feature_selector特征选择器.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://mp.weixin.qq.com/s?__biz=MzIwODI2NDkxNQ==&mid=2247491475&idx=2&sn=337c9eca28ab24eea5d697687eb4303c&chksm=9704942fa0731d39ee5b61186014c8fed17b08ed56fe86c5fb113b92ad240199daa406078445&scene=126&sessionid=1603865144&key=14beeba64fb9d2879d9eeacced8321894be8b0994585577a03befb1631ebd97e571cd0a266544ec889edfe148d9e86a57198f74484544b051762a3223d8cecd43b337f6aaf52781c6cc3c461fa6df5b0d42598cdf5cdcccdcd645a558ec212e132652d1600cbadb6dac3e1f158c002c74f04309fb8b1805e707c9d0d20568e3d&ascene=1&uin=MjA1MjAyODkxNg%3D%3D&devicetype=Windows+10+x64&version=6300002f&lang=zh_CN&exportkey=AR8lJAn3Ve9B4j1XK0NyGpw%3D&pass_ticket=0BVkXjGRc1leewhvh%2B41NnEtNzoa3bggQ%2B036XEOyQFaXzNPgIrBNJi9VtLfpUfr&wx_header=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from feature_selector import FeatureSelector\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<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",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>247408</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>108000.0</td>\n",
       "      <td>172512.0</td>\n",
       "      <td>13477.5</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>153916</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>229065</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>463500.0</td>\n",
       "      <td>20547.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>282013</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>549882.0</td>\n",
       "      <td>17739.0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142266</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>518562.0</td>\n",
       "      <td>20695.5</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      247408       0         Cash loans           F            Y   \n",
       "1      153916       0    Revolving loans           F            Y   \n",
       "2      229065       0         Cash loans           F            N   \n",
       "3      282013       0         Cash loans           F            N   \n",
       "4      142266       0         Cash loans           F            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               N             2          108000.0    172512.0      13477.5   \n",
       "1               Y             2          135000.0    180000.0       9000.0   \n",
       "2               Y             0          112500.0    463500.0      20547.0   \n",
       "3               Y             0          135000.0    549882.0      17739.0   \n",
       "4               Y             0           90000.0    518562.0      20695.5   \n",
       "\n",
       "   ...  FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "0  ...                 0                0                0                0   \n",
       "1  ...                 0                0                0                0   \n",
       "2  ...                 0                0                0                0   \n",
       "3  ...                 0                0                0                0   \n",
       "4  ...                 0                0                0                0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0                        0.0                       0.0   \n",
       "1                        0.0                       0.0   \n",
       "2                        0.0                       0.0   \n",
       "3                        0.0                       0.0   \n",
       "4                        0.0                       0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         1.0                        0.0   \n",
       "3                         0.0                        0.0   \n",
       "4                         0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        0.0                         1.0  \n",
       "1                        0.0                         0.0  \n",
       "2                        0.0                         7.0  \n",
       "3                        0.0                         1.0  \n",
       "4                        1.0                         1.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('../dataset/FeatureSelectorUsage/credit_example.csv')\n",
    "train_labels = train['TARGET']\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(columns=['TARGET'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = FeatureSelector(data=train,labels=train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17 features with greater than 0.60 missing values.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 缺失值\n",
    "fs.identify_missing(missing_threshold=0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_features = fs.ops['missing']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['OWN_CAR_AGE',\n",
       " 'YEARS_BUILD_AVG',\n",
       " 'COMMONAREA_AVG',\n",
       " 'FLOORSMIN_AVG',\n",
       " 'LIVINGAPARTMENTS_AVG',\n",
       " 'NONLIVINGAPARTMENTS_AVG',\n",
       " 'YEARS_BUILD_MODE',\n",
       " 'COMMONAREA_MODE',\n",
       " 'FLOORSMIN_MODE',\n",
       " 'LIVINGAPARTMENTS_MODE',\n",
       " 'NONLIVINGAPARTMENTS_MODE',\n",
       " 'YEARS_BUILD_MEDI',\n",
       " 'COMMONAREA_MEDI',\n",
       " 'FLOORSMIN_MEDI',\n",
       " 'LIVINGAPARTMENTS_MEDI',\n",
       " 'NONLIVINGAPARTMENTS_MEDI',\n",
       " 'FONDKAPREMONT_MODE']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c5301495c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.plot_missing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>missing_fraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_AVG</th>\n",
       "      <td>0.6953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MODE</th>\n",
       "      <td>0.6953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MEDI</th>\n",
       "      <td>0.6953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_AVG</th>\n",
       "      <td>0.6945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <td>0.6945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>0.6945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>0.6846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_AVG</th>\n",
       "      <td>0.6846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <td>0.6846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FONDKAPREMONT_MODE</th>\n",
       "      <td>0.6820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          missing_fraction\n",
       "COMMONAREA_AVG                      0.6953\n",
       "COMMONAREA_MODE                     0.6953\n",
       "COMMONAREA_MEDI                     0.6953\n",
       "NONLIVINGAPARTMENTS_AVG             0.6945\n",
       "NONLIVINGAPARTMENTS_MODE            0.6945\n",
       "NONLIVINGAPARTMENTS_MEDI            0.6945\n",
       "LIVINGAPARTMENTS_MEDI               0.6846\n",
       "LIVINGAPARTMENTS_AVG                0.6846\n",
       "LIVINGAPARTMENTS_MODE               0.6846\n",
       "FONDKAPREMONT_MODE                  0.6820"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.missing_stats.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 features with a single unique value.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 唯一值\n",
    "fs.identify_single_unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 features with a correlation magnitude greater than 0.97.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 共线(高相关性)特征\n",
    "fs.identify_collinear(correlation_threshold=0.975)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Gradient Boosting Model\n",
      "\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[77]\tvalid_0's auc: 0.731179\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[94]\tvalid_0's auc: 0.76212\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[40]\tvalid_0's auc: 0.751866\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[73]\tvalid_0's auc: 0.729399\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[324]\tvalid_0's auc: 0.776752\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[93]\tvalid_0's auc: 0.732742\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[55]\tvalid_0's auc: 0.745687\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[112]\tvalid_0's auc: 0.752371\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[85]\tvalid_0's auc: 0.732701\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[50]\tvalid_0's auc: 0.702807\n",
      "\n",
      "64 features with zero importance after one-hot encoding.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_zero_importance(task = 'classification', eval_metric = 'auc',n_iterations = 10, early_stopping = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c530326b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c53014e780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123 features required for 0.99 of cumulative importance\n"
     ]
    }
   ],
   "source": [
    "fs.plot_feature_importances(threshold = 0.99, plot_n = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}