From 44417ebbb53a15248626655dcd8b7ee47728ec61 Mon Sep 17 00:00:00 2001
From: benjas <909336740@qq.com>
Date: Sun, 14 Feb 2021 09:57:35 +0800
Subject: [PATCH] =?UTF-8?q?Delete=204-=E6=A8=A1=E5=9E=8B=E9=A2=84=E6=B5=8B?=
 =?UTF-8?q?=E5=8F=8A=E8=AF=84=E4=BC=B0-checkpoint.ipynb?=
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diff --git a/机器学习竞赛实战_优胜解决方案/京东用户购买意向预测/.ipynb_checkpoints/4-模型预测及评估-checkpoint.ipynb b/机器学习竞赛实战_优胜解决方案/京东用户购买意向预测/.ipynb_checkpoints/4-模型预测及评估-checkpoint.ipynb
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--- a/机器学习竞赛实战_优胜解决方案/京东用户购买意向预测/.ipynb_checkpoints/4-模型预测及评估-checkpoint.ipynb
+++ /dev/null
@@ -1,1055 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import lightgbm as lgb  # 模型\n",
-    "import pandas as pd  # 数据处理包\n",
-    "import numpy as np  # 数据处理包\n",
-    "from sklearn import metrics  # 混淆矩阵\n",
-    "from sklearn.model_selection import StratifiedKFold, GridSearchCV, train_test_split  # 分层五折验证包、寻找最优参函数、切分数据\n",
-    "from sklearn.metrics import accuracy_score, roc_curve, auc, confusion_matrix  # 准确率、roc计算、auc计算、混淆矩阵\n",
-    "import matplotlib.pyplot as plt  # 图形处理包\n",
-    "import itertools  # 处理混淆矩阵\n",
-    "import gc  # 处理缓存，有兴趣的可以搜搜怎么使用\n",
-    "import warnings  # 忽略普通警告，不打印太多东西\n",
-    "warnings.filterwarnings('ignore')\n",
-    "plt.rcParams['font.sans-serif']=['SimHei']  # 让图形可以显示中文\n",
-    "plt.rcParams['axes.unicode_minus']=False"
-   ]
-  },
-  {
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-    "data = pd.read_csv('data/train_set.csv')  # 读取训练数据\n",
-    "data.head()"
-   ]
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-  {
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-       "\n",
-       "   action_before_3_6.0_x  action_before_3_1.0_y  ...  cate_action_4_mean  \\\n",
-       "0                    2.0                    1.0  ...                 8.4   \n",
-       "1                    4.0                    2.0  ...                 8.4   \n",
-       "2                    5.0                   79.0  ...                 8.4   \n",
-       "3                   26.0                   10.0  ...                 8.4   \n",
-       "4                   28.0                   55.0  ...                 8.4   \n",
-       "\n",
-       "   cate_action_5_mean  cate_action_6_mean  has_bad_comment  bad_comment_rate  \\\n",
-       "0           20.866667              5167.6              1.0            0.0260   \n",
-       "1           20.866667              5167.6              1.0            0.0403   \n",
-       "2           20.866667              5167.6              1.0            0.0245   \n",
-       "3           20.866667              5167.6              1.0            0.0208   \n",
-       "4           20.866667              5167.6              1.0            0.0166   \n",
-       "\n",
-       "   comment_num_0  comment_num_1  comment_num_2  comment_num_3  comment_num_4  \n",
-       "0            0.0            0.0            0.0            0.0            1.0  \n",
-       "1            0.0            0.0            0.0            0.0            1.0  \n",
-       "2            0.0            0.0            0.0            0.0            1.0  \n",
-       "3            0.0            0.0            0.0            0.0            1.0  \n",
-       "4            0.0            0.0            0.0            0.0            1.0  \n",
-       "\n",
-       "[5 rows x 236 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_x = data.loc[:,data.columns != 'label']  # 将训练数据集分成特征和标签\n",
-    "train_y = data.loc[:,data.columns == 'label']\n",
-    "train_x.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "   label\n",
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-    "train_y.head()"
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-       "      <td>...</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>1.0</td>\n",
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-       "      <td>...</td>\n",
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-       "      <td>1.0</td>\n",
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-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 234 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   cate  action_before_3_1.0_x  action_before_3_2.0_x  action_before_3_3.0_x  \\\n",
-       "0   8.0                    1.0                    0.0                    0.0   \n",
-       "1   8.0                    4.0                    0.0                    0.0   \n",
-       "2   8.0                    3.0                    0.0                    0.0   \n",
-       "3   8.0                   20.0                    1.0                    0.0   \n",
-       "4   8.0                   30.0                    1.0                    0.0   \n",
-       "\n",
-       "   action_before_3_4.0_x  action_before_3_5.0_x  action_before_3_6.0_x  \\\n",
-       "0                    0.0                    0.0                    2.0   \n",
-       "1                    0.0                    0.0                    4.0   \n",
-       "2                    0.0                    0.0                    5.0   \n",
-       "3                    0.0                    0.0                   26.0   \n",
-       "4                    0.0                    0.0                   28.0   \n",
-       "\n",
-       "   action_before_3_1.0_y  action_before_3_2.0_y  action_before_3_3.0_y  ...  \\\n",
-       "0                    1.0                    0.0                    0.0  ...   \n",
-       "1                    2.0                    0.0                    0.0  ...   \n",
-       "2                   79.0                    0.0                    0.0  ...   \n",
-       "3                   10.0                    0.0                    0.0  ...   \n",
-       "4                   55.0                    0.0                    0.0  ...   \n",
-       "\n",
-       "   cate_action_4_mean  cate_action_5_mean  cate_action_6_mean  \\\n",
-       "0                 8.4           20.866667              5167.6   \n",
-       "1                 8.4           20.866667              5167.6   \n",
-       "2                 8.4           20.866667              5167.6   \n",
-       "3                 8.4           20.866667              5167.6   \n",
-       "4                 8.4           20.866667              5167.6   \n",
-       "\n",
-       "   has_bad_comment  bad_comment_rate  comment_num_0  comment_num_1  \\\n",
-       "0              1.0            0.0260            0.0            0.0   \n",
-       "1              1.0            0.0403            0.0            0.0   \n",
-       "2              1.0            0.0245            0.0            0.0   \n",
-       "3              1.0            0.0208            0.0            0.0   \n",
-       "4              1.0            0.0166            0.0            0.0   \n",
-       "\n",
-       "   comment_num_2  comment_num_3  comment_num_4  \n",
-       "0            0.0            0.0            1.0  \n",
-       "1            0.0            0.0            1.0  \n",
-       "2            0.0            0.0            1.0  \n",
-       "3            0.0            0.0            1.0  \n",
-       "4            0.0            0.0            1.0  \n",
-       "\n",
-       "[5 rows x 234 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "del train_x['user_id']\n",
-    "del train_x['sku_id']\n",
-    "\n",
-    "train_x.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(14619, 234)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_x.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>200007.0</td>\n",
-       "      <td>26229.0</td>\n",
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-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>200007.0</td>\n",
-       "      <td>63315.0</td>\n",
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-       "      <td>4.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>200007.0</td>\n",
-       "      <td>126404.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>20.766667</td>\n",
-       "      <td>56.700000</td>\n",
-       "      <td>12937.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 236 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    user_id    sku_id  cate  action_before_3_1.0_x  action_before_3_2.0_x  \\\n",
-       "0  200005.0   67444.0   4.0                    2.0                    0.0   \n",
-       "1  200005.0   72967.0   4.0                   26.0                    1.0   \n",
-       "2  200007.0   26229.0   9.0                    2.0                    0.0   \n",
-       "3  200007.0   63315.0   9.0                    4.0                    0.0   \n",
-       "4  200007.0  126404.0   9.0                    4.0                    0.0   \n",
-       "\n",
-       "   action_before_3_3.0_x  action_before_3_4.0_x  action_before_3_5.0_x  \\\n",
-       "0                    0.0                    0.0                    0.0   \n",
-       "1                    0.0                    1.0                    0.0   \n",
-       "2                    0.0                    0.0                    0.0   \n",
-       "3                    0.0                    0.0                    0.0   \n",
-       "4                    0.0                    0.0                    0.0   \n",
-       "\n",
-       "   action_before_3_6.0_x  action_before_3_1.0_y  ...  cate_action_4_mean  \\\n",
-       "0                    3.0                   26.0  ...           73.400000   \n",
-       "1                   30.0                    2.0  ...           73.400000   \n",
-       "2                    2.0                   12.0  ...           20.766667   \n",
-       "3                    3.0                   10.0  ...           20.766667   \n",
-       "4                    3.0                   10.0  ...           20.766667   \n",
-       "\n",
-       "   cate_action_5_mean  cate_action_6_mean  has_bad_comment  bad_comment_rate  \\\n",
-       "0          169.366667             48251.0              1.0            0.0821   \n",
-       "1          169.366667             48251.0              1.0            0.0196   \n",
-       "2           56.700000             12937.7              1.0            0.0198   \n",
-       "3           56.700000             12937.7              1.0            0.0476   \n",
-       "4           56.700000             12937.7              0.0            0.0000   \n",
-       "\n",
-       "   comment_num_0  comment_num_1  comment_num_2  comment_num_3  comment_num_4  \n",
-       "0            0.0            0.0            0.0            0.0            1.0  \n",
-       "1            0.0            0.0            0.0            0.0            1.0  \n",
-       "2            0.0            0.0            0.0            0.0            1.0  \n",
-       "3            0.0            0.0            0.0            0.0            1.0  \n",
-       "4            0.0            0.0            0.0            0.0            1.0  \n",
-       "\n",
-       "[5 rows x 236 columns]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = pd.read_csv('data/val_set.csv')  # 读取验证数据\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "val_x = data.loc[:,data.columns != 'label']  # 将验证数据集分成特征和标签\n",
-    "val_y = data.loc[:,data.columns == 'label']\n",
-    "val_x.head()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}