baseline1.1560.ipynb

{
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  {
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   "id": "18030b8f",
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   "source": [
    "## 导入第三方包\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import lightgbm as lgb\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import f1_score, roc_auc_score,mean_squared_error\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6d52f78f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 训练数据及测试数据准备\n",
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8566e6c2",
   "metadata": {},
   "outputs": [
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       "         article_id  date  baike_id_1h   price  price_diff  author  level1  \\\n",
       "0               356     1          104  5969.0      -597.8   21562       8   \n",
       "1               388     1       219783   190.0         NaN   51522      14   \n",
       "2               872     1        92240   139.0        20.0   69683       7   \n",
       "3               938     1        88894    99.0         NaN   63556      19   \n",
       "4              1957     1        85380   779.0        10.0   63860       5   \n",
       "...             ...   ...          ...     ...         ...     ...     ...   \n",
       "1807652     1804751   116       219783    99.0         0.0   44655       0   \n",
       "1807653     1805971   116       219783   217.0         NaN   68203       0   \n",
       "1807654     1807148   116       219783   138.0         NaN   20391      19   \n",
       "1807655     1807424   116       219783   559.0         9.0   22941       7   \n",
       "1807656     1808873   116       219783   148.5         0.0   21432      19   \n",
       "\n",
       "         level2  level3  level4  ...  buzhi_1h  favorite_1h  orders_1h  \\\n",
       "0            53      63     212  ...         0            0          0   \n",
       "1            15     580       0  ...         3           12          5   \n",
       "2            22       0       0  ...         0            0          0   \n",
       "3             2     753       0  ...         0            0          0   \n",
       "4            40     429       0  ...         0            0          0   \n",
       "...         ...     ...     ...  ...       ...          ...        ...   \n",
       "1807652       6     713       0  ...         0            0          0   \n",
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       "1807654       4     763       0  ...         0            0          0   \n",
       "1807655      50     237       0  ...         0            0          0   \n",
       "1807656      30     747       0  ...         1            0          0   \n",
       "\n",
       "         baike_id_2h  comments_2h  zhi_2h  buzhi_2h  favorite_2h  orders_2h  \\\n",
       "0                104            0       0         0            0          0   \n",
       "1             219783           11       3         1            8          5   \n",
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       "...              ...          ...     ...       ...          ...        ...   \n",
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       "1807655       219783            0       0         0            0          0   \n",
       "1807656       219783            0       0         0            0          0   \n",
       "\n",
       "         orders_3h_15h  \n",
       "0                    0  \n",
       "1                    5  \n",
       "2                    0  \n",
       "3                    0  \n",
       "4                    4  \n",
       "...                ...  \n",
       "1807652              0  \n",
       "1807653              0  \n",
       "1807654              0  \n",
       "1807655              0  \n",
       "1807656              0  \n",
       "\n",
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     "execution_count": 3,
     "metadata": {},
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   "source": [
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   ]
  },
  {
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       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>219783</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>149584</th>\n",
       "      <td>1955932</td>\n",
       "      <td>123</td>\n",
       "      <td>219783</td>\n",
       "      <td>123.50</td>\n",
       "      <td>18.05</td>\n",
       "      <td>69329</td>\n",
       "      <td>8</td>\n",
       "      <td>57</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>219783</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149585</th>\n",
       "      <td>1956580</td>\n",
       "      <td>123</td>\n",
       "      <td>29553</td>\n",
       "      <td>2.40</td>\n",
       "      <td>1.45</td>\n",
       "      <td>71272</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>754</td>\n",
       "      <td>1199</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29553</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149586</th>\n",
       "      <td>1956675</td>\n",
       "      <td>123</td>\n",
       "      <td>219783</td>\n",
       "      <td>310.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>665</td>\n",
       "      <td>13</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>219783</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149587</th>\n",
       "      <td>1956922</td>\n",
       "      <td>123</td>\n",
       "      <td>49355</td>\n",
       "      <td>99.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>55420</td>\n",
       "      <td>5</td>\n",
       "      <td>43</td>\n",
       "      <td>319</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>49355</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149588</th>\n",
       "      <td>1957113</td>\n",
       "      <td>123</td>\n",
       "      <td>39348</td>\n",
       "      <td>369.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28612</td>\n",
       "      <td>9</td>\n",
       "      <td>58</td>\n",
       "      <td>356</td>\n",
       "      <td>666</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39348</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149589 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        article_id  date  baike_id_1h   price  price_diff  author  level1  \\\n",
       "0          1809360   117       219783    9.40         NaN   39424      19   \n",
       "1          1809857   117       219783  149.60      -38.77   51888      19   \n",
       "2          1810275   117       219783   97.00       41.00    8649      13   \n",
       "3          1810622   117        68785    5.42        2.50   52372       3   \n",
       "4          1811139   117       219783   79.00         NaN   20410      13   \n",
       "...            ...   ...          ...     ...         ...     ...     ...   \n",
       "149584     1955932   123       219783  123.50       18.05   69329       8   \n",
       "149585     1956580   123        29553    2.40        1.45   71272      19   \n",
       "149586     1956675   123       219783  310.80         NaN     665      13   \n",
       "149587     1956922   123        49355   99.00       10.00   55420       5   \n",
       "149588     1957113   123        39348  369.00        0.00   28612       9   \n",
       "\n",
       "        level2  level3  level4  ...  zhi_1h  buzhi_1h  favorite_1h  orders_1h  \\\n",
       "0           31     744       0  ...       0         0            0          0   \n",
       "1           30     750       0  ...       0         0            0          0   \n",
       "2           13     664       0  ...       0         0            0          0   \n",
       "3            5      27     798  ...       0         0            1          1   \n",
       "4           96     146       0  ...       0         0            0          0   \n",
       "...        ...     ...     ...  ...     ...       ...          ...        ...   \n",
       "149584      57      84       0  ...       0         0            1          0   \n",
       "149585       2     754    1199  ...       0         0            0          0   \n",
       "149586      96       1       0  ...       0         0            0          0   \n",
       "149587      43     319       0  ...       0         0            2          0   \n",
       "149588      58     356     666  ...       0         0            0          0   \n",
       "\n",
       "        baike_id_2h  comments_2h  zhi_2h  buzhi_2h  favorite_2h  orders_2h  \n",
       "0            219783            0       0         0            0          0  \n",
       "1            219783            0       0         0            0          0  \n",
       "2            219783            0       0         0            0          0  \n",
       "3             68785            0       0         0            1          1  \n",
       "4            219783            0       0         0            0          0  \n",
       "...             ...          ...     ...       ...          ...        ...  \n",
       "149584       219783            0       0         0            0          0  \n",
       "149585        29553            0       0         0            0          0  \n",
       "149586       219783            0       0         0            0          0  \n",
       "149587        49355            0       0         0            0          0  \n",
       "149588        39348            0       0         0            0          0  \n",
       "\n",
       "[149589 rows x 24 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0193f048",
   "metadata": {},
   "outputs": [],
   "source": [
    "#频数特征\n",
    "\n",
    "temp=train['author'].value_counts().to_dict()\n",
    "train['author_cnt']=train['author'].map(temp)\n",
    "\n",
    "temp=train['brand'].value_counts().to_dict()\n",
    "train['brand_cnt']=train['brand'].map(temp)\n",
    "\n",
    "temp=train['mall'].value_counts().to_dict()\n",
    "train['mall_cnt']=train['mall'].map(temp)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "temp=test['author'].value_counts().to_dict()\n",
    "test['author_cnt']=test['author'].map(temp)\n",
    "\n",
    "temp=test['brand'].value_counts().to_dict()\n",
    "test['brand_cnt']=test['brand'].map(temp)\n",
    "\n",
    "temp=test['mall'].value_counts().to_dict()\n",
    "test['mall_cnt']=test['mall'].map(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2de6111a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#聚合特征\n",
    "#level1,level2,level3,level4\n",
    "###############################################train\n",
    "\n",
    "#计数\n",
    "train['gp_author_level1_cnt']=train.groupby('author')['level1'].transform(lambda x: x.count())\n",
    "train['gp_author_level2_cnt']=train.groupby('author')['level2'].transform(lambda x: x.count())\n",
    "train['gp_author_level3_cnt']=train.groupby('author')['level3'].transform(lambda x: x.count())\n",
    "train['gp_author_level4_cnt']=train.groupby('author')['level4'].transform(lambda x: x.count())\n",
    "\n",
    "train['gp_mall_level1_cnt']=train.groupby('mall')['level1'].transform(lambda x: x.count())\n",
    "train['gp_mall_level2_cnt']=train.groupby('mall')['level2'].transform(lambda x: x.count())\n",
    "train['gp_mall_level3_cnt']=train.groupby('mall')['level3'].transform(lambda x: x.count())\n",
    "train['gp_mall_level4_cnt']=train.groupby('mall')['level4'].transform(lambda x: x.count())\n",
    "\n",
    "train['gp_brand_level1_cnt']=train.groupby('brand')['level1'].transform(lambda x: x.count())\n",
    "train['gp_brand_level2_cnt']=train.groupby('brand')['level2'].transform(lambda x: x.count())\n",
    "train['gp_brand_level3_cnt']=train.groupby('brand')['level3'].transform(lambda x: x.count())\n",
    "train['gp_brand_level4_cnt']=train.groupby('brand')['level4'].transform(lambda x: x.count())\n",
    "\n",
    "#平均\n",
    "train['gp_author_level1_mean']=train.groupby('author')['level1'].transform(lambda x: x.mean())\n",
    "train['gp_author_level2_mean']=train.groupby('author')['level2'].transform(lambda x: x.mean())\n",
    "train['gp_author_level3_mean']=train.groupby('author')['level3'].transform(lambda x: x.mean())\n",
    "train['gp_author_level4_mean']=train.groupby('author')['level4'].transform(lambda x: x.mean())\n",
    "\n",
    "train['gp_mall_level1_mean']=train.groupby('mall')['level1'].transform(lambda x: x.mean())\n",
    "train['gp_mall_level2_mean']=train.groupby('mall')['level2'].transform(lambda x: x.mean())\n",
    "train['gp_mall_level3_mean']=train.groupby('mall')['level3'].transform(lambda x: x.mean())\n",
    "train['gp_mall_level4_mean']=train.groupby('mall')['level4'].transform(lambda x: x.mean())\n",
    "\n",
    "train['gp_brand_level1_mean']=train.groupby('brand')['level1'].transform(lambda x: x.mean())\n",
    "train['gp_brand_level2_mean']=train.groupby('brand')['level2'].transform(lambda x: x.mean())\n",
    "train['gp_brand_level3_mean']=train.groupby('brand')['level3'].transform(lambda x: x.mean())\n",
    "train['gp_brand_level4_mean']=train.groupby('brand')['level4'].transform(lambda x: x.mean())\n",
    "\n",
    "#中位数\n",
    "train['gp_author_level1_median']=train.groupby('author')['level1'].transform(lambda x: x.median())\n",
    "train['gp_author_level2_median']=train.groupby('author')['level2'].transform(lambda x: x.median())\n",
    "train['gp_author_level3_median']=train.groupby('author')['level3'].transform(lambda x: x.median())\n",
    "train['gp_author_level4_median']=train.groupby('author')['level4'].transform(lambda x: x.median())\n",
    "\n",
    "train['gp_mall_level1_median']=train.groupby('mall')['level1'].transform(lambda x: x.median())\n",
    "train['gp_mall_level2_median']=train.groupby('mall')['level2'].transform(lambda x: x.median())\n",
    "train['gp_mall_level3_median']=train.groupby('mall')['level3'].transform(lambda x: x.median())\n",
    "train['gp_mall_level4_median']=train.groupby('mall')['level4'].transform(lambda x: x.median())\n",
    "\n",
    "train['gp_brand_level1_median']=train.groupby('brand')['level1'].transform(lambda x: x.median())\n",
    "train['gp_brand_level2_median']=train.groupby('brand')['level2'].transform(lambda x: x.median())\n",
    "train['gp_brand_level3_median']=train.groupby('brand')['level3'].transform(lambda x: x.median())\n",
    "train['gp_brand_level4_median']=train.groupby('brand')['level4'].transform(lambda x: x.median())\n",
    "\n",
    "#方差\n",
    "train['gp_author_level1_var']=train.groupby('author')['level1'].transform(lambda x: x.var())\n",
    "train['gp_author_level2_var']=train.groupby('author')['level2'].transform(lambda x: x.var())\n",
    "train['gp_author_level3_var']=train.groupby('author')['level3'].transform(lambda x: x.var())\n",
    "train['gp_author_level4_var']=train.groupby('author')['level4'].transform(lambda x: x.var())\n",
    "\n",
    "train['gp_mall_level1_var']=train.groupby('mall')['level1'].transform(lambda x: x.var())\n",
    "train['gp_mall_level2_var']=train.groupby('mall')['level2'].transform(lambda x: x.var())\n",
    "train['gp_mall_level3_var']=train.groupby('mall')['level3'].transform(lambda x: x.var())\n",
    "train['gp_mall_level4_var']=train.groupby('mall')['level4'].transform(lambda x: x.var())\n",
    "\n",
    "train['gp_brand_level1_var']=train.groupby('brand')['level1'].transform(lambda x: x.var())\n",
    "train['gp_brand_level2_var']=train.groupby('brand')['level2'].transform(lambda x: x.var())\n",
    "train['gp_brand_level3_var']=train.groupby('brand')['level3'].transform(lambda x: x.var())\n",
    "train['gp_brand_level4_var']=train.groupby('brand')['level4'].transform(lambda x: x.var())\n",
    "\n",
    "\n",
    "#标准差\n",
    "train['gp_author_level1_std']=train.groupby('author')['level1'].transform(lambda x: x.std())\n",
    "train['gp_author_level2_std']=train.groupby('author')['level2'].transform(lambda x: x.std())\n",
    "train['gp_author_level3_std']=train.groupby('author')['level3'].transform(lambda x: x.std())\n",
    "train['gp_author_level4_std']=train.groupby('author')['level4'].transform(lambda x: x.std())\n",
    "\n",
    "train['gp_mall_level1_std']=train.groupby('mall')['level1'].transform(lambda x: x.std())\n",
    "train['gp_mall_level2_std']=train.groupby('mall')['level2'].transform(lambda x: x.std())\n",
    "train['gp_mall_level3_std']=train.groupby('mall')['level3'].transform(lambda x: x.std())\n",
    "train['gp_mall_level4_std']=train.groupby('mall')['level4'].transform(lambda x: x.std())\n",
    "\n",
    "train['gp_brand_level1_std']=train.groupby('brand')['level1'].transform(lambda x: x.std())\n",
    "train['gp_brand_level2_std']=train.groupby('brand')['level2'].transform(lambda x: x.std())\n",
    "train['gp_brand_level3_std']=train.groupby('brand')['level3'].transform(lambda x: x.std())\n",
    "train['gp_brand_level4_std']=train.groupby('brand')['level4'].transform(lambda x: x.std())\n",
    "\n",
    "#最大值\n",
    "train['gp_author_level1_max']=train.groupby('author')['level1'].transform(lambda x: x.max())\n",
    "train['gp_author_level2_max']=train.groupby('author')['level2'].transform(lambda x: x.max())\n",
    "train['gp_author_level3_max']=train.groupby('author')['level3'].transform(lambda x: x.max())\n",
    "train['gp_author_level4_max']=train.groupby('author')['level4'].transform(lambda x: x.max())\n",
    "\n",
    "train['gp_mall_level1_max']=train.groupby('mall')['level1'].transform(lambda x: x.max())\n",
    "train['gp_mall_level2_max']=train.groupby('mall')['level2'].transform(lambda x: x.max())\n",
    "train['gp_mall_level3_max']=train.groupby('mall')['level3'].transform(lambda x: x.max())\n",
    "train['gp_mall_level4_max']=train.groupby('mall')['level4'].transform(lambda x: x.max())\n",
    "\n",
    "train['gp_brand_level1_max']=train.groupby('brand')['level1'].transform(lambda x: x.max())\n",
    "train['gp_brand_level2_max']=train.groupby('brand')['level2'].transform(lambda x: x.max())\n",
    "train['gp_brand_level3_max']=train.groupby('brand')['level3'].transform(lambda x: x.max())\n",
    "train['gp_brand_level4_max']=train.groupby('brand')['level4'].transform(lambda x: x.max())\n",
    "\n",
    "#最小值\n",
    "train['gp_author_level1_min']=train.groupby('author')['level1'].transform(lambda x: x.min())\n",
    "train['gp_author_level2_min']=train.groupby('author')['level2'].transform(lambda x: x.min())\n",
    "train['gp_author_level3_min']=train.groupby('author')['level3'].transform(lambda x: x.min())\n",
    "train['gp_author_level4_min']=train.groupby('author')['level4'].transform(lambda x: x.min())\n",
    "\n",
    "train['gp_mall_level1_min']=train.groupby('mall')['level1'].transform(lambda x: x.min())\n",
    "train['gp_mall_level2_min']=train.groupby('mall')['level2'].transform(lambda x: x.min())\n",
    "train['gp_mall_level3_min']=train.groupby('mall')['level3'].transform(lambda x: x.min())\n",
    "train['gp_mall_level4_min']=train.groupby('mall')['level4'].transform(lambda x: x.min())\n",
    "\n",
    "train['gp_brand_level1_min']=train.groupby('brand')['level1'].transform(lambda x: x.min())\n",
    "train['gp_brand_level2_min']=train.groupby('brand')['level2'].transform(lambda x: x.min())\n",
    "train['gp_brand_level3_min']=train.groupby('brand')['level3'].transform(lambda x: x.min())\n",
    "train['gp_brand_level4_min']=train.groupby('brand')['level4'].transform(lambda x: x.min())\n",
    "\n",
    "#众数\n",
    "\n",
    "\n",
    "#####################################################test\n",
    "\n",
    "#计数\n",
    "test['gp_author_level1_cnt']=test.groupby('author')['level1'].transform(lambda x: x.count())\n",
    "test['gp_author_level2_cnt']=test.groupby('author')['level2'].transform(lambda x: x.count())\n",
    "test['gp_author_level3_cnt']=test.groupby('author')['level3'].transform(lambda x: x.count())\n",
    "test['gp_author_level4_cnt']=test.groupby('author')['level4'].transform(lambda x: x.count())\n",
    "\n",
    "test['gp_mall_level1_cnt']=test.groupby('mall')['level1'].transform(lambda x: x.count())\n",
    "test['gp_mall_level2_cnt']=test.groupby('mall')['level2'].transform(lambda x: x.count())\n",
    "test['gp_mall_level3_cnt']=test.groupby('mall')['level3'].transform(lambda x: x.count())\n",
    "test['gp_mall_level4_cnt']=test.groupby('mall')['level4'].transform(lambda x: x.count())\n",
    "\n",
    "test['gp_brand_level1_cnt']=test.groupby('brand')['level1'].transform(lambda x: x.count())\n",
    "test['gp_brand_level2_cnt']=test.groupby('brand')['level2'].transform(lambda x: x.count())\n",
    "test['gp_brand_level3_cnt']=test.groupby('brand')['level3'].transform(lambda x: x.count())\n",
    "test['gp_brand_level4_cnt']=test.groupby('brand')['level4'].transform(lambda x: x.count())\n",
    "\n",
    "#平均\n",
    "test['gp_author_level1_mean']=test.groupby('author')['level1'].transform(lambda x: x.mean())\n",
    "test['gp_author_level2_mean']=test.groupby('author')['level2'].transform(lambda x: x.mean())\n",
    "test['gp_author_level3_mean']=test.groupby('author')['level3'].transform(lambda x: x.mean())\n",
    "test['gp_author_level4_mean']=test.groupby('author')['level4'].transform(lambda x: x.mean())\n",
    "\n",
    "test['gp_mall_level1_mean']=test.groupby('mall')['level1'].transform(lambda x: x.mean())\n",
    "test['gp_mall_level2_mean']=test.groupby('mall')['level2'].transform(lambda x: x.mean())\n",
    "test['gp_mall_level3_mean']=test.groupby('mall')['level3'].transform(lambda x: x.mean())\n",
    "test['gp_mall_level4_mean']=test.groupby('mall')['level4'].transform(lambda x: x.mean())\n",
    "\n",
    "test['gp_brand_level1_mean']=test.groupby('brand')['level1'].transform(lambda x: x.mean())\n",
    "test['gp_brand_level2_mean']=test.groupby('brand')['level2'].transform(lambda x: x.mean())\n",
    "test['gp_brand_level3_mean']=test.groupby('brand')['level3'].transform(lambda x: x.mean())\n",
    "test['gp_brand_level4_mean']=test.groupby('brand')['level4'].transform(lambda x: x.mean())\n",
    "\n",
    "#中位数\n",
    "test['gp_author_level1_median']=test.groupby('author')['level1'].transform(lambda x: x.median())\n",
    "test['gp_author_level2_median']=test.groupby('author')['level2'].transform(lambda x: x.median())\n",
    "test['gp_author_level3_median']=test.groupby('author')['level3'].transform(lambda x: x.median())\n",
    "test['gp_author_level4_median']=test.groupby('author')['level4'].transform(lambda x: x.median())\n",
    "\n",
    "test['gp_mall_level1_median']=test.groupby('mall')['level1'].transform(lambda x: x.median())\n",
    "test['gp_mall_level2_median']=test.groupby('mall')['level2'].transform(lambda x: x.median())\n",
    "test['gp_mall_level3_median']=test.groupby('mall')['level3'].transform(lambda x: x.median())\n",
    "test['gp_mall_level4_median']=test.groupby('mall')['level4'].transform(lambda x: x.median())\n",
    "\n",
    "test['gp_brand_level1_median']=test.groupby('brand')['level1'].transform(lambda x: x.median())\n",
    "test['gp_brand_level2_median']=test.groupby('brand')['level2'].transform(lambda x: x.median())\n",
    "test['gp_brand_level3_median']=test.groupby('brand')['level3'].transform(lambda x: x.median())\n",
    "test['gp_brand_level4_median']=test.groupby('brand')['level4'].transform(lambda x: x.median())\n",
    "\n",
    "#方差\n",
    "test['gp_author_level1_var']=test.groupby('author')['level1'].transform(lambda x: x.var())\n",
    "test['gp_author_level2_var']=test.groupby('author')['level2'].transform(lambda x: x.var())\n",
    "test['gp_author_level3_var']=test.groupby('author')['level3'].transform(lambda x: x.var())\n",
    "test['gp_author_level4_var']=test.groupby('author')['level4'].transform(lambda x: x.var())\n",
    "\n",
    "test['gp_mall_level1_var']=test.groupby('mall')['level1'].transform(lambda x: x.var())\n",
    "test['gp_mall_level2_var']=test.groupby('mall')['level2'].transform(lambda x: x.var())\n",
    "test['gp_mall_level3_var']=test.groupby('mall')['level3'].transform(lambda x: x.var())\n",
    "test['gp_mall_level4_var']=test.groupby('mall')['level4'].transform(lambda x: x.var())\n",
    "\n",
    "test['gp_brand_level1_var']=test.groupby('brand')['level1'].transform(lambda x: x.var())\n",
    "test['gp_brand_level2_var']=test.groupby('brand')['level2'].transform(lambda x: x.var())\n",
    "test['gp_brand_level3_var']=test.groupby('brand')['level3'].transform(lambda x: x.var())\n",
    "test['gp_brand_level4_var']=test.groupby('brand')['level4'].transform(lambda x: x.var())\n",
    "\n",
    "\n",
    "#标准差\n",
    "test['gp_author_level1_std']=train.groupby('author')['level1'].transform(lambda x: x.std())\n",
    "test['gp_author_level2_std']=test.groupby('author')['level2'].transform(lambda x: x.std())\n",
    "test['gp_author_level3_std']=test.groupby('author')['level3'].transform(lambda x: x.std())\n",
    "test['gp_author_level4_std']=test.groupby('author')['level4'].transform(lambda x: x.std())\n",
    "\n",
    "test['gp_mall_level1_std']=test.groupby('mall')['level1'].transform(lambda x: x.std())\n",
    "test['gp_mall_level2_std']=test.groupby('mall')['level2'].transform(lambda x: x.std())\n",
    "test['gp_mall_level3_std']=test.groupby('mall')['level3'].transform(lambda x: x.std())\n",
    "test['gp_mall_level4_std']=test.groupby('mall')['level4'].transform(lambda x: x.std())\n",
    "\n",
    "test['gp_brand_level1_std']=test.groupby('brand')['level1'].transform(lambda x: x.std())\n",
    "test['gp_brand_level2_std']=test.groupby('brand')['level2'].transform(lambda x: x.std())\n",
    "test['gp_brand_level3_std']=test.groupby('brand')['level3'].transform(lambda x: x.std())\n",
    "test['gp_brand_level4_std']=test.groupby('brand')['level4'].transform(lambda x: x.std())\n",
    "\n",
    "#最大值\n",
    "test['gp_author_level1_max']=test.groupby('author')['level1'].transform(lambda x: x.max())\n",
    "test['gp_author_level2_max']=test.groupby('author')['level2'].transform(lambda x: x.max())\n",
    "test['gp_author_level3_max']=test.groupby('author')['level3'].transform(lambda x: x.max())\n",
    "test['gp_author_level4_max']=test.groupby('author')['level4'].transform(lambda x: x.max())\n",
    "\n",
    "test['gp_mall_level1_max']=test.groupby('mall')['level1'].transform(lambda x: x.max())\n",
    "test['gp_mall_level2_max']=test.groupby('mall')['level2'].transform(lambda x: x.max())\n",
    "test['gp_mall_level3_max']=test.groupby('mall')['level3'].transform(lambda x: x.max())\n",
    "test['gp_mall_level4_max']=test.groupby('mall')['level4'].transform(lambda x: x.max())\n",
    "\n",
    "test['gp_brand_level1_max']=test.groupby('brand')['level1'].transform(lambda x: x.max())\n",
    "test['gp_brand_level2_max']=test.groupby('brand')['level2'].transform(lambda x: x.max())\n",
    "test['gp_brand_level3_max']=test.groupby('brand')['level3'].transform(lambda x: x.max())\n",
    "test['gp_brand_level4_max']=test.groupby('brand')['level4'].transform(lambda x: x.max())\n",
    "\n",
    "#最小值\n",
    "test['gp_author_level1_min']=test.groupby('author')['level1'].transform(lambda x: x.min())\n",
    "test['gp_author_level2_min']=test.groupby('author')['level2'].transform(lambda x: x.min())\n",
    "test['gp_author_level3_min']=test.groupby('author')['level3'].transform(lambda x: x.min())\n",
    "test['gp_author_level4_min']=test.groupby('author')['level4'].transform(lambda x: x.min())\n",
    "\n",
    "test['gp_mall_level1_min']=test.groupby('mall')['level1'].transform(lambda x: x.min())\n",
    "test['gp_mall_level2_min']=test.groupby('mall')['level2'].transform(lambda x: x.min())\n",
    "test['gp_mall_level3_min']=test.groupby('mall')['level3'].transform(lambda x: x.min())\n",
    "test['gp_mall_level4_min']=test.groupby('mall')['level4'].transform(lambda x: x.min())\n",
    "\n",
    "test['gp_brand_level1_min']=test.groupby('brand')['level1'].transform(lambda x: x.min())\n",
    "test['gp_brand_level2_min']=test.groupby('brand')['level2'].transform(lambda x: x.min())\n",
    "test['gp_brand_level3_min']=test.groupby('brand')['level3'].transform(lambda x: x.min())\n",
    "test['gp_brand_level4_min']=test.groupby('brand')['level4'].transform(lambda x: x.min())\n",
    "\n",
    "#众数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4508fde0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#聚合特征\n",
    "#orders_1h,orders_2h\n",
    "###############################################train\n",
    "\n",
    "#计数\n",
    "train['gp_author_orders_1h_cnt']=train.groupby('author')['orders_1h'].transform(lambda x: x.count())\n",
    "train['gp_author_orders_2h_cnt']=train.groupby('author')['orders_2h'].transform(lambda x: x.count())\n",
    "\n",
    "\n",
    "train['gp_mall_orders_1h_cnt']=train.groupby('mall')['orders_1h'].transform(lambda x: x.count())\n",
    "train['gp_mall_orders_2h_cnt']=train.groupby('mall')['orders_2h'].transform(lambda x: x.count())\n",
    "\n",
    "\n",
    "train['gp_brand_orders_1h_cnt']=train.groupby('brand')['orders_1h'].transform(lambda x: x.count())\n",
    "train['gp_brand_orders_2h_cnt']=train.groupby('brand')['orders_2h'].transform(lambda x: x.count())\n",
    "\n",
    "\n",
    "#平均\n",
    "train['gp_author_orders_1h_mean']=train.groupby('author')['orders_1h'].transform(lambda x: x.mean())\n",
    "train['gp_author_orders_2h_mean']=train.groupby('author')['orders_2h'].transform(lambda x: x.mean())\n",
    "\n",
    "\n",
    "train['gp_mall_orders_1h_mean']=train.groupby('mall')['orders_1h'].transform(lambda x: x.mean())\n",
    "train['gp_mall_orders_2h_mean']=train.groupby('mall')['orders_2h'].transform(lambda x: x.mean())\n",
    "\n",
    "\n",
    "train['gp_brand_orders_1h_mean']=train.groupby('brand')['orders_1h'].transform(lambda x: x.mean())\n",
    "train['gp_brand_orders_2h_mean']=train.groupby('brand')['orders_2h'].transform(lambda x: x.mean())\n",
    "\n",
    "\n",
    "#中位数\n",
    "train['gp_author_orders_1h_median']=train.groupby('author')['orders_1h'].transform(lambda x: x.median())\n",
    "train['gp_author_orders_2h_median']=train.groupby('author')['orders_2h'].transform(lambda x: x.median())\n",
    "\n",
    "\n",
    "train['gp_mall_orders_1h_median']=train.groupby('mall')['orders_1h'].transform(lambda x: x.median())\n",
    "train['gp_mall_orders_2h_median']=train.groupby('mall')['orders_2h'].transform(lambda x: x.median())\n",
    "\n",
    "\n",
    "train['gp_brand_orders_1h_median']=train.groupby('brand')['orders_1h'].transform(lambda x: x.median())\n",
    "train['gp_brand_orders_2h_median']=train.groupby('brand')['orders_2h'].transform(lambda x: x.median())\n",
    "\n",
    "\n",
    "#方差\n",
    "train['gp_author_orders_1h_var']=train.groupby('author')['orders_1h'].transform(lambda x: x.var())\n",
    "train['gp_author_orders_2h_var']=train.groupby('author')['orders_2h'].transform(lambda x: x.var())\n",
    "\n",
    "\n",
    "train['gp_mall_orders_1h_var']=train.groupby('mall')['orders_1h'].transform(lambda x: x.var())\n",
    "train['gp_mall_orders_2h_var']=train.groupby('mall')['orders_2h'].transform(lambda x: x.var())\n",
    "\n",
    "\n",
    "train['gp_brand_orders_1h_var']=train.groupby('brand')['orders_1h'].transform(lambda x: x.var())\n",
    "train['gp_brand_orders_2h_var']=train.groupby('brand')['orders_2h'].transform(lambda x: x.var())\n",
    "\n",
    "\n",
    "\n",
    "#标准差\n",
    "train['gp_author_orders_1h_std']=train.groupby('author')['orders_1h'].transform(lambda x: x.std())\n",
    "train['gp_author_orders_2h_std']=train.groupby('author')['orders_2h'].transform(lambda x: x.std())\n",
    "\n",
    "\n",
    "train['gp_mall_orders_1h_std']=train.groupby('mall')['orders_1h'].transform(lambda x: x.std())\n",
    "train['gp_mall_orders_2h_std']=train.groupby('mall')['orders_2h'].transform(lambda x: x.std())\n",
    "\n",
    "\n",
    "train['gp_brand_orders_1h_std']=train.groupby('brand')['orders_1h'].transform(lambda x: x.std())\n",
    "train['gp_brand_orders_2h_std']=train.groupby('brand')['orders_2h'].transform(lambda x: x.std())\n",
    "\n",
    "\n",
    "#最大值\n",
    "train['gp_author_orders_1h_max']=train.groupby('author')['orders_1h'].transform(lambda x: x.max())\n",
    "train['gp_author_orders_2h_max']=train.groupby('author')['orders_2h'].transform(lambda x: x.max())\n",
    "\n",
    "\n",
    "train['gp_mall_orders_1h_max']=train.groupby('mall')['orders_1h'].transform(lambda x: x.max())\n",
    "train['gp_mall_orders_2h_max']=train.groupby('mall')['orders_2h'].transform(lambda x: x.max())\n",
    "\n",
    "\n",
    "train['gp_brand_orders_1h_max']=train.groupby('brand')['orders_1h'].transform(lambda x: x.max())\n",
    "train['gp_brand_orders_2h_max']=train.groupby('brand')['orders_2h'].transform(lambda x: x.max())\n",
    "\n",
    "\n",
    "#最小值\n",
    "train['gp_author_orders_1h_min']=train.groupby('author')['orders_1h'].transform(lambda x: x.min())\n",
    "train['gp_author_orders_2h_min']=train.groupby('author')['orders_2h'].transform(lambda x: x.min())\n",
    "\n",
    "\n",
    "train['gp_mall_orders_1h_min']=train.groupby('mall')['orders_1h'].transform(lambda x: x.min())\n",
    "train['gp_mall_orders_2h_min']=train.groupby('mall')['orders_2h'].transform(lambda x: x.min())\n",
    "\n",
    "\n",
    "train['gp_brand_orders_1h_min']=train.groupby('brand')['orders_1h'].transform(lambda x: x.min())\n",
    "train['gp_brand_orders_2h_min']=train.groupby('brand')['orders_2h'].transform(lambda x: x.min())\n",
    "\n",
    "\n",
    "#众数\n",
    "\n",
    "\n",
    "#####################################################test\n",
    "\n",
    "#计数\n",
    "test['gp_author_orders_1h_cnt']=test.groupby('author')['orders_1h'].transform(lambda x: x.count())\n",
    "test['gp_author_orders_2h_cnt']=test.groupby('author')['orders_2h'].transform(lambda x: x.count())\n",
    "\n",
    "\n",
    "test['gp_mall_orders_1h_cnt']=test.groupby('mall')['orders_1h'].transform(lambda x: x.count())\n",
    "test['gp_mall_orders_2h_cnt']=test.groupby('mall')['orders_2h'].transform(lambda x: x.count())\n",
    "\n",
    "\n",
    "test['gp_brand_orders_1h_cnt']=test.groupby('brand')['orders_1h'].transform(lambda x: x.count())\n",
    "test['gp_brand_orders_2h_cnt']=test.groupby('brand')['orders_2h'].transform(lambda x: x.count())\n",
    "\n",
    "\n",
    "#平均\n",
    "test['gp_author_orders_1h_mean']=test.groupby('author')['orders_1h'].transform(lambda x: x.mean())\n",
    "test['gp_author_orders_2h_mean']=test.groupby('author')['orders_2h'].transform(lambda x: x.mean())\n",
    "\n",
    "\n",
    "test['gp_mall_orders_1h_mean']=test.groupby('mall')['orders_1h'].transform(lambda x: x.mean())\n",
    "test['gp_mall_orders_2h_mean']=test.groupby('mall')['orders_2h'].transform(lambda x: x.mean())\n",
    "\n",
    "\n",
    "test['gp_brand_orders_1h_mean']=test.groupby('brand')['orders_1h'].transform(lambda x: x.mean())\n",
    "test['gp_brand_orders_2h_mean']=test.groupby('brand')['orders_2h'].transform(lambda x: x.mean())\n",
    "\n",
    "\n",
    "#中位数\n",
    "test['gp_author_orders_1h_median']=test.groupby('author')['orders_1h'].transform(lambda x: x.median())\n",
    "test['gp_author_orders_2h_median']=test.groupby('author')['orders_2h'].transform(lambda x: x.median())\n",
    "\n",
    "\n",
    "test['gp_mall_orders_1h_median']=test.groupby('mall')['orders_1h'].transform(lambda x: x.median())\n",
    "test['gp_mall_orders_2h_median']=test.groupby('mall')['orders_2h'].transform(lambda x: x.median())\n",
    "\n",
    "\n",
    "test['gp_brand_orders_1h_median']=test.groupby('brand')['orders_1h'].transform(lambda x: x.median())\n",
    "test['gp_brand_orders_2h_median']=test.groupby('brand')['orders_2h'].transform(lambda x: x.median())\n",
    "\n",
    "\n",
    "#方差\n",
    "test['gp_author_orders_1h_var']=test.groupby('author')['orders_1h'].transform(lambda x: x.var())\n",
    "test['gp_author_orders_2h_var']=test.groupby('author')['orders_2h'].transform(lambda x: x.var())\n",
    "\n",
    "\n",
    "test['gp_mall_orders_1h_var']=test.groupby('mall')['orders_1h'].transform(lambda x: x.var())\n",
    "test['gp_mall_orders_2h_var']=test.groupby('mall')['orders_2h'].transform(lambda x: x.var())\n",
    "\n",
    "\n",
    "test['gp_brand_orders_1h_var']=test.groupby('brand')['orders_1h'].transform(lambda x: x.var())\n",
    "test['gp_brand_orders_2h_var']=test.groupby('brand')['orders_2h'].transform(lambda x: x.var())\n",
    "\n",
    "\n",
    "\n",
    "#标准差\n",
    "test['gp_author_orders_1h_std']=test.groupby('author')['orders_1h'].transform(lambda x: x.std())\n",
    "test['gp_author_orders_2h_std']=test.groupby('author')['orders_2h'].transform(lambda x: x.std())\n",
    "\n",
    "\n",
    "test['gp_mall_orders_1h_std']=test.groupby('mall')['orders_1h'].transform(lambda x: x.std())\n",
    "test['gp_mall_orders_2h_std']=test.groupby('mall')['orders_2h'].transform(lambda x: x.std())\n",
    "\n",
    "\n",
    "test['gp_brand_orders_1h_std']=test.groupby('brand')['orders_1h'].transform(lambda x: x.std())\n",
    "test['gp_brand_orders_2h_std']=test.groupby('brand')['orders_2h'].transform(lambda x: x.std())\n",
    "\n",
    "\n",
    "#最大值\n",
    "test['gp_author_orders_1h_max']=test.groupby('author')['orders_1h'].transform(lambda x: x.max())\n",
    "test['gp_author_orders_2h_max']=test.groupby('author')['orders_2h'].transform(lambda x: x.max())\n",
    "\n",
    "\n",
    "test['gp_mall_orders_1h_max']=test.groupby('mall')['orders_1h'].transform(lambda x: x.max())\n",
    "test['gp_mall_orders_2h_max']=test.groupby('mall')['orders_2h'].transform(lambda x: x.max())\n",
    "\n",
    "\n",
    "test['gp_brand_orders_1h_max']=test.groupby('brand')['orders_1h'].transform(lambda x: x.max())\n",
    "test['gp_brand_orders_2h_max']=test.groupby('brand')['orders_2h'].transform(lambda x: x.max())\n",
    "\n",
    "\n",
    "#最小值\n",
    "test['gp_author_orders_1h_min']=test.groupby('author')['orders_1h'].transform(lambda x: x.min())\n",
    "test['gp_author_orders_2h_min']=test.groupby('author')['orders_2h'].transform(lambda x: x.min())\n",
    "\n",
    "\n",
    "test['gp_mall_orders_1h_min']=test.groupby('mall')['orders_1h'].transform(lambda x: x.min())\n",
    "test['gp_mall_orders_2h_min']=test.groupby('mall')['orders_2h'].transform(lambda x: x.min())\n",
    "\n",
    "\n",
    "test['gp_brand_orders_1h_min']=test.groupby('brand')['orders_1h'].transform(lambda x: x.min())\n",
    "test['gp_brand_orders_2h_min']=test.groupby('brand')['orders_2h'].transform(lambda x: x.min())\n",
    "\n",
    "#众数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5762bb95",
   "metadata": {},
   "outputs": [],
   "source": [
    "#合成特征\n",
    "\n",
    "#####################train\n",
    "train['baike_iou']=len(set(train['baike_id_1h'] & train['baike_id_2h']))/len(set(train['baike_id_1h'] | train['baike_id_2h']))\n",
    "train['price_this']=train['price']+train['price_diff']\n",
    "train['price_ratio']=train['price_diff']/train['price']\n",
    "train['favorite_diff']=train['favorite_2h']-train['favorite_1h']\n",
    "train['zhi_diff']=train['zhi_2h']-train['zhi_1h']\n",
    "train['buzhi_diff']=train['buzhi_2h']-train['buzhi_1h']\n",
    "train['orders_diff']=train['orders_2h']-train['orders_1h']\n",
    "train['comments_diff']=train['comments_2h']-train['comments_1h']\n",
    "\n",
    "\n",
    "#####################test\n",
    "test['baike_iou']=len(set(test['baike_id_1h'] & test['baike_id_2h']))/len(set(test['baike_id_1h'] | test['baike_id_2h']))\n",
    "test['price_this']=test['price']+test['price_diff']\n",
    "test['price_ratio']=test['price_diff']/test['price']\n",
    "test['favorite_diff']=test['favorite_2h']-test['favorite_1h']\n",
    "test['zhi_diff']=test['zhi_2h']-test['zhi_1h']\n",
    "test['buzhi_diff']=test['buzhi_2h']-test['buzhi_1h']\n",
    "test['orders_diff']=test['orders_2h']-test['orders_1h']\n",
    "test['comments_diff']=test['comments_2h']-test['comments_1h']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "67fd85c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['price_last']=train['price']-train['price_diff']\n",
    "train['price_increase_ratio']=(train['price_last']+train['price_diff'])/train['price_last']\n",
    "train['favorite_increase_ratio']=train['favorite_2h']/(1+train['favorite_1h'])\n",
    "train['zhi_increase_ratio']=train['zhi_2h']/(1+train['zhi_1h'])\n",
    "train['buzhi_increase_ratio']=train['buzhi_2h']/(1+train['buzhi_1h'])\n",
    "train['orders_increase_ratio']=train['orders_2h']/(1+train['orders_1h'])\n",
    "train['comments_increase_ratio']=train['comments_2h']/(1+train['comments_1h'])\n",
    "\n",
    "\n",
    "\n",
    "test['price_last']=test['price']-test['price_diff']\n",
    "test['price_increase_ratio']=(test['price_last']+test['price_diff'])/test['price_last']\n",
    "test['favorite_increase_ratio']=test['favorite_2h']/(1+test['favorite_1h'])\n",
    "test['zhi_increase_ratio']=test['zhi_2h']/(1+test['zhi_1h'])\n",
    "test['buzhi_increase_ratio']=test['buzhi_2h']/(1+test['buzhi_1h'])\n",
    "test['orders_increase_ratio']=test['orders_2h']/(1+test['orders_1h'])\n",
    "test['comments_increase_ratio']=test['comments_2h']/(1+test['comments_1h'])"
   ]
  },
  {
   "cell_type": "code",
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   "id": "6501fdc1",
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   "outputs": [
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      ],
      "text/plain": [
       "         article_id  date  baike_id_1h   price  price_diff  author  level1  \\\n",
       "0               356     1          104  5969.0      -597.8   21562       8   \n",
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       "\n",
       "         level2  level3  level4  ...  buzhi_diff  orders_diff  comments_diff  \\\n",
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       "1807656      30     747       0  ...          -1            0              0   \n",
       "\n",
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       "...             ...                   ...                      ...   \n",
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       "1807656       148.5              1.000000                 0.000000   \n",
       "\n",
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       "4                       0.0                  0.00               0.000000   \n",
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       "1807656                 0.0                  0.00               0.000000   \n",
       "\n",
       "         comments_increase_ratio  \n",
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       "1                         0.6875  \n",
       "2                         0.0000  \n",
       "3                         0.0000  \n",
       "4                         0.0000  \n",
       "...                          ...  \n",
       "1807652                   0.0000  \n",
       "1807653                   0.0000  \n",
       "1807654                   0.0000  \n",
       "1807655                   0.0000  \n",
       "1807656                   0.0000  \n",
       "\n",
       "[1807657 rows x 169 columns]"
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     },
     "execution_count": 10,
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  },
  {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1810622</td>\n",
       "      <td>117</td>\n",
       "      <td>68785</td>\n",
       "      <td>5.42</td>\n",
       "      <td>2.50</td>\n",
       "      <td>52372</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>798</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.92</td>\n",
       "      <td>1.856164</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1811139</td>\n",
       "      <td>117</td>\n",
       "      <td>219783</td>\n",
       "      <td>79.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20410</td>\n",
       "      <td>13</td>\n",
       "      <td>96</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>149584</th>\n",
       "      <td>1955932</td>\n",
       "      <td>123</td>\n",
       "      <td>219783</td>\n",
       "      <td>123.50</td>\n",
       "      <td>18.05</td>\n",
       "      <td>69329</td>\n",
       "      <td>8</td>\n",
       "      <td>57</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105.45</td>\n",
       "      <td>1.171171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149585</th>\n",
       "      <td>1956580</td>\n",
       "      <td>123</td>\n",
       "      <td>29553</td>\n",
       "      <td>2.40</td>\n",
       "      <td>1.45</td>\n",
       "      <td>71272</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>754</td>\n",
       "      <td>1199</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>2.526316</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149586</th>\n",
       "      <td>1956675</td>\n",
       "      <td>123</td>\n",
       "      <td>219783</td>\n",
       "      <td>310.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>665</td>\n",
       "      <td>13</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149587</th>\n",
       "      <td>1956922</td>\n",
       "      <td>123</td>\n",
       "      <td>49355</td>\n",
       "      <td>99.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>55420</td>\n",
       "      <td>5</td>\n",
       "      <td>43</td>\n",
       "      <td>319</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.00</td>\n",
       "      <td>1.112360</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149588</th>\n",
       "      <td>1957113</td>\n",
       "      <td>123</td>\n",
       "      <td>39348</td>\n",
       "      <td>369.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28612</td>\n",
       "      <td>9</td>\n",
       "      <td>58</td>\n",
       "      <td>356</td>\n",
       "      <td>666</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>369.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149589 rows × 168 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        article_id  date  baike_id_1h   price  price_diff  author  level1  \\\n",
       "0          1809360   117       219783    9.40         NaN   39424      19   \n",
       "1          1809857   117       219783  149.60      -38.77   51888      19   \n",
       "2          1810275   117       219783   97.00       41.00    8649      13   \n",
       "3          1810622   117        68785    5.42        2.50   52372       3   \n",
       "4          1811139   117       219783   79.00         NaN   20410      13   \n",
       "...            ...   ...          ...     ...         ...     ...     ...   \n",
       "149584     1955932   123       219783  123.50       18.05   69329       8   \n",
       "149585     1956580   123        29553    2.40        1.45   71272      19   \n",
       "149586     1956675   123       219783  310.80         NaN     665      13   \n",
       "149587     1956922   123        49355   99.00       10.00   55420       5   \n",
       "149588     1957113   123        39348  369.00        0.00   28612       9   \n",
       "\n",
       "        level2  level3  level4  ...  buzhi_diff  orders_diff  comments_diff  \\\n",
       "0           31     744       0  ...           0            0              0   \n",
       "1           30     750       0  ...           0            0              0   \n",
       "2           13     664       0  ...           0            0              0   \n",
       "3            5      27     798  ...           0            0              0   \n",
       "4           96     146       0  ...           0            0              0   \n",
       "...        ...     ...     ...  ...         ...          ...            ...   \n",
       "149584      57      84       0  ...           0            0              0   \n",
       "149585       2     754    1199  ...           0            0              0   \n",
       "149586      96       1       0  ...           0            0              0   \n",
       "149587      43     319       0  ...           0            0              0   \n",
       "149588      58     356     666  ...           0            0              0   \n",
       "\n",
       "        price_last  price_increase_ratio  favorite_increase_ratio  \\\n",
       "0              NaN                   NaN                      0.0   \n",
       "1           188.37              0.794182                      0.0   \n",
       "2            56.00              1.732143                      0.0   \n",
       "3             2.92              1.856164                      0.5   \n",
       "4              NaN                   NaN                      0.0   \n",
       "...            ...                   ...                      ...   \n",
       "149584      105.45              1.171171                      0.0   \n",
       "149585        0.95              2.526316                      0.0   \n",
       "149586         NaN                   NaN                      0.0   \n",
       "149587       89.00              1.112360                      0.0   \n",
       "149588      369.00              1.000000                      0.0   \n",
       "\n",
       "        zhi_increase_ratio  buzhi_increase_ratio  orders_increase_ratio  \\\n",
       "0                      0.0                   0.0                    0.0   \n",
       "1                      0.0                   0.0                    0.0   \n",
       "2                      0.0                   0.0                    0.0   \n",
       "3                      0.0                   0.0                    0.5   \n",
       "4                      0.0                   0.0                    0.0   \n",
       "...                    ...                   ...                    ...   \n",
       "149584                 0.0                   0.0                    0.0   \n",
       "149585                 0.0                   0.0                    0.0   \n",
       "149586                 0.0                   0.0                    0.0   \n",
       "149587                 0.0                   0.0                    0.0   \n",
       "149588                 0.0                   0.0                    0.0   \n",
       "\n",
       "        comments_increase_ratio  \n",
       "0                           0.0  \n",
       "1                           0.0  \n",
       "2                           0.0  \n",
       "3                           0.0  \n",
       "4                           0.0  \n",
       "...                         ...  \n",
       "149584                      0.0  \n",
       "149585                      0.0  \n",
       "149586                      0.0  \n",
       "149587                      0.0  \n",
       "149588                      0.0  \n",
       "\n",
       "[149589 rows x 168 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5cee4ee8",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = train.drop(['article_id','orders_3h_15h'],axis=1)\n",
    "x_test = test.drop(['article_id'],axis=1)\n",
    "y_train = train['orders_3h_15h']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bb1ce494",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 5, 4, 3, 1, 6, 2, 8, 7, 9], dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2bb218ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cv_model(clf, train_x, train_y, test_x, clf_name='lgb'):\n",
    "    folds = 5\n",
    "    seed = 2021\n",
    "    kf = KFold(n_splits=folds, shuffle=True, random_state=seed)\n",
    "\n",
    "    train = np.zeros(train_x.shape[0])\n",
    "    test = np.zeros(test_x.shape[0])\n",
    "\n",
    "    cv_scores = []\n",
    "\n",
    "    for i, (train_index, valid_index) in enumerate(kf.split(train_x, train_y)):\n",
    "        print('************************************ {} ************************************'.format(str(i+1)))\n",
    "        trn_x, trn_y, val_x, val_y = train_x.iloc[train_index], train_y[train_index], train_x.iloc[valid_index], train_y[valid_index]\n",
    "\n",
    "        train_matrix = clf.Dataset(trn_x, label=trn_y)\n",
    "        valid_matrix = clf.Dataset(val_x, label=val_y)\n",
    "\n",
    "        params = {\n",
    "            'boosting_type': 'gbdt',\n",
    "            'objective': 'regression',\n",
    "            'metric': 'mse',\n",
    "            'min_child_weight': 5,\n",
    "            'max_depth': 10,\n",
    "            'num_leaves': 2 ** 4,\n",
    "            'lambda_l2': 10,\n",
    "            'feature_fraction': 0.9,\n",
    "            'bagging_fraction': 0.9,\n",
    "            'bagging_freq': 4,\n",
    "            'learning_rate': 0.1,\n",
    "            'seed': 2021,\n",
    "            'nthread': 28,\n",
    "            'n_jobs':-1,\n",
    "            'silent': True,\n",
    "            'verbose': -1,\n",
    "        }\n",
    "\n",
    "        model = clf.train(params, train_matrix, 5000, valid_sets=[train_matrix, valid_matrix], verbose_eval=500,early_stopping_rounds=200)\n",
    "        val_pred = model.predict(val_x, num_iteration=model.best_iteration)\n",
    "        test_pred = model.predict(test_x, num_iteration=model.best_iteration)\n",
    "\n",
    "        train[valid_index] = val_pred\n",
    "        test += test_pred / kf.n_splits\n",
    "        cv_scores.append(mean_squared_error(val_y, val_pred))\n",
    "        \n",
    "        print(cv_scores)\n",
    "        \n",
    "    print(\"%s_scotrainre_list:\" % clf_name, cv_scores)\n",
    "    print(\"%s_score_mean:\" % clf_name, np.mean(cv_scores))\n",
    "    print(\"%s_score_std:\" % clf_name, np.std(cv_scores))\n",
    "    return train, test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0212fc2a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************************************ 1 ************************************\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[500]\ttraining's l2: 1.22301\tvalid_1's l2: 1.23671\n",
      "[1000]\ttraining's l2: 1.192\tvalid_1's l2: 1.22362\n",
      "[1500]\ttraining's l2: 1.16783\tvalid_1's l2: 1.21607\n",
      "[2000]\ttraining's l2: 1.14716\tvalid_1's l2: 1.21082\n",
      "[2500]\ttraining's l2: 1.12863\tvalid_1's l2: 1.207\n",
      "[3000]\ttraining's l2: 1.11161\tvalid_1's l2: 1.20412\n",
      "[3500]\ttraining's l2: 1.0957\tvalid_1's l2: 1.20193\n",
      "[4000]\ttraining's l2: 1.08131\tvalid_1's l2: 1.20041\n",
      "[4500]\ttraining's l2: 1.06732\tvalid_1's l2: 1.19874\n",
      "[5000]\ttraining's l2: 1.05395\tvalid_1's l2: 1.19734\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[5000]\ttraining's l2: 1.05395\tvalid_1's l2: 1.19734\n",
      "[1.1973364466150944]\n",
      "************************************ 2 ************************************\n",
      "[LightGBM] [Warning] num_threads is set with nthread=28, will be overridden by n_jobs=-1. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] Unknown parameter: silent\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[500]\ttraining's l2: 1.22357\tvalid_1's l2: 1.2317\n",
      "[1000]\ttraining's l2: 1.193\tvalid_1's l2: 1.21781\n",
      "[1500]\ttraining's l2: 1.16901\tvalid_1's l2: 1.21016\n",
      "[2000]\ttraining's l2: 1.1489\tvalid_1's l2: 1.20479\n",
      "[2500]\ttraining's l2: 1.13085\tvalid_1's l2: 1.2016\n",
      "[3000]\ttraining's l2: 1.11406\tvalid_1's l2: 1.1984\n",
      "[3500]\ttraining's l2: 1.09842\tvalid_1's l2: 1.19594\n",
      "[4000]\ttraining's l2: 1.08362\tvalid_1's l2: 1.19386\n",
      "[4500]\ttraining's l2: 1.0697\tvalid_1's l2: 1.19192\n",
      "[5000]\ttraining's l2: 1.05654\tvalid_1's l2: 1.19034\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[5000]\ttraining's l2: 1.05654\tvalid_1's l2: 1.19034\n",
      "[1.1973364466150944, 1.1903431287975743]\n",
      "************************************ 3 ************************************\n",
      "[LightGBM] [Warning] num_threads is set with nthread=28, will be overridden by n_jobs=-1. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] Unknown parameter: silent\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[500]\ttraining's l2: 1.21915\tvalid_1's l2: 1.25022\n",
      "[1000]\ttraining's l2: 1.18835\tvalid_1's l2: 1.23692\n",
      "[1500]\ttraining's l2: 1.16441\tvalid_1's l2: 1.22926\n",
      "[2000]\ttraining's l2: 1.14461\tvalid_1's l2: 1.22432\n",
      "[2500]\ttraining's l2: 1.12594\tvalid_1's l2: 1.2202\n",
      "[3000]\ttraining's l2: 1.10918\tvalid_1's l2: 1.21714\n",
      "[3500]\ttraining's l2: 1.09371\tvalid_1's l2: 1.21491\n",
      "[4000]\ttraining's l2: 1.07909\tvalid_1's l2: 1.21322\n",
      "[4500]\ttraining's l2: 1.06513\tvalid_1's l2: 1.21149\n",
      "[5000]\ttraining's l2: 1.05179\tvalid_1's l2: 1.21015\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[5000]\ttraining's l2: 1.05179\tvalid_1's l2: 1.21015\n",
      "[1.1973364466150944, 1.1903431287975743, 1.2101500451647549]\n",
      "************************************ 4 ************************************\n",
      "[LightGBM] [Warning] num_threads is set with nthread=28, will be overridden by n_jobs=-1. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] Unknown parameter: silent\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[500]\ttraining's l2: 1.2233\tvalid_1's l2: 1.23484\n",
      "[1000]\ttraining's l2: 1.1919\tvalid_1's l2: 1.22149\n",
      "[1500]\ttraining's l2: 1.16826\tvalid_1's l2: 1.21407\n",
      "[2000]\ttraining's l2: 1.14831\tvalid_1's l2: 1.20979\n",
      "[2500]\ttraining's l2: 1.12957\tvalid_1's l2: 1.20569\n",
      "[3000]\ttraining's l2: 1.11296\tvalid_1's l2: 1.20311\n",
      "[3500]\ttraining's l2: 1.09713\tvalid_1's l2: 1.20082\n",
      "[4000]\ttraining's l2: 1.08286\tvalid_1's l2: 1.19906\n",
      "[4500]\ttraining's l2: 1.06939\tvalid_1's l2: 1.1979\n",
      "[5000]\ttraining's l2: 1.0563\tvalid_1's l2: 1.19642\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[5000]\ttraining's l2: 1.0563\tvalid_1's l2: 1.19642\n",
      "[1.1973364466150944, 1.1903431287975743, 1.2101500451647549, 1.1964238138626975]\n",
      "************************************ 5 ************************************\n",
      "[LightGBM] [Warning] num_threads is set with nthread=28, will be overridden by n_jobs=-1. Current value: num_threads=-1\n",
      "[LightGBM] [Warning] Unknown parameter: silent\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[500]\ttraining's l2: 1.22181\tvalid_1's l2: 1.24213\n",
      "[1000]\ttraining's l2: 1.19075\tvalid_1's l2: 1.22843\n",
      "[1500]\ttraining's l2: 1.16707\tvalid_1's l2: 1.22086\n",
      "[2000]\ttraining's l2: 1.14675\tvalid_1's l2: 1.21573\n",
      "[2500]\ttraining's l2: 1.1289\tvalid_1's l2: 1.2122\n",
      "[3000]\ttraining's l2: 1.11203\tvalid_1's l2: 1.20922\n",
      "[3500]\ttraining's l2: 1.09624\tvalid_1's l2: 1.20658\n",
      "[4000]\ttraining's l2: 1.08139\tvalid_1's l2: 1.2046\n",
      "[4500]\ttraining's l2: 1.06752\tvalid_1's l2: 1.20293\n",
      "[5000]\ttraining's l2: 1.05443\tvalid_1's l2: 1.20173\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[5000]\ttraining's l2: 1.05443\tvalid_1's l2: 1.20173\n",
      "[1.1973364466150944, 1.1903431287975743, 1.2101500451647549, 1.1964238138626975, 1.201730734709395]\n",
      "lgb_scotrainre_list: [1.1973364466150944, 1.1903431287975743, 1.2101500451647549, 1.1964238138626975, 1.201730734709395]\n",
      "lgb_score_mean: 1.1991968338299033\n",
      "lgb_score_std: 0.006571640059224859\n"
     ]
    }
   ],
   "source": [
    "lgb_train, lgb_test = cv_model(lgb, x_train, y_train, x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5b280afb",
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb_test=lgb_test.clip(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dbadd145",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_submit=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6fa2e6f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 预测结果\n",
    "sample_submit['article_id']=test['article_id']\n",
    "sample_submit['orders_3h_15h'] = lgb_test\n",
    "#sample_submit['loan_default'] = sample_submit['loan_default'].apply(lambda x:1 if x>0.25 else 0).values\n",
    "sample_submit.to_csv('eee.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5db544a4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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