test*两个表"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1648ae22",
   "metadata": {},
   "source": [
    "提交的是概率，而不是分类结果"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00b92db0",
   "metadata": {},
   "source": [
    "## 导入包"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c5a93a",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9f8af1b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "726a591d",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (5, 3) # 设置默认长宽\n",
    "# 设置中文字体（Windows常用SimHei，Mac常用Arial Unicode MS）\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] \n",
    "# 解决负号 '-' 显示为方块的问题\n",
    "plt.rcParams['axes.unicode_minus'] = False "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3e0e5ef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28611a2a",
   "metadata": {},
   "source": [
    "## 数据读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "27ca191a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['application_test.csv',\n",
       " 'application_train.csv',\n",
       " 'bureau.csv',\n",
       " 'bureau_balance.csv',\n",
       " 'credit_card_balance.csv',\n",
       " 'HomeCredit_columns_description.csv',\n",
       " 'installments_payments.csv',\n",
       " 'log_regress_model_baseline.csv',\n",
       " 'POS_CASH_balance.csv',\n",
       " 'previous_application.csv',\n",
       " 'random_forest_baseline.csv',\n",
       " 'sample_submission.csv']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ad1051f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "application_train = pd.read_csv('data/application_train.csv')\n",
    "application_test = pd.read_csv('data/application_test.csv')\n",
    "bureau = pd.read_csv('data/bureau.csv')\n",
    "bureau_balance = pd.read_csv('data/bureau_balance.csv')\n",
    "credit_card_balance = pd.read_csv('data/credit_card_balance.csv')\n",
    "installments_payments = pd.read_csv('data/installments_payments.csv')\n",
    "previous_application = pd.read_csv('data/previous_application.csv')\n",
    "pos_cash_balance = pd.read_csv('data/POS_CASH_balance.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5b837eb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307511, 122)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "468eb3e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SK_ID_CURR',\n",
       " 'TARGET',\n",
       " 'NAME_CONTRACT_TYPE',\n",
       " 'CODE_GENDER',\n",
       " 'FLAG_OWN_CAR',\n",
       " 'FLAG_OWN_REALTY',\n",
       " 'CNT_CHILDREN',\n",
       " 'AMT_INCOME_TOTAL',\n",
       " 'AMT_CREDIT',\n",
       " 'AMT_ANNUITY',\n",
       " 'AMT_GOODS_PRICE',\n",
       " 'NAME_TYPE_SUITE',\n",
       " 'NAME_INCOME_TYPE',\n",
       " 'NAME_EDUCATION_TYPE',\n",
       " 'NAME_FAMILY_STATUS',\n",
       " 'NAME_HOUSING_TYPE',\n",
       " 'REGION_POPULATION_RELATIVE',\n",
       " 'DAYS_BIRTH',\n",
       " 'DAYS_EMPLOYED',\n",
       " 'DAYS_REGISTRATION',\n",
       " 'DAYS_ID_PUBLISH',\n",
       " 'OWN_CAR_AGE',\n",
       " 'FLAG_MOBIL',\n",
       " 'FLAG_EMP_PHONE',\n",
       " 'FLAG_WORK_PHONE',\n",
       " 'FLAG_CONT_MOBILE',\n",
       " 'FLAG_PHONE',\n",
       " 'FLAG_EMAIL',\n",
       " 'OCCUPATION_TYPE',\n",
       " 'CNT_FAM_MEMBERS',\n",
       " 'REGION_RATING_CLIENT',\n",
       " 'REGION_RATING_CLIENT_W_CITY',\n",
       " 'WEEKDAY_APPR_PROCESS_START',\n",
       " 'HOUR_APPR_PROCESS_START',\n",
       " 'REG_REGION_NOT_LIVE_REGION',\n",
       " 'REG_REGION_NOT_WORK_REGION',\n",
       " 'LIVE_REGION_NOT_WORK_REGION',\n",
       " 'REG_CITY_NOT_LIVE_CITY',\n",
       " 'REG_CITY_NOT_WORK_CITY',\n",
       " 'LIVE_CITY_NOT_WORK_CITY',\n",
       " 'ORGANIZATION_TYPE',\n",
       " 'EXT_SOURCE_1',\n",
       " 'EXT_SOURCE_2',\n",
       " 'EXT_SOURCE_3',\n",
       " 'APARTMENTS_AVG',\n",
       " 'BASEMENTAREA_AVG',\n",
       " 'YEARS_BEGINEXPLUATATION_AVG',\n",
       " 'YEARS_BUILD_AVG',\n",
       " 'COMMONAREA_AVG',\n",
       " 'ELEVATORS_AVG',\n",
       " 'ENTRANCES_AVG',\n",
       " 'FLOORSMAX_AVG',\n",
       " 'FLOORSMIN_AVG',\n",
       " 'LANDAREA_AVG',\n",
       " 'LIVINGAPARTMENTS_AVG',\n",
       " 'LIVINGAREA_AVG',\n",
       " 'NONLIVINGAPARTMENTS_AVG',\n",
       " 'NONLIVINGAREA_AVG',\n",
       " 'APARTMENTS_MODE',\n",
       " 'BASEMENTAREA_MODE',\n",
       " 'YEARS_BEGINEXPLUATATION_MODE',\n",
       " 'YEARS_BUILD_MODE',\n",
       " 'COMMONAREA_MODE',\n",
       " 'ELEVATORS_MODE',\n",
       " 'ENTRANCES_MODE',\n",
       " 'FLOORSMAX_MODE',\n",
       " 'FLOORSMIN_MODE',\n",
       " 'LANDAREA_MODE',\n",
       " 'LIVINGAPARTMENTS_MODE',\n",
       " 'LIVINGAREA_MODE',\n",
       " 'NONLIVINGAPARTMENTS_MODE',\n",
       " 'NONLIVINGAREA_MODE',\n",
       " 'APARTMENTS_MEDI',\n",
       " 'BASEMENTAREA_MEDI',\n",
       " 'YEARS_BEGINEXPLUATATION_MEDI',\n",
       " 'YEARS_BUILD_MEDI',\n",
       " 'COMMONAREA_MEDI',\n",
       " 'ELEVATORS_MEDI',\n",
       " 'ENTRANCES_MEDI',\n",
       " 'FLOORSMAX_MEDI',\n",
       " 'FLOORSMIN_MEDI',\n",
       " 'LANDAREA_MEDI',\n",
       " 'LIVINGAPARTMENTS_MEDI',\n",
       " 'LIVINGAREA_MEDI',\n",
       " 'NONLIVINGAPARTMENTS_MEDI',\n",
       " 'NONLIVINGAREA_MEDI',\n",
       " 'FONDKAPREMONT_MODE',\n",
       " 'HOUSETYPE_MODE',\n",
       " 'TOTALAREA_MODE',\n",
       " 'WALLSMATERIAL_MODE',\n",
       " 'EMERGENCYSTATE_MODE',\n",
       " 'OBS_30_CNT_SOCIAL_CIRCLE',\n",
       " 'DEF_30_CNT_SOCIAL_CIRCLE',\n",
       " 'OBS_60_CNT_SOCIAL_CIRCLE',\n",
       " 'DEF_60_CNT_SOCIAL_CIRCLE',\n",
       " 'DAYS_LAST_PHONE_CHANGE',\n",
       " 'FLAG_DOCUMENT_2',\n",
       " 'FLAG_DOCUMENT_3',\n",
       " 'FLAG_DOCUMENT_4',\n",
       " 'FLAG_DOCUMENT_5',\n",
       " 'FLAG_DOCUMENT_6',\n",
       " 'FLAG_DOCUMENT_7',\n",
       " 'FLAG_DOCUMENT_8',\n",
       " 'FLAG_DOCUMENT_9',\n",
       " 'FLAG_DOCUMENT_10',\n",
       " 'FLAG_DOCUMENT_11',\n",
       " 'FLAG_DOCUMENT_12',\n",
       " 'FLAG_DOCUMENT_13',\n",
       " 'FLAG_DOCUMENT_14',\n",
       " 'FLAG_DOCUMENT_15',\n",
       " 'FLAG_DOCUMENT_16',\n",
       " 'FLAG_DOCUMENT_17',\n",
       " 'FLAG_DOCUMENT_18',\n",
       " 'FLAG_DOCUMENT_19',\n",
       " 'FLAG_DOCUMENT_20',\n",
       " 'FLAG_DOCUMENT_21',\n",
       " 'AMT_REQ_CREDIT_BUREAU_HOUR',\n",
       " 'AMT_REQ_CREDIT_BUREAU_DAY',\n",
       " 'AMT_REQ_CREDIT_BUREAU_WEEK',\n",
       " 'AMT_REQ_CREDIT_BUREAU_MON',\n",
       " 'AMT_REQ_CREDIT_BUREAU_QRT',\n",
       " 'AMT_REQ_CREDIT_BUREAU_YEAR']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_train.columns.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14d88a47",
   "metadata": {},
   "source": [
    "## 字段说明\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8e561e9",
   "metadata": {},
   "source": [
    "### application_train.csv\n",
    "`application_train` 共有122个字段，主要分类，转换为还款能力和还款意愿    \n",
    "- `SK_ID_CURR`: 贷款申请ID\n",
    "- `TARGET`: 贷款是否违约，1为违约，0为正常还\n",
    "\n",
    "**个人信息**\n",
    "- `CODE_GENDER`: 性别\n",
    "- `DAYS_BIRTH`: 年龄\n",
    "- `DAYS_REGISTRATION`: 客户修改居住地址。\n",
    "- `DAYS_ID_PUBLISH`: 身份证更新时间，身份证更新越频繁，说明个人信息变动越大，风险也越大\n",
    "- `FLAG_MOBIL`: 是否有手机; `FLAG_EMP_PHONE`: 是否有工作电话; `FLAG_WORK_PHONE`: 是否有工作单位电话; `FLAG_CONT_MOBILE`: 是否有紧急联系人电话; `FLAG_PHONE`: 是否有电话; `FLAG_EMAIL`: 是否有电子邮箱 \n",
    "  - 都是01值\n",
    "- `DAYS_LAST_PHONE_CHANGE`: 最后一次更换联系电话的时间，天数表示\n",
    "  - 联系电话变动越频繁，说明个人信息变动越大，风险也越大\n",
    "- `NAME_EDUCATION_TYPE` : 教育水平\n",
    "  - Lower secondary：初中（最低）;Secondary / secondary special：高中 / 中专（数据中的“大部队”，占比约 70%）；Incomplete higher：大学肄业（上过大学但没毕业）；Higher education：本科 / 研究生（高等教育）；academic degree：博士 / 教授（最高，但人数极少）\n",
    "\n",
    "\n",
    "**车**\n",
    "- `FLAG_OWN_CAR`: 是否拥有汽车; \n",
    "    - 应该是很重要的特征，\n",
    "- `OWN_CAR_AGE`: 汽车年龄，车龄越大，说明经济状况越差\n",
    "- \n",
    "\n",
    "**房产**\n",
    "- `FLAG_OWN_REALTY`: 是否拥有房产\n",
    "- `FONDKAPREMONT_MODE`: 房屋管理模式\n",
    "- `HOUSETYPE_MODE`: 房屋类型\n",
    "- `TOTALAREA_MODE`: 房屋总面积\n",
    "- `WALLSMATERIAL_MODE`: 墙体材料\n",
    "- `EMERGENCYSTATE_MODE`: 房屋是否危急\n",
    "- 反映所在建筑的情况：\n",
    "  - 平均值情况\n",
    "    - `APARTMENTS_AVG`: g 房屋面积\n",
    "    - `YEARS_BUILD_AVG`: 房龄，房龄越大，说明经济状况越差\n",
    "    - `BASEMENTAREA_AVG`: 地下室面积\n",
    "    - `YEARS_BEGINEXPLUATATION_AVG`: 房屋使用年限\n",
    "    - `YEAR_BUILD_AVG`: 房屋建成年代\n",
    "    - `COMMONAREA_AVG`: 公共区域面积\n",
    "    - `ELEVATORS_AVG`: 电梯数量\n",
    "    - `ENTRANCES_AVG`: 入口数量\n",
    "    - `FLOORSMAX_AVG`: 最大楼层数\n",
    "    - `FLOORSMIN_AVG`: 最小楼层数\n",
    "    - `LANDAREA_AVG`: 土地面积\n",
    "    - `LIVINGAPARTMENTS_AVG`: 居住公寓面积\n",
    "    - `LIVINGAREA_AVG`: 居住面积\n",
    "    - `NONLIVINGAPARTMENTS_AVG`: 非居住公寓面积\n",
    "    - `NONLIVINGAREA_AVG`: 非居住面积\n",
    "  - 众数情况\n",
    "    - `NONLIVINGAREA_MODE`: 非居住面积\n",
    "    - `APARTMENTS_MODE`: g 房屋面积\n",
    "    - `YEARS_BUILD_MODE`: 房龄，房龄越大，说明经济状况越差\n",
    "    - `BASEMENTAREA_MODE`: 地下室面积\n",
    "    - `YEARS_BEGINEXPLUATATION_MODE`: 房屋使用年限\n",
    "    - `YEAR_BUILD_MODE`: 房屋建成年代\n",
    "    - `COMMONAREA_MODE`: 公共区域面积\n",
    "    - `ELEVATORS_MODE`: 电梯数量\n",
    "    - `ENTRANCES_MODE`: 入口数量\n",
    "    - `FLOORSMAX_MODE`: 最大楼层数        \n",
    "    - `FLOORSMIN_MODE`: 最小楼层数\n",
    "    - `LANDAREA_MODE`: 土地面积\n",
    "    - `LIVINGAPARTMENTS_MODE`: 居住公寓面积\n",
    "    - `LIVINGAREA_MODE`: 居住面积\n",
    "    - `NONLIVINGAPARTMENTS_MODE`: 非居住公寓面积\n",
    "  - 中数情况：\n",
    "    - ..... 同上\n",
    "\n",
    "**地区住址**\n",
    "- `REGION_POPULATION_RELATIVE`: 居住地区人口密度。数值大为大城市\n",
    "- `REGION_RATING_CLIENT`: 居住地区评级; `REGION_RATING_CLIENT_W_CITY`: 考虑了城市因素的地区评级\n",
    "  - 大城市风险较低\n",
    "- `REG_REGION_NOT_LIVE_REGION`: 是否注册地区等于所在地区（省）; \n",
    "- `REG_REGION_NOT_WORK_REGION`: 是否注册地区等于工作地区（省）; \n",
    "- `LIVE_REGION_NOT_WORK_REGION`: 是否居住地区等于工作地区（省）\n",
    "- `REG_CITY_NOT_LIVE_CITY`: 是否在登记城市居住; \n",
    "- `REG_CITY_NOT_WORK_CITY`: 是否在登记城市工作; \n",
    "- `LIVE_CITY_NOT_WORK_CITY`: 是否在居住城市工作\n",
    "  - 是否跨省流动、跨市流动、异地务工...\n",
    "  - 如果不在登记地区居住或者工作，说明流动性大，风险也大\n",
    "\n",
    "**社交圈**\n",
    "- `OBS_30_CNT_SOCIAL_CIRCLE`: 过去30天内社交圈中有多少人借钱; `OBS_60_CNT_SOCIAL_CIRCLE`\n",
    "- `DEF_30_CNT_SOCIAL_CIRCLE`: 过去30天内社交圈中有多少人违约, `DEF_60_CNT_SOCIAL_CIRCLE`\n",
    "\n",
    "**家庭状况**：\n",
    "- `NAME_FAMILY_STATUS`: 家庭状况，已婚，单身，离异等\n",
    "- `CNT_CHILDREN`: 子女数量; `CNT_FAM_MEMBERS`: 家庭成员数量\n",
    "    - 家庭负担越重，抗风险能力也差\n",
    "\n",
    "**经济能力**\n",
    "- `AMT_INCOME_TOTAL`: 年收入\n",
    "- `AMT_CREDIT`: 贷款金额;\n",
    "- `AMT_ANNUITY`: 贷款年金，每个月还多少\n",
    "- `AMT_GOODS_PRICE`: 贷款购买商品的价格。比如我想买1000的电脑，银行借给我多少\n",
    "- `NAME_INCOME_TYPE`: 收入类型\n",
    "    - 工资收入，养老金，商业贷款等，不同收入类型的还款能力不同\n",
    "- `NAME_HOUSING_TYPE`: 住房类型\n",
    "    - 自有住房，租赁住房等，不同住房类型的还款能力不同\n",
    "\n",
    "**信用历史，外部评分**\n",
    "- `EXT_SOURCE_1`, `EXT_SOURCE_2`, `EXT_SOURCE_3`: 外部其他征信机构对他的评分\n",
    "  -  这个应该是很重要的\n",
    "- 征信查询次数:\n",
    "  - `AMT_REQ_CREDIT_BUREAU_HOUR`: 过去一小时内查询信用局的次数; \n",
    "  - `AMT_REQ_CREDIT_BUREAU_DAY`: 过去一天内查询信用局的次数; \n",
    "  - `AMT_REQ_CREDIT_BUREAU_WEEK`: 过去一周内查询信用局的次数; \n",
    "  - `AMT_REQ_CREDIT_BUREAU_MON`: 过去一个月内查询信用局的次数; \n",
    "  - `AMT_REQ_CREDIT_BUREAU_QRT`: 过去一个季度内查询信用局的次数; \n",
    "  - `AMT_REQ_CREDIT_BUREAU_YEAR`: 过去一年内查询信用局的次数\n",
    "    - 查询次数越多，说明借款人越急需资金，风险也越大\n",
    "\n",
    "**工作情况**\n",
    "- `DAYS_EMPLOYED`: 工作年限天数\n",
    "- `OCCUPATION_TYPE`: 职业类型\n",
    "- `NAME_INCOME_TYPE`: 收入类型\n",
    "  - 负值表示倒退的天数\n",
    "- `ORGANIZATION_TYPE`: 工作单位类型\n",
    "    - 国企，私企，事业单位等，不同单位类型的还款能力\n",
    "\n",
    "**申请行为**\n",
    "- `NAME_CONTRACT_TYPE`: 贷款合同类型，现金贷款还是循环贷款。现金贷款直接给全部，循环贷款可以分期还款。\n",
    "- `NAME_TYPE_SUITE`: 申请时候的陪同人员类型\n",
    "- `WEEKDAY_APPR_PROCESS_START`: 申请的星期几\n",
    "- `HOUR_APPR_PROCESS_START`: 几点申请的， 0-23\n",
    "  - 半夜申请和周末申请贷款，可能风险更大\n",
    "- `FLAG_DOCUMENT_2` 到 `FLAG_DOCUMENT_21`: 提交的证明文件数。 都是01值\n",
    "  - 提交的身份证件越多，说明资料越齐全，风险也越小\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "429de2ba",
   "metadata": {},
   "source": [
    "### bureau.csv\n",
    "`bureau` 共有17个字段，每行为借款人在**其他贷款机构**的贷历史情况    \n",
    "- `SK_ID_CURR`: 贷款申请ID. 与`application_train`中的`SK_ID_CURR`对应\n",
    "- `SK_ID_BUREAU`: 其他贷款机构的贷款ID. 关联`bureau_balance`中的`SK_ID_BUREAU`\n",
    "- `CREDIT_ACTIVE`: 贷款状态，活跃，结清等\n",
    "- `CREDIT_CURRENCY`: 贷款币种\n",
    "- `CREDIT_TYPE`: 贷款类型，信用卡，汽车贷款等\n",
    "- 日期时间\n",
    "  - `DAYS_CREDIT`: 贷款申请距今的天数\n",
    "  - `DAYS_CREDIT_ENDDATE`: 贷款结束距今的天数(到期日)\n",
    "  - `DAYS_ENDDATE_FACT`: 贷款实际结束距今的天数. 对已经结清的贷款有效\n",
    "  - `DAYS_CREDIT_UPDATE`: 贷款最后一次更新距今的天数\n",
    "- 额度，金额, 逾期\n",
    "  - `CREDIT_DAY_OVERDUE`: 贷款逾期天数\n",
    "  - `AMT_CREDIT_MAX_OVERDUE`: 这笔贷款历史上最大逾期金额\n",
    "  - `CNT_CREDIT_PROLONG`: 这笔贷款展期次数. 延长还款期限的次数\n",
    "  - `AMT_CREDIT_SUM`: 这笔贷款的总授信额度\n",
    "  - `AMT_CREDIT_SUM_DEBT`: 这笔贷款的未还金额\n",
    "  - `AMT_CREDIT_SUM_LIMIT`: 这笔贷款的额度上限\n",
    "  - `AMT_CREDIT_SUM_OVERDUE`: 这笔贷款的逾期金额\n",
    "  - `AMT_ANNUITY`: 这笔贷款的年金，每个月还多少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abebac7e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3c251fa4",
   "metadata": {},
   "source": [
    "### bureau_balance.csv\n",
    "`bureau_balance` 共有3个字段，记录`bureau`中每笔贷款的每月状态\n",
    "- `SK_ID_BUREAU`: 其他贷款机构的贷款ID. 关联`bureau`中的`SK_ID_BUREAU`\n",
    "- `MONTHS_BALANCE`: 记录月份，贷款申请到现在的月数\n",
    "- `STATUS`: 贷款状态\n",
    "    - 0: 按时还款\n",
    "    - 1: 逾期1-30天\n",
    "    - 2: 逾期31-60天\n",
    "    - 3: 逾期61-90天\n",
    "    - 4: 逾期91-120天\n",
    "    - 5: 逾期120天以上\n",
    "    - C: 结清\n",
    "    - X: 无贷款"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f56f0ddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_BUREAU</th>\n",
       "      <th>MONTHS_BALANCE</th>\n",
       "      <th>STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5715448</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5715448</td>\n",
       "      <td>-1</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5715448</td>\n",
       "      <td>-2</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5715448</td>\n",
       "      <td>-3</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5715448</td>\n",
       "      <td>-4</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_BUREAU  MONTHS_BALANCE STATUS\n",
       "0       5715448               0      C\n",
       "1       5715448              -1      C\n",
       "2       5715448              -2      C\n",
       "3       5715448              -3      C\n",
       "4       5715448              -4      C"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bureau_balance.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4690287",
   "metadata": {},
   "source": [
    "### credit_card_balance.csv\n",
    "`credit_card_balance` 共有23个字段，记录信用卡每月的账单\n",
    "- `SK_ID_CURR`: 贷款申请ID. 与`application_train`中的`SK_ID_CURR`对应\n",
    "- `SK_ID_PREV`: 贷款ID. 关联`previous_application`中的`SK_ID_PREV`\n",
    "- `MONTHS_BALANCE`: 记录月份，贷款申请到现在的月数\n",
    "- `NAME_CONTRACT_STATUS`: 信用卡合同状态\n",
    "    - Active: 活跃\n",
    "    - Closed: 关闭\n",
    "    - Sold: 出售\n",
    "    - Demand: 需求\n",
    "- `AMT_BALANCE`: 账单余额\n",
    "- `AMT_CREDIT_LIMIT_ACTUAL`: 实际信用额度\n",
    "- `AMT_DRAWINGS_ATM_CURRENT`: 目前ATM取现额度\n",
    "- `AMT_DRAWINGS_CURRENT`: 目前取现额度\n",
    "- `AMT_DRAWINGS_OTHER_CURRENT`: 目前其他取现额度\n",
    "- `AMT_DRAWINGS_POS_CURRENT`: 目前POS取现额度\n",
    "- `AMT_INST_MIN_REGULARITY`: 最低还款金额\n",
    "- `AMT_PAYMENT_CURRENT`: 本期还款金额\n",
    "- `AMT_PAYMENT_TOTAL_CURRENT`: 本期应还金额\n",
    "- `AMT_RECEIVABLE_PRINCIPAL`: 应收本金\n",
    "- `AMT_RECIVABLE`: 应收总额\n",
    "- `AMT_TOTAL_RECEIVABLE`: 应收总额\n",
    "- `CNT_DRAWINGS_ATM_CURRENT`: 目前ATM取现次数\n",
    "- `CNT_DRAWINGS_CURRENT`: 目前取现次数\n",
    "- `CNT_DRAWINGS_OTHER_CURRENT`: 目前其他取现次数\n",
    "- `CNT_DRAWINGS_POS_CURRENT`: 目前POS取现次数\n",
    "- `CNT_INSTALMENT_MATURE_CUM`: 累计分期付款次数\n",
    "- `SK_DPD`: 逾期天数\n",
    "- `SK_DPD_DEF`: 逾期天数，严重逾期"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "62d7fe50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SK_ID_PREV', 'SK_ID_CURR', 'MONTHS_BALANCE', 'AMT_BALANCE',\n",
       "       'AMT_CREDIT_LIMIT_ACTUAL', 'AMT_DRAWINGS_ATM_CURRENT',\n",
       "       'AMT_DRAWINGS_CURRENT', 'AMT_DRAWINGS_OTHER_CURRENT',\n",
       "       'AMT_DRAWINGS_POS_CURRENT', 'AMT_INST_MIN_REGULARITY',\n",
       "       'AMT_PAYMENT_CURRENT', 'AMT_PAYMENT_TOTAL_CURRENT',\n",
       "       'AMT_RECEIVABLE_PRINCIPAL', 'AMT_RECIVABLE', 'AMT_TOTAL_RECEIVABLE',\n",
       "       'CNT_DRAWINGS_ATM_CURRENT', 'CNT_DRAWINGS_CURRENT',\n",
       "       'CNT_DRAWINGS_OTHER_CURRENT', 'CNT_DRAWINGS_POS_CURRENT',\n",
       "       'CNT_INSTALMENT_MATURE_CUM', 'NAME_CONTRACT_STATUS', 'SK_DPD',\n",
       "       'SK_DPD_DEF'],\n",
       "      dtype='str')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_balance.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1030a6b7",
   "metadata": {},
   "source": [
    "### previous_application.csv\n",
    "记录了借款人之前在home credit机构的贷款申请情况，共有37个字段\n",
    "- `SK_ID_CURR`: 贷款申请ID. 与`application_train`中的`SK_ID_CURR`对应\n",
    "- `SK_ID_PREV`: 贷款ID. 关联`credit_card_balance`中的`SK_ID_PREV` 。 表示之前的贷款ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3417605f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SK_ID_PREV', 'SK_ID_CURR', 'NAME_CONTRACT_TYPE', 'AMT_ANNUITY',\n",
       "       'AMT_APPLICATION', 'AMT_CREDIT', 'AMT_DOWN_PAYMENT', 'AMT_GOODS_PRICE',\n",
       "       'WEEKDAY_APPR_PROCESS_START', 'HOUR_APPR_PROCESS_START',\n",
       "       'FLAG_LAST_APPL_PER_CONTRACT', 'NFLAG_LAST_APPL_IN_DAY',\n",
       "       'RATE_DOWN_PAYMENT', 'RATE_INTEREST_PRIMARY',\n",
       "       'RATE_INTEREST_PRIVILEGED', 'NAME_CASH_LOAN_PURPOSE',\n",
       "       'NAME_CONTRACT_STATUS', 'DAYS_DECISION', 'NAME_PAYMENT_TYPE',\n",
       "       'CODE_REJECT_REASON', 'NAME_TYPE_SUITE', 'NAME_CLIENT_TYPE',\n",
       "       'NAME_GOODS_CATEGORY', 'NAME_PORTFOLIO', 'NAME_PRODUCT_TYPE',\n",
       "       'CHANNEL_TYPE', 'SELLERPLACE_AREA', 'NAME_SELLER_INDUSTRY',\n",
       "       'CNT_PAYMENT', 'NAME_YIELD_GROUP', 'PRODUCT_COMBINATION',\n",
       "       'DAYS_FIRST_DRAWING', 'DAYS_FIRST_DUE', 'DAYS_LAST_DUE_1ST_VERSION',\n",
       "       'DAYS_LAST_DUE', 'DAYS_TERMINATION', 'NFLAG_INSURED_ON_APPROVAL'],\n",
       "      dtype='str')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "previous_application.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ff61d17",
   "metadata": {},
   "source": [
    "\n",
    "贷款金额 (Core Info & Amounts)\n",
    "- NAME_CONTRACT_TYPE: 贷款类型（如：现金贷款、消费贷款等）。\n",
    "- AMT_APPLICATION: 客户申请的贷款金额。\n",
    "- AMT_CREDIT: 银行最终实际审批的贷款金额。\n",
    "- AMT_ANNUITY: 该笔贷款的每月年金（月还款额）。\n",
    "- AMT_GOODS_PRICE: 如果是消费贷，代表商品本身的实际价格。\n",
    "- AMT_DOWN_PAYMENT: 首付款金额。\n",
    "\n",
    "利率与审批状态 (Status & Interest)\n",
    "- NAME_CONTRACT_STATUS: 审批状态（非常关键：Approved 批准, Refused 拒绝, Canceled 取消, Unused offer 未使用的额度）\n",
    "- DAYS_DECISION: 相比于当前申请，这笔旧申请是在多少天前决定的（负数，如 -100 表示 100 天前）。\n",
    "- CODE_REJECT_REASON: 如果被拒绝，拒绝的原因代码。\n",
    "- RATE_DOWN_PAYMENT: 首付款比例（归一化后的数据）。\n",
    "- RATE_INTEREST_PRIMARY / PRIVILEGED: 利率相关信息（这两列通常缺失值极多）。\n",
    "\n",
    "客户属性与贷款用途 (Client & Purpose)\n",
    "- NAME_CASH_LOAN_PURPOSE: 现金贷款的用途。\n",
    "- NAME_PAYMENT_TYPE: 客户选择的还款方式。\n",
    "- NAME_CLIENT_TYPE: 客户类型（新客户、老客户、续约客户等）。\n",
    "- NAME_GOODS_CATEGORY: 购买商品的类别（电脑、手机、建材等）。\n",
    "- NAME_PORTFOLIO: 贷款组合（POS 贷、现金贷、卡类等）。\n",
    "- NAME_PRODUCT_TYPE: 产品类型（x-sell 交叉销售, walk-in 步入式等）。\n",
    "\n",
    "销售渠道与地点 (Sales & Channel)\n",
    "- CHANNEL_TYPE: 获取客户的渠道（如：石材店、电子产品店、电话销售等）。\n",
    "- SELLERPLACE_AREA: 卖方地点的面积（反映店铺规模）。\n",
    "- NAME_SELLER_INDUSTRY: 卖方的行业（如：连通器、家具、汽车等）。\n",
    "- PRODUCT_COMBINATION: 产品的详细组合名称。\n",
    "\n",
    "时间线与还款细节 (Timeline & Insure)\n",
    "- CNT_PAYMENT: 申请时的分期期数（比如分 12 个月还）。\n",
    "- NAME_YIELD_GROUP: 收益率分组（反映这笔贷款对银行的获利高低，如 high, low, normal）。\n",
    "- DAYS_FIRST_DRAWING: 第一次放款的时间。\n",
    "- DAYS_FIRST_DUE: 第一笔款项应还的时间。\n",
    "- DAYS_LAST_DUE_1ST_VERSION: 原计划最后一笔款项应还的时间。\n",
    "- DAYS_LAST_DUE: 实际最后一笔款项还清的时间。\n",
    "- DAYS_TERMINATION: 贷款合同预期的终止时间。\n",
    "- NFLAG_INSURED_ON_APPROVAL: 申请时客户是否申请了保险。\n",
    "- NFLAG_LAST_APPL_IN_DAY 这是否是该客户在当天申请的最后一笔贷款？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ffb5456",
   "metadata": {},
   "source": [
    "### installments_payments.csv\n",
    "所有流水， 包括 信用卡、和pos贷款\n",
    "\n",
    "- NUM_INSTALMENT_VERSION记录了该笔贷款的还款计划变动了多少次。\n",
    "- NUM_INSTALMENT_NUMBER：第几期还款。\n",
    "- DAYS_INSTALMENT：计划还款日。\n",
    "- DAYS_ENTRY_PAYMENT：实际还款日（关键！）。\n",
    "- AMT_INSTALMENT：计划还款金额。\n",
    "- AMT_PAYMENT：实际还款金额（关键！）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c4d0d5e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SK_ID_PREV', 'SK_ID_CURR', 'NUM_INSTALMENT_VERSION',\n",
       "       'NUM_INSTALMENT_NUMBER', 'DAYS_INSTALMENT', 'DAYS_ENTRY_PAYMENT',\n",
       "       'AMT_INSTALMENT', 'AMT_PAYMENT'],\n",
       "      dtype='str')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "installments_payments.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34856c92",
   "metadata": {},
   "source": [
    "### pos_cash_balance.csv\n",
    "- 于previous_banlance关联，描述pos贷状态"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6a87103f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_PREV</th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>MONTHS_BALANCE</th>\n",
       "      <th>CNT_INSTALMENT</th>\n",
       "      <th>CNT_INSTALMENT_FUTURE</th>\n",
       "      <th>NAME_CONTRACT_STATUS</th>\n",
       "      <th>SK_DPD</th>\n",
       "      <th>SK_DPD_DEF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1803195</td>\n",
       "      <td>182943</td>\n",
       "      <td>-31</td>\n",
       "      <td>48.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1715348</td>\n",
       "      <td>367990</td>\n",
       "      <td>-33</td>\n",
       "      <td>36.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1784872</td>\n",
       "      <td>397406</td>\n",
       "      <td>-32</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1903291</td>\n",
       "      <td>269225</td>\n",
       "      <td>-35</td>\n",
       "      <td>48.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2341044</td>\n",
       "      <td>334279</td>\n",
       "      <td>-35</td>\n",
       "      <td>36.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_PREV  SK_ID_CURR  MONTHS_BALANCE  CNT_INSTALMENT  \\\n",
       "0     1803195      182943             -31            48.0   \n",
       "1     1715348      367990             -33            36.0   \n",
       "2     1784872      397406             -32            12.0   \n",
       "3     1903291      269225             -35            48.0   \n",
       "4     2341044      334279             -35            36.0   \n",
       "\n",
       "   CNT_INSTALMENT_FUTURE NAME_CONTRACT_STATUS  SK_DPD  SK_DPD_DEF  \n",
       "0                   45.0               Active       0           0  \n",
       "1                   35.0               Active       0           0  \n",
       "2                    9.0               Active       0           0  \n",
       "3                   42.0               Active       0           0  \n",
       "4                   35.0               Active       0           0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_cash_balance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d2a05f3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SK_ID_PREV                 int64\n",
       "SK_ID_CURR                 int64\n",
       "MONTHS_BALANCE             int64\n",
       "CNT_INSTALMENT           float64\n",
       "CNT_INSTALMENT_FUTURE    float64\n",
       "NAME_CONTRACT_STATUS         str\n",
       "SK_DPD                     int64\n",
       "SK_DPD_DEF                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_cash_balance.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa75371f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b6379929",
   "metadata": {},
   "source": [
    "### metric: ROC\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a3e9996",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "- 发现有趣的现象和特征"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63387d50",
   "metadata": {},
   "source": [
    "### Distribution of TARGET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9634740c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.9192711805431351)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_train['TARGET'].value_counts()[0] /application_train['TARGET'].value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3e15af81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='TARGET'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "application_train['TARGET'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf0c0949",
   "metadata": {},
   "source": [
    "可以看到，这是一个不平衡的分类，违约的样本只占大约8%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25f9b01a",
   "metadata": {},
   "source": [
    "### Missing Values\n",
    "- 看看哪些字段缺失值较多\n",
    "- 后续 大部分模型都需要处理缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "08cd8ccc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_values_table(df):\n",
    "    mis_val = df.isnull().sum()\n",
    "    mis_val_percent = 100 * mis_val / len(df)\n",
    "    mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n",
    "    mis_val_table_ren_columns = mis_val_table.rename(\n",
    "        columns={0: 'Missing Values', 1: '% of Total Values'}\n",
    "        )\n",
    "    mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
    "        mis_val_table_ren_columns['Missing Values'] > 0\n",
    "    ]\n",
    "    mis_val_table_ren_columns = mis_val_table_ren_columns.sort_values(\n",
    "        '% of Total Values', ascending=False\n",
    "    ).round(1)\n",
    "    print(\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\n\"\n",
    "          \"There are \" + str(mis_val_table_ren_columns[mis_val_table_ren_columns['Missing Values'] > 0].shape[0]) +\n",
    "          \" columns that have missing values.\")\n",
    "    return mis_val_table_ren_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d67be9ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 122 columns.\n",
      "There are 67 columns that have missing values.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MEDI</th>\n",
       "      <td>214865</td>\n",
       "      <td>69.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MODE</th>\n",
       "      <td>214865</td>\n",
       "      <td>69.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_AVG</th>\n",
       "      <td>214865</td>\n",
       "      <td>69.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <td>213514</td>\n",
       "      <td>69.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>213514</td>\n",
       "      <td>69.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_AVG</th>\n",
       "      <td>213514</td>\n",
       "      <td>69.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FONDKAPREMONT_MODE</th>\n",
       "      <td>210295</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_AVG</th>\n",
       "      <td>210199</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>210199</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <td>210199</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Missing Values  % of Total Values\n",
       "COMMONAREA_MEDI                   214865               69.9\n",
       "COMMONAREA_MODE                   214865               69.9\n",
       "COMMONAREA_AVG                    214865               69.9\n",
       "NONLIVINGAPARTMENTS_MODE          213514               69.4\n",
       "NONLIVINGAPARTMENTS_MEDI          213514               69.4\n",
       "NONLIVINGAPARTMENTS_AVG           213514               69.4\n",
       "FONDKAPREMONT_MODE                210295               68.4\n",
       "LIVINGAPARTMENTS_AVG              210199               68.4\n",
       "LIVINGAPARTMENTS_MEDI             210199               68.4\n",
       "LIVINGAPARTMENTS_MODE             210199               68.4"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_table(application_train).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd985d8b",
   "metadata": {},
   "source": [
    "### column types\n",
    "- int64, float64 数值特征\n",
    "- object 分类特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bee357a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64    65\n",
       "int64      41\n",
       "str        16\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_train.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b47c7e2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\63517\\AppData\\Local\\Temp\\ipykernel_12096\\3850001950.py:1: Pandas4Warning: For backward compatibility, 'str' dtypes are included by select_dtypes when 'object' dtype is specified. This behavior is deprecated and will be removed in a future version. Explicitly pass 'str' to `include` to select them, or to `exclude` to remove them and silence this warning.\n",
      "See https://pandas.pydata.org/docs/user_guide/migration-3-strings.html#string-migration-select-dtypes for details on how to write code that works with pandas 2 and 3.\n",
      "  application_train.select_dtypes('object').apply(pd.Series.nunique, axis=0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NAME_CONTRACT_TYPE             2\n",
       "CODE_GENDER                    3\n",
       "FLAG_OWN_CAR                   2\n",
       "FLAG_OWN_REALTY                2\n",
       "NAME_TYPE_SUITE                7\n",
       "NAME_INCOME_TYPE               8\n",
       "NAME_EDUCATION_TYPE            5\n",
       "NAME_FAMILY_STATUS             6\n",
       "NAME_HOUSING_TYPE              6\n",
       "OCCUPATION_TYPE               18\n",
       "WEEKDAY_APPR_PROCESS_START     7\n",
       "ORGANIZATION_TYPE             58\n",
       "FONDKAPREMONT_MODE             4\n",
       "HOUSETYPE_MODE                 3\n",
       "WALLSMATERIAL_MODE             7\n",
       "EMERGENCYSTATE_MODE            2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_train.select_dtypes('object').apply(pd.Series.nunique, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecc1d943",
   "metadata": {},
   "source": [
    "大多数类别变量都有比较少的分类值"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c6b5d81",
   "metadata": {},
   "source": [
    "### Encoding categorical variable\n",
    "- 大部分模型都需要预处理这些分类特征，编码为数字\n",
    "  - `label encoding` 和 `one-hot encoding`\n",
    "- 对二分类特征使用`label encoding`\n",
    "- 对多酚类特征使用`one-hot encoding`\n",
    "  - 也可以使用pandas `get_dummies(df)`  更方便"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cf47eadb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 columns are label encoded.\n"
     ]
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "le_cnt = 0\n",
    "\n",
    "for col in application_train:\n",
    "    if application_train[col].dtype == 'object':\n",
    "        if len(application_train[col].unique()) <= 2:\n",
    "            # 二分类特征\n",
    "            le.fit(application_train[col])\n",
    "            application_train[col] = le.transform(application_train[col])\n",
    "            application_test[col] = le.transform(application_test[col])\n",
    "            le_cnt += 1\n",
    "print('%d columns are label encoded.' % le_cnt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c22884dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features shape with one-hot : (307511, 246)\n",
      "Testing Features shape with one-hot : (48744, 242)\n"
     ]
    }
   ],
   "source": [
    "application_train = pd.get_dummies(application_train)\n",
    "application_test = pd.get_dummies(application_test)\n",
    "print(f\"Training Features shape with one-hot : {application_train.shape}\")\n",
    "print(f\"Testing Features shape with one-hot : {application_test.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9f60f40",
   "metadata": {},
   "source": [
    "- 这里特征数量翻了一倍！\n",
    "- 特征数量训练和测试也不匹配"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c6e82d",
   "metadata": {},
   "source": [
    "### align train and test features\n",
    "- 对齐训练数据和测试数据特征，因为onehot\n",
    "- 采取inner交集的方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c7f62d72",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保留下拉，inner会除掉\n",
    "train_labels = application_train['TARGET'] \n",
    "\n",
    "application_train, application_test = application_train.align(application_test, join='inner', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a9211cba",
   "metadata": {},
   "outputs": [],
   "source": [
    "application_train['TARGET'] = train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4c093988",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features shape:  (307511, 243)\n",
      "Testing Features shape:  (48744, 242)\n"
     ]
    }
   ],
   "source": [
    "print('Training Features shape: ', application_train.shape)\n",
    "print('Testing Features shape: ', application_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8e738373",
   "metadata": {},
   "outputs": [],
   "source": [
    "application_train.to_feather('checkpoints/01_train_app_base.feather')\n",
    "application_test.to_feather('checkpoints/01_test_app_base.feather')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a73c81c",
   "metadata": {},
   "source": [
    "### Anomalies 异常数据处理\n",
    "- 一些异常的数据。 通过`describe`查看统计量筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d715022e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    307511.000000\n",
       "mean         43.936973\n",
       "std          11.956133\n",
       "min          20.517808\n",
       "25%          34.008219\n",
       "50%          43.150685\n",
       "75%          53.923288\n",
       "max          69.120548\n",
       "Name: DAYS_BIRTH, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(application_train['DAYS_BIRTH'] / -365).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "471874f0",
   "metadata": {},
   "source": [
    "这看起来没什么异常\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1dcaa603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    307511.000000\n",
       "mean      63815.045904\n",
       "std      141275.766519\n",
       "min      -17912.000000\n",
       "25%       -2760.000000\n",
       "50%       -1213.000000\n",
       "75%        -289.000000\n",
       "max      365243.000000\n",
       "Name: DAYS_EMPLOYED, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_train['DAYS_EMPLOYED'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e36c9255",
   "metadata": {},
   "source": [
    "这明显不对，最大值是+36万天。我们通过分布频次直方图看一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "207795f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Days employed')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O9e5rvQhdAEC6Bm7Hjh3llltusXuxuXPnlvRQokQJu8Ss8zQXLVrUOkAln8dZ31vLfDtweR5XgZalRMPZ305hAAAEJXAPHTpkqwXdcccdEiw61Oeee+7xdqbSHsc5cuSwVqQOB9L7vr7vr/dUNTS1bNGiRd4y7dlcunRp+1nL9HV0OcHUypo3b35dGQAAIXEPV3v36nCaYKpVq5a8+OKL8tlnn9lau/369ZPu3btbRyntuayXcGNiYuy52ktZlwLU+7g6JEiXB9SOW1evXrUZr1q2bOltiS9ZssQ6ZF24cMFmx/KUaS9rvWx97NgxW2ZQezZ7ygAACIkWrrYsdaYp7SylC9BrhyMPHZYTCF1xSIcFaUhqkOq+BqtVMjzchvhoRy1tCWvLd+PGjVamk11MmTJFWrduLQUKFLDhRJ4xuhri2nO6bt26do9Whw3179/fynRcrw5p0seUtnQ7dOgQUN0BALiZMCd5ryc/9OzZM+UXCwuTt99+W9LLyZMnbeiODhFKvkqRXmbev3+/NG7c2II3eQ9obcnql4Pk92m3bdsmFy9etDLP0CJ/aItbeyvr+GDfLxxpVW7Eagklh19lLWMASI8sCChwQeACACRNgRvwOFztqKS9lO+//34bJqQzTemUjQAAIEiBO2rUKFsZSMfJ6oxNek9V9e3bN5CXAwAgywuo05QO0dHexDo/8VtvvSW5cuWyqR3vvPPO4NcQAIDs2sLVnsA6y5Qv7ZSkS90BAIAgtXB1vGz79u1tGI1OQKHDclauXCljx44N5OUAAMjyAmrh6oQUn376qS2f98ADD9iwmnfeeUeeeOKJ4NcQAIDs2sJVOvuTbgAAIJ0CV3snpzZJxA8//BDISwIAkKUFFLieqROVLmkXFxdncysHOq0jAABZXUCBq9Mg+nrwwQdtnmNdlad3797BqhsAAFlGwDNNJVeuXDkbGgQAAILUwtXFC3zv4epC8bqoQKVKlQJ5OQAAsrzwQFuzvjR8dZUevawMAACCFLhjxowJ5DAAALKtgAI3KirqpmvHxsbGBlonAACynIACt3LlyrJixQrrkVy2bFk5ePCgLTyvl5Tr1KkT/FoCAJAdA3fLli2yatUque+++7yPPfzwwza149SpU4NZPwAAsu+wIF1oXnsm+9J9XfkeAAAEqYXbr18/iY6Olm7dukmpUqVsqb5FixbJwIEDA3k5AACyvIACV5fhq1mzpnz44Yeyb98+ufXWW2Xu3LnSrl274NcQAIDsvFpQx44dbQMAAOl0D/fq1asybtw4adCggZQuXVq++eYbqV+/vvVWBgAAQQrc/v37y3vvvWdTPCYkJEi+fPmkUaNG0rdv30BeDgCALC+gwF22bJm8//77FrA5c+a07W9/+5t8+eWXwa8hAADZNXDLlCkjmzdv9u7rrFN6WVkXpgcAAEHqNDVx4kSb6EIXndcF6IcOHSqff/65vPPOO4G8HAAAWV5AgduqVSvZs2eP3ce955575Pbbb5cJEyZIhQoVgl9DAACy87AgXfv2hRdeCG5tAADIogK6h/vJJ5/IuXPngl8bAACyqByBTu0YFxcX/NoAAJBFBRS4OmfyG2+8IdeuXQt+jQAAyIICCtxixYrZJWVd+3bGjBnWO9mzpWXFIR1GdPjwYe9j2hGrXr16UqRIERk+fLg4juMt27Rpk1SvXl2KFy8ukydPvm5csK7LqwspLF68OEnZtGnTpGTJktahKzY2NknZqFGj7L10Xuhdu3YFcCYAAEjHTlPz5s2TPHny2KY9lX3H43bv3t2vsG3Tpk2SsL18+bK0bdtWWrZsKUuWLLFWtL6PzmZ1+vRpWxjhueees0XuO3fubL2jo6KiLKS7du1qwarr83bo0EHuvfdeqVq1qqxdu1aGDRtmr3fLLbfY6kbbtm2zLwwzZ860TRdg0C8P+ppff/215M6dO5BTAgDADYU5vs3IG9DW4QMPPCA5cgTUKE6iRYsWFqCDBg2SQ4cOSbly5WTFihXy17/+VX788UebKnLnzp3yzDPPyBdffGGXrzUc9+7da6G+cuVKWbp0qSxYsEAGDx4s+/fvt45c6s0337SAfvnll22ssK5k9NZbb1nZkCFD5M4775RevXpJ7dq1LWRHjBhhZe3bt7f307r5Q9f+LVSokMTHx0tkZGTA56LciNUSSg6/+lBGVwEAMo20ZIHf6fnnP/9ZLly44N3XmaUCvYc7e/bs69bO1YDVxRA0bJVe5tWA9ZRpa1bDVulCCdu3b/eWNWvWzPs6/pTpd4zdu3enelxKtAWuJ9Z3AwDAX34HbvKGcOPGjeX48eMSiJSmgNQA831cw1XnaNbLvcnL9FuE570DKdMvDomJiakel5Lx48fbtxjPptNbAgAQ9MD1tC49/LwS7bfw8HC7J+wrIiLCpo5MXuZ5PKXj/CnTx1Vqx6Vk5MiRdsnAsx09ejQonxsAkD2kqdOUb+jqz8lD+I8oWrSodYDypUv/aScmLdP7sskf9xyX1rK8efPapmWea+6+x6XE00kMAIB0v6T8pz/9yXoA66aXax988EHvvmcLlA4H2rp1q3dfO1PpfVMNzeRlO3bssIXvUzrO37K6deumWgYAQIa1cGNiYiQ9NWnSxEJc30eHAo0bN856DOt9XO3RrD2I169fL02bNrXVinT4kOrYsaPcf//91uNZ78lOnTrVhv+oTp062axY+np6GXnu3LnWi9lTpgsu6GufPHnSxvL6LjkIAECGBG6PHj3StyLh4TJnzhwbZ6uTXujwo40bN1qZTnYxZcoUad26tRQoUEAKFy5sY3RVrVq1LGy1xar3YStXriz9+/e3Mh3Xq8OH9DHVvHlzG6er+vbta8OLdKUjbUnrUCGdyAMAgAwdh+sWbW3q8BwdIqQTVPjSy8w65lZ7SGvw+tIhRMeOHbMWcPJ7sTrZxcWLF63M976z9lTesmWL3ZvVYUFpwThcAMD5NIzDDbnAzSwIXADA+fSY+AIAAASOwAUAwAUELgAALiBwAQBwAYELAIALCFwAAFxA4AIA4AICFwAAFxC4AAC4gMAFAMAFBC4AAAQuAABZAy1cAABcQOACAOACAhcAABcQuAAAuIDABQDABQQuAAAuIHABAHABgQsAgAsIXAAAXEDgAgDgAgIXAAAXELgAALiAwAUAwAUELgAALiBwAQBwAYELAIALCFwAAFxA4AIA4AICFwAAF4S78SYAAKhyI1ZLKDn86kPZt4U7cOBACQsL826VKlWyx/fs2SP16tWTIkWKyPDhw8VxHO8xmzZtkurVq0vx4sVl8uTJSV5v2bJlUrZsWSlVqpQsXrw4Sdm0adOkZMmSUqFCBYmNjXXpEwIAsqOQC9y4uDhZvXq1nDt3zrYdO3bI5cuXpW3btlKnTh0r37t3r8ybN8+ef/r0aWnXrp106dJFtm7dKgsXLpQNGzZ4Q7pr164yevRoWbt2rbz00kty4MABK9P9YcOGyaxZs2TBggXSq1cvOXv2bIZ+dgBA1hVSgXvt2jX55ptvpEmTJlK4cGHbChYsKGvWrJH4+HhrvVasWFHGjRsnc+fOtWM0YLX1qqFauXJlC1VP2Zw5cyQqKsrC9O6775YBAwbI/PnzrWzGjBnSo0cPiY6OlkaNGtm/y5cvz9DPDwDIukIqcHfv3i2JiYlSu3ZtyZs3r7Rq1UqOHDkiO3fulAYNGki+fPnseTVr1rRWrtIyDVW9/Kzq168v27dv95Y1a9bM+/r+lqVEW9nnz59PsgEAkCkDV0O0atWq1grdtWuXhIeHS58+fSzcypcv732ehmvOnDntknPyssjISDl+/Lj9HGhZSsaPHy+FChXybmXKlAn65wcAZF0hFbh6v1Xv0TZs2NAuD0+fPl0+/fRTa/XmyZMnyXMjIiLk0qVLFsq+ZZ7HVaBlKRk5cqRd1vZsR48eDepnBwBkbSE9LKhEiRIWtrfeeqt1gPKVkJAguXPnlqJFi1rHqeSPq0DLUqLhnDz0AQDIlC1cHe6zaNEi7772Os6RI4d1eNKfPQ4dOmT3VDU0daiQb5n2ai5durT9HGgZAABZOnBr1aolL774onz22Weybt066devn3Tv3l3+8pe/2D3XmJgYe572Um7RooXdx9UhQVu2bJH169fL1atXZeLEidKyZUt7XseOHWXJkiXWGevChQsydepUb1mnTp3skvWxY8fk1KlT1rPZUwYAQJa+pNytWzcbFqRBqWGq+xquer9Vh/joWFttBWurd+PGjXaMTnYxZcoUad26tRQoUMCGEnnG6GqADxo0SOrWrWv3aPW+cP/+/a1Mx/UuXbrUHlPNmzeXDh06ZOCnBwBkZWGO75RNIe7kyZM2dEeHCBUrVixJmV5m3r9/vzRu3NiCN3nvZ23JNm3a9Lr7tNu2bZOLFy9amWdokT+0xa29lbUDlfZwDlR2nuYMQPZTLov9n5eWLAipFu7NaOephx5K+eToEB/fYT6+atSoYVtK9F4uAADZ6h4uAABZFYELAIALCFwAAFxA4AIA4AICFwAAFxC4AAC4gMAFAMAFBC4AAC4gcAEAcAGBCwCACwhcAABcQOACAOACAhcAABcQuAAAuIDABQDABQQuAAAuIHABAHABgQsAgAsIXAAAXEDgAgDgAgIXAAAXELgAALiAwAUAwAUELgAALiBwAQBwAYELAIALCFwAAFxA4AIA4AICFwAAFxC4AAC4gMAFAMAFBC4AAC7I9oG7Z88eqVevnhQpUkSGDx8ujuO4cd4BANlMtg7cy5cvS9u2baVOnToSFxcne/fulXnz5mV0tQAAWVC2Dtw1a9ZIfHy8TJ48WSpWrCjjxo2TuXPnZnS1AABZULhkYzt37pQGDRpIvnz5bL9mzZrWyk2tNaybhwa1On/+/B+qQ+LlSxJK/ujnAYDs9H/e+f9/vD+3I7N14OqJKl++vHc/LCxMcubMKefOnbN7ur7Gjx8vY8eOve41ypQpI1lJoTcyugYAkPn+z0tISJBChQrd8DnZOnDDw8MlT548SR6LiIiQS5cuXRe4I0eOlKFDh3r3ExMT5eeff5ZixYpZUGfklwYN/aNHj0pkZKRkFtSb883vSejh7zLttGWrYVuqVKmbPjdbB27RokWtl7IvPXG5c+e+7rkazMnDuXDhwhIqNGwzU+B6UG/ON78noYe/y7S5WcvWI1t3mtLhQFu3bvXuHzp0yO7TahADABBM2TpwmzRpYpdQYmJibF97Kbdo0cLu4wIAEEzh2f0e7pw5c6RLly426UWOHDlk48aNkpnoZe4xY8Zcd7k71FFvzje/J6GHv8v0FeYwtZKcPHlStm/fbkOEtBMUAADBRuACAOCCbH0PFwAAtxC4AJAF/PLLL/Lll1/axD2ZxS+ZsM5/BIGbiWX0SkcDBw60ST88W6VKlW5ar02bNkn16tWlePHiNoe1r2XLlknZsmVtAPnixYuTlE2bNk1KliwpFSpUkNjY2DTX9cyZMzar2OHDh72PuV3PUaNG2XvpFKK7du0KuN6pnfeM+EwpWblypT1XOyXWrl1b9u3blynOd2r1DvXzrZYuXSrlypWTXr16ye233277oX7Ol6ZS58xwvgOmnaaQ+fz2229OuXLlnL59+zrff/+907p1a+ftt992tQ4NGzZ0Vq9e7Zw7d8628+fP37BeP/30kxMZGemMHTvW+fbbb517773XiY2NtbLdu3c7uXPndmbPnu3s2rXLqVSpkrN//34r++STT5yIiAhnxYoVzpYtW5zy5cs7Z86c8buep0+fdu677z79y3QOHTpkj7ldz7feesspVqyYs3nzZmflypVO9erVncuXL6e53qmd94z4TCnR9y1SpIjz7rvvOidPnnQeeeQRp1GjRiF/vlOrd6ifb/XLL784xYsXd3bu3Gn7MTExTtmyZUP6nP+SSp0zw/n+IwjcTGr58uX2H8TFixdt/+uvv3buv/9+197/6tWr9guekJDgd72mTJniVKtWzUlMTLR9/SXv2rWr/Txo0CCnZcuW3td54403nFGjRtnP0dHR9kfmMXjwYPvj8Vfz5s2dN998M0lwuV3PWrVqOePHj/eWPfzww86nn36a5nqndt4z4jOlZNWqVc7MmTO9+/qfXt68eUP+fKdW71A/3+rIkSPOggULvPsaYgUKFAjpc34klTpnhvP9R3BJORusdJQedu/ebfNJ66W3vHnzSqtWreTIkSM3rJeWRUVFeeeerl+/vg3H8pQ1a9bM+/r+lvlj9uzZdpnKl5v11C+2er7S+hlSqndq593tz5SaNm3aSJ8+fbz7Bw4ckMqVK4f8+U6t3qF+vpXOpd61a1f7+erVqzJlyhRp3759SJ/zMqnUOTOc7z+CwM2CKx25QX/Rq1atKvPnz7d7NXrfS//DulG9kpfpfK3Hjx9P8fP4W+YP32M93KznhQsX7D+RtH6GlOqd2nl3+zP548qVKzJp0iTp169fpjjfKdU7M51vDY9bb71VPvnkE5k6dWqmOOc7k9U5M53vQGTrmaayy0pH6UG/nXq+oarp06fbL652WkitXsnr7Hk8pc/jb1l6nL9g11MfV8H4DKmdd/2Pw83P5A+dAS1//vzWKebFF1/MNOfbt965cuXKNOdbW3zr1q2TIUOGWN0rVqwY8ue8ZrI6a+emzHK+A0ELN5PSBRZOnz7t10pHbihRooR9w9Vvq6nVK3mdfesbaFl6nL9g11MvjekW7M/ge95PnDjh6me6Ge3tqb0/Fy1aZKGVWc538npnlvPtafHVqVNH/v3vf8sHH3yQKc55WLI66zChzHK+A0HgZlIZvdKRdsnX/5Q8tC46F/Xdd9+dar2S13nHjh1SunTpFD+Pv2Xpcf7So55169YNymdI7bzrPTG3P1Nq9H11fnINrho1amSa851SvTPD+dbhMFpPDw0MDTK92hSq53xTKnUeO3ZsyJ/vPyTdumMhXWlvvltuucXbLb5Xr15OmzZtXDvr8+fPty7069evd9auXetUqVLFefLJJ29YLx3mol3wtefilStXnFatWjkDBgzw9jjMnz+/ddvXHoq1a9d2Xn/9dSvTIQa33Xab8+OPP9qQjdKlSzvLli1Lc52T9/Z1s57a2/iuu+5y4uPjnQMHDjj58uVz4uLi0lzv1M57RnymlFy6dMmpUaOG07t3b3stz6bvGcrnO7V6v/POOyF9vtXx48etZ6/2stbev927d7f3C+Xf8eOp1DnUf7//KAI3E9NfFv2l1rFv+ov4zTffuPr+I0aMcAoVKuQULVrUGThwoHPhwoWb1mvGjBlOrly5rHu//mHpL7nHCy+8YOPl9A+xTp069p+g0q7+3bp1s2Eauukfmaf7f1okH8/qZj11DGGzZs2cggUL2rH9+/cPuN6pnXe3P1NKdDiG1jf5pvUP5fN9o3qH8vn2WLdunX1h0M/bqVMnG5fqdv3Ses7XpVLnzHC+A0XgZnInTpxwPvroo3QdrB3sev3www/Oxx9/nOJYO/0D0j/ElAbMf/XVV86GDRuC+gfhZj1///13mxTgyy+/dLLrued8u3u+s+I5PxHCv983w2pBAAC4gE5TAAC4gMAFAMAFBC4AAC4gcAEAcAGBCwCACwhcIETMmzfPOyH7HXfcIc8//7xNpJ8VP+cDDzzg+vv+/e9/lyeffNL19wU8CFwghNx11122Wsk///lPWbhwofTv3z+jqwQgSAhcIIRo67ZkyZISHR0tMTEx8s4778jPP/+c0dUCEAQELhCiPAtjf/311/bvli1b5J577rEFuHWhbM/i27p26J133uk9Ttcm1ZVb9u/fb/vr16+3yfj1uEaNGsn333/v1/vrGqW6GEXhwoVt6TSdKF7pZe9nn31WChQoIOPGjbNJ62+//Xb53//+Z5dsO3fubCvA6KTyzzzzjC0w7g9dmk3XQi1evLgMGDBAfvvtN3v8qaeestfx0M9TqlQpW0VG6ZcSXSxej3vhhRdsMXSl7ztw4EBbrlLrqPUDMhKBC4QoXatTQ+Snn36ycOnUqZN06NBBfvjhB2nSpIkMGzbMnqet4YMHD8qBAwdsf82aNVKlShWpVq2a7Xfr1k169uxp5Rq8o0ePvul7ayjr6w4aNEi2bdsmX331lbz22mvecg3voUOH2n3Rf/3rX7Z+7BdffGFlH374obz88suyceNGWbt2rZXfjL5Hjx49ZMKECfY6cXFxMmLECCt79NFHZcWKFd4gXb58uTzyyCO2iszmzZvty8DkyZPls88+s2Xe9FK8mjFjhqxatcrqoWvc+q5CA2QEAhcIYdqa9ASNLh2mHamOHj1q64Z6AjYyMlJatWol77//vu1rOD322GPe19DWrrb2tMU5a9YsC6Wbeffdd6V27doWZtp61HvJGqQeGuAVKlSQe++9Vxo0aCC33XabtyWrXwoefPBBW1xcW6YrV6686fvNmTPHFh5/+OGH7YvCpEmTrK762Zs3b26ta11GTff19TyfT1v37du3l7Zt20qtWrXkiSee8NZTg1nfXx/Xcv0CAWQkAhcIUb///rucOXPG7ulqa27KlCm2VqeGyLlz56zcQ1uBuoC3ht7q1auTBO6CBQtkw4YNdmxUVJTs2bPnpu/9448/WsDr5WTdtDV95MgRb3lERESSf33p2qUe+p6nTp266fvplwgNcI+KFSvKr7/+aouDa0tfQ1w/n7aE9Vw0bNjQW08NYE89p06d6q2nLlquvb19XxPISOEZ+u4AUqWLdCttReplUW0F7tu3T0qUKCEff/yxbN++3fvcdu3aWWtUO1pVqlTJGy6XLl2Sa9euyaeffmr/6qVVvc+6a9euG555vSerrUJtaSoNd30tfxw+fDhJkN566603PUaDUS+Ve+jP2jK/5ZZbbF+/QPTu3dsWKtcvF9ry99Szb9++MmTIENvXLxyee7t6nrTHt4cGca5cufz6DEB6oIULhBANNm0RfvTRRxaMTz/9tLXcEhISrFwvJWvnKb1/6rnUrLQDk17G1UvOvq1bDdmWLVvafU29F6zH6GM3ox2fPv/8c/nuu+8kT548NkxJLyP7Qy9p6xcCDfVp06ZJx44db3qMflnQOuqxeqlcP58GqSdYddzuxYsXZfr06Uk+X/fu3a2Fe/LkSWsJjxo1yjbPlxB9/927d1sHMG0hAxkqXRf/A+C3mJgYW/Q8R44ctoD2yy+/7Fy7ds3Krly54jz++ONO/vz5nbvuust5/fXXnfDw8CSLbL/77rt2/OHDh5O87tKlS51q1ao5ERERduymTZv8qs+aNWvs+brgd1RUlPPtt9/a456F2bW+TZs2tcf0X93v0aOH88QTT9hxuoj4gAEDrO7JP6fnuOT1rFKlii0urouX//rrr0nKn376aTsvyc2bN8+pVKmSU6BAASc6Oto5deqUd0H0vn37OoULF3buvfdep3PnzlY/IKOwHi6QBeglWG2RakcjbQFnFG2VlytXznovB4vnXq72fNZLzK+88krQXhtwE/dwgSxAL59qKL333nuS1ejEHzrMScforlu3LqOrAwSMFi4AAC6g0xQAAC4gcAEAcAGBCwCACwhcAABcQOACAOACAhcAABcQuAAAuIDABQDABQQuAACS/v4f41p8zaug1coAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "application_train['DAYS_EMPLOYED'].plot.hist()\n",
    "plt.xlabel('Days employed')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1dd1514",
   "metadata": {},
   "source": [
    "我们可以看到，\n",
    "- 右侧还是有很多人的异常数据集中分布\n",
    "- 由于max值很异常，导致左边的正常数据缩成一团\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3714f032",
   "metadata": {},
   "source": [
    "需要对这些异常数据的人观察，看他们target如何"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3fb7e9cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The non-anom people 违约 on avg:8.66%\n",
      "The anom people 违约 on avg:5.40%\n",
      "There are 55374 anomalous days of employment\n"
     ]
    }
   ],
   "source": [
    "anom = application_train[application_train['DAYS_EMPLOYED'] == 365243]\n",
    "non_anom = application_train[application_train['DAYS_EMPLOYED'] != 365243]\n",
    "print(f'The non-anom people 违约 on avg:{non_anom['TARGET'].mean() * 100:.2f}%')\n",
    "print(f'The anom people 违约 on avg:{anom['TARGET'].mean() * 100:.2f}%')\n",
    "print(f'There are {len(anom)} anomalous days of employment')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6693806e",
   "metadata": {},
   "source": [
    "这样看来，异常这些的人 违约率更低！ 😊\n",
    "- 不能随便删除这些行！\n",
    "- 用空缺 `np.nan`代替，是个**安全**的方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "32c83fc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 我们还创建了一个辅助的列 标识这个字段异常的行\n",
    "application_train['DAYS_EMPLOYED_ANOM'] = application_train[\"DAYS_EMPLOYED\"] == 365243\n",
    "\n",
    "application_train['DAYS_EMPLOYED'] = application_train['DAYS_EMPLOYED'].replace({365243: np.nan})\n",
    "application_train['DAYS_EMPLOYED'].plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99d02bc7",
   "metadata": {},
   "source": [
    "现在看起来好多了呐😊，此外，我们创建了一个列，表明这个字段最初是异常的。（后续可能会进行均值等代替）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d791943",
   "metadata": {},
   "source": [
    "对train的操作，也做到test上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2af5396e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "application_test['DAYS_EMPLOYED_ANOM'] = application_test[\"DAYS_EMPLOYED\"] == 365243\n",
    "\n",
    "application_test['DAYS_EMPLOYED'] = application_test['DAYS_EMPLOYED'].replace({365243: np.nan})\n",
    "application_test['DAYS_EMPLOYED'].plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "107d21b8",
   "metadata": {},
   "source": [
    "### correlations\n",
    "- 类别特征处理后，现在都是数值列了，可以计算与target相关性\n",
    "- `df.corr()` 提供了快速的方法，计算相关系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "120c96fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlations = application_train.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1790b0f9",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bdb79487",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlations = correlations['TARGET'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "dd9f7f04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 positive features: \n",
      " REGION_RATING_CLIENT           0.058899\n",
      "REGION_RATING_CLIENT_W_CITY    0.060893\n",
      "DAYS_EMPLOYED                  0.074958\n",
      "DAYS_BIRTH                     0.078239\n",
      "TARGET                         1.000000\n",
      "Name: TARGET, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Top 5 positive features: \\n', correlations.tail(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1d26e408",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOP 5 negative features: \n",
      " EXT_SOURCE_3                           -0.178919\n",
      "EXT_SOURCE_2                           -0.160472\n",
      "EXT_SOURCE_1                           -0.155317\n",
      "NAME_EDUCATION_TYPE_Higher education   -0.056593\n",
      "CODE_GENDER_F                          -0.054704\n",
      "Name: TARGET, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('TOP 5 negative features: \\n', correlations.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d70b08fb",
   "metadata": {},
   "source": [
    "#### DAYS_BIRTH 年龄因素"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a90b8a04",
   "metadata": {},
   "source": [
    "最正相关的是`DAYS_BIRTH` 和`DAYS_EMPLOYED`\n",
    "- 实际意义，因为是负值，所以实际是负相关的。！\n",
    "  - 也就是说，年龄增长，违约风险越低\n",
    "\n",
    "可以做绝对值看看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "df368fa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "application_train['DAYS_BIRTH'] = abs(application_train['DAYS_BIRTH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "658b02df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(-0.07823930830982709)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_train['DAYS_BIRTH'].corr(application_train['TARGET'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23b37cdf",
   "metadata": {},
   "source": [
    "我们可以认为，年龄越大，违约风险越低吗？ "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cfd2f70",
   "metadata": {},
   "source": [
    "年龄分布直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a1d67d62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'AGE')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "plt.hist(application_train['DAYS_BIRTH'] / 365, bins=25, edgecolor='k')\n",
    "plt.title('age distribution')\n",
    "plt.xlabel('AGE')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdb54cc4",
   "metadata": {},
   "source": [
    "年龄分布是合理的。 我们看下分开target的平滑直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e38ef89b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1229cf8aba0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(\n",
    "    application_train.loc[application_train['TARGET']==0, 'DAYS_BIRTH'] / 365, \n",
    "    label = 'target=0'\n",
    ")\n",
    "sns.kdeplot(\n",
    "    application_train.loc[application_train['TARGET']==1, 'DAYS_BIRTH'] / 365, \n",
    "    label = 'target=1'\n",
    ")\n",
    "plt.xlabel('Age (years)')\n",
    "plt.title('Age distribution ')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46e09771",
   "metadata": {},
   "source": [
    "可以看到， target=1的曲线，明显倾向于年轻人。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7883b3d",
   "metadata": {},
   "source": [
    "换个角度，从年龄段看看，看看每个年龄段平均违约率。 条形图\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ba941e56",
   "metadata": {},
   "outputs": [],
   "source": [
    "age_data = application_train[['TARGET', 'DAYS_BIRTH']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "eb980749",
   "metadata": {},
   "outputs": [],
   "source": [
    "age_data['YEARS_BIRTH'] = age_data['DAYS_BIRTH'] / 365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e20d5c0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bin age data\n",
    "age_data['YEARS_BINNED'] = pd.cut(\n",
    "    age_data['YEARS_BIRTH'],\n",
    "    bins = np.linspace(20, 70, num=11),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d3e6c3ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "age_groups = age_data.groupby('YEARS_BINNED').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4286b6c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '违约概率年龄分布')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vXGH85TQnAS+ID5PhLWHe4VEgHye2/TUJrXjdmCOxeTcYJRikfJ+MhsxGRoMQ/wOFm9suiXk1w8kp3kz/B+r0fVgip3wEQKFSDslqndUz3pBgvwa/DXS8ygbv2saVT+yd3AKSE7mx8gwmQua1Z0Kwe6FvfBULCpHvxDNB7gJKrU6dOi7E4r0UrKBzWuHyPjL76U6IQUWMHwOE1TEKzPfQyIlEx9a98vPHy29S5Y+zf9674jm2fq+G3MCLROgqHiR4ejjuwWOPxwbo+RHP0wTeA5BX8iTGJ4aa39SMElgMvdyM4jAoSTLzkdEgxP+gKRIXPUIWKCSfeEbtPSt2lFi85k5kmuPaj9e0hxUmbaXxVqAQcEsTa2ZVihLghgEQXA2y0melxyrWN5by+BV/7CocLwMrYZQFCgBlgPseJcYKMDZGjbysDoOeDBQ8Cnz//fd3yXjIiJER61r34HbH/c9qmHHxMvFdlKlSZooxwRjiVWBMfaIdyX28H9c+yaIkqZKoh0cC4y0vgwHi5XwUJf7zmROEcdgQCgih4A3w4SevKHmMhMN4CaSEamjGhaFR0HbXvr8Dnoac4JhBbg2k8BxhyJG/AhwHDIaiyENQkmTmI6NBiP+RUxIeyYB5tT6G2BU8BgFKmYt0bqWXGAZBUEIkCRKLp9dCsN10vJwGHvOfgUs72NkSJRYLz5NTwSoWA8YrOcICeDJwv9OEye9lkBN4A/Bi0PAn6GUhj4GbVyAYCr4Hhfd4kBTId+CmDyqaeImogAeI44AhhFGH8s1v46Xcqke8Bye4sg9uVMbYYMycccYZ7piQL+GNOZ9bERtKCuJ7VeBFym8raW/QEd4BjMK8jIbcPA2MN7ITkiI8hYcokbk9IrOQ0SBEHnijIZ7xwCrZlyzG7hXBhZsEOt9pkQszCoPVN8qT1tEoz3g7RdKimMRClE1wvwav3IKeBpQAq3oaC9EwKLiRUzz4bj4fRYinwHcepFETsApFoYdxN8fbijnsajM2KTM3MEIwcOLB783tNxMKyCkcECS2JbU3Srj3XRAxovA6UDpJomQ8jwfJksG4PkYTuQBU5+S2JXpuYJwxzzge8QxBH+4hLAV5taqmZwNJs4SdEu2xEZmFjAYh8oCLcfAexU05IitP72VghRqbEQ8oCYwClCtKMj/u23g19T48EcxpQJGQA4Giz8lbEoSEQ8IQQLdBlB+/g3wGFFKwNC+npDbyGXBp+w2+/E6J8fAlnazq84qZ+x4KvIdOk94zg5JDWfvGXHyXv+VEThtWxdu4imNKL43Y54GkQxI9OY7kE5ALwGMYabFgdNx2221ubAhb8dkYf37MwjbMijVCfIkjeSo5JYgSAvMGbJiGUPH2NsnNq5YbReH1EemBjAYh8sCHF/w97nwUjFe8KLF+/frFfS8bL+W2+VJ+iedpAIyR3EoV/TbHhDFQdigMWkyzCiZr3+9TENyTwn9uPFDqvo8BRoPf9Cgevl8C5FSF4WGMufH53tiAJ5980vJLYTes8grU97mg5wHweRgGVJf45NNgeIDOojR5wjvBfhOEFRhr9jspCORC4NFifOOVlnoo1/UKPD9dJIP4fUfyi08gltci85HRIIo1JCrG6+iYU5MgD0lvhBZYmR9//PFZW2aHgQoMvpdyRXr5c5HPrX11UA5ufnUfFlzWNFsCDAZK6VBoKBnKSPGQUBmAhyF2YySvBFAmwbg3yYDBfThyA8MET4vfUTNd8J4lvDh0cKTvgoekzXircroqEnaiKoSEUMaM40uOSkFW4ySk+iZdGC14L3LCe70IgxQ0RyE/RgPHFfkwGHzb69y6eorMQEaDKHbgOvbtlmmqhMJk1UjiYbwVGhf72NgvTXty2ljJKxya9eAip1EOjYFILER5x27B7ZPk8sLvCZFTuV0sXqnhRfBua1oTo8AwPGjYxO8lDMBvozdC7ErR/8/KGdc8fQBQisUB71nyZZJ4EPyNJFevpL1xEcy/wPtEtQxJquRK5LeUkWNH8igeDf4m+dHv4ZET3lsUzIEpSqPB/04/rwj50K462OgpXidJkVnIaBDFCtzJKD4MAVZueAjovkd4Adc92fz0GiBZkTg65Ya47IlLs7pn1c6KmQtoMPMe5UCOA4qduDMXUsISsfs6+CQ1lAid/4iTh00IxCuRUyw6iPeI+G2qKV8k6Q0Pg99UCZCXizyeD8pKgwl2flVMl0PGCDc7JanxulTmRUHj5IXFK8BYpR4WP36EIziuOf2OeMeYUBHfj8eBNs6EOHwDrTDHmcROH/6idTnGbWx/D4xQdtbkeGOcosChMOEwDEnmdbxeIvHCdHhfCL8wtzC6MS5FZiOjQRQbcKfTUc/Hu31vBBLbMBxwtRIy4FZQaOKE0YDLnxUm5XlcUFE8XMwxFvITb/ZhCxSY7+yYV8meV3b+HmI3IMLbQQdLOhiyMo7N6ve9GVACGA24oAlfxEv2zAuvVIMr92QaDfGUehjYO4PqF4w8Vu94WDh2JFYyDhhvuONzWp0zvhhmGKNUteDdoadFTuCRoqU2jbG8gUJXTzprxgufYdSyH4jf+ROQkRBJQfG9G+Lhj19wPHPbg0JkJjIaRLGBskQa8rCyDzZTIqmQ8AQ3mh4RriCGzcUYNzRZ6WzNHEzMy4lge10UMcYJXouCggs8Nt8BRZIbvpdDUN6gwcDKG7nIL6AKhM+PjbfTmZExwqggCQ+lyWMFIZgLEluOmEi8Mg8aT/kBZR2G3OYFxgJeCkINPhk1t1U+3ieaSGGo0OI5t5U744hhgjFJqAAjFQOQhlmJwP/Ogo6nyAx22LBhQ2p8h0KkANy4XGx9k6H84Hd8RPGx2vKZ/n7nROL/RR3vJ1GRpDvkxWOBEiG+XhTQOwCXcqLL5dj0CWUGGGV5hVeKCnIKCAvQfMq77lMF+S2xJZ05gcwc5/xsTZ4MCEXgiSCURzhLFE9kNAghhBAiFGoFJoQQQohQyGgQQgghRChkNAghhBAiFDIahBBCCBEKGQ1CCCGEyCyjgY58LVu2jFuTTs05jWdoQsP2xDRTKWgtMRvLUBIWe/NbBQshhBDFlbRo7jRs2DC3y13jxo23e+7SSy91LXI7dOjgGqPQsvXxxx937W/ZaCY/0PqXrmw0ZMF4CHLUUUdZOkADFhoS0bkuXivYVCP5NIapJupzMB1klHyZP4ZpaTTQRIeuevRdj7eLIE1j2LVvzJgxrkmN3/wHj8QLL7zgOqrlp5HM3Llz3X3Xrl2dxyJdSXa73vwi+TSGqSbqczAdZJR8mT+GaReeYCdCDAO6udEmNZZy5co5w8AbDB42HeJg+D3e82M00NEvnQ0GIYQQolh6GtiKl53ecuqlzu5v3GJh1zW2Ms5vq2B28aNHP21n6bnPFsT0c2fHufxsMiSEEEJkIpE2Ggqyo96sWbNs3rx5Li8huElPmFDI/Pnz3f4BLVq0sE6dOtkXX3zhdj9kAyOMlzJlyuT6GWE2NErWJj057byXaiSfxjDVRH0OpoOMki+zxjA/ORVps/eEr5xgA5+coJICLwEJjRgO7GgYFnaiI9myWbNmVrdu3azHJ0yY4HIcHnzwQZcvkRvsipiOMSohhBDFkxIlSuQrJJ9RRkOPHj1s3Lhx9uyzz9oZZ5xRJN/LzobsTte8eXPndUgHT8PatWutcuXKtvPOO1vUkHwaw1QT9TmYDjJKvswaw/x4GiIdnsgPI0aMcFUW9HMoKoMB2DaYAf3+++/zfG2UymaYhFGSJxbJpzFMNVGfg+kgo+TL/DFMq+qJsFBhQUMnvBE33nhjgT+jXbt2zrMQZNWqVS7cUaFChSKSVgghhEhP0t7T8P7777ukxTp16tjw4cOdZ6CgvPnmmy68QaMo+Oeff2zgwIHub5Ijk8Eeo1YVwaeQsLm+wO/e0KVqEcgghBAi00hro4E+DJ07d3axofPOO89effXVbM83aNDAatSo4f5+7rnnXG5Co0aN4n4WOQv169e3a665xqZPn+48CzNmzLCFCxdakyZNnBdCCCGEKM6ktdFAeaVv4DRgwIDtnn/kkUeyjIYrrrjCWrVqlaPRQKkllRJ33HGHaya1efNm23///e22227Ld/mmEEIIkYmkjSaMVzVBJ8gNGzaEen+Y19Fy+p577nE3IYQQQmRgIqQQQgghEo+MBiGEEEKEQkaDEEIIIUIho0EIIYQQoZDRIIQQQohQyGgQQgghRChkNAghhBAiFDIahBBCCBEKGQ1CCCGECIWMBiGEEEKEQkaDEEIIIUIho0EIIYQQoZDRIIQQQohQyGgQQgghRChkNAghhBAiFDIahBBCCBEKGQ1CCCGECIWMBiGEEEKEomS4lwnx/+wxalUhh6OMma0v8Ls3dKmqwyGEEClAngYhhBBChEJGgxBCCCFCIaNBCCGEEKGQ0SCEEEKIUMhoEEIIIUQoZDQIIYQQIhQyGoQQQggRChkNQgghhAiFjAYhhBBChEJGgxBCCCFCIaNBCCGEEKGQ0SCEEEKIUMhoEEIIIUQoZDQIIYQQIhQyGoQQQggRipKWBvTp08c+//xze+2117Z7jseGDBliS5cutZo1a9rgwYOtadOmBfqen376yQYNGmRvvPGGbd261Vq3bm133nmnlS5dugh+hUgWe4xaVchPKGNm6wv87g1dqhby+4UQIppE3tMwbNgwe/LJJ+M+N3HiROvYsaP9888/dvPNN1utWrWsTZs2tmjRonx/D0bCueeea88++6wzFnr37m2vvPKKXXnllUXwK4QQQoj0J7Kehj///NOuv/56Gzt2rJUvX36757ds2eKex7swZcoUK1eunF1xxRXWsmVLu+GGG5zCzw/Dhw+3//znPzZ06FC75JJL3GNHHHGEMyQuvfRSa9y4cZH9NiGEECIdiaynYfbs2TZ16lSbNGmSHXzwwds9P3PmTFu3bp117drVGQyw4447OgU/a9YsW78+f+7ll156ySpUqGCdOnXKeuykk06yAw44wCZPnlwEv0gIIYRIbyLraahdu7Z98MEHtvvuu8d9fvHixe7+hBNOyPZ4vXr1XLhi4cKF+cpt4PNOPfVUK1GixHaf9+mnn4YOcWQCUf8dxV2+P/74I9t91JB8GsNUE/U5GDUZS5Uqlf5GQ6VKlXJ9fsOGDe6+Ro0a2R6vWLGiu//uu+9Cf9dvv/1mv//++3af5T8Pz0UYVq9ebX///bcVPgkvtaxYsSLSMqa/fEXD2rVrLcpIPo1hqon6HIyCjCyUyQdMe6MhL/AmQJky2RWE/3/jxo2F/iz/WNjPqlKlihWegmftFxX77bdfpGVMf/kKBysTLjSVK1e2nXfe2aKG5NMYppqoz8F0kTGjjAbKIMlhYGVfsuT//4xt27bl20XMZ+2www5ZxkMQPi/sZ+XHxRNlov47JN9/4UIT5bGQfBrDVBP1OZguMqZFImSYVT1KfuXKldkeJzkSfHJkGDA+sPbiuZX5vPx8lhBCCJGppK3RQDkkfPTRR9ke//jjj9393nvvne/Po2IjFj4vv58lhBBCZCJpazTUrVvX9t13Xxs5cqT99ddfWY+PHj3aJXbEVlXkxZlnnmnLly+3N998M5vBQKOognaYFEIIITKJtDUaCCn069fP5s6da5dffrm99957du2117reDm3bts3WEOrVV1+1t956K9fPo5MkPRloEDV+/HjXH+Liiy92sSbf7EkIIYQozqRtIiRcdNFFbr8I9pugOROcdtppbi+KIHSIxIg45ZRTck2GnDBhgl122WXWrVs399gee+xhTzzxhOsZIYQQQhR30sJoiLdRleeaa66x9u3bu1DCPvvs48IWsdDoKQzUqr7zzjs2Z84c1weiQYMGznAQQgghRJoYDWEaQbVo0aJIPovSS4wFIYQQQiTBaKAZ0q+//mo77bSTCwugiIUQQgiR3hRZIiQ9E/r37+9c/Ow8edhhh7lcAMoV2Wr622+/LaqvEkIIIUSUPQ0///yzDRgwwLp37+4Mglgee+wxt700lQvHHnusqzqgWyMNk0gmJLmQLayFEEIIkeFGw5577ulKEO+55x7bvHmzK01kV0jPN9984+4vvPBCq1+/vu2yyy7O+8AmTiQyLlu2LDG/QAghhBDRy2nAGODmvQeUNmIkUL3Qs2dPmzx5sp199tnbvY+chocffrgo5RZCCCFEOiRCsovf7bffbr/88ouNGTPGzjjjDDvppJOcR2Hp0qXOq8B20yRCsqdDo0aNrHr16kUvvRBCCCGiazRs2rTJPvnkk6yNoQ499FCbOHGizZw503r37u0SH3v06BG3X4IQQgghionRQDvmrl27uq2i/RbUUKZMGRsxYoS9/PLLzqB45JFH7IcffnDtndnTQQghhBDFrOSSkkq6Lr7yyiuuAyM3/iYEwT4QUK9ePWdAUE0xb948F7Z4/PHHbcuWLYn6DUIIIYSImtHA/gzkKqxZs8YlQ3Ljbx7juSBVqlSxQYMGuTyHkiVLWq9evYpadiGEEEJENTzxwAMP2KWXXurCDkHwNAwbNizuewhdsAkUNyGE2R6jVhXBMJQxs/UFfveGLlV1KIQQiTUaGjdubIsXL7bly5dnJUJWqFDBdYBkq2ohhBBCZC75rp7AONh///3dTQghhBDFB7kHhBBCCBEKGQ1CCCGECIWMBiGEEEKEQkaDEEIIIUIho0EIIYQQoZDRIIQQQohQyGgQQgghRChkNAghhBAiFDIahBBCCBEKGQ1CCCGECIWMBiGEEEKk1mj4448/7Omnn7YvvvgiUV8hhBBCiCgaDT///LO9/vrr9uuvv+b4mmXLltnKlSvd33/99Zf16tXLPvroo6KRVAghhBDpYTR8/fXX1rFjR2c8TJs2zYYNG7adZ+Giiy6y1q1b29atW22XXXaxbdu2uXshhBBCFCOjoVSpUu6+TJkyduWVV9qQIUPs888/z3r+1ltvtcWLF1vTpk3da0uUKPHfL9hRaRNCCCFEJhBao++0007uvly5cjZy5Ej3d/v27W39+vUuBPHYY4/ZiSee6IyJbF8go0EIIYQoXkaDV/4lS5a04447zu677z777rvv7O2337ZffvnFqlatak888YRt2LAhkfIKIYQQIkUUOHaAl+G1116zdu3aWfPmzW3evHk2c+ZMO+qoo+yzzz4rWimFEEIIkXJKFvSNL7zwgp1//vnu799++81VSjz33HMul+HTTz+1ww47zBLNt99+a0ceeWSur8mP56Nnz542ZsyY7R6//PLL7Z577imQjEKkI3uMWlXITyhjZusL/O4NXaoW8vuFEJExGqZPn25du3Z1YYnOnTvbU089ZePHj7edd97ZzjnnHOeFSAYVKlRwIZF43H333fn+PLwlJ598svOeBDnwwAMLLKMQQghR7IyGv//+O+vv3XbbzapVq2Z9+vSx3Xff3a3E8TCsWLHCVq9ebcli11133U7Bw9y5c12JaDyvQU5s3LjRvvzyS+vevXvczxRCCCGKOyXDhABIenz55Zfd///884/LW3jnnXfs3HPPdR6H4cOHW7du3VzvhsmTJ9sVV1yR9X68ECRL0rMBw2PLli3OK5FIbr75ZpesedZZZ4V+z8cff+x+W6NGjRIqmxBCCJGxRsO6deuyVuw77LCDPfvssy5PgPh/mzZtnILGSNhjjz2sfPnytmrVKpswYULW+ynHDHaF5DMSCQbKhx9+aFOmTMnX++bMmePCK3379nUGBEZOgwYNrH///nbMMcckTF4hhBAiY4wGEg1vv/12O/zww12+wgMPPOBKLQ8++GD3fK1atax27drWqVMn6927t+25557O24DSbdKkiTMqqK7gf1pL42lIJHg7UPJ4GvLD7Nmz7c8//3TGz8CBA13/CfpRtGzZ0hkiderUyfMz6ISZCUT9d0g+jWFhoHtt8D6KRF1GyZdZY+ibNxaJ0UBfBjpALl261HkJnn/+ebv66qudkYBR8P3339sHH3xgHTp0sLvuussNAEaEbx+97777hlK4RcGCBQtsxowZ9swzz+T7vYQySOo8++yzsx7Dk9KwYUN76KGHXAgmL8jnCOZ+FDzrPLWQmxJlGSWfxrAoWLt2rUWdqMso+dJ/DMlHZPGfsOoJPpz8Btz2I0aMcIYEF/FRo0a5fAAU58KFC1Pi0kexV65c2c4888x8v/eSSy7Z7rGaNWs6w+jdd98N9RlVqlSxwlPwMrWiYr/99ou0jJJPY1gYWNhwoeZaQUgyikRdRsmX+WNYpCWXdIekpHH+/PmueoI+DVRUXHvttc6ln+i8hXiw+ybGDJtq+X0vigL22iCvg9CFb6VdFC6eKBP13yH5NIZF00dic+T7SKBMojzfJV/mj2GRdYQkLEE44sUXX3Tll+Q9EIYgCfHoo4+2ZPPqq6+6vhG+4VR+WL58uSuz/Oqrr7Z7jkZV5DnkZTAIIYQQmU5oo4FERp+ERmnipZdeam3btrUff/zRxfHHjRtn1113nUt2TAUkX1aqVKlAYRGMAhpWDR06NNvjeE2WLVtmLVq0KEJJhRBCiPQkdHjCGwOEAejbQPIjjZAqVqzoHidZ8LzzzrMbbrjBtVz2RgYGRqJBNhIgmzVrFvf5adOm2aZNm7IlOQah4oPfQmUIHpRDDjnEJX5OnTrV5SncdNNNCf4FQgghRAYZDb///rszBAgBoKRZ0Q8ePDjredovk4DI6pwwhe+qmIxyEnosYBTk1Jjp3nvvtc8//zxHowEoDcVTQXInG2/tvfferv8EXS+9YSSEEEIUZ0IbDSjOfv36Wbly5ZyCXbNmzXavueWWW1wr5nr16rnEQfD3iQRjIbeNqdiNM0xyZ48ePdxNCCGEEIUwGui3QJllbuWFlGOSCEn1Ak2czjjjjBDlcUIIIYTI6K2xc8KXO7KZFC2nhRBCCJEZFLjkMr/cf//9riwyCi0zhRBCCJEgo4GKCXoY5FROiUeBCoWcYJvqW2+91S6++GK3M6YQQgghMtRomDVrltWvX9+VI8bjkUcecV6EnNhnn33chlAXXHCBzZ07t+DSCiGEECLaOQ1+86mcWl3yvH8NsAsmG13RHpMcByoTKlSoYKVLl05K3wYhhBBCpMhoQOnTo2H8+PGueyLGAImO7DvBnhP0cAhyyimnuE6RsfAZRxxxRNFJL4QQQohoVk8MGDAg2//BjakwEsh9oMnT5ZdfHvf9GBu5NVgSQgghRAYYDRgIbBGNh4EKCPag+Pnnn+2nn35ym1VhMEyYMMGeeeYZF4bA23DRRRdZ8+bNE/sLhBAighTNTpzrI78TpyhehEqEZEMqoFHTgQce6P7HYMA4qFq1qstnYPOqb7/91nVf7NSpk2vyxGPHHXdcrkmSQgghhMggowGvAvkI3MPrr79ubdq0sbPOOsvtN+G3lGb7aFo633nnnbZo0SIbNmyYbd682ZVadunSxe1bIYQQQogMDk+QvPj000+7JEhgN0tKMMuWLevCFr/99psrq4zNX2jcuLGddtppLpHyqaeesiVLltjEiROtcuXKifk1QgghhEit0YBBMGTIEHvwwQddKSVGgIcSSjalooKCMATbTG/cuNG9nh0veQ6Pw7///W/r2rWrtWrVyqZMmWJ77bVX4n6VEEIIIVLXRpqtpUl6JAmSbahp1rR+/Xr3PyEInvcdI6+99lp77LHHnEHRrFkzlxDZokUL1zmSz+D9QgghhMjgvSfYEpt20WPHjnU5Dg899JD7f+jQodleN2jQIJs0aZLzTLz99ts2evRo9/iJJ55o06dPtxo1ahTtrxBCCCFEtIwGPAsrV6507aTJZfjhhx9sxYoVWY2cVq1a5f4nfME22ccff7w1bdrUbrjhBlu8eLF7DdUWQgghhMjw5k7XX3+9uwGeBkorPfxPY6dYeBzYd4JyTMo2hRBCCJHBRkP//v2tXLlyrqwyJ2jwhCeidu3aWY+tW7fOJk+ebBUrVnTvF0IIIUSGGw39+vXL+rtv377Og3Dvvfe6/xcsWOBKMimnrFatmqucCFZY9O7d270+uKmVEEIIITLMaKD745YtW9zGVJ7Vq1dndYmkYqJbt26ucVPnzp1dlURwTwrfs4FKCjwRlGZSlimEEEKIDDMali1b5ho50Z8BTwEGge8M6fMTKLukH8PDDz/sbrnB+0moFEIIIUSGGQ14GC688EK3URUeA6A5E/0WyG+ggsJDSKJhw4Z21FFHbfc5eCYwLPA2CCGEECIDjQZaPsd6D/A+kKNAvwZKLGfNmmVvvPGGTZ061W1UhSHB9tjsOVGqVKlEyi+EEEKIKPZp8BCOCO58STkliZCfffaZ/etf/7K1a9e6agvf1EkIIYQQxaxPg+f555+P+zgJjn369HFto+n82K5du8LKJ4QQQoh09jSECWnIYBBCCCEyi4QYDUIIIYTIPAoUnjjrrLNc+SXVFCQ6UllRtmxZd6Pro//b36jAKF++vFpICyGEEMXNaJg5c6Yrr6RhU27ENnk64IAD3PbYBx54YEG+VgghhBDpZjRAx44d7YEHHnCdIOm9wC2nvzds2OC20J4xY4Yr3xw2bFjR/gohhBD5Yo9Rqwo5YmXY+7jA797QRTseFyujwXsSCEeE2Yjquuuuc9tlf/LJJ4X5SiGEEEJkUiLkN998E/fxMmXK2HfffZeIrxRCCCFEFIwGwgq0jc4LchwGDRpkDRo0sDlz5mz3PF0iX3vttYJJKoQQQojohyeuv/56+/bbb+3ss8/Osf/C999/73a5nD17th188MFuX4pYrrnmmsJLLIQQQohoGg2bNm2y4447zjZu3Gjjx4+35557zuUyYCTQSrpEiRLudRgTn376qV155ZU2cODAuEZDImATLFpZ+503g5B8Wa9evdCfhafkwQcfdC2x+X2NGjWyoUOHWo0aNYpYaiGEEFFO1AQlaxZwl0sUJ7e5c+faqFGjbOLEiW5zqmOPPdbtMdG2bVu75557bM2aNc4bkUwWLVrkDIYbb7zRqlWrlu25/Cr7vn372siRI61FixbWrVs3Vx56zjnnuA256EUhhBBCFGfyVT2BkcDttttusyeeeMIef/xxp1zHjRvnVuj169e3ZEPuBF6Nq666ykqXLl3gz/n444+dwdC6dWt76qmn3GNt2rRxngrKRPv161eEUgshhBDFpHqC7o7sZjlv3jy3Emdzqvnz52eFC4LQpyGR4P1AsRfGYIBJkya5+169emX7nRgRkydPLrScQgghRLHs08CW11WqVLHmzZu7VTkhCR+WWLhwoUuI5DV169Z1zz/yyCO2ePHi7TpEFpWngVJO8i6WL19uFSpUsFatWrnkzT322CP05yAfu3Qefvjh2R7HIOG3sB04bbNzI15eRToS9d8h+TSGqSbqczAdZIy6fImW8Y8//sh2n0rYDiJhRgOllzfffLNt3rzZVUOQSxDMY9h7771txYoV7obRgBImqfCLL76wQw45xIoScijo+4BHgO24q1ev7sIMjz32mPNAvPXWW6E/i66V8XIgKlas6BIkV65c6ZpT5cbq1atdcmjhk3dSC8cuyjJKPo1h9OdgOsgo+Qo/hoVn7dq1lkooZshLtxXKaNhrr72c94DEyPvvv99VKAwfPjxrP4lKlSo5j8KPP/7o/vcrdzpBFrXRgIImTEIiplf4Xbp0cX/ffvvtLmzStGnTUJ+FYYDHIhb/GNUjeYH3pfAULtu3KKAaJcoySj6NYfTnYDrIKPkKP4YFBw8DBkPlypXz9GJHiVBGA4YBP6px48bu/5NPPtndSIDs0aOHNWnSxAYMGGDdu3e3kiVLOjc/K3MgXMANo6FDhw5FKvy+++7rwhCxEB7BaHj33XdDGw3kRMTbgGvbtm2h3VT5cfFEmaj/DsmnMUw1UZ+D6SBj1OVLlow7/2+36IxKhMSrQAiCksTgivuYY45x9yRDEqY488wzXRMoXPpLly7Net1BBx1kCxYssGThvQOERcKClyCeK2rdunXuPsz+GkIIIUQmE8pooP0zSY80PcJQGDNmTNZzhCLuvvtue/XVV92eE8cff7wLTSxZsiTrNfvvv7/rp1D4eH928HTg3YiFJlOAhyMsRxxxhK1atWo7w4EcCZ+rIYQQQhRnQhkNVCPQDfKjjz6yE0880SVAnn766a7iAIjL0D3x/ffft5o1a7pkya+++sp+/vln9zw5Br///rurrChKtmzZ4gwH9sbwEEagjwTQpCksLVu2dPfkZwQ//8UXX3R5GfkxQIQQQggr7n0a8BjQAIlNp3D9k3QIy5Yty0qSpFskrvwjjzzSKV2gqoHcgNdff71IhW/fvr0zSLjv2bOn605J3gUdHHnshBNOyCrLpP31X3/9letvI6GSRk54TsjjOP/8853XJJ43QwghhChuFKhPAz0RqEygYyIhiR13/H/bgxU5Hodg+SLlHCQtkiValNDaGUOEnTUJj5CNymZZdKekBNND62s8EngTypYtm2vuBsbN4MGDs0pRevfubRdeeGGRyi2EEEIUG6MBaJyEV4HMz1122SXbc7H9DsgXIM8gEc2dSGAMhhTiQd8GbmGMED6rT58+LpGTsAReEiGEEEIUwmjIT0VB0BORDtSuXdvdhBBCCPH/JE2b//bbb0ktuxRCCCFECo2GSy65ZLvtpz3kE7z33nuuXTQtpmOh6RMNoZo1a2a//PJLwSUWQgghRPTDE4QZfIdEoFqBx0gcZO8Gmjz5vAX2gyCXYeDAge6e7atJQqQzJGWRuSUkCiGEECLNjQa8Bdw8H374Ybb/geRBDAt2nKSN86ZNm7I2jqJTI16GdMtxEEIIIUQ+wxOUIAYVPlUTsRttXHfdda7ZErtPHnrooTZ//nxnOPjXQyKqKIQQQggRIaMBgwHDwROv3DL4Wlov8xrfUtq/V54GIYQQIsPDE/E8BLl5Ddjgql69elmGhTcWYkMaQgghhIg++dbe+QktNGzYMNv/fsMqGQ1CCCFEBhoNt956q9toqnTp0q6rI/kJnTt3ds9RXknIgf/ZkArovDh58mSXDPnPP/+4/R7+/PNPt8mVNxpi8yCEEEIIkQFGw9dff21vv/12tscwCnL6f+7cuXG9Ex06dHC9HDAYlAgphBBCZKDRcPPNN9v111/v+izQj2HmzJk2ZcoU50lg90dCDewMuW7dOjvjjDPs9ttvt1NOOcU9j2eBG8YC+1Fs3LjR7VkhhBBCiAw0Gtgy2rPbbru5ZMYDDzzQ/U/IgvAE/+++++7usX322Sfuvg0YDjR1Ouigg4r2FwghhBAieomQ5Chw8/gcBf9c7GNBVq1albVNthBCCCEy3GggqTFoFJDg6NtK+8eDRkWQxYsXu/sjjzyyMPIKIYQQIh2MBowEbh7yE3z5pH/cV1HEwp4TJECedNJJhZNYCCGEiAB7jPqvB73glDGz9QV+94YuVS3SRgNtodni2vPCCy9kMyDuvvtuO+aYY+K+9/XXX7fq1au7zauEEEIIkX7ky2jo3bt3js9hNFx++eVxn5s3b559/vnndt999+VfQiGEEEKkn9EwZ84ce/bZZ10zJ9pDw4IFC5xRQKvonDo9jhw50sqVK2cdO3YsGqmFEEIIEU2j4amnnnKKH2+BT3Z86KGHsrbHvvHGG3N9P8mSlGf+9NNPbhMrIYQQQmSo0TBu3DhX/UC5ZPv27a1Vq1bZDAJutJvOiRUrVtiIESPstNNOs2eeecbq1q1bNNILIYQQIlpGQ79+/VxHyCZNmsQtw6Qq4qqrrsq1PfRee+1lQ4YMsVNPPdWmT5/ukiqFEEIIkWFGQ/PmzXN8bsuWLc7TQLdHQhA50b9/f/v111+d8SGDQQghhCgGW2PHcvXVV9ull16aq8HgyS2EIYQQQogMNxp23XVXdxNCCCFEZrNjqgUQQgghRHogo0EIIYQQoZDRIIQQQohQyGgQQgghRChkNAghhBAiFDIahBBCCBEKGQ1CCCGEKD5Gw9NPP23169e3ihUrWtWqVa1Dhw62cuXKfH9Oz5493Rbfsbe+ffsmRG4hhBCiWDV3SjUPP/yw3XTTTdasWTPr1q2bLV++3B577DH78ssv7YMPPnBbdoeFLb5PPvlka9euXbbHDzzwwARILoQQQqQXaW00rF+/3gYPHmxXXHGF2wzLQ0vru+++26ZNm2ann356qM/auHGjMzS6d+++ndEghBBCiDQ3GjZv3my9evVyHoYgderUcfc//PBD6M/6+OOP7Z9//rFGjRoVuZxCCCFEJpDWOQ01atSwPn36WNmyZbczAODwww8P/Vlz5syxnXfe2eUvVKtWzfbbbz87//zzXchCCCGEEGluNMRjw4YNNnr0aOdtqFevXuj3zZ492/7880+X+Dhw4ECXFLlw4UJr2bKluxdCCCGKO2kdnohH//797eeff7ZnnnnGdthhh9DvO+uss6xz58529tlnZz3Wpk0ba9iwoT300EM2fPjwPD9j69atlglE/XdIPo1hqon6HEwHGaMuXzrIuLWI5CtVqlTxNBrGjh1r48ePtyuvvNJOOOGEfL33kksu2e6xmjVrWpMmTezdd98N9RmrV6+2v//+2wpHGUs1K1asiLSMkk9jGP05mA4ySr7MH8O8KVGihNWqVcuKndHwySefuKTIxo0b2y233FJkn1umTBlbt26dC13stNNOub62SpUqRfCN6y3VkM8RZRkln8Yw+nMwHWSUfJk/hkVPRhgN3333nbVv39723ntvGzNmjJUsmb+fRW8Hwhp33HGHHXDAAdme+/TTT12eQ14GQ35dPFEm6r9D8mkMU03U52A6yBh1+dJBxlIpkC/tEyHXrFnj8hCI7UyYMMH22muvfH8GRsH06dNt6NCh2R4fOXKkLVu2zFq0aFGEEgshhBDpSdp7Gi6//HLnKSCJkRAFt2BOAu2lafK0adOmbEmOQfbcc0/X1OmBBx6w77//3g455BBbunSpTZ061YUc6DgphBBCFHfS2migSmLmzJlZ+09wC0LIAqPh3nvvtc8//zxHowFuvvlmq1Spko0YMcJ9JqEOOk3SB4I9LYQQQojiTlobDXgI6MuQF6+99lqer9lxxx2tR48e7iaEEEKIDMxpEEIIIURykNEghBBCiFDIaBBCCCFEKGQ0CCGEECIUMhqEEEIIEQoZDUIIIYQIhYwGIYQQQoRCRoMQQgghQiGjQQghhBChkNEghBBCiFDIaBBCCCFEKGQ0CCGEECIUMhqEEEIIEQoZDUIIIYQIhYwGIYQQQoRCRoMQQgghQiGjQQghhBChkNEghBBCiFDIaBBCCCFEKGQ0CCGEECIUMhqEEEIIEQoZDUIIIYQIhYwGIYQQQoRCRoMQQgghQiGjQQghhBChkNEghBBCiFDIaBBCCCFEKGQ0CCGEECIUMhqEEEIIEQoZDUIIIYQIhYwGIYQQQoRCRoMQQgghQiGjQQghhBChkNEghBBCiOJjNCxevNjOP/98q1atmh1yyCH26KOPFuhz/vnnH3vggQesbt26tvfee1vr1q3tm2++KXJ5hRBCiHQk7Y2GpUuX2hlnnGHz58+3a6+91hkPN910kz311FP5/qy+ffvaoEGDnOHB/Y8//mjnnHOObdmyJSGyCyGEEOlESUtzbrzxRqfUp02bZocffrh7bOedd7aBAwdau3btbNdddw31OR9//LGNHDnSeRe8wdGmTRurV6+ePfzww9avX7+E/g4hhBAi6qS10bBx40Z75513rEWLFlkGA1x++eV23333uedatWoV6rMmTZrk7nv16pX1WPny5Z0RMXny5KQZDeV3ib7zJ+oySj6NYRTQPMzs8UsXGYuaHTZs2LDN0pS5c+faKaecYkOGDLErrrgi23OEGC688EIXqgjDeeed50Icy5cvz/b4M888Y9ddd519//33zoMhhBBCFFfS2kzasGGDu69Ro8Z2z1WsWNG+++67fH1WTp9DguTKlSsLKa0QQgiR3qS10YAyhzJlymz3HI8RvsjPZ+X0OZCfzxJCCCEykbQ2GkqXLp3NeAiybds227p1a74+K6fPgfx8lhBCCJGJpLXRUKVKFXe/YsWK7Z5bt26dlStXLl+fldPnQH4+SwghhMhE0tpoqFmzpu2222720UcfbZefsGzZMtegKSxHHHGErVq1ajvDgVJMyM9nCSGEEJlIWhsNJUqUsNNPP91efvllW7NmTdbjo0ePdmGFpk2bhv6sli1buvvhw4dnPUb/hxdffNGVc1aoUKGIpRdCCCHSi7Q2GoByyD/++MM1Ypo6dao98sgjdscdd9iBBx5ozZs3d6+ZM2eOPffcc/bXX3/l+Dn777+/tW3b1jVyuvvuu12zKLpL0hWye/fuSfxFQgghRDRJ6z4NHpo4XXnlla6XAhx00EH29NNPu14NgNIfN26cK5ssW7Zsjp+DZwEjZMKECVmeDFpTDxgwIEm/RAghhIguGWE0wG+//WYffvihU/SNGjWynXbaqcCftWTJErenBWGJ6tWrF6mcQgghRLqSMUaDEEIIIRJL2uc0iNwhj+Onn36K9DBFXUbJl/lj+Pvvv9uCBQucjH///bdF0ZP6yiuv2KeffurKwP/880+LGlEfw6jPwb8iLl9GbFgl8ub111+3Tp06WcOGDe2EE06wU0891YVdSpUqFZnhi7qMki/zx5Ak6osvvtjlQR199NF20kknuTJsSq3ZKXeHHXZIqXxvv/22kw+ZDjjgAKtbt64deuihVq1aNdfqfvfdd0+5jFEfw6jPwdcjLp9H4YkMhyRQqkdOO+00e//99+2LL75wrbGPPfZYNyk5sak0SeUJHXUZJV/mjyEb3i1evNhVTL311ls2b948V7aNAjzxxBPdjQRrFHQqLuLI9+WXX1qXLl3sgw8+sE8++cR+/fVXp5APO+wwq1evnhu/fffd15WHo6RTIWOUxzDqc7B7xOXzyGjIcDhhu3bt6qpCYO3ata4Z1ptvvmkzZsxwDa248Bx//PFuYjZu3Nj22WcfyZhGYxh1+dJBRuTjon311VdnPYZipgcMMnIBp/IK5czFm2RrmsvtsccehUq6DsvBBx/s5LvmmmuyHkNBv/vuu278kA9lgueB1f2RRx7pysgZw7322st22WUXK+5jmA5zsGuE5fPIaMhgvvnmGzvqqKPspZdesiZNmsR9DZ0z33vvPXvjjTeclbtp0yZnzdIYq0WLFu7iw0ldXGWUfJk/huyGiyKjkRtuYaAKKzbejIJGAXJPMzku4LiSvRseBUiYoKihSy3jRyk4q/V48pFDgIJBtlmzZrnyclaphDJ4r7/HqCiOYxj1OfhNxOULIqMhg1m0aJGNHTvWevfubeXLl8/z9TTJYvVC/BT34meffeaSm0aNGuU6ZibCLRZ1GSVf5o/h8uXLXTz5wgsvzHbRZQM7bnxfrAIkYY2LNwqQC/jPP/9sI0eOtHPOOcd23LFo88t/+OEHV06OYqVtvgfXv5cv9jtppT9z5kzXw4bVPgmUNK5r167ddr+lOIxh1OfgoojLF0RGg8iRhQsXupXN+PHjXZwtikRdRsmXOWPoFSAKLajU1q9f71byUZAPQwLZgkqDrra1a9d2XXFxa6eSqI9h1OdgFORTyWUG47f1jgVXZvC52Nf5/9msi6QlEq2Kq4ySr/iOIcotCIq4ZMmSTtnxHl/2yEo50edJWJmDBoNvm//qq69apUqVXLVFsuWJyhim6xz8OyLyBZHRkMHk5KLCTRh8LqfXkYBDyQ+JVMVVRslXfMfQKzav+GJl9cl7uIdRyIk8T2Lh+5GNuLZXvLEeBpSzlw9PQyrO46iMYbrOwRIRkS+I+jQUE3BREveaNGmSi32RjU3S0X777edWIdw4gYMnd9WqVe24445zCVWSMfpjGHX50kFGEtLIH3jhhRecwqtRo4ZrJc+NJEL+J1EPRehDAfXr13e/IxnyEfsmd2DixInu+8iep8wS2TAM6tSp45ILwYcBWrdu7R5L1jGO+hhGfQ7+GHH5lNOQweDawlKlXGfw4ME2e/Zs1xSmdOnS7sRmgy9KsZiUxDrPO+88l43rYVKyiklkuVbUZZR8xWcMScq7/fbbXVIf8WEuzOxBw43XoJx5nD4EJCV6UHyEAhJVNujle/755+3OO++0rVu32hlnnOGUBUYEY0jHSMaHsr1zzz3XOnbsmC3x0BsQiSJdxjDqc3BGROULIqMhg+FE5MJCPfSee+5pDzzwgFtx+BavtKMla5mVC81EsGp79OhhvXr1snLlyknGNBjDqMuXDjJ6+WiiU6tWLbvvvvucfN69T6Y65YyUw+EGplSQKoRbbrnFKleunDT5KMmjrPGuu+5y8esg8+fPtylTprjV6VdffWXNmjWze+65x61Qk0G6jGHU52CjiMoXRDkNGQyTkLIm6ntZfWCZMsEoieJGtnKHDh3smWeesWeffdbVST/44IM2efLkXJNzipOMkq94jCHlfigyVsGshr2yYwW38847u8fvv/9+e+2119wqj0oESuSSJR8llIwhngRvMLAy90oFg+LGG290PRoo26PU8oknnkjKOZwuYxj1OfhThOULIqMhwyFRilgYMcagGzA4yXCL0ZAF9ycrE6zcX375JWntSqMuo+TL/DFk5cYKGWUbxLv0fTkjcg0bNsytqB9//HHbuHFj0s4TDAZWmh6Usg9BYDzgnkY59+nTx5XdoVySuQFS1Mcw6nNwU8Tl88hoyGCYbCQdNW/e3CVO0dzFlzz57GsmpS/NwqLlYkMiDn3tJWP0xzDq8qWLjLiCW7VqZdOmTbP+/fu7FXNQfu8+BvZ1oAsfeQQovGTAmLRp08Y1arrooovcbpLBckaUic8HYF+HBg0aZK3+k0WUxzDqc3BbxOULIqMhg/EnKJvc4M4aMGCAcwvS6pUViJ+U3o349ddfu7gjm8oky3KNuoySL/PH0NO5c2c7+eSTnVufzobco/h8N0MfCkBh0/uAmvhkVSQAMuGi5rsZy1tvvdXFuVG8KBOvfGk1/PTTT7u2wsksA43yGEZ9Du4Qcfmyybphw4bkBUNEyiAmSjyRSchGKCTbsKEN2bi4FMluJpGK1csjjzxip59+umRMszGMunzpIiOtjMeNG+cUm8/4J5Od8kaSDMkboOyN1WBO+wQkEloHE9tmt0sUCm2HKbdk9Umm/eeff+5CGcS8jznmGEsFUR7DqM/BDRGXT0ZDMcCXW+HGYqc5Vif/+c9/7Ntvv3U7p/E4JzYlUJdddpmr95WM6TWGUZcvHWT08pEbwAZM9PanXn7JkiVuAygy2FnpsTlQ27Ztk9oBEoLufWTBQEA2xpIEOmTEgDjllFNcSWaiNqdK5zFMlzn4a0TlAxkNxRisWFyaWLKsUjjRcRUmuqY7k2SUfJk/hsiG+99vJMTfKL6gEk8llDOSABlloj6GUZ+DayMkn4yGYoZPnsppokXhJI66jJIv88fQZ6wHZUi1THnJFxUFly5jGPU5+E9E5ZPRIIQQQohQRMcsFUIIIUSkkdEghBBCiFDIaBBCCCFEKGQ0CCGEECIUMhqKOWTgrl+/3nWViypRl1HyZf4YkslOFz56I0QRavfZ6dLvWxBFoj6GUZ+D2yIin4yGYg6TkNavtKRNBcHNWLiocPGL3bEt1TLmRSrly4TxS6WMsWOV0xiyh8M111zjNgiK975E4PcZiMXv4xCEGn5aD9M+GoL7UhTnMcwPOo/DIaMhw+Di8cILL2y3CUzsRcT/T4OV+vXru81PwPeGTxbUGXtZqUemP72vPU6VjOk0hlEcv3QaQ8aKldv3338fdwy98mMTqPPPP9/at28f93cUFV6R0jmRLa7jrcqRLSgfspQtW9auu+46u/LKK7N9TnEcw3Sbg1E+j+OhPg0ZAhOIiUYvciz5N9980/UpD0IrUrqIVaxYMdvjdBfzu6klC/rPjxkzxvVRZ3c+bmyuU69ePdcalR3fki1jOo1hFMcvHcYw2BBn3rx59thjj9nq1avdY3x/hQoVrFGjRnbqqae6zYCCJKN5kh8/toy+77773FbYBx54YJbsKOYvv/zS7eVwwAEHWCpIlzGM6hxMh/M4N2Q0ZAi+LSs7o3HCcCLTspVJhstywoQJWfuucwLxOvrT77LLLkmT0Z/Mo0ePtrvvvtvKlStn1atXd21wkZPNd1hhISM7vZ177rl25plnutclg6iPYdTHL53G8KGHHnKbJbEREJs9we+//+5W9vT8Z9MgHmd/BHZrZNvnZI5f69atnQJ59NFH3fhxXFGAzz77rFvVIysthdn18oorrrD99tvPkkW6jGHU5+DoCJ/HufHffTZF2uO3TGW71H79+jl3Jdx1113u5Mbi5wTGon3ttdds5syZLsZ4ySWXJK39LCcK3HHHHW5ntmuvvdZq1qzpHuMkZtMddujjN7zyyitORizxPn36WOnSpa24j2HUxy+dxhAFjLLo27dv1mpz69atrsc/Gy19/PHHNnHiRDfWjOHgwYOdkk7W+M2ePduuv/76rG2jb7nlFrdrJMbEoYce6pQKW0uzmt6yZYsb32QpvXQZw6jPwTsifB7nhnIaMgAfv1y4cKHbBe3II490FxAs/REjRjhL/6WXXsraTnfatGlWuXJld0IxOZNhMHgZOSFYKRGb40ThIuNjsljV7dq1c9vCvv/++y7+OXToUGeRF/cxjPr4pcMYelgJM2a491F2jKGPubPiO+GEE9yFnK2JuVCPHz/eKedEx5Bjx+/oo492SmLz5s32/PPPW8+ePW348OFOtttvv93effddu+CCC9zqmfFMJukyhlGbg9vS4DzOCxkNGYBPlHnrrbeytqWFRYsWOau7Y8eOLs6I+4tJy772AwcOdK/hwpMMfAyOe3Zpmzp1qvufiwwyIxcXFJ80VaVKFXciH3LIIc4FmuikqaiPYdTHLx3G0MOOkPvss49NmjTJubIZQ8bVZ/17xYYyJNu/QYMGNmrUqByrGYpaobAdMrDC5DG2RuaYsyoF5OBxYt8YEritGfNkEtUxjPoc3CENzuO8kNGQAXh3F5OQyXfRRRdZ//79bfLkyS5hatddd3XPB7fPxcWJq5C66WRmW2NVn3LKKfbyyy+7k5UVCycIJxG/I5g1XLVqVWvSpElWfXIiSZcxjOr4pdMYciEmhk1Pgx49ethnn33mlJnPWve/A5C5YcOGTuZEj6Ff5bJ6b9q0qQ0bNsxatmzp8hgwELzCiJWPXAFCApAspRLVMUyXOVgzwudxXiinIYMgJkdiFG645557zpXwrFy50sU/gxcUJiMxR5KWfHYuzwVP9ESCa5WL3IMPPujcb8RFjz32WJfMhXtut912c68j65nscTKJgyd5cR9DYqFRHb90GEO+46abbnK5AFQpzJgxw8466yyXrU6lAqtjXNaAwiY0wHN+XBMNCoLb3LlzneEwffp0VzWxZMkSJ4df0XNPXH7NmjVuvJNdahnlMYz6HEyH8zgnVD2RQfisYWJluDRJnCKmeOONN1r37t2zlRsRZ8QNihuPpKBk7c3uv2fFihUui5lYJ0k+uFiRg9UALsUff/zRXTSxyP/973+71UAyZMPaj/IY+sxrFAUXw7FjxzrFEYXxA8YA1y9xZO4//fRT9/3Uy0dlDD0kv3Exxm1O0h4wftxISCPOzNzEhT1y5MjtyvaSAcpuypQp7li3aNHCunbt6h7n+JMsSfb9pk2b3EqaKoZkn8fxxhAvBIoPgyJVY+ivhbFz8F//+pebg36MUjEHt27d6uTCKMCQQa6once5IaMhw+Giw4mNFY1Ffdlll7lyKJQPK4XgRTyR5HQi+pjtrFmzXAyXZCQyxnHJ1alTx5WT+RVLMuSLJydjSBkUJy5uRC7cyR7DnMaPCwuuYZK6GENq+LlYctEhoSpZ45ebjLHzEIOxS5cubuWcrDH0WfG4oHFF4+4PwvFlRU9cm2PMHMTtz6qZVat3aydaPs6FSpUquXyBWCWIjDzH+LGKJk5PrJvxI1SQapAPQ4a5uHjxYvd7GEOUYDLGMC9IhmQekqhJX4lOnTq58yVZc/Cv/xkyJDQyPr169XLH0z/H8WTs/PgRXkn2dTAMMhoyBDKFcXERY+RkPemkk1zyUbAMCxcnMTROnGOOOca9JplwMaaEiAsjFxOyglmBxMLKCeWT7LrkoHyslihvQ/EGM6q58OB9YAxxt+JGTjReobBS45gefvjhub6eCxDGRDLLs8LIiEz0HkBxY+D88MMPzh2bzDHkmOJGJ9bNBdx7bmJhlcxFO1khOy8f5+TZZ5/tOjsyVvGMMVapzFHOZ+YnSjCZ8P3eTY7cyMc4cd6uWrXKGQd4Gng8WRUxOYEMPtzAPec45wf/v/feey5cQQUDOSTJOsZ16tRxHgauwYRPYhs4eXwviVQbW7HIaMgAcHfhdsNF6MFCpbabOFnwwpPTRTLRoGS5GOIqJHsZmVmBUPqErFjX3pjxNd/J6h+Rm3ysCjAgMMYofeMCQ5JVsiFcwoWY44db86ijjnKrS1rKVqtWLet1lGrRppcEOo63r1lPpYxk/QebD9Hoh2Y1vp1wslzqGHx4iyhbbNOmTZYhg1udmDxGDBd0wgCpmINePhQJBrU3Xt555x3nOmf+YWRzzPEwpAJkILHwzjvvdMfYjw+GNKESlDIGBccejxyNiZIJipZSRXpHYPT5ufX222+7fU0wtFDCGPwYjtwnew7WqlXLtfym8oTchXvvvdcZzz4fxcvi5UlmyC4Mqp5IY3xZE2U7GAy426hPfuqpp5zbi1AEZTrgJx0GA6/BFZtMUL7E7DAScAnSGAZ3NZnDN9xwg7uI43JlhUqMEc9JMlcpOcl38803O/m4iKOQaQxD0x0unsmECzEycc+4YNQMGjTI1aEjExdo6vWZCxg5rFKTaTDkJiOr4aCMwdbIkIw8EEBxoOj8d3MBp+EPRgL3JOtRHsgcpBERJGMOevkwDJDPt4cmU57zg/OanIVPPvnEJc/RopmKhWTPQSCkRPyd5D0/Pm+88YaTB+8hxxujhmvMVVdd5ZopJQOvcBkjehqQK+XnFtc6ri8YtXiaWKSQp3TxxRe7Fs7+dck6xmXLlnUGM/0i8ARjhC1YsCCboRCUJ0oGA8jTkMZ4rwHGAVm4ZDFzQgAWPzXcrJBRiD7mSZYw8bsnn3zSKcVE41ciXJhZHXFCe/j7tttucxfx448/3t3jtqZLGycS9cnFXb5YhgwZ4hQcx5be/lyc2RKZi44vGcNd3bx5c9fIhhVMcZfRx5JRvniUnnjiCRcfxuPA6pMLOOEAkgvZS4GVPn+zAuQ9iSYoH4YA5zFGPy2kWdF369YtSz7yVzh3yR1AQWPkJBOMec4L9sUgsZVYO8YB1xWMCW9kkcBH3gq5P4RNMWSTAYYgXlc8H7SPxtOKHMiNweorEkguZExRyBg9ie5U+df/jjGGCsYgizyOMecFXhHOC8bz8ssvt6gjT0Ma48MMxBGxWHffffcsY4I4GRMTFztb5i5fvjzrpCLJxrtnE91sxV9E+N5g8xfgwk3eAhc+LkQkIpEhjPualZUvjyrO8sWupDBUSCoj94IVPTKhoHF1onw57hxzjj2lbsncDS+qMnqPC93/cA371swouZNPPtntJolMuKoxdDi2hCeoWIi3xXOi5CMJkznn85BYDWN04aEhJIZRi8eLVTRZ9DxP+CKZMHYsOjD28MagmMmvoDMl5xLGAzfGD/c/q3vCK8kqB2WcMFrJk6EjJXMMoxVljcHA8eSaxzzE4CG3gHmRaPlK/u8Ys+ho3LhxlpFCGIfjiAGNV4n55w3rVO5kmRsyGtIcJjonCDkBvvuZT/ohNk/rVrKtWRkAHglWfMS8k+n6wrXPxRhZvLFDiRgnro97kmAFND3BGqccyv/G4i5f8DihlHFho4w5nmzGg4cJw5CLIyEpVk/eE5KsYxxlGcnsxwBkhc5FGmWGQUN4x2ewc85wjMkrICGRaiMu4MmMdfveDHwvMXnmIGPn5eMcRzHSphnPQ7ytsxOJn+t4QfBe4gnhPPCllJw7XkF674LP+k/GecJ3cJzxrmJk0ROG4+gTc31/C0BmlLJfbCVKvm3/+1yOFfOQ9uA+yRV5OJ54j8hBI4SGwU3COqQi/ywvZDSkOZwAJPBxouAm9B3D/AqahiC461jR4Z5DKeKqa9asWVInJUoZ65oTgosd0CUOl5xf+XER5wQjbIKMBx98cNZvLO7yBUHJEdvmgsNqzu8siEsYmVnlkVDqy/ZSkcEeNRlJfiNRj4RMktBYadL/nx4Cvn0v9/4YsyollEHpajIgzu1XyIRNWHmyQiYcEZQPZcOYIideBr/RUbLw3jiuGyhkX4HFKpnxwmDw1xQWJ/zPyjpZ89Ane3P8yLvwlQl+0cT4efk49shHtU8y5Js1a5YzArl5/LEFwij33HOPMwz5myof8n+ihnIaMgRcv7gBcckFJyXgjsOdSGUACWlkibOiSWZmOJBgSB8GLohcJIPfzwqPxDl6NRDjI4sdN2wyZYy6fLGQ9HXOOec4lzV9B4jjslIhIS2VckVdRpQxpaHswHjYYYe5v4Mxd4wbvCHIRhJdsuXE8GflSSgMowXlRz6Nz2EiYZcQGQsFzvlUHmsMg6uvvtqNKcYB1xnODbxILFJatWrlFGGqZGSukViNNwRZmIfIR54FRiRGGt7YZMi3evVq52nD04AXKSc4znhyMCLxlpA8GaUKChkNGQTGge9d7vEXGrZZ5ULISo8YLnFJn5yTDHI6Kf3JQEc2PCKUv+H+J8mLuG2yLjZRly8Wf+xoMUs1By5Yqk9I3ovC9rlRlNH39ud4kY+CXLiLUSR4PDCkUcIYgxiJJEnyXLLKlIM9DwifYKByvmLE4rmhrwBdVNkSG68YnhzCjKkqo/bgsSFnhQRhPHCAPJdeeqk77oRXUmnYELrF9c85zHxkDrCax8ChOoqwWVSM7L//dyyRj+ONh45jnMxrdV7IaMgQcrNE/UQkREH5ILF7whZROVEAZYwrDpc1CWmp6IWQbvJxzLmR7c+N/ACyxaNyTNNFRg/udlb4rEQxrFk5+9hzFKB8kXJqwjtk2dOzIdXj6K8thEroaIgBQTgPDw6hk1TH5P01jtAJCyc8IoR2KFtFxmQar9si5C0oDDIaihGsXijBJGNcZBa4iblI+6SuKBIlGb0xE1S6fstpwlDJ7kYaj5yMelafwU6vIjP5O8UepJyQ0SAiQdSt8KjLJzL/eEdRpiC+MiGqMvqdLVPtnUl3ohEkEcWeqF5o0kU+kfnHO4oyBYm6Mo66fOmCRlEIIYQQoZDRIIQQQohQyGgQQgghRChkNAghhBAiFDIahBBCCBEKGQ1CCCGECIWMBiGEEEKEQkaDEEIIIUIho0EIIYQQoZDRIIQQQohQyGgQQgghRChkNAghhBAiFDIaigF+G+AoE3UZJZ/GMNW7M8b+HSXSQUbQeVx4ZDRkKL/88ou999579sUXX0R2u9qoyyj5NIbJIDdjefPmzTZ58mR76623UrpTYzrImBM6j4uWaB1dUaATmP+x7oOPr1u3zm6++WZr0qSJDRgwwH788cekjm7UZZR8GsNUz0GPN5Y5P/7+++9sc3PDhg02fPhw69q1q/Xs2dOWL1+edPmiLKPO4+QjoyHN4ITlBF6xYoWtWbPGncT8j3UfXKmXL1/errrqKlu4cKHVqlUrqa7/qMso+TSGqZiDwc/+7bff7KeffrL58+fb0qVL3Zzk/ChRokTWOcLrK1Wq5IyZBQsW2EknnWSlS5dOmHzpIqNH53Fq2GHDhg3RDSQLd1JyguJie+ONN+zZZ591J+fee+/tTtY999zTqlevbkcffbQde+yxts8++2w3apz4e+21V7GVUfJpDFM9BzGcUbh4Np5//nkbM2aMW5FXqVLFdt11VytZsqST7/jjj7eTTz7Zatasud1n/PXXX+51iSLqMuo8jgYyGiKOP1F69+5tEyZMcJb8iSeeaN9//70tWbLEVq1a5U7yP//80/bdd19r27atderUyXbaaSfJmCZjGHX50kHGdJHv0ksvtWnTptkFF1xgjRo1stWrV9t3331nK1eutGXLljl5S5UqZa1atbKrr77aGd3JIuoypssx7h1R+YoKGQ1pwNq1a+3www+3O++80y677LJsz23dutUWLVpk7777ro0dO9a+/fZbO/fcc+3ee+9NqHch3WSUfBrDqMzB+++/3zp27JjtORQJq/rZs2fbv//9b5szZ46dcMIJ9uijjzoFkyyiLqPO49SjnIYI4+OLXOyw7CtWrOj+//33390JjDuRx4855hi7/vrrXUige/fu9tJLLzn3omSM/hhGXb50kDFd5KNKiHi/zwcgZwB3PvKx2qxdu7ZddNFF9uabbzrje8aMGTZ69OiEy5cOMqbLMV4UUfmKEhkNEcafuCRo7bLLLu5EBf7mBPalTSQEMTmBiXjIIYe4vAJIdHJh1GWUfBrDqMzBgw8+2HbffXebNGmS+z6UM/F/5OP/oHznn3++1a9f3ymVRMuXDjLqPI4OMhrSABKO2rRpY+PGjXNlTfPmzbNff/0163mymVHSQHxxv/32cyc7pVDJ6n0QdRkln8Yw1XOwcuXKbpX+9ttvW8uWLe311193JZ6+uigoH4mHhE1Q4CRoJus8jrqMOo9TT+JScUWRcuONN7pEJFxZxBRPP/10a9asmXMX+hU9Jy+xyKlTp7ra87Jlyyb1KERdRsmnMUz1HMQ1vXHjRpcHQJln06ZNXbLcoYceauXKlXOKGEOGXAsqkW6//XbbY489kiZfOsio8zi1KBEyzSCRiySkDz74wH744Qd3slLayAqAzGZW9x06dLBhw4YltDwrnWWUfBrDVMxBn10PKGNCI++88447J5CBslDOEaoVgP4Rt912W1JkSycZPTqPU4OMhjQheDJv2bLFlfFwIpO4xI1SniOPPNIaN27syqRSUcYTdRkln8Yw1XMwFowXmqAtXrzY/vOf/7jzpk6dOnbEEUdY3bp1LQpETUadx6lFRkMGETyZokrUZZR8GkOR/ug8ThxKhMwAfC/4KCvjqMso+TSGqcRXJkSZdJBR53HikadBCCGEEKGQp0EIIYQQoZDRIIQQQohQyGjIIJK5/XWmyij5NIaphkZKUSfqMuo8ThwyGjKIqCYZppOMkk9jmGp82+soE3UZdR4njmgfeRHKat60aZNrwvLxxx9HdsRSLWO6j2EU5NMYFm7s8ho/2lk/8MADrplSVEm1jJqDqUdGQ4TxJ0heVjP7tPfp08fteR98XyLJrfQqnusyFTJGeQzTZfyC36UxLDiMXV7jRzM0WjLTgCpZIQB2YMyJeN+fChlBczA6qOQywrCb3Pz5861ChQp2wAEHuMeCFx6/euHGCpROd34zmUQqO9rIvvDCC66NKxcQNq3JCX9RSaaMUR7DdBs/0BgWDlqpsxdLlSpVrHz58rbzzju7/Ru4+RbWHGdc/ihlXpPoTpV+Hj799NM2ceJEGzFihNusKjeSLWMQzcHooA2rIoRvfsSOcffcc4+99tpr7iTFqmdP+4MOOsiOPfZYO+GEE6xJkyZuTwevANmiNlkyAhcbNq8JxjbZ5OaTTz6xn3/+2fbff3+3Ix2v8SRDxqiPYdTHz8uoMSw4XrniFWJfhhkzZrjxXL9+vRtbjIfDDjvMtbFmHmII+nnAHi3J8CL578BgKFOmjDNkPGw6995779lvv/3mdgPF2GazqmTKqDkYXeRpiCBnnXWWLV++3C655BJ3grIiWbt2rS1YsMBtOc1+DiiTCy64wK644gqrUaNG0rstVq1a1fr372/dunVzKw7kuuGGG9yq3q+OK1asaBdeeKH17Nkz19V0cRzDqI8faAwLx8knn+z2aejRo4dVr17d7a7JHORYv//++/bpp586T0OLFi3s2muvdYaENziSRew8fOutt2zgwIH2+eefu/8xtHfbbTc3F3gdr08mmoPRQ56GiOAV1meffWYffvihPfHEE3beeedle574I4lIKD52kXz88cfdquDhhx92F6RE4y9obFrDKuTwww93FxYujNddd52tWbPG7rjjDue6ZJXFFtkkTfEaLjiJvhhGfQyjPn6gMSya8WNzJ3aJxO1/zjnnZHsNW8YDipnk1kceecS++uorGzt2rO2zzz6FlCC8jMzDrVu3Zs3DX375xW07/ccffzi58TIQiiCUxrnCe/DeJTo8pjkYbWQ0RIyvv/7auQtLlSrl/ucExnXICcs9J/Kpp55qxxxzjB1yyCF29913u5O+X79+CZfNK71XXnnFrThw9cPbb79t3377rT300EN29tlnZ70ehU3yHheaTp06JW2VEtUxTJfxA41h4SBxlflHLD6YQxCEuYd3CS9X37593XEeOnSoJRpkwcPBPMRIIfQAVERgJAwfPjzLsAHCeHgfxowZY1deeWXWvE00moPRRNUTEcG7xTmBcQeOHDnSxbZ9rJETHVehd13jrvZuz2nTprnHEh1n9Bc9TmZkY/WBXLjUca0effTRWbIiCxfDiy66yK1iklGiFfUxjPr4gcawaMaP44mRhxdhyZIlWcc+dg4SNuMYs8008yCZ5/Hq1audcUMyLt42vh9DxhsRyIgsJEi2b9/enUf+PEkkmoPRRkZDxCDpqE2bNu7kvOqqq1z805/orA5YqfoLDicXq2bc6ri4Ex2P95/frl07a9asmXPpE3PEzcmKqmzZslmy+gufX/H7/5NRohXVMUyX8QONYeEg2bFr164uRMEcfOONN3Kcg5s3b3aPoZzpx5Gs85hcHmSbNWuWtW3b1j766COXQIwh41/n5x3GDvPQV0wkY7dLzcFoovBEBPnXv/7lTlyUypQpU6xBgwZ23HHH2VFHHeXc6MS8ybIfNWqUzZ0718Uf/YmeDHBdcps5c6Y99thj7h6lR2LXiSee6F7j4+8obC6KPvM/WYmGUR7DqI+fjyn7MWS1HOUxJC+FCoWojKFXtF26dHGK9v7773crdQwJXzHhZcFgxOPEKp97PGTJgqoNbueff76r5nn99dedBwzjoWXLltl6S5C4iUGD/JCsZE3Nweih6okIEczeJymJE5UTmQsK5VrAapgYPasUVsesZpKRz+CJze7+9ddf7eWXX3aJhyR8nXTSSU52lAgudS5GKJsXX3zRkg1jhbeBVV5wDJGPhMhUjGHs+CEjseUojl9wHmI00CciCmMYC3OQMfzggw+yjeGcOXPcGD7zzDNJG8N4FTgYLcxD5KGih74NGDHe23DggQe6Vf+ll16acPlykhHwOGCEYRhieJEwzLhSfjlp0iRnSDCWycLLyfFlDlI+zVj++OOP7jmSiX2oJ9lzcNv/erv4cxmDlTFiDuI9bN68uTvGyBuF87gokdEQEfwJghKJXa1Rv88Kimxr/mY1giubuGndunUjISNZ2Jy8uNNJ7MPtyetatWplvXv3tlq1aiWtpJGLHfFXZPGwSmJV/OWXX7q/GT9KLlkxs9pKJsiHmzc4hlx0uHFsuciQa8HFksRIxo+eDckaP1abjF1sljxzj6oT5iFKOVVjmJN8zEGUCHIxhiTtMQeTNYbeIJwwYYJTGrFlsowZRgO5BMxBfgNhCfIIqlWrlhCZcpOREBneIh7zXgXmoE8YRlH36tXLPYYxdvXVV7sy4GSXd8eO4TfffOPOY/IwOI+Yg+SEJPs8jgeGtA/hYGxddtll7jw+99xzk34eJwoZDRGBExe34F133eUuyqw+qNHngudj3VGTkSxqZMQwCCpALt5UA7ASSKZRg8IYPXq0y0Ln4lKnTh2nOM4444xInKSx8nGhw7jCze7lI1bMRXrFihVupcL4+a6ByQBldvHFF7uVJseXZExi7azkgh0AuXDvu+++SZUtVr6DDz7YXYSRD2MrKAtGNsoRrxzzIFndC8kJoDEXoRrc/sFzwuemcB7FVlIkk5xkZN5hiDHvmIc0PmMeMoa8PpnnCQmaJCiDV7Io5OnTpzuZmJskDvsKqWTyV4x8HuTDWPXXbzwLPEZjOQzDZHbQTCTKaUgxvhQLtyAuSlZGXAxZjaBQmIBkN/vJ6i+MnNgQ7BiYbBmx9FkR0xfBywicyJzUnOxc5JMVp6WenNI1SilxsRLauf76651HBplxEXLxoSMkiZLJJp58lFRyQWE1gusXRUfOgJcv2Vv84gXheLJKYm7RfptsemLZjCMXSmRt3LixC/twYYySfBgRGKuUCfIaxjIZ+HMEpbHnnnta7dq1s87ZRYsWuX4beGk4F3gOVz+rz2QSVkaeQ+nRdMrLmIzVsZePUBh5MnSr9J4RrjWEHjhnPBx7rpF4QJKxKMivfBUrVnTnN2GTTEJGQ4rxSoFJWLp0abcSxYLGlU6SHEloTMzBgwdnGQxY/pMnT3bNgFBCUZIxuLLCBUoHvGTI6OXDM0PiGRdnPCDXXHONtW7d2rlbcWmSjMYKi99AXwRW0skirHwk9mHgkMSJEkwmuMk5np07d3ZVJXQ15DhTp8+FGWWC29132KSunxVpslZ8+ZGPUAAXbpRgouXz5wjnJUYMsgG5DCTzsQjAUGQlT+4AsW0UDD1CkC8Z5EdGEktpLvbqq686z1gyOpJ67wtyItPNN99st956q3vszjvvdDKxOCHUiSeJMRw0aJDzxCYjH6Qg8vXr188ZFYQpMgWVXKYYbwiQ+EYCDS5rJhlK+cknn3RtjlHMZFYHV1soFsq5klH+VBAZ8TDQJCZZMgIrUFZwuNJRIKwyGzZs6JQHGetcALlQE14hKY0OfMlczedHPhKoxo0bl1T5PDS+QpmRsEcLbloLo5gfffRR5yUhqRTPDQYPhs/UqVMjKR+tmTHSkiGfP0cI351yyilZhsCwYcOyGimhhDlvOU/IJ8Dz5FemyTjGBZGR1TQr62TJCFxj6JCKUc/eHbR8RyEPGTLEhRvxgKCMUdwcbxYBPkE3ivI9+eSTSZUv0choiAAkmLFaQ3lg5bNq90qWiXn88ce7VTwuRCCeRpvhYDyyuMuIIUUzHd8gx4M7nVUVnfdwqfM/IRaUCeEWFE+ykjOjLF8QlEPHjh2dh+i+++5zxgz5CxxLFAwJfLiFBwwY4BLpfPvtZG6THEX5yPNAOXBe+CRNVpysguvVq+eOIyEVjG08Xaz0SRomnJKsY5wOMvI9rOLJXcErh2eJ84fcJCBPgBveJIxw8qe4Jcuw2SHi8iUaGQ0phknERQ03K+U6nNSs4n2DH1YAuLy4Z/8BwI1NTgMZ2pDopKqoy4gMGDQdOnRwrW5990SUBCthFAnu1aC3g1X+0qVLk5JvEXX5YvHKAUXCRY9VFf0EAM8RiV2sttgMjFUo+QPJrN2PqnwcPzxHrNQvv/xyt8Ik3ORbmPsyPSDMR5Ire1QkMykyHWT0xh0ufwzBp556ysmHYgYSCrkhJ94S5CYvCJJh2PwTcfkSjYyGFOMnESsnTlLcWtQhB5+jlIiEGmKhxPC4QBI3Y0WVjNVd1GX0MmD5kyk/fvx4V52AkiAGjoswtn0uSXKsTol1F3f5coJjR6gE7wetjlHGhKjwmPgSwVSunKImHxUGbJJG2AaPG6tRjGc2o/IK288FjjHVCn5b6mQd43SQkfOC48b33nTTTXbaaac57wjXF2T1cpB8iJcJDwnXJcmXHJQImSI4OSkdIhGPyU4m80svveRWl/G66qFwqJsmbosiIREMErmVbtRljJUPq54GMCTnBXs0+MxvXP3kCrCy531cMP3ziSDq8sXKGJshj8x4QPCOUKdPjgAhJ1aqPh6ejIz6dJEPMFaIazP3SYQj+ZWEUY4trn7KHXH/8xy5BSjCZJ7HUZQx3jH2xw1ZCd0hH2ESzh2MCIwYEq0xtFmkJOs83hZB+ZKNPA0p4osvvnBxV+/qpyKCXutMuKBCCU42tq3FXY3b3++GmEiXa9RljJWPOCKQHBf8Tn+Ck9SFSxYvCa5FX05WXOWLlRE5gqs137GQnRDZ/AvvEe5/wlR+E7BEk07yAceYcwGjGiOaVT3PU8UByInLn2oPymrx3kEi3f9RlzG3Y+y9mHSdxcOJ25/SZCo6MHDIaeF6VJzlSzZq7pQiKEkk5kqCEav3sLCSZ1JyYhd3GfMrn2/fyyqUFWqia8+jLl9+ZaRUj+x6el9QQQNRGsOoy4ernxwCPHVULzRt2tQZ31E6xqmQMS/5vIeD51nJs3ihoRePsUBJ9TH+J8XyJRsZDSmEFSWrSWJylO6QLBds6ZpbJYPPCC/uMuYkXzIT89JZvoLKmMwLYbrK52WIwnGOuoxRP0+iLl8yKV6/NkJwsuIOJP5PnS9xbDrtMQFj47ax7jASgEj6Ku4y5iZf8EQOlod6KDNLdO101OXLj4x0DWQV6uH4R2kMoygf7mgvI8cYGaN2jKMgY36OseRLPTIaUoRXurhRSeCiAQ29Du644w7n3sdV7Q7Q/04cmiVh7VKbTqY4WeLFXcb8yMfF0cuH+5okMBo8FWf58iMj5bTU9Ud1DKMuH8cYGaN8jFMlY36OseRLPQpPRAiywGfPnu3K8UhS8tu+8jfNkshk5mSi7XAq9k5IBxkln8ZQc1DnSXG/ziQSGQ0RwG+EwqSjcxgtcXHv46LjxiSkzTAdA6nd9zXJyYylRV1Gyacx1BzUeVLcrzPJQEZDhCELN17pWJQmYdRllHwaw1QT9TmYDjJKvuggoyFi+Dau/kSN/T8KRF1GyacxTDVRn4PpIKPkiyYyGoQQQggRimiYlEIIIYSIPDIahBBCCBEKGQ1CCCGECIWMBiGEEEKEQkaDEEIIIUIho0EIIYQQoZDRIIQQQohQyGgQohjwxhtvuO186YcvhBAFRUaDEMWAt99+290vWrTI1qxZk2pxhBBpiowGIYoBb7311nYGhBBC5BcZDUJkOEuXLnU78nlkNAghCoqMBiGKiZehW7dubifDadOm2V9//ZVqsYQQaYiMBiEyHO9ZOPPMM61Bgwa2adMmmzNnTtzXrly50i677DI76KCDrEqVKtamTRubN2+e9ezZ06pVq2b9+/fP9vqPP/7YOnToYDVr1rTKlSvbCSecYOPGjSsSufMjy4wZM1yiZ/fu3bNyN6644go7/PDD7Z577on7+WPHjrWTTjrJ9t57b6tVq5Z17tzZvvjii+1ex+fWqVMn7mcce+yx7nnPs88+6/6/88477YknnrCjjjrKKlWqZPXq1bOhQ4fa33//XQQjI0TqKJnC7xZCJJhff/3VZs2aZWXLlnUGw8knn+wULIZEo0aNsr32xx9/dM//8MMPThnusssu9s477zhPxXHHHWfNmze3/fbbL+v1EydOtK5du9o///xj9evXt1KlStlHH33kFDcKv2/fvgWWO7+yBEEuZPjjjz9szz33tBIlSmz3mquvvtpGjx7tZD766KNt3bp1NmnSJPf5GBNNmjSxwjB+/HgXEjr44IOd/J988ondeuut9umnn9rTTz9dqM8WIpXI0yBEBoOB8Pvvv1vjxo1tp512coo4NjHS88wzzzglfc0117jnX3311axVOivlp556yq688kr3/9q1a+2qq65yypxyTm4oXQyU3XbbzYYMGVKoKo38yBLkq6++co+fc845tnjxYlu2bJn16tUr22swCjAY8Fa8//779tprr9ns2bPt7rvvti1btliXLl1s48aNVhgwGPCEYETx+XwPHgfGiP+FSFdkNAhRDEIT3lg44ogjrGLFis59//3332d7Ld4BCPZywNiAzz//PNtrCUHgxbj44oudl8Gz//77O4VNzsTUqVMLLHd+ZAkyd+5cO/30011oYJ999on7mkcffdTdE0KoXbt21uN4TXjvTz/9VOgQC+GOoKflgAMOcDklgOEgRLoio0GIDMZ7FF5++WWn4Dt16uT+37Zt23ZVFBgUMHPmzKzH8BzAvvvum+218+fPd/ePPfaYi+EHb6ziYcmSJQWWOz+yBClTpozzGOQEnoTPPvvMdthhB2vWrNl2z5966qnuPqecj7AQ+okNi3jjKl7ehBDpgnIahMhQcNV/88032RRuEIyGjh07Zv2PUTFmzBgbNmyYffDBBy70wD2K2CcYekimBPIL8FzEA69DQcmPLEFOO+20HOUBwg4YTHvttZfLZ4jFeyd+/vnnUHJu3bo17uPxZChfvnyW4SJEuiKjQYgM9zJQ3eBd8rB69Wo79NBDXekl2fx+RYxhgXeA8AJ///nnn241PmjQIPf6IOQtAAq8VatWRS57fmQJEs8QCLL77rs7LwNGAQo/9vU+ZEMCZV4Qnskpb4Ocj3jJnVCuXLk8P1uIqKLwhBAZig8/nHjiidkep3wRLwCr7qAb/rbbbnMxfrL7aQiFl+L555+3ww47bLvPppTQ5xDEMn36dOcpIHRRUPIjS37Ydddd3WfgbXj33Xe3e97nYQTzNHbccce43gGqNHLqd0HiY+xz3tuTm9EjRNSR0SBEBvLbb79lKalYoyH4WDCvgZXzd999l7Uizo327du7UAFVDBgJns2bN9vAgQNdDgUKuqDkR5b80qNHD3d/ww03uBCOh98yZcoUF7q44IILsh6nyoLkyGAuAomalFDmBM/TlyFYTTFy5Ej3N/0mhEhXFJ4QIkNLLXG/41HAsxALTZhGjRrlQhgDBgxwj7Vt29a5/4888kirW7euc9Gj+CtUqOA8C2eddZbLLQAaOT388MOu4oAQAomLPEazJxQsoYQLL7ywwPLnR5b8QriGUkgSNqnOoE8DMlOVgSHEuAQbNvE77rjjDrvooousd+/ezutw3333udJPQijLly/f7jvwJgwePNiVV5Lf8OGHH7r3YYz4ShYh0hEZDUJkcD5DPC+DNxqI7S9cuNDF31H4JDXSBIqVNitwFGnQxd6wYUPXL6Fkyf9eNlq3bm3Vq1e3e++91ynFr7/+2pUW3nTTTS48Ea+pUljyK0t+efDBB12zqxEjRjhDh9yGs88+23kfaMgU5LrrrnONoiZMmGDXXnutq97AWKIzJXLEA6OGPhZ4GzBGeA8dJ3lMiHRmhw0bNmxLtRBCiNRC3wZaKt94441OMXro9kjSIO2cSZx877333Oq/uMiSX2gjTXOpfv36OQNEiExDngYhhP3yyy+uQgE3PCt4XOqs4ukmSTz+yy+/tKpVqxaqjDIdZRFCZEdGgxDCudlffPFFl6zH/giELQgHENuvUaOG627ICp+QQXGSRQiRHYUnhBBCCBEKlVwKIYQQIhQyGoQQQggRChkNQgghhAiFjAYhhBBChEJGgxBCCCFCIaNBCCGEEKGQ0SCEEEKIUMhoEEIIIUQoZDQIIYQQwsLwf+oV414F1IYkAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(age_groups.index.astype(str), 100 * age_groups['TARGET'])\n",
    "plt.xticks(rotation=75)\n",
    "plt.xlabel('Age group')\n",
    "plt.ylabel('违约概率 %')\n",
    "plt.title('违约概率年龄分布')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dbe8a8d",
   "metadata": {},
   "source": [
    "确实是这样，越年轻的客户更容易违约"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e698f22",
   "metadata": {},
   "source": [
    "#### Exterior Sources\n",
    "`EXIT_SOURCE_1..3`是最负相关的三个特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "17d9f29c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ext_data = application_train[['TARGET', 'EXT_SOURCE_1',  'EXT_SOURCE_2', 'EXT_SOURCE_3']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3bdc4304",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TARGET</th>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TARGET</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.155317</td>\n",
       "      <td>-0.160472</td>\n",
       "      <td>-0.178919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <td>-0.155317</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.213982</td>\n",
       "      <td>0.186846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <td>-0.160472</td>\n",
       "      <td>0.213982</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.109167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <td>-0.178919</td>\n",
       "      <td>0.186846</td>\n",
       "      <td>0.109167</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                TARGET  EXT_SOURCE_1  EXT_SOURCE_2  EXT_SOURCE_3\n",
       "TARGET        1.000000     -0.155317     -0.160472     -0.178919\n",
       "EXT_SOURCE_1 -0.155317      1.000000      0.213982      0.186846\n",
       "EXT_SOURCE_2 -0.160472      0.213982      1.000000      0.109167\n",
       "EXT_SOURCE_3 -0.178919      0.186846      0.109167      1.000000"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ext_data_corrs = ext_data.corr()\n",
    "ext_data_corrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8213bac8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'correlations map')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(\n",
    "    ext_data_corrs,\n",
    "    cmap = plt.cm.RdYlBu_r,\n",
    "    vmin = -0.25,\n",
    "    vmax = 0.5,\n",
    "    annot = True\n",
    ")\n",
    "plt.title('correlations map')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f080906",
   "metadata": {},
   "source": [
    "观察下不同target下的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "eb0fdb46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (6,6))\n",
    "for i, source in enumerate(['EXT_SOURCE_1',  'EXT_SOURCE_2', 'EXT_SOURCE_3']):\n",
    "    plt.subplot(3, 1 , i + 1)\n",
    "    sns.kdeplot(\n",
    "        application_train.loc[application_train['TARGET'] == 0, source],\n",
    "        label = 'target = 0'\n",
    "    )\n",
    "    sns.kdeplot(\n",
    "        application_train.loc[application_train['TARGET'] == 1, source],\n",
    "        label = 'target = 1'\n",
    "    )\n",
    "    plt.title(f'Distribution of {source} by target')\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f8c6d96",
   "metadata": {},
   "source": [
    "可以看到，相对而言,`EXIT_SOURCE_3`和`EXIT_SOURCE_1`可能对target有比较强的关联"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3adec37",
   "metadata": {},
   "source": [
    "### pairs plot\n",
    "- 我们找到了`DAYS_BIRTH` `EXIT_SOUCE_` 与目标最相关的特征，但对于这两特征之间，我们还要探索\n",
    "- 通过pairs plot 可以探索 两两变量的关系 和 单变量的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "4f8cc9ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_data = application_train[['TARGET', 'DAYS_BIRTH', 'EXT_SOURCE_3', 'EXT_SOURCE_1']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "6099c6f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_data = plot_data.dropna().loc[:1000, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2e614e77",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_crr(x, y, **kwargs):\n",
    "    r = np.corrcoef(x, y)[0][1]\n",
    "    ax = plt.gca() # 获取当前坐标轴\n",
    "    ax.annotate(f\"r = {r:.2f}\", xy=(0.5, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6ec4fd4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\63517\\AppData\\Local\\Temp\\ipykernel_12096\\2750256857.py:8: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "  plt.legend()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x121c717df90>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.PairGrid(data = plot_data, height = 3, diag_sharey=False,\n",
    "                    hue = 'TARGET', \n",
    "                    vars = [x for x in list(plot_data.columns) if x != 'TARGET'])\n",
    "\n",
    "grid.map_upper(plt.scatter, alpha = 0.2)\n",
    "grid.map_diag(sns.kdeplot)\n",
    "grid.map_lower(sns.kdeplot);\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70240e59",
   "metadata": {},
   "source": [
    "注意：由于TARGET分布是不均匀的，所以这里看起来怪怪的"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "942c0235",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f6c06a4f",
   "metadata": {},
   "source": [
    "## 特征工程\n",
    "- 胜负关键就来自于特征工程，  获胜模型往往是梯度提升变体\n",
    "- 特征工程比模型构建和超参数调优具有更高的投资回报率。\n",
    "- 特征工程：就是 构建新特征、选择特征"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0316438c",
   "metadata": {},
   "source": [
    "简单的特征构造方法：\n",
    "- Polynomial features 多项式特征\n",
    "- Domain knowledge features 领域知识特性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6f7de1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "application_train = pd.read_feather('checkpoints/01_train_app_base.feather')\n",
    "application_test = pd.read_feather('checkpoints/01_test_app_base.feather')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59496f87",
   "metadata": {},
   "source": [
    "### Polynomial Features 多项式特征\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "6c8dd643",
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_features = application_train[['EXT_SOURCE_1','EXT_SOURCE_2','EXT_SOURCE_3', 'DAYS_BIRTH']]\n",
    "poly_features_test = application_test[['EXT_SOURCE_1','EXT_SOURCE_2','EXT_SOURCE_3', 'DAYS_BIRTH']]\n",
    "poly_target = application_train['TARGET']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "d652a8ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(strategy = 'median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "63fbe6c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_features = imputer.fit_transform(poly_features)\n",
    "poly_features_test = imputer.fit_transform(poly_features_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3f9ba9df",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import  PolynomialFeatures\n",
    "poly_transformer = PolynomialFeatures(degree=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "8997c50b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\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-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 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-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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: 0.5em;\n",
       "  text-align: center;\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-5 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-5 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-5 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-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PolynomialFeatures(degree=3)</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PolynomialFeatures</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.PolynomialFeatures.html\">?<span>Documentation for PolynomialFeatures</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('degree',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">degree&nbsp;</td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('interaction_only',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">interaction_only&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('include_bias',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">include_bias&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('order',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">order&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;C&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
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       "</script></body>"
      ],
      "text/plain": [
       "PolynomialFeatures(degree=3)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "poly_transformer.fit(poly_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "4561ebcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Polynomial features shape:  (307511, 35)\n"
     ]
    }
   ],
   "source": [
    "poly_features = poly_transformer.transform(poly_features)\n",
    "poly_features_test = poly_transformer.transform(poly_features_test)\n",
    "print('Polynomial features shape: ', poly_features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "fb30102e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', 'x0', 'x1', 'x2', 'x3', 'x0^2', 'x0 x1', 'x0 x2', 'x0 x3',\n",
       "       'x1^2', 'x1 x2', 'x1 x3', 'x2^2', 'x2 x3', 'x3^2', 'x0^3',\n",
       "       'x0^2 x1', 'x0^2 x2', 'x0^2 x3', 'x0 x1^2', 'x0 x1 x2', 'x0 x1 x3',\n",
       "       'x0 x2^2', 'x0 x2 x3', 'x0 x3^2', 'x1^3', 'x1^2 x2', 'x1^2 x3',\n",
       "       'x1 x2^2', 'x1 x2 x3', 'x1 x3^2', 'x2^3', 'x2^2 x3', 'x2 x3^2',\n",
       "       'x3^3'], dtype=object)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_transformer.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "344ab640",
   "metadata": {},
   "source": [
    "现在我们 看下生成的新特征与target关系如何？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "7ab59670",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.021834</td>\n",
       "      <td>0.011573</td>\n",
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       "      <td>0.018181</td>\n",
       "      <td>0.009637</td>\n",
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       "      <td>0.005108</td>\n",
       "      <td>346.733022</td>\n",
       "      <td>2.353667e+07</td>\n",
       "      <td>0.002707</td>\n",
       "      <td>183.785678</td>\n",
       "      <td>1.247560e+07</td>\n",
       "      <td>8.468590e+11</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.311267</td>\n",
       "      <td>0.622246</td>\n",
       "      <td>0.535276</td>\n",
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       "      <td>0.166614</td>\n",
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       "      <td>0.387190</td>\n",
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       "      <td>0.240927</td>\n",
       "      <td>0.207254</td>\n",
       "      <td>6491.237078</td>\n",
       "      <td>0.178286</td>\n",
       "      <td>5583.975307</td>\n",
       "      <td>1.748916e+08</td>\n",
       "      <td>0.153368</td>\n",
       "      <td>4803.518937</td>\n",
       "      <td>1.504475e+08</td>\n",
       "      <td>4.712058e+12</td>\n",
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       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.505998</td>\n",
       "      <td>0.555912</td>\n",
       "      <td>0.729567</td>\n",
       "      <td>19046.0</td>\n",
       "      <td>0.256034</td>\n",
       "      <td>0.281290</td>\n",
       "      <td>0.369159</td>\n",
       "      <td>9637.236584</td>\n",
       "      <td>0.309038</td>\n",
       "      <td>...</td>\n",
       "      <td>0.171798</td>\n",
       "      <td>0.225464</td>\n",
       "      <td>5885.942404</td>\n",
       "      <td>0.295894</td>\n",
       "      <td>7724.580288</td>\n",
       "      <td>2.016572e+08</td>\n",
       "      <td>0.388325</td>\n",
       "      <td>10137.567875</td>\n",
       "      <td>2.646504e+08</td>\n",
       "      <td>6.908939e+12</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.505998</td>\n",
       "      <td>0.650442</td>\n",
       "      <td>0.535276</td>\n",
       "      <td>19005.0</td>\n",
       "      <td>0.256034</td>\n",
       "      <td>0.329122</td>\n",
       "      <td>0.270849</td>\n",
       "      <td>9616.490669</td>\n",
       "      <td>0.423074</td>\n",
       "      <td>...</td>\n",
       "      <td>0.275185</td>\n",
       "      <td>0.226462</td>\n",
       "      <td>8040.528832</td>\n",
       "      <td>0.186365</td>\n",
       "      <td>6616.894625</td>\n",
       "      <td>2.349331e+08</td>\n",
       "      <td>0.153368</td>\n",
       "      <td>5445.325225</td>\n",
       "      <td>1.933364e+08</td>\n",
       "      <td>6.864416e+12</td>\n",
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       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.505998</td>\n",
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       "      <td>0.535276</td>\n",
       "      <td>19932.0</td>\n",
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       "      <td>0.033616</td>\n",
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       "      <td>2076.117157</td>\n",
       "      <td>0.092471</td>\n",
       "      <td>3443.335521</td>\n",
       "      <td>1.282190e+08</td>\n",
       "      <td>0.153368</td>\n",
       "      <td>5710.929881</td>\n",
       "      <td>2.126570e+08</td>\n",
       "      <td>7.918677e+12</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>307506</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.145570</td>\n",
       "      <td>0.681632</td>\n",
       "      <td>0.535276</td>\n",
       "      <td>9327.0</td>\n",
       "      <td>0.021191</td>\n",
       "      <td>0.099226</td>\n",
       "      <td>0.077920</td>\n",
       "      <td>1357.735625</td>\n",
       "      <td>0.464623</td>\n",
       "      <td>...</td>\n",
       "      <td>0.316702</td>\n",
       "      <td>0.248701</td>\n",
       "      <td>4333.535804</td>\n",
       "      <td>0.195302</td>\n",
       "      <td>3403.064320</td>\n",
       "      <td>5.929720e+07</td>\n",
       "      <td>0.153368</td>\n",
       "      <td>2672.378236</td>\n",
       "      <td>4.656525e+07</td>\n",
       "      <td>8.113830e+11</td>\n",
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       "    <tr>\n",
       "      <th>307507</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.505998</td>\n",
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       "      <td>0.535276</td>\n",
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       "      <td>0.013454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001561</td>\n",
       "      <td>0.007202</td>\n",
       "      <td>279.510194</td>\n",
       "      <td>0.033234</td>\n",
       "      <td>1289.874083</td>\n",
       "      <td>5.006225e+07</td>\n",
       "      <td>0.153368</td>\n",
       "      <td>5952.466801</td>\n",
       "      <td>2.310256e+08</td>\n",
       "      <td>8.966503e+12</td>\n",
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       "    <tr>\n",
       "      <th>307508</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.744026</td>\n",
       "      <td>0.535722</td>\n",
       "      <td>0.218859</td>\n",
       "      <td>14966.0</td>\n",
       "      <td>0.553575</td>\n",
       "      <td>0.398591</td>\n",
       "      <td>0.162837</td>\n",
       "      <td>11135.099105</td>\n",
       "      <td>0.286998</td>\n",
       "      <td>...</td>\n",
       "      <td>0.153751</td>\n",
       "      <td>0.062812</td>\n",
       "      <td>4295.209004</td>\n",
       "      <td>0.025661</td>\n",
       "      <td>1754.727146</td>\n",
       "      <td>1.199916e+08</td>\n",
       "      <td>0.010483</td>\n",
       "      <td>716.860892</td>\n",
       "      <td>4.902031e+07</td>\n",
       "      <td>3.352102e+12</td>\n",
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       "    <tr>\n",
       "      <th>307509</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.505998</td>\n",
       "      <td>0.514163</td>\n",
       "      <td>0.661024</td>\n",
       "      <td>11961.0</td>\n",
       "      <td>0.256034</td>\n",
       "      <td>0.260165</td>\n",
       "      <td>0.334477</td>\n",
       "      <td>6052.241247</td>\n",
       "      <td>0.264363</td>\n",
       "      <td>...</td>\n",
       "      <td>0.135926</td>\n",
       "      <td>0.174750</td>\n",
       "      <td>3162.050698</td>\n",
       "      <td>0.224665</td>\n",
       "      <td>4065.229651</td>\n",
       "      <td>7.355897e+07</td>\n",
       "      <td>0.288836</td>\n",
       "      <td>5226.384299</td>\n",
       "      <td>9.456968e+07</td>\n",
       "      <td>1.711207e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307510</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.734460</td>\n",
       "      <td>0.708569</td>\n",
       "      <td>0.113922</td>\n",
       "      <td>16856.0</td>\n",
       "      <td>0.539431</td>\n",
       "      <td>0.520415</td>\n",
       "      <td>0.083671</td>\n",
       "      <td>12380.052173</td>\n",
       "      <td>0.502070</td>\n",
       "      <td>...</td>\n",
       "      <td>0.355751</td>\n",
       "      <td>0.057197</td>\n",
       "      <td>8462.889915</td>\n",
       "      <td>0.009196</td>\n",
       "      <td>1360.647784</td>\n",
       "      <td>2.013220e+08</td>\n",
       "      <td>0.001479</td>\n",
       "      <td>218.762433</td>\n",
       "      <td>3.236817e+07</td>\n",
       "      <td>4.789207e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>307511 rows × 35 columns</p>\n",
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       "          1  EXT_SOURCE_1  EXT_SOURCE_2  EXT_SOURCE_3  DAYS_BIRTH  \\\n",
       "0       1.0      0.083037      0.262949      0.139376      9461.0   \n",
       "1       1.0      0.311267      0.622246      0.535276     16765.0   \n",
       "2       1.0      0.505998      0.555912      0.729567     19046.0   \n",
       "3       1.0      0.505998      0.650442      0.535276     19005.0   \n",
       "4       1.0      0.505998      0.322738      0.535276     19932.0   \n",
       "...     ...           ...           ...           ...         ...   \n",
       "307506  1.0      0.145570      0.681632      0.535276      9327.0   \n",
       "307507  1.0      0.505998      0.115992      0.535276     20775.0   \n",
       "307508  1.0      0.744026      0.535722      0.218859     14966.0   \n",
       "307509  1.0      0.505998      0.514163      0.661024     11961.0   \n",
       "307510  1.0      0.734460      0.708569      0.113922     16856.0   \n",
       "\n",
       "        EXT_SOURCE_1^2  EXT_SOURCE_1 EXT_SOURCE_2  EXT_SOURCE_1 EXT_SOURCE_3  \\\n",
       "0             0.006895                   0.021834                   0.011573   \n",
       "1             0.096887                   0.193685                   0.166614   \n",
       "2             0.256034                   0.281290                   0.369159   \n",
       "3             0.256034                   0.329122                   0.270849   \n",
       "4             0.256034                   0.163305                   0.270849   \n",
       "...                ...                        ...                        ...   \n",
       "307506        0.021191                   0.099226                   0.077920   \n",
       "307507        0.256034                   0.058692                   0.270849   \n",
       "307508        0.553575                   0.398591                   0.162837   \n",
       "307509        0.256034                   0.260165                   0.334477   \n",
       "307510        0.539431                   0.520415                   0.083671   \n",
       "\n",
       "        EXT_SOURCE_1 DAYS_BIRTH  EXT_SOURCE_2^2  ...  EXT_SOURCE_2^3  \\\n",
       "0                    785.612748        0.069142  ...        0.018181   \n",
       "1                   5218.396475        0.387190  ...        0.240927   \n",
       "2                   9637.236584        0.309038  ...        0.171798   \n",
       "3                   9616.490669        0.423074  ...        0.275185   \n",
       "4                  10085.550751        0.104160  ...        0.033616   \n",
       "...                         ...             ...  ...             ...   \n",
       "307506              1357.735625        0.464623  ...        0.316702   \n",
       "307507             10512.107006        0.013454  ...        0.001561   \n",
       "307508             11135.099105        0.286998  ...        0.153751   \n",
       "307509              6052.241247        0.264363  ...        0.135926   \n",
       "307510             12380.052173        0.502070  ...        0.355751   \n",
       "\n",
       "        EXT_SOURCE_2^2 EXT_SOURCE_3  EXT_SOURCE_2^2 DAYS_BIRTH  \\\n",
       "0                          0.009637                 654.152107   \n",
       "1                          0.207254                6491.237078   \n",
       "2                          0.225464                5885.942404   \n",
       "3                          0.226462                8040.528832   \n",
       "4                          0.055754                2076.117157   \n",
       "...                             ...                        ...   \n",
       "307506                     0.248701                4333.535804   \n",
       "307507                     0.007202                 279.510194   \n",
       "307508                     0.062812                4295.209004   \n",
       "307509                     0.174750                3162.050698   \n",
       "307510                     0.057197                8462.889915   \n",
       "\n",
       "        EXT_SOURCE_2 EXT_SOURCE_3^2  EXT_SOURCE_2 EXT_SOURCE_3 DAYS_BIRTH  \\\n",
       "0                          0.005108                            346.733022   \n",
       "1                          0.178286                           5583.975307   \n",
       "2                          0.295894                           7724.580288   \n",
       "3                          0.186365                           6616.894625   \n",
       "4                          0.092471                           3443.335521   \n",
       "...                             ...                                   ...   \n",
       "307506                     0.195302                           3403.064320   \n",
       "307507                     0.033234                           1289.874083   \n",
       "307508                     0.025661                           1754.727146   \n",
       "307509                     0.224665                           4065.229651   \n",
       "307510                     0.009196                           1360.647784   \n",
       "\n",
       "        EXT_SOURCE_2 DAYS_BIRTH^2  EXT_SOURCE_3^3  EXT_SOURCE_3^2 DAYS_BIRTH  \\\n",
       "0                    2.353667e+07        0.002707                 183.785678   \n",
       "1                    1.748916e+08        0.153368                4803.518937   \n",
       "2                    2.016572e+08        0.388325               10137.567875   \n",
       "3                    2.349331e+08        0.153368                5445.325225   \n",
       "4                    1.282190e+08        0.153368                5710.929881   \n",
       "...                           ...             ...                        ...   \n",
       "307506               5.929720e+07        0.153368                2672.378236   \n",
       "307507               5.006225e+07        0.153368                5952.466801   \n",
       "307508               1.199916e+08        0.010483                 716.860892   \n",
       "307509               7.355897e+07        0.288836                5226.384299   \n",
       "307510               2.013220e+08        0.001479                 218.762433   \n",
       "\n",
       "        EXT_SOURCE_3 DAYS_BIRTH^2  DAYS_BIRTH^3  \n",
       "0                    1.247560e+07  8.468590e+11  \n",
       "1                    1.504475e+08  4.712058e+12  \n",
       "2                    2.646504e+08  6.908939e+12  \n",
       "3                    1.933364e+08  6.864416e+12  \n",
       "4                    2.126570e+08  7.918677e+12  \n",
       "...                           ...           ...  \n",
       "307506               4.656525e+07  8.113830e+11  \n",
       "307507               2.310256e+08  8.966503e+12  \n",
       "307508               4.902031e+07  3.352102e+12  \n",
       "307509               9.456968e+07  1.711207e+12  \n",
       "307510               3.236817e+07  4.789207e+12  \n",
       "\n",
       "[307511 rows x 35 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_features = pd.DataFrame(\n",
    "    poly_features,\n",
    "    columns = poly_transformer.get_feature_names_out(['EXT_SOURCE_1','EXT_SOURCE_2','EXT_SOURCE_3', 'DAYS_BIRTH'])\n",
    ")\n",
    "poly_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "39f01b83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOP 5 positive feature\n",
      " EXT_SOURCE_2 EXT_SOURCE_3                -0.193939\n",
      "EXT_SOURCE_1 EXT_SOURCE_2 EXT_SOURCE_3   -0.189605\n",
      "EXT_SOURCE_2 EXT_SOURCE_3 DAYS_BIRTH     -0.181283\n",
      "EXT_SOURCE_2^2 EXT_SOURCE_3              -0.176428\n",
      "EXT_SOURCE_2 EXT_SOURCE_3^2              -0.172282\n",
      "Name: TARGET, dtype: float64\n",
      "TOP 5 negetive feature\n",
      " DAYS_BIRTH     -0.078239\n",
      "DAYS_BIRTH^2   -0.076672\n",
      "DAYS_BIRTH^3   -0.074273\n",
      "TARGET          1.000000\n",
      "1                    NaN\n",
      "Name: TARGET, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "poly_features['TARGET'] = poly_target\n",
    "poly_corrs = poly_features.corr()['TARGET'].sort_values()\n",
    "print('TOP 5 positive feature\\n', poly_corrs.head(5))\n",
    "print('TOP 5 negetive feature\\n', poly_corrs.tail(5))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d800392d",
   "metadata": {},
   "source": [
    "可以看到，一些新的特征与target 相关性大于原特征，  我们可以试着采用他"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "4a503df9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>EXT_SOURCE_1^2</th>\n",
       "      <th>EXT_SOURCE_1 EXT_SOURCE_2</th>\n",
       "      <th>EXT_SOURCE_1 EXT_SOURCE_3</th>\n",
       "      <th>EXT_SOURCE_1 DAYS_BIRTH</th>\n",
       "      <th>EXT_SOURCE_2^2</th>\n",
       "      <th>...</th>\n",
       "      <th>EXT_SOURCE_2^3</th>\n",
       "      <th>EXT_SOURCE_2^2 EXT_SOURCE_3</th>\n",
       "      <th>EXT_SOURCE_2^2 DAYS_BIRTH</th>\n",
       "      <th>EXT_SOURCE_2 EXT_SOURCE_3^2</th>\n",
       "      <th>EXT_SOURCE_2 EXT_SOURCE_3 DAYS_BIRTH</th>\n",
       "      <th>EXT_SOURCE_2 DAYS_BIRTH^2</th>\n",
       "      <th>EXT_SOURCE_3^3</th>\n",
       "      <th>EXT_SOURCE_3^2 DAYS_BIRTH</th>\n",
       "      <th>EXT_SOURCE_3 DAYS_BIRTH^2</th>\n",
       "      <th>DAYS_BIRTH^3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.752614</td>\n",
       "      <td>0.789654</td>\n",
       "      <td>0.159520</td>\n",
       "      <td>-19241.0</td>\n",
       "      <td>0.566429</td>\n",
       "      <td>0.594305</td>\n",
       "      <td>0.120057</td>\n",
       "      <td>-14481.055414</td>\n",
       "      <td>0.623554</td>\n",
       "      <td>...</td>\n",
       "      <td>0.492392</td>\n",
       "      <td>0.099469</td>\n",
       "      <td>-11997.802403</td>\n",
       "      <td>0.020094</td>\n",
       "      <td>-2423.698322</td>\n",
       "      <td>2.923427e+08</td>\n",
       "      <td>0.004059</td>\n",
       "      <td>-489.615795</td>\n",
       "      <td>5.905670e+07</td>\n",
       "      <td>-7.123328e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.564990</td>\n",
       "      <td>0.291656</td>\n",
       "      <td>0.432962</td>\n",
       "      <td>-18064.0</td>\n",
       "      <td>0.319214</td>\n",
       "      <td>0.164783</td>\n",
       "      <td>0.244619</td>\n",
       "      <td>-10205.983005</td>\n",
       "      <td>0.085063</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024809</td>\n",
       "      <td>0.036829</td>\n",
       "      <td>-1536.577117</td>\n",
       "      <td>0.054673</td>\n",
       "      <td>-2281.043619</td>\n",
       "      <td>9.516956e+07</td>\n",
       "      <td>0.081161</td>\n",
       "      <td>-3386.201665</td>\n",
       "      <td>1.412789e+08</td>\n",
       "      <td>-5.894429e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.506771</td>\n",
       "      <td>0.699787</td>\n",
       "      <td>0.610991</td>\n",
       "      <td>-20038.0</td>\n",
       "      <td>0.256817</td>\n",
       "      <td>0.354632</td>\n",
       "      <td>0.309633</td>\n",
       "      <td>-10154.682538</td>\n",
       "      <td>0.489702</td>\n",
       "      <td>...</td>\n",
       "      <td>0.342687</td>\n",
       "      <td>0.299203</td>\n",
       "      <td>-9812.640816</td>\n",
       "      <td>0.261238</td>\n",
       "      <td>-8567.521115</td>\n",
       "      <td>2.809794e+08</td>\n",
       "      <td>0.228089</td>\n",
       "      <td>-7480.393855</td>\n",
       "      <td>2.453261e+08</td>\n",
       "      <td>-8.045687e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.525734</td>\n",
       "      <td>0.509677</td>\n",
       "      <td>0.612704</td>\n",
       "      <td>-13976.0</td>\n",
       "      <td>0.276396</td>\n",
       "      <td>0.267955</td>\n",
       "      <td>0.322119</td>\n",
       "      <td>-7347.658072</td>\n",
       "      <td>0.259771</td>\n",
       "      <td>...</td>\n",
       "      <td>0.132399</td>\n",
       "      <td>0.159163</td>\n",
       "      <td>-3630.555667</td>\n",
       "      <td>0.191336</td>\n",
       "      <td>-4364.443591</td>\n",
       "      <td>9.955450e+07</td>\n",
       "      <td>0.230013</td>\n",
       "      <td>-5246.681115</td>\n",
       "      <td>1.196786e+08</td>\n",
       "      <td>-2.729912e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.202145</td>\n",
       "      <td>0.425687</td>\n",
       "      <td>0.519097</td>\n",
       "      <td>-13040.0</td>\n",
       "      <td>0.040863</td>\n",
       "      <td>0.086051</td>\n",
       "      <td>0.104933</td>\n",
       "      <td>-2635.970697</td>\n",
       "      <td>0.181210</td>\n",
       "      <td>...</td>\n",
       "      <td>0.077139</td>\n",
       "      <td>0.094065</td>\n",
       "      <td>-2362.974127</td>\n",
       "      <td>0.114707</td>\n",
       "      <td>-2881.489762</td>\n",
       "      <td>7.238455e+07</td>\n",
       "      <td>0.139877</td>\n",
       "      <td>-3513.785087</td>\n",
       "      <td>8.826814e+07</td>\n",
       "      <td>-2.217342e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48739</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.506771</td>\n",
       "      <td>0.648575</td>\n",
       "      <td>0.643026</td>\n",
       "      <td>-19970.0</td>\n",
       "      <td>0.256817</td>\n",
       "      <td>0.328679</td>\n",
       "      <td>0.325867</td>\n",
       "      <td>-10120.222092</td>\n",
       "      <td>0.420649</td>\n",
       "      <td>...</td>\n",
       "      <td>0.272823</td>\n",
       "      <td>0.270488</td>\n",
       "      <td>-8400.368742</td>\n",
       "      <td>0.268174</td>\n",
       "      <td>-8328.493414</td>\n",
       "      <td>2.586523e+08</td>\n",
       "      <td>0.265879</td>\n",
       "      <td>-8257.233066</td>\n",
       "      <td>2.564392e+08</td>\n",
       "      <td>-7.964054e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48740</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.506771</td>\n",
       "      <td>0.684596</td>\n",
       "      <td>0.519097</td>\n",
       "      <td>-11186.0</td>\n",
       "      <td>0.256817</td>\n",
       "      <td>0.346933</td>\n",
       "      <td>0.263064</td>\n",
       "      <td>-5668.743331</td>\n",
       "      <td>0.468671</td>\n",
       "      <td>...</td>\n",
       "      <td>0.320850</td>\n",
       "      <td>0.243286</td>\n",
       "      <td>-5242.555692</td>\n",
       "      <td>0.184473</td>\n",
       "      <td>-3975.188577</td>\n",
       "      <td>8.566112e+07</td>\n",
       "      <td>0.139877</td>\n",
       "      <td>-3014.202453</td>\n",
       "      <td>6.495288e+07</td>\n",
       "      <td>-1.399666e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48741</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.733503</td>\n",
       "      <td>0.632770</td>\n",
       "      <td>0.283712</td>\n",
       "      <td>-15922.0</td>\n",
       "      <td>0.538027</td>\n",
       "      <td>0.464139</td>\n",
       "      <td>0.208104</td>\n",
       "      <td>-11678.842724</td>\n",
       "      <td>0.400397</td>\n",
       "      <td>...</td>\n",
       "      <td>0.253359</td>\n",
       "      <td>0.113597</td>\n",
       "      <td>-6375.125880</td>\n",
       "      <td>0.050933</td>\n",
       "      <td>-2858.384957</td>\n",
       "      <td>1.604135e+08</td>\n",
       "      <td>0.022837</td>\n",
       "      <td>-1281.600508</td>\n",
       "      <td>7.192382e+07</td>\n",
       "      <td>-4.036388e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48742</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.373090</td>\n",
       "      <td>0.445701</td>\n",
       "      <td>0.595456</td>\n",
       "      <td>-13968.0</td>\n",
       "      <td>0.139196</td>\n",
       "      <td>0.166287</td>\n",
       "      <td>0.222159</td>\n",
       "      <td>-5211.322249</td>\n",
       "      <td>0.198649</td>\n",
       "      <td>...</td>\n",
       "      <td>0.088538</td>\n",
       "      <td>0.118287</td>\n",
       "      <td>-2774.734348</td>\n",
       "      <td>0.158031</td>\n",
       "      <td>-3707.043157</td>\n",
       "      <td>8.695850e+07</td>\n",
       "      <td>0.211130</td>\n",
       "      <td>-4952.607075</td>\n",
       "      <td>1.161765e+08</td>\n",
       "      <td>-2.725227e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48743</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.506771</td>\n",
       "      <td>0.456541</td>\n",
       "      <td>0.272134</td>\n",
       "      <td>-13962.0</td>\n",
       "      <td>0.256817</td>\n",
       "      <td>0.231362</td>\n",
       "      <td>0.137910</td>\n",
       "      <td>-7075.540353</td>\n",
       "      <td>0.208429</td>\n",
       "      <td>...</td>\n",
       "      <td>0.095156</td>\n",
       "      <td>0.056721</td>\n",
       "      <td>-2910.091018</td>\n",
       "      <td>0.033810</td>\n",
       "      <td>-1734.640191</td>\n",
       "      <td>8.899687e+07</td>\n",
       "      <td>0.020153</td>\n",
       "      <td>-1033.980235</td>\n",
       "      <td>5.304904e+07</td>\n",
       "      <td>-2.721717e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48744 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         1  EXT_SOURCE_1  EXT_SOURCE_2  EXT_SOURCE_3  DAYS_BIRTH  \\\n",
       "0      1.0      0.752614      0.789654      0.159520    -19241.0   \n",
       "1      1.0      0.564990      0.291656      0.432962    -18064.0   \n",
       "2      1.0      0.506771      0.699787      0.610991    -20038.0   \n",
       "3      1.0      0.525734      0.509677      0.612704    -13976.0   \n",
       "4      1.0      0.202145      0.425687      0.519097    -13040.0   \n",
       "...    ...           ...           ...           ...         ...   \n",
       "48739  1.0      0.506771      0.648575      0.643026    -19970.0   \n",
       "48740  1.0      0.506771      0.684596      0.519097    -11186.0   \n",
       "48741  1.0      0.733503      0.632770      0.283712    -15922.0   \n",
       "48742  1.0      0.373090      0.445701      0.595456    -13968.0   \n",
       "48743  1.0      0.506771      0.456541      0.272134    -13962.0   \n",
       "\n",
       "       EXT_SOURCE_1^2  EXT_SOURCE_1 EXT_SOURCE_2  EXT_SOURCE_1 EXT_SOURCE_3  \\\n",
       "0            0.566429                   0.594305                   0.120057   \n",
       "1            0.319214                   0.164783                   0.244619   \n",
       "2            0.256817                   0.354632                   0.309633   \n",
       "3            0.276396                   0.267955                   0.322119   \n",
       "4            0.040863                   0.086051                   0.104933   \n",
       "...               ...                        ...                        ...   \n",
       "48739        0.256817                   0.328679                   0.325867   \n",
       "48740        0.256817                   0.346933                   0.263064   \n",
       "48741        0.538027                   0.464139                   0.208104   \n",
       "48742        0.139196                   0.166287                   0.222159   \n",
       "48743        0.256817                   0.231362                   0.137910   \n",
       "\n",
       "       EXT_SOURCE_1 DAYS_BIRTH  EXT_SOURCE_2^2  ...  EXT_SOURCE_2^3  \\\n",
       "0                -14481.055414        0.623554  ...        0.492392   \n",
       "1                -10205.983005        0.085063  ...        0.024809   \n",
       "2                -10154.682538        0.489702  ...        0.342687   \n",
       "3                 -7347.658072        0.259771  ...        0.132399   \n",
       "4                 -2635.970697        0.181210  ...        0.077139   \n",
       "...                        ...             ...  ...             ...   \n",
       "48739            -10120.222092        0.420649  ...        0.272823   \n",
       "48740             -5668.743331        0.468671  ...        0.320850   \n",
       "48741            -11678.842724        0.400397  ...        0.253359   \n",
       "48742             -5211.322249        0.198649  ...        0.088538   \n",
       "48743             -7075.540353        0.208429  ...        0.095156   \n",
       "\n",
       "       EXT_SOURCE_2^2 EXT_SOURCE_3  EXT_SOURCE_2^2 DAYS_BIRTH  \\\n",
       "0                         0.099469              -11997.802403   \n",
       "1                         0.036829               -1536.577117   \n",
       "2                         0.299203               -9812.640816   \n",
       "3                         0.159163               -3630.555667   \n",
       "4                         0.094065               -2362.974127   \n",
       "...                            ...                        ...   \n",
       "48739                     0.270488               -8400.368742   \n",
       "48740                     0.243286               -5242.555692   \n",
       "48741                     0.113597               -6375.125880   \n",
       "48742                     0.118287               -2774.734348   \n",
       "48743                     0.056721               -2910.091018   \n",
       "\n",
       "       EXT_SOURCE_2 EXT_SOURCE_3^2  EXT_SOURCE_2 EXT_SOURCE_3 DAYS_BIRTH  \\\n",
       "0                         0.020094                          -2423.698322   \n",
       "1                         0.054673                          -2281.043619   \n",
       "2                         0.261238                          -8567.521115   \n",
       "3                         0.191336                          -4364.443591   \n",
       "4                         0.114707                          -2881.489762   \n",
       "...                            ...                                   ...   \n",
       "48739                     0.268174                          -8328.493414   \n",
       "48740                     0.184473                          -3975.188577   \n",
       "48741                     0.050933                          -2858.384957   \n",
       "48742                     0.158031                          -3707.043157   \n",
       "48743                     0.033810                          -1734.640191   \n",
       "\n",
       "       EXT_SOURCE_2 DAYS_BIRTH^2  EXT_SOURCE_3^3  EXT_SOURCE_3^2 DAYS_BIRTH  \\\n",
       "0                   2.923427e+08        0.004059                -489.615795   \n",
       "1                   9.516956e+07        0.081161               -3386.201665   \n",
       "2                   2.809794e+08        0.228089               -7480.393855   \n",
       "3                   9.955450e+07        0.230013               -5246.681115   \n",
       "4                   7.238455e+07        0.139877               -3513.785087   \n",
       "...                          ...             ...                        ...   \n",
       "48739               2.586523e+08        0.265879               -8257.233066   \n",
       "48740               8.566112e+07        0.139877               -3014.202453   \n",
       "48741               1.604135e+08        0.022837               -1281.600508   \n",
       "48742               8.695850e+07        0.211130               -4952.607075   \n",
       "48743               8.899687e+07        0.020153               -1033.980235   \n",
       "\n",
       "       EXT_SOURCE_3 DAYS_BIRTH^2  DAYS_BIRTH^3  \n",
       "0                   5.905670e+07 -7.123328e+12  \n",
       "1                   1.412789e+08 -5.894429e+12  \n",
       "2                   2.453261e+08 -8.045687e+12  \n",
       "3                   1.196786e+08 -2.729912e+12  \n",
       "4                   8.826814e+07 -2.217342e+12  \n",
       "...                          ...           ...  \n",
       "48739               2.564392e+08 -7.964054e+12  \n",
       "48740               6.495288e+07 -1.399666e+12  \n",
       "48741               7.192382e+07 -4.036388e+12  \n",
       "48742               1.161765e+08 -2.725227e+12  \n",
       "48743               5.304904e+07 -2.721717e+12  \n",
       "\n",
       "[48744 rows x 35 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_features_test = pd.DataFrame(\n",
    "    poly_features_test,\n",
    "    columns = poly_transformer.get_feature_names_out(['EXT_SOURCE_1','EXT_SOURCE_2','EXT_SOURCE_3', 'DAYS_BIRTH'])\n",
    ")\n",
    "poly_features_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "efbc41da",
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_features['SK_ID_CURR'] = application_train['SK_ID_CURR']\n",
    "application_train_poly = application_train.merge(poly_features, on = 'SK_ID_CURR', how='left')\n",
    "\n",
    "poly_features_test['SK_ID_CURR'] = application_test['SK_ID_CURR']\n",
    "application_test_poly = application_test.merge(poly_features_test, on = 'SK_ID_CURR', how='left')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29c6842c",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "a2596028",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(307511, 280) (48744, 278)\n"
     ]
    }
   ],
   "source": [
    "print(application_train_poly.shape, application_test_poly.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e056bfcb",
   "metadata": {},
   "source": [
    "对齐一下特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "04483cc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(307511, 278) (48744, 278)\n"
     ]
    }
   ],
   "source": [
    "application_train_poly, application_test_poly = application_train_poly.align(\n",
    "    application_test_poly, join='inner',axis = 1\n",
    ")\n",
    "print(application_train_poly.shape, application_test_poly.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbffb959",
   "metadata": {},
   "source": [
    "### Domain Knowledge Features\n",
    "- 领域知识：就是利用对业务的理解，手动构造一些特征。\n",
    "  - 比如信贷：原始数据：月收入、月还款额。领域知识特征：“负债率”（月还款 / 月收入）。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "717aef12",
   "metadata": {},
   "source": [
    "我们得到了一些领域知识特征：\n",
    "- CREDIT_INCOME_PERCENT ： 贷款金额 与 收入 百分比\n",
    "- ANNUITY_INCOME_PERCENT： 月还款 与 收入百分比\n",
    "- CREDIT_TERM： 支付期数\n",
    "- DAYS_EMPLOYED_PERCENT： 工作天数 与 年龄 百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d53ec7ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(307511, 244)\n"
     ]
    }
   ],
   "source": [
    "print(application_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "90d965f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "application_train_domain = application_train.copy()\n",
    "application_test_domain = application_test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "4a56ef7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "application_train_domain['CREDIT_INCOME_PERCENT'] = application_train_domain['AMT_CREDIT']/application_train_domain['AMT_INCOME_TOTAL']\n",
    "application_train_domain['ANNUITY_INCOME_PERCENT'] = application_train_domain['AMT_ANNUITY'] / application_train_domain['AMT_INCOME_TOTAL']\n",
    "application_train_domain['CREDIT_TERM'] = application_train_domain['AMT_ANNUITY'] / application_train_domain['AMT_CREDIT']\n",
    "application_train_domain['DAYS_EMPLOYED_PERCENT'] = application_train_domain['DAYS_EMPLOYED'] / application_train_domain['DAYS_BIRTH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "49290997",
   "metadata": {},
   "outputs": [],
   "source": [
    "application_test_domain['CREDIT_INCOME_PERCENT'] = application_test_domain['AMT_CREDIT']/application_test_domain['AMT_INCOME_TOTAL']\n",
    "application_test_domain['ANNUITY_INCOME_PERCENT'] = application_test_domain['AMT_ANNUITY'] / application_test_domain['AMT_INCOME_TOTAL']\n",
    "application_test_domain['CREDIT_TERM'] = application_test_domain['AMT_ANNUITY'] / application_train_domain['AMT_CREDIT']\n",
    "application_test_domain['DAYS_EMPLOYED_PERCENT'] = application_test_domain['DAYS_EMPLOYED'] / application_test_domain['DAYS_BIRTH']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fffb35bb",
   "metadata": {},
   "source": [
    "我们来看下这几个新变量和目标的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "53a629ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x2000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,20))\n",
    "for i, feature in enumerate(['CREDIT_INCOME_PERCENT', 'ANNUITY_INCOME_PERCENT', 'CREDIT_TERM', 'DAYS_EMPLOYED_PERCENT']):\n",
    "    plt.subplot(4,1, i+1)\n",
    "    sns.kdeplot(application_train_domain.loc[application_train_domain['TARGET'] == 0, feature], label = 'target = 0')\n",
    "    sns.kdeplot(application_train_domain.loc[application_train_domain['TARGET'] == 1, feature], label = 'target = 1')\n",
    "    plt.title(f'distribution of {feature} by target')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1af1e2b",
   "metadata": {},
   "source": [
    "可以看到， 对于每个新特征而言， 违约的人和不违约的没什么区别。 🙌 没感觉他有啥用\n",
    "就是亲自试试呢~"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e195d65",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "db08f41c",
   "metadata": {},
   "source": [
    "## baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d142610d",
   "metadata": {},
   "source": [
    "作为baseline：\n",
    "- 使用了全特征\n",
    "- 填充缺失值\n",
    "- 数据归一化，统一量度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b3f22f3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(307511, 244)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['SK_ID_CURR',\n",
       " 'CNT_CHILDREN',\n",
       " 'AMT_INCOME_TOTAL',\n",
       " 'AMT_CREDIT',\n",
       " 'AMT_ANNUITY',\n",
       " 'AMT_GOODS_PRICE',\n",
       " 'REGION_POPULATION_RELATIVE',\n",
       " 'DAYS_BIRTH',\n",
       " 'DAYS_EMPLOYED',\n",
       " 'DAYS_REGISTRATION',\n",
       " 'DAYS_ID_PUBLISH',\n",
       " 'OWN_CAR_AGE',\n",
       " 'FLAG_MOBIL',\n",
       " 'FLAG_EMP_PHONE',\n",
       " 'FLAG_WORK_PHONE',\n",
       " 'FLAG_CONT_MOBILE',\n",
       " 'FLAG_PHONE',\n",
       " 'FLAG_EMAIL',\n",
       " 'CNT_FAM_MEMBERS',\n",
       " 'REGION_RATING_CLIENT',\n",
       " 'REGION_RATING_CLIENT_W_CITY',\n",
       " 'HOUR_APPR_PROCESS_START',\n",
       " 'REG_REGION_NOT_LIVE_REGION',\n",
       " 'REG_REGION_NOT_WORK_REGION',\n",
       " 'LIVE_REGION_NOT_WORK_REGION',\n",
       " 'REG_CITY_NOT_LIVE_CITY',\n",
       " 'REG_CITY_NOT_WORK_CITY',\n",
       " 'LIVE_CITY_NOT_WORK_CITY',\n",
       " 'EXT_SOURCE_1',\n",
       " 'EXT_SOURCE_2',\n",
       " 'EXT_SOURCE_3',\n",
       " 'APARTMENTS_AVG',\n",
       " 'BASEMENTAREA_AVG',\n",
       " 'YEARS_BEGINEXPLUATATION_AVG',\n",
       " 'YEARS_BUILD_AVG',\n",
       " 'COMMONAREA_AVG',\n",
       " 'ELEVATORS_AVG',\n",
       " 'ENTRANCES_AVG',\n",
       " 'FLOORSMAX_AVG',\n",
       " 'FLOORSMIN_AVG',\n",
       " 'LANDAREA_AVG',\n",
       " 'LIVINGAPARTMENTS_AVG',\n",
       " 'LIVINGAREA_AVG',\n",
       " 'NONLIVINGAPARTMENTS_AVG',\n",
       " 'NONLIVINGAREA_AVG',\n",
       " 'APARTMENTS_MODE',\n",
       " 'BASEMENTAREA_MODE',\n",
       " 'YEARS_BEGINEXPLUATATION_MODE',\n",
       " 'YEARS_BUILD_MODE',\n",
       " 'COMMONAREA_MODE',\n",
       " 'ELEVATORS_MODE',\n",
       " 'ENTRANCES_MODE',\n",
       " 'FLOORSMAX_MODE',\n",
       " 'FLOORSMIN_MODE',\n",
       " 'LANDAREA_MODE',\n",
       " 'LIVINGAPARTMENTS_MODE',\n",
       " 'LIVINGAREA_MODE',\n",
       " 'NONLIVINGAPARTMENTS_MODE',\n",
       " 'NONLIVINGAREA_MODE',\n",
       " 'APARTMENTS_MEDI',\n",
       " 'BASEMENTAREA_MEDI',\n",
       " 'YEARS_BEGINEXPLUATATION_MEDI',\n",
       " 'YEARS_BUILD_MEDI',\n",
       " 'COMMONAREA_MEDI',\n",
       " 'ELEVATORS_MEDI',\n",
       " 'ENTRANCES_MEDI',\n",
       " 'FLOORSMAX_MEDI',\n",
       " 'FLOORSMIN_MEDI',\n",
       " 'LANDAREA_MEDI',\n",
       " 'LIVINGAPARTMENTS_MEDI',\n",
       " 'LIVINGAREA_MEDI',\n",
       " 'NONLIVINGAPARTMENTS_MEDI',\n",
       " 'NONLIVINGAREA_MEDI',\n",
       " 'TOTALAREA_MODE',\n",
       " 'OBS_30_CNT_SOCIAL_CIRCLE',\n",
       " 'DEF_30_CNT_SOCIAL_CIRCLE',\n",
       " 'OBS_60_CNT_SOCIAL_CIRCLE',\n",
       " 'DEF_60_CNT_SOCIAL_CIRCLE',\n",
       " 'DAYS_LAST_PHONE_CHANGE',\n",
       " 'FLAG_DOCUMENT_2',\n",
       " 'FLAG_DOCUMENT_3',\n",
       " 'FLAG_DOCUMENT_4',\n",
       " 'FLAG_DOCUMENT_5',\n",
       " 'FLAG_DOCUMENT_6',\n",
       " 'FLAG_DOCUMENT_7',\n",
       " 'FLAG_DOCUMENT_8',\n",
       " 'FLAG_DOCUMENT_9',\n",
       " 'FLAG_DOCUMENT_10',\n",
       " 'FLAG_DOCUMENT_11',\n",
       " 'FLAG_DOCUMENT_12',\n",
       " 'FLAG_DOCUMENT_13',\n",
       " 'FLAG_DOCUMENT_14',\n",
       " 'FLAG_DOCUMENT_15',\n",
       " 'FLAG_DOCUMENT_16',\n",
       " 'FLAG_DOCUMENT_17',\n",
       " 'FLAG_DOCUMENT_18',\n",
       " 'FLAG_DOCUMENT_19',\n",
       " 'FLAG_DOCUMENT_20',\n",
       " 'FLAG_DOCUMENT_21',\n",
       " 'AMT_REQ_CREDIT_BUREAU_HOUR',\n",
       " 'AMT_REQ_CREDIT_BUREAU_DAY',\n",
       " 'AMT_REQ_CREDIT_BUREAU_WEEK',\n",
       " 'AMT_REQ_CREDIT_BUREAU_MON',\n",
       " 'AMT_REQ_CREDIT_BUREAU_QRT',\n",
       " 'AMT_REQ_CREDIT_BUREAU_YEAR',\n",
       " 'NAME_CONTRACT_TYPE_Cash loans',\n",
       " 'NAME_CONTRACT_TYPE_Revolving loans',\n",
       " 'CODE_GENDER_F',\n",
       " 'CODE_GENDER_M',\n",
       " 'FLAG_OWN_CAR_N',\n",
       " 'FLAG_OWN_CAR_Y',\n",
       " 'FLAG_OWN_REALTY_N',\n",
       " 'FLAG_OWN_REALTY_Y',\n",
       " 'NAME_TYPE_SUITE_Children',\n",
       " 'NAME_TYPE_SUITE_Family',\n",
       " 'NAME_TYPE_SUITE_Group of people',\n",
       " 'NAME_TYPE_SUITE_Other_A',\n",
       " 'NAME_TYPE_SUITE_Other_B',\n",
       " 'NAME_TYPE_SUITE_Spouse, partner',\n",
       " 'NAME_TYPE_SUITE_Unaccompanied',\n",
       " 'NAME_INCOME_TYPE_Businessman',\n",
       " 'NAME_INCOME_TYPE_Commercial associate',\n",
       " 'NAME_INCOME_TYPE_Pensioner',\n",
       " 'NAME_INCOME_TYPE_State servant',\n",
       " 'NAME_INCOME_TYPE_Student',\n",
       " 'NAME_INCOME_TYPE_Unemployed',\n",
       " 'NAME_INCOME_TYPE_Working',\n",
       " 'NAME_EDUCATION_TYPE_Academic degree',\n",
       " 'NAME_EDUCATION_TYPE_Higher education',\n",
       " 'NAME_EDUCATION_TYPE_Incomplete higher',\n",
       " 'NAME_EDUCATION_TYPE_Lower secondary',\n",
       " 'NAME_EDUCATION_TYPE_Secondary / secondary special',\n",
       " 'NAME_FAMILY_STATUS_Civil marriage',\n",
       " 'NAME_FAMILY_STATUS_Married',\n",
       " 'NAME_FAMILY_STATUS_Separated',\n",
       " 'NAME_FAMILY_STATUS_Single / not married',\n",
       " 'NAME_FAMILY_STATUS_Widow',\n",
       " 'NAME_HOUSING_TYPE_Co-op apartment',\n",
       " 'NAME_HOUSING_TYPE_House / apartment',\n",
       " 'NAME_HOUSING_TYPE_Municipal apartment',\n",
       " 'NAME_HOUSING_TYPE_Office apartment',\n",
       " 'NAME_HOUSING_TYPE_Rented apartment',\n",
       " 'NAME_HOUSING_TYPE_With parents',\n",
       " 'OCCUPATION_TYPE_Accountants',\n",
       " 'OCCUPATION_TYPE_Cleaning staff',\n",
       " 'OCCUPATION_TYPE_Cooking staff',\n",
       " 'OCCUPATION_TYPE_Core staff',\n",
       " 'OCCUPATION_TYPE_Drivers',\n",
       " 'OCCUPATION_TYPE_HR staff',\n",
       " 'OCCUPATION_TYPE_High skill tech staff',\n",
       " 'OCCUPATION_TYPE_IT staff',\n",
       " 'OCCUPATION_TYPE_Laborers',\n",
       " 'OCCUPATION_TYPE_Low-skill Laborers',\n",
       " 'OCCUPATION_TYPE_Managers',\n",
       " 'OCCUPATION_TYPE_Medicine staff',\n",
       " 'OCCUPATION_TYPE_Private service staff',\n",
       " 'OCCUPATION_TYPE_Realty agents',\n",
       " 'OCCUPATION_TYPE_Sales staff',\n",
       " 'OCCUPATION_TYPE_Secretaries',\n",
       " 'OCCUPATION_TYPE_Security staff',\n",
       " 'OCCUPATION_TYPE_Waiters/barmen staff',\n",
       " 'WEEKDAY_APPR_PROCESS_START_FRIDAY',\n",
       " 'WEEKDAY_APPR_PROCESS_START_MONDAY',\n",
       " 'WEEKDAY_APPR_PROCESS_START_SATURDAY',\n",
       " 'WEEKDAY_APPR_PROCESS_START_SUNDAY',\n",
       " 'WEEKDAY_APPR_PROCESS_START_THURSDAY',\n",
       " 'WEEKDAY_APPR_PROCESS_START_TUESDAY',\n",
       " 'WEEKDAY_APPR_PROCESS_START_WEDNESDAY',\n",
       " 'ORGANIZATION_TYPE_Advertising',\n",
       " 'ORGANIZATION_TYPE_Agriculture',\n",
       " 'ORGANIZATION_TYPE_Bank',\n",
       " 'ORGANIZATION_TYPE_Business Entity Type 1',\n",
       " 'ORGANIZATION_TYPE_Business Entity Type 2',\n",
       " 'ORGANIZATION_TYPE_Business Entity Type 3',\n",
       " 'ORGANIZATION_TYPE_Cleaning',\n",
       " 'ORGANIZATION_TYPE_Construction',\n",
       " 'ORGANIZATION_TYPE_Culture',\n",
       " 'ORGANIZATION_TYPE_Electricity',\n",
       " 'ORGANIZATION_TYPE_Emergency',\n",
       " 'ORGANIZATION_TYPE_Government',\n",
       " 'ORGANIZATION_TYPE_Hotel',\n",
       " 'ORGANIZATION_TYPE_Housing',\n",
       " 'ORGANIZATION_TYPE_Industry: type 1',\n",
       " 'ORGANIZATION_TYPE_Industry: type 10',\n",
       " 'ORGANIZATION_TYPE_Industry: type 11',\n",
       " 'ORGANIZATION_TYPE_Industry: type 12',\n",
       " 'ORGANIZATION_TYPE_Industry: type 13',\n",
       " 'ORGANIZATION_TYPE_Industry: type 2',\n",
       " 'ORGANIZATION_TYPE_Industry: type 3',\n",
       " 'ORGANIZATION_TYPE_Industry: type 4',\n",
       " 'ORGANIZATION_TYPE_Industry: type 5',\n",
       " 'ORGANIZATION_TYPE_Industry: type 6',\n",
       " 'ORGANIZATION_TYPE_Industry: type 7',\n",
       " 'ORGANIZATION_TYPE_Industry: type 8',\n",
       " 'ORGANIZATION_TYPE_Industry: type 9',\n",
       " 'ORGANIZATION_TYPE_Insurance',\n",
       " 'ORGANIZATION_TYPE_Kindergarten',\n",
       " 'ORGANIZATION_TYPE_Legal Services',\n",
       " 'ORGANIZATION_TYPE_Medicine',\n",
       " 'ORGANIZATION_TYPE_Military',\n",
       " 'ORGANIZATION_TYPE_Mobile',\n",
       " 'ORGANIZATION_TYPE_Other',\n",
       " 'ORGANIZATION_TYPE_Police',\n",
       " 'ORGANIZATION_TYPE_Postal',\n",
       " 'ORGANIZATION_TYPE_Realtor',\n",
       " 'ORGANIZATION_TYPE_Religion',\n",
       " 'ORGANIZATION_TYPE_Restaurant',\n",
       " 'ORGANIZATION_TYPE_School',\n",
       " 'ORGANIZATION_TYPE_Security',\n",
       " 'ORGANIZATION_TYPE_Security Ministries',\n",
       " 'ORGANIZATION_TYPE_Self-employed',\n",
       " 'ORGANIZATION_TYPE_Services',\n",
       " 'ORGANIZATION_TYPE_Telecom',\n",
       " 'ORGANIZATION_TYPE_Trade: type 1',\n",
       " 'ORGANIZATION_TYPE_Trade: type 2',\n",
       " 'ORGANIZATION_TYPE_Trade: type 3',\n",
       " 'ORGANIZATION_TYPE_Trade: type 4',\n",
       " 'ORGANIZATION_TYPE_Trade: type 5',\n",
       " 'ORGANIZATION_TYPE_Trade: type 6',\n",
       " 'ORGANIZATION_TYPE_Trade: type 7',\n",
       " 'ORGANIZATION_TYPE_Transport: type 1',\n",
       " 'ORGANIZATION_TYPE_Transport: type 2',\n",
       " 'ORGANIZATION_TYPE_Transport: type 3',\n",
       " 'ORGANIZATION_TYPE_Transport: type 4',\n",
       " 'ORGANIZATION_TYPE_University',\n",
       " 'ORGANIZATION_TYPE_XNA',\n",
       " 'FONDKAPREMONT_MODE_not specified',\n",
       " 'FONDKAPREMONT_MODE_org spec account',\n",
       " 'FONDKAPREMONT_MODE_reg oper account',\n",
       " 'FONDKAPREMONT_MODE_reg oper spec account',\n",
       " 'HOUSETYPE_MODE_block of flats',\n",
       " 'HOUSETYPE_MODE_specific housing',\n",
       " 'HOUSETYPE_MODE_terraced house',\n",
       " 'WALLSMATERIAL_MODE_Block',\n",
       " 'WALLSMATERIAL_MODE_Mixed',\n",
       " 'WALLSMATERIAL_MODE_Monolithic',\n",
       " 'WALLSMATERIAL_MODE_Others',\n",
       " 'WALLSMATERIAL_MODE_Panel',\n",
       " 'WALLSMATERIAL_MODE_Stone, brick',\n",
       " 'WALLSMATERIAL_MODE_Wooden',\n",
       " 'EMERGENCYSTATE_MODE_No',\n",
       " 'EMERGENCYSTATE_MODE_Yes',\n",
       " 'TARGET',\n",
       " 'DAYS_EMPLOYED_ANOM']"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(application_train.shape)\n",
    "list(application_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1ad431fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "def impute_and_scaler(train, test):\n",
    "    features = list(train.columns)\n",
    "    imputer = SimpleImputer(strategy='median')\n",
    "    scaler = MinMaxScaler(feature_range=(0,1))\n",
    "\n",
    "    imputer.fit(train)\n",
    "    train = imputer.transform(train)\n",
    "    test = imputer.transform(test)\n",
    "\n",
    "    scaler.fit(train)\n",
    "    train = scaler.transform(train)\n",
    "    test = scaler.transform(test)\n",
    "\n",
    "    return train, test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df67c877",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = application_train.copy()\n",
    "train_labels = train['TARGET']\n",
    "train = train.drop(columns = ['TARGET'])\n",
    "test = application_test.copy()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74b9643c",
   "metadata": {},
   "source": [
    "### logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "0b29704e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "174eba54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\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-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6 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-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 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-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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: 0.5em;\n",
       "  text-align: center;\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-6 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-6 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-6 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-6 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.001)</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">penalty&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">dual&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">C&nbsp;</td>\n",
       "            <td class=\"value\">0.001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">class_weight&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">solver&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_iter&nbsp;</td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('multi_class',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">multi_class&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">warm_start&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "LogisticRegression(C=0.001)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_regress_model = LogisticRegression(C=0.001)\n",
    "log_regress_model.fit(train, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "69aa00f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_regress_model_pred = log_regress_model.predict_proba(test)\n",
    "log_regress_model_pred = log_regress_model_pred[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e75f4d2",
   "metadata": {},
   "source": [
    "得到了违约的概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "8c17cf31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>0.094349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100005</td>\n",
       "      <td>0.269978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100013</td>\n",
       "      <td>0.069365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100028</td>\n",
       "      <td>0.082479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100038</td>\n",
       "      <td>0.178499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR    TARGET\n",
       "0      100001  0.094349\n",
       "1      100005  0.269978\n",
       "2      100013  0.069365\n",
       "3      100028  0.082479\n",
       "4      100038  0.178499"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "submit = application_test[['SK_ID_CURR']]\n",
    "submit['TARGET'] = log_regress_model_pred\n",
    "submit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "e971c9be",
   "metadata": {},
   "outputs": [],
   "source": [
    "submit.to_csv('data/log_regress_model_baseline.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d1f6f7b",
   "metadata": {},
   "source": [
    "我们得到了 71%得分"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42b25a77",
   "metadata": {},
   "source": [
    "### 改进：random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "bc8a1664",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a0a842a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 16 concurrent workers.\n"
     ]
    }
   ],
   "source": [
    "random_forest_model = RandomForestClassifier(\n",
    "    n_estimators = 100, random_state = 50, verbose = 1, n_jobs = -1\n",
    ")\n",
    "random_forest_model.fit(train, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31cfa8ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "767e6781",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.\n",
      "[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    }
   ],
   "source": [
    "random_forest_model_pred = random_forest_model.predict_proba(test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06291854",
   "metadata": {},
   "outputs": [],
   "source": [
    "submit = application_test[['SK_ID_CURR']]\n",
    "submit['TARGET'] = random_forest_model_pred\n",
    "submit.to_csv('data/random_forest_baseline.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78e3a1ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100005</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100013</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100028</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100038</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48739</th>\n",
       "      <td>456221</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48740</th>\n",
       "      <td>456222</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48741</th>\n",
       "      <td>456223</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48742</th>\n",
       "      <td>456224</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48743</th>\n",
       "      <td>456250</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48744 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SK_ID_CURR  TARGET\n",
       "0          100001    0.15\n",
       "1          100005    0.15\n",
       "2          100013    0.09\n",
       "3          100028    0.15\n",
       "4          100038    0.21\n",
       "...           ...     ...\n",
       "48739      456221    0.12\n",
       "48740      456222    0.16\n",
       "48741      456223    0.22\n",
       "48742      456224    0.14\n",
       "48743      456250    0.19\n",
       "\n",
       "[48744 rows x 2 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abd25fd3",
   "metadata": {},
   "source": [
    "得分68.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1d12c4c",
   "metadata": {},
   "source": [
    "### 改进：random forest with 多项式特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "806db41a",
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_features_names = list(application_train_poly.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0d32e3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307511, 278)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_train_poly.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07678498",
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(strategy='median')\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "\n",
    "poly_features = imputer.fit_transform(application_train_poly)\n",
    "poly_features_test = imputer.transform(application_test_poly)\n",
    "\n",
    "poly_features = scaler.fit_transform(poly_features)\n",
    "poly_features_test = scaler.transform(poly_features_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5292cad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 16 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:   12.8s\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[84]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m random_forest_poly = RandomForestClassifier(n_estimators = \u001b[32m100\u001b[39m, random_state = \u001b[32m50\u001b[39m, verbose = \u001b[32m1\u001b[39m, n_jobs = -\u001b[32m1\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[43mrandom_forest_poly\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoly_features\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_labels\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\63517\\miniconda3\\envs\\data-analysis\\Lib\\site-packages\\sklearn\\base.py:1365\u001b[39m, in \u001b[36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[39m\u001b[34m(estimator, *args, **kwargs)\u001b[39m\n\u001b[32m   1358\u001b[39m     estimator._validate_params()\n\u001b[32m   1360\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[32m   1361\u001b[39m     skip_parameter_validation=(\n\u001b[32m   1362\u001b[39m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[32m   1363\u001b[39m     )\n\u001b[32m   1364\u001b[39m ):\n\u001b[32m-> \u001b[39m\u001b[32m1365\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\63517\\miniconda3\\envs\\data-analysis\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:486\u001b[39m, in \u001b[36mBaseForest.fit\u001b[39m\u001b[34m(self, X, y, sample_weight)\u001b[39m\n\u001b[32m    475\u001b[39m trees = [\n\u001b[32m    476\u001b[39m     \u001b[38;5;28mself\u001b[39m._make_estimator(append=\u001b[38;5;28;01mFalse\u001b[39;00m, random_state=random_state)\n\u001b[32m    477\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n_more_estimators)\n\u001b[32m    478\u001b[39m ]\n\u001b[32m    480\u001b[39m \u001b[38;5;66;03m# Parallel loop: we prefer the threading backend as the Cython code\u001b[39;00m\n\u001b[32m    481\u001b[39m \u001b[38;5;66;03m# for fitting the trees is internally releasing the Python GIL\u001b[39;00m\n\u001b[32m    482\u001b[39m \u001b[38;5;66;03m# making threading more efficient than multiprocessing in\u001b[39;00m\n\u001b[32m    483\u001b[39m \u001b[38;5;66;03m# that case. However, for joblib 0.12+ we respect any\u001b[39;00m\n\u001b[32m    484\u001b[39m \u001b[38;5;66;03m# parallel_backend contexts set at a higher level,\u001b[39;00m\n\u001b[32m    485\u001b[39m \u001b[38;5;66;03m# since correctness does not rely on using threads.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m486\u001b[39m trees = \u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    487\u001b[39m \u001b[43m    \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    488\u001b[39m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    489\u001b[39m \u001b[43m    \u001b[49m\u001b[43mprefer\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mthreads\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m    490\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    491\u001b[39m \u001b[43m    \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_parallel_build_trees\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    492\u001b[39m \u001b[43m        \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    493\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbootstrap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    494\u001b[39m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    495\u001b[39m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    496\u001b[39m \u001b[43m        \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    497\u001b[39m \u001b[43m        \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    498\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrees\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    499\u001b[39m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    500\u001b[39m \u001b[43m        \u001b[49m\u001b[43mclass_weight\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mclass_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    501\u001b[39m \u001b[43m        \u001b[49m\u001b[43mn_samples_bootstrap\u001b[49m\u001b[43m=\u001b[49m\u001b[43mn_samples_bootstrap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    502\u001b[39m \u001b[43m        \u001b[49m\u001b[43mmissing_values_in_feature_mask\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmissing_values_in_feature_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    503\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    504\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrees\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    505\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    507\u001b[39m \u001b[38;5;66;03m# Collect newly grown trees\u001b[39;00m\n\u001b[32m    508\u001b[39m \u001b[38;5;28mself\u001b[39m.estimators_.extend(trees)\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\63517\\miniconda3\\envs\\data-analysis\\Lib\\site-packages\\sklearn\\utils\\parallel.py:82\u001b[39m, in \u001b[36mParallel.__call__\u001b[39m\u001b[34m(self, iterable)\u001b[39m\n\u001b[32m     73\u001b[39m warning_filters = warnings.filters\n\u001b[32m     74\u001b[39m iterable_with_config_and_warning_filters = (\n\u001b[32m     75\u001b[39m     (\n\u001b[32m     76\u001b[39m         _with_config_and_warning_filters(delayed_func, config, warning_filters),\n\u001b[32m   (...)\u001b[39m\u001b[32m     80\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m delayed_func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m iterable\n\u001b[32m     81\u001b[39m )\n\u001b[32m---> \u001b[39m\u001b[32m82\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miterable_with_config_and_warning_filters\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\63517\\miniconda3\\envs\\data-analysis\\Lib\\site-packages\\joblib\\parallel.py:2072\u001b[39m, in \u001b[36mParallel.__call__\u001b[39m\u001b[34m(self, iterable)\u001b[39m\n\u001b[32m   2066\u001b[39m \u001b[38;5;66;03m# The first item from the output is blank, but it makes the interpreter\u001b[39;00m\n\u001b[32m   2067\u001b[39m \u001b[38;5;66;03m# progress until it enters the Try/Except block of the generator and\u001b[39;00m\n\u001b[32m   2068\u001b[39m \u001b[38;5;66;03m# reaches the first `yield` statement. This starts the asynchronous\u001b[39;00m\n\u001b[32m   2069\u001b[39m \u001b[38;5;66;03m# dispatch of the tasks to the workers.\u001b[39;00m\n\u001b[32m   2070\u001b[39m \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[32m-> \u001b[39m\u001b[32m2072\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.return_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\63517\\miniconda3\\envs\\data-analysis\\Lib\\site-packages\\joblib\\parallel.py:1682\u001b[39m, in \u001b[36mParallel._get_outputs\u001b[39m\u001b[34m(self, iterator, pre_dispatch)\u001b[39m\n\u001b[32m   1679\u001b[39m     \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[32m   1681\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._backend.retrieval_context():\n\u001b[32m-> \u001b[39m\u001b[32m1682\u001b[39m         \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m._retrieve()\n\u001b[32m   1684\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mGeneratorExit\u001b[39;00m:\n\u001b[32m   1685\u001b[39m     \u001b[38;5;66;03m# The generator has been garbage collected before being fully\u001b[39;00m\n\u001b[32m   1686\u001b[39m     \u001b[38;5;66;03m# consumed. This aborts the remaining tasks if possible and warn\u001b[39;00m\n\u001b[32m   1687\u001b[39m     \u001b[38;5;66;03m# the user if necessary.\u001b[39;00m\n\u001b[32m   1688\u001b[39m     \u001b[38;5;28mself\u001b[39m._exception = \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\63517\\miniconda3\\envs\\data-analysis\\Lib\\site-packages\\joblib\\parallel.py:1800\u001b[39m, in \u001b[36mParallel._retrieve\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m   1789\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.return_ordered:\n\u001b[32m   1790\u001b[39m     \u001b[38;5;66;03m# Case ordered: wait for completion (or error) of the next job\u001b[39;00m\n\u001b[32m   1791\u001b[39m     \u001b[38;5;66;03m# that have been dispatched and not retrieved yet. If no job\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m   1795\u001b[39m     \u001b[38;5;66;03m# control only have to be done on the amount of time the next\u001b[39;00m\n\u001b[32m   1796\u001b[39m     \u001b[38;5;66;03m# dispatched job is pending.\u001b[39;00m\n\u001b[32m   1797\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m (nb_jobs == \u001b[32m0\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[32m   1798\u001b[39m         \u001b[38;5;28mself\u001b[39m._jobs[\u001b[32m0\u001b[39m].get_status(timeout=\u001b[38;5;28mself\u001b[39m.timeout) == TASK_PENDING\n\u001b[32m   1799\u001b[39m     ):\n\u001b[32m-> \u001b[39m\u001b[32m1800\u001b[39m         \u001b[43mtime\u001b[49m\u001b[43m.\u001b[49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m   1801\u001b[39m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m   1803\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m nb_jobs == \u001b[32m0\u001b[39m:\n\u001b[32m   1804\u001b[39m     \u001b[38;5;66;03m# Case unordered: jobs are added to the list of jobs to\u001b[39;00m\n\u001b[32m   1805\u001b[39m     \u001b[38;5;66;03m# retrieve `self._jobs` only once completed or in error, which\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m   1811\u001b[39m     \u001b[38;5;66;03m# timeouts before any other dispatched job has completed and\u001b[39;00m\n\u001b[32m   1812\u001b[39m     \u001b[38;5;66;03m# been added to `self._jobs` to be retrieved.\u001b[39;00m\n",
      "\u001b[31mKeyboardInterrupt\u001b[39m: "
     ]
    }
   ],
   "source": [
    "random_forest_poly = RandomForestClassifier(n_estimators = 100, random_state = 50, verbose = 1, n_jobs = -1)\n",
    "random_forest_poly.fit(poly_features, train_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "225c0efa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.\n",
      "[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "random_forest_poly_pred = random_forest_poly.predict_proba(poly_features_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53e73751",
   "metadata": {},
   "outputs": [],
   "source": [
    "submit = application_test[['SK_ID_CURR']]\n",
    "submit['TARGET'] = random_forest_poly_pred\n",
    "\n",
    "submit.to_csv('random_forest_baseline_engineered.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77711f62",
   "metadata": {},
   "source": [
    "得分65，特征工程并没有起到作用"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19a00746",
   "metadata": {},
   "source": [
    "### 改进：random forest with 领域知识特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d49fa0e",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5df6eef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SK_ID_CURR', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL', 'AMT_CREDIT',\n",
       "       'AMT_ANNUITY', 'AMT_GOODS_PRICE', 'REGION_POPULATION_RELATIVE',\n",
       "       'DAYS_BIRTH', 'DAYS_EMPLOYED', 'DAYS_REGISTRATION',\n",
       "       ...\n",
       "       'WALLSMATERIAL_MODE_Stone, brick', 'WALLSMATERIAL_MODE_Wooden',\n",
       "       'EMERGENCYSTATE_MODE_No', 'EMERGENCYSTATE_MODE_Yes', 'TARGET',\n",
       "       'DAYS_EMPLOYED_ANOM', 'CREDIT_INCOME_PERCENT', 'ANNUITY_INCOME_PERCENT',\n",
       "       'CREDIT_TERM', 'DAYS_EMPLOYED_PERCENT'],\n",
       "      dtype='str', length=248)"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_train_domain.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3590417c",
   "metadata": {},
   "outputs": [],
   "source": [
    "application_train_domain = application_train_domain.drop(columns=['TARGET'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5dfa8255",
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(strategy='median')\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "\n",
    "domain_features = imputer.fit_transform(application_train_domain)\n",
    "domain_features_test = imputer.transform(application_test_domain)\n",
    "\n",
    "domain_features = scaler.fit_transform(domain_features)\n",
    "domain_features_test = scaler.transform(domain_features_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ba840c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 16 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    8.0s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   29.8s finished\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\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-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 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-5 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-5 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-5 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-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 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-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 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: 0.5em;\n",
       "  text-align: center;\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-5 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-5 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-5 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-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_jobs=-1, random_state=50, 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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('criterion',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">criterion&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_depth',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_depth&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_split',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_features',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_features&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
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       "\n",
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       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
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       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
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       "    \n",
       "\n",
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       "            ></i></td>\n",
       "            <td class=\"param\">bootstrap&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
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       "    \n",
       "\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">oob_score&nbsp;</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
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       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">-1</td>\n",
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       "    \n",
       "\n",
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       "            <td class=\"param\">verbose&nbsp;</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
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       "        <tr class=\"default\">\n",
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       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
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       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
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      ],
      "text/plain": [
       "RandomForestClassifier(n_jobs=-1, random_state=50, verbose=1)"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest_domain = RandomForestClassifier(n_estimators = 100, random_state = 50, verbose = 1, n_jobs = -1)\n",
    "random_forest_domain.fit(domain_features, train_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28d2f04e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.\n",
      "[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.2s finished\n"
     ]
    }
   ],
   "source": [
    "random_forest_domain_pred = random_forest_domain.predict_proba(domain_features_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31a71cb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "submit = application_test[['SK_ID_CURR']]\n",
    "submit['TARGET'] = random_forest_domain_pred\n",
    "\n",
    "submit.to_csv('random_forest_baseline_domain_engineered.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54c1ce29",
   "metadata": {},
   "source": [
    "得分65， 没什么改变呢"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "123a15c9",
   "metadata": {},
   "source": [
    "### random forest 模型解释\n",
    "- 我们之前预期到`EXT_SOURCE`和`DAYS_BIRTH`是最重要的，\n",
    "- "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fadc9d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>EXT_SOURCE_2</td>\n",
       "      <td>0.049029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>EXT_SOURCE_3</td>\n",
       "      <td>0.046400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DAYS_ID_PUBLISH</td>\n",
       "      <td>0.031736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DAYS_BIRTH</td>\n",
       "      <td>0.031524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DAYS_REGISTRATION</td>\n",
       "      <td>0.031100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FLAG_MOBIL</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>FLAG_DOCUMENT_12</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>FLAG_DOCUMENT_10</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>FLAG_DOCUMENT_4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>NAME_INCOME_TYPE_Businessman</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>243 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          feature  importance\n",
       "29                   EXT_SOURCE_2    0.049029\n",
       "30                   EXT_SOURCE_3    0.046400\n",
       "10                DAYS_ID_PUBLISH    0.031736\n",
       "7                      DAYS_BIRTH    0.031524\n",
       "9               DAYS_REGISTRATION    0.031100\n",
       "..                            ...         ...\n",
       "12                     FLAG_MOBIL    0.000000\n",
       "89               FLAG_DOCUMENT_12    0.000000\n",
       "87               FLAG_DOCUMENT_10    0.000000\n",
       "81                FLAG_DOCUMENT_4    0.000000\n",
       "120  NAME_INCOME_TYPE_Businessman    0.000000\n",
       "\n",
       "[243 rows x 2 columns]"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = random_forest_model.feature_importances_\n",
    "feature_importances = pd.DataFrame({\n",
    "    'feature': features,\n",
    "    'importance':feature_importances\n",
    "    }\n",
    ")\n",
    "feature_importances.sort_values(by='importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d123182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.9999999999999997)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances['importance'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b7fd8a2",
   "metadata": {},
   "source": [
    "画个倒立直方图看看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd1323aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'impotance of features')"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importances_plot = feature_importances.sort_values(by='importance', ascending=False)[:15]\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(\n",
    "    data = feature_importances_plot,\n",
    "    x = 'importance',\n",
    "    y = 'feature'\n",
    ")\n",
    "plt.title('impotance of features')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9220a5f",
   "metadata": {},
   "source": [
    "## model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "684e6516",
   "metadata": {},
   "source": [
    "### Adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6f55637",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f78c3687",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a35f4e53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(307511, 243)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2b2d391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\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-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6 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-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 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-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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: 0.5em;\n",
       "  text-align: center;\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-6 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-6 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-6 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-6 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(n_estimators=100)</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>AdaBoostClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.AdaBoostClassifier.html\">?<span>Documentation for AdaBoostClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('estimator',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">estimator&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('algorithm',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">algorithm&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "AdaBoostClassifier(n_estimators=100)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adaboost_model = AdaBoostClassifier(\n",
    "    n_estimators=100\n",
    ")\n",
    "adaboost_model.fit(train, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c996c2a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "adaboost_model_pred = adaboost_model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc264525",
   "metadata": {},
   "outputs": [],
   "source": [
    "submit = application_test[['SK_ID_CURR']]\n",
    "submit['TARGET'] = adaboost_model_pred\n",
    "\n",
    "submit.to_csv('adaboost.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "205ad99b",
   "metadata": {},
   "source": [
    "得分 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd20024c",
   "metadata": {},
   "outputs": [],
   "source": [
    "adaboost_model.feature_importances"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "793edffe",
   "metadata": {},
   "source": [
    "### Gradient boost "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8ad2a4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_feather('checkpoints/01_train_app_base.feather')\n",
    "test = pd.read_feather('checkpoints/01_test_app_base.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "81447ea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>REGION_POPULATION_RELATIVE</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>DAYS_REGISTRATION</th>\n",
       "      <th>...</th>\n",
       "      <th>WALLSMATERIAL_MODE_Mixed</th>\n",
       "      <th>WALLSMATERIAL_MODE_Monolithic</th>\n",
       "      <th>WALLSMATERIAL_MODE_Others</th>\n",
       "      <th>WALLSMATERIAL_MODE_Panel</th>\n",
       "      <th>WALLSMATERIAL_MODE_Stone, brick</th>\n",
       "      <th>WALLSMATERIAL_MODE_Wooden</th>\n",
       "      <th>EMERGENCYSTATE_MODE_No</th>\n",
       "      <th>EMERGENCYSTATE_MODE_Yes</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>DAYS_EMPLOYED_ANOM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>351000.0</td>\n",
       "      <td>0.018801</td>\n",
       "      <td>9461</td>\n",
       "      <td>-637.0</td>\n",
       "      <td>-3648.0</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>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>1129500.0</td>\n",
       "      <td>0.003541</td>\n",
       "      <td>16765</td>\n",
       "      <td>-1188.0</td>\n",
       "      <td>-1186.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>0.010032</td>\n",
       "      <td>19046</td>\n",
       "      <td>-225.0</td>\n",
       "      <td>-4260.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>297000.0</td>\n",
       "      <td>0.008019</td>\n",
       "      <td>19005</td>\n",
       "      <td>-3039.0</td>\n",
       "      <td>-9833.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>0.028663</td>\n",
       "      <td>19932</td>\n",
       "      <td>-3038.0</td>\n",
       "      <td>-4311.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307506</th>\n",
       "      <td>456251</td>\n",
       "      <td>0</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>254700.0</td>\n",
       "      <td>27558.0</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>0.032561</td>\n",
       "      <td>9327</td>\n",
       "      <td>-236.0</td>\n",
       "      <td>-8456.0</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>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307507</th>\n",
       "      <td>456252</td>\n",
       "      <td>0</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>269550.0</td>\n",
       "      <td>12001.5</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>0.025164</td>\n",
       "      <td>20775</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4388.0</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>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307508</th>\n",
       "      <td>456253</td>\n",
       "      <td>0</td>\n",
       "      <td>153000.0</td>\n",
       "      <td>677664.0</td>\n",
       "      <td>29979.0</td>\n",
       "      <td>585000.0</td>\n",
       "      <td>0.005002</td>\n",
       "      <td>14966</td>\n",
       "      <td>-7921.0</td>\n",
       "      <td>-6737.0</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>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307509</th>\n",
       "      <td>456254</td>\n",
       "      <td>0</td>\n",
       "      <td>171000.0</td>\n",
       "      <td>370107.0</td>\n",
       "      <td>20205.0</td>\n",
       "      <td>319500.0</td>\n",
       "      <td>0.005313</td>\n",
       "      <td>11961</td>\n",
       "      <td>-4786.0</td>\n",
       "      <td>-2562.0</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>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307510</th>\n",
       "      <td>456255</td>\n",
       "      <td>0</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>49117.5</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>0.046220</td>\n",
       "      <td>16856</td>\n",
       "      <td>-1262.0</td>\n",
       "      <td>-5128.0</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>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>307511 rows × 244 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SK_ID_CURR  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0           100002             0          202500.0    406597.5      24700.5   \n",
       "1           100003             0          270000.0   1293502.5      35698.5   \n",
       "2           100004             0           67500.0    135000.0       6750.0   \n",
       "3           100006             0          135000.0    312682.5      29686.5   \n",
       "4           100007             0          121500.0    513000.0      21865.5   \n",
       "...            ...           ...               ...         ...          ...   \n",
       "307506      456251             0          157500.0    254700.0      27558.0   \n",
       "307507      456252             0           72000.0    269550.0      12001.5   \n",
       "307508      456253             0          153000.0    677664.0      29979.0   \n",
       "307509      456254             0          171000.0    370107.0      20205.0   \n",
       "307510      456255             0          157500.0    675000.0      49117.5   \n",
       "\n",
       "        AMT_GOODS_PRICE  REGION_POPULATION_RELATIVE  DAYS_BIRTH  \\\n",
       "0              351000.0                    0.018801        9461   \n",
       "1             1129500.0                    0.003541       16765   \n",
       "2              135000.0                    0.010032       19046   \n",
       "3              297000.0                    0.008019       19005   \n",
       "4              513000.0                    0.028663       19932   \n",
       "...                 ...                         ...         ...   \n",
       "307506         225000.0                    0.032561        9327   \n",
       "307507         225000.0                    0.025164       20775   \n",
       "307508         585000.0                    0.005002       14966   \n",
       "307509         319500.0                    0.005313       11961   \n",
       "307510         675000.0                    0.046220       16856   \n",
       "\n",
       "        DAYS_EMPLOYED  DAYS_REGISTRATION  ...  WALLSMATERIAL_MODE_Mixed  \\\n",
       "0              -637.0            -3648.0  ...                     False   \n",
       "1             -1188.0            -1186.0  ...                     False   \n",
       "2              -225.0            -4260.0  ...                     False   \n",
       "3             -3039.0            -9833.0  ...                     False   \n",
       "4             -3038.0            -4311.0  ...                     False   \n",
       "...               ...                ...  ...                       ...   \n",
       "307506         -236.0            -8456.0  ...                     False   \n",
       "307507            NaN            -4388.0  ...                     False   \n",
       "307508        -7921.0            -6737.0  ...                     False   \n",
       "307509        -4786.0            -2562.0  ...                     False   \n",
       "307510        -1262.0            -5128.0  ...                     False   \n",
       "\n",
       "        WALLSMATERIAL_MODE_Monolithic  WALLSMATERIAL_MODE_Others  \\\n",
       "0                               False                      False   \n",
       "1                               False                      False   \n",
       "2                               False                      False   \n",
       "3                               False                      False   \n",
       "4                               False                      False   \n",
       "...                               ...                        ...   \n",
       "307506                          False                      False   \n",
       "307507                          False                      False   \n",
       "307508                          False                      False   \n",
       "307509                          False                      False   \n",
       "307510                          False                      False   \n",
       "\n",
       "        WALLSMATERIAL_MODE_Panel  WALLSMATERIAL_MODE_Stone, brick  \\\n",
       "0                          False                             True   \n",
       "1                          False                            False   \n",
       "2                          False                            False   \n",
       "3                          False                            False   \n",
       "4                          False                            False   \n",
       "...                          ...                              ...   \n",
       "307506                     False                             True   \n",
       "307507                     False                             True   \n",
       "307508                      True                            False   \n",
       "307509                     False                             True   \n",
       "307510                      True                            False   \n",
       "\n",
       "        WALLSMATERIAL_MODE_Wooden  EMERGENCYSTATE_MODE_No  \\\n",
       "0                           False                    True   \n",
       "1                           False                    True   \n",
       "2                           False                   False   \n",
       "3                           False                   False   \n",
       "4                           False                   False   \n",
       "...                           ...                     ...   \n",
       "307506                      False                    True   \n",
       "307507                      False                    True   \n",
       "307508                      False                    True   \n",
       "307509                      False                    True   \n",
       "307510                      False                    True   \n",
       "\n",
       "        EMERGENCYSTATE_MODE_Yes  TARGET  DAYS_EMPLOYED_ANOM  \n",
       "0                         False       1               False  \n",
       "1                         False       0               False  \n",
       "2                         False       0               False  \n",
       "3                         False       0               False  \n",
       "4                         False       0               False  \n",
       "...                         ...     ...                 ...  \n",
       "307506                    False       0               False  \n",
       "307507                    False       0                True  \n",
       "307508                    False       0               False  \n",
       "307509                    False       1               False  \n",
       "307510                    False       0               False  \n",
       "\n",
       "[307511 rows x 244 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c074db87",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = train['TARGET']\n",
    "train_ids = train['SK_ID_CURR']\n",
    "test_ids = test['SK_ID_CURR']\n",
    "train_features = train.drop(columns=['TARGET', 'SK_ID_CURR'])\n",
    "test_features = test.drop(columns=['SK_ID_CURR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f649a5d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_features, test_features = impute_and_scaler(train_features, test_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "29bfcf7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(307511, 242) (48744, 242)\n"
     ]
    }
   ],
   "source": [
    "print(train_features.shape, test_features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0de38131",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f5d9d633",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\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: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\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",
       "  display: none;\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",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\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: 0.5em;\n",
       "  text-align: center;\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",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.3)</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\"><div><div>GradientBoostingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('loss',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">loss&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;log_loss&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
       "            <td class=\"value\">0.3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('subsample',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">subsample&nbsp;</td>\n",
       "            <td class=\"value\">1.0</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('criterion',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">criterion&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;friedman_mse&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_split',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
       "            <td class=\"value\">1</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">max_depth&nbsp;</td>\n",
       "            <td class=\"value\">3</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('init',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">init&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
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       "            <td class=\"param\">max_features&nbsp;</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">verbose&nbsp;</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "            <td class=\"param\">warm_start&nbsp;</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('validation_fraction',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
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       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_iter_no_change&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">0.0001</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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      ],
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gradient_boost_model = GradientBoostingClassifier(\n",
    "    n_estimators = 100,\n",
    "    learning_rate = 0.3\n",
    ")\n",
    "gradient_boost_model.fit(train_features, train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5332fb80",
   "metadata": {},
   "source": [
    "查看训练集上效果\n",
    "- roc_and_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec54282d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7695779581683545"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "train_probs = gradient_boost_model.predict_proba(train_features)[:, 1]\n",
    "train_auc = roc_auc_score(train_labels, train_probs)\n",
    "train_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdab9cd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "gradient_boost_model_pred = gradient_boost_model.predict_proba(test_features)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "300c9416",
   "metadata": {},
   "outputs": [],
   "source": [
    "submit = pd.DataFrame({\n",
    "    'SK_ID_CURR': test_ids\n",
    "})\n",
    "submit['TARGET'] = gradient_boost_model_pred\n",
    "\n",
    "submit.to_csv('gradient_boost.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d547f3",
   "metadata": {},
   "source": [
    "得分 52分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32dcb060",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c05e7c5a",
   "metadata": {},
   "source": [
    "TODO 为什么随机森林和boost显示出大的区别？\n",
    "- 随机森林在65分左右\n",
    "- boost在50分左右\n",
    "\n",
    "可能是异常数据过大了"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b310385f",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "945ec684",
   "metadata": {},
   "source": []
  }
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