Product_search_relevance(jupyter notebook).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding:utf8 -*-\n",
    "# @TIME : 2018/4/26 下午6:15\n",
    "# @Author : Allen\n",
    "# @File : product_search_relevance.py\n",
    "\n",
    "#目的：给出输入关键字与搜索结果，评价搜索准确度\n",
    "\n",
    "#处理思路\n",
    "#1，导入包、数据  -> 合并数据格式concat,merge,\n",
    "#2，文本预处理 -> 【简单方法】：看输入词是在搜索结果中出现几次，需要先统一数据集格式 -> str_stemmer and str_commond_words 处理数据\n",
    "#3，自制文本特征 -> 关键词长度/搜索词语与title和describtion中重复词语数 -> 去掉之前的英文，保留自制特征\n",
    "#4，重塑训练／测试集 -> 拆分出X_train, X_test, y_train, 去除label\n",
    "#5，建立模型：Ridge回归模型RandomForestRegressor 找出最佳参数max_depth=7 通过多种参数导入 -> 画图\n",
    "#6，上传结果：生成csv文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1) 导入需要用的库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier, BaggingRegressor\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "import os\n",
    "os.chdir('/Users/a1/Desktop/算法实战/Word2vec/Product_search_relevance/data')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读取数据\n",
    "df_train = pd.read_csv('train.csv', encoding = \"ISO-8859-1\")\n",
    "df_test = pd.read_csv('test.csv', encoding = \"ISO-8859-1\")\n",
    "df_desc = pd.read_csv('product_descriptions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>product_uid</th>\n",
       "      <th>product_title</th>\n",
       "      <th>search_term</th>\n",
       "      <th>relevance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>100001</td>\n",
       "      <td>Simpson Strong-Tie 12-Gauge Angle</td>\n",
       "      <td>angle bracket</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>100001</td>\n",
       "      <td>Simpson Strong-Tie 12-Gauge Angle</td>\n",
       "      <td>l bracket</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>100002</td>\n",
       "      <td>BEHR Premium Textured DeckOver 1-gal. #SC-141 ...</td>\n",
       "      <td>deck over</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>100005</td>\n",
       "      <td>Delta Vero 1-Handle Shower Only Faucet Trim Ki...</td>\n",
       "      <td>rain shower head</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>100005</td>\n",
       "      <td>Delta Vero 1-Handle Shower Only Faucet Trim Ki...</td>\n",
       "      <td>shower only faucet</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  product_uid                                      product_title  \\\n",
       "0   2       100001                  Simpson Strong-Tie 12-Gauge Angle   \n",
       "1   3       100001                  Simpson Strong-Tie 12-Gauge Angle   \n",
       "2   9       100002  BEHR Premium Textured DeckOver 1-gal. #SC-141 ...   \n",
       "3  16       100005  Delta Vero 1-Handle Shower Only Faucet Trim Ki...   \n",
       "4  17       100005  Delta Vero 1-Handle Shower Only Faucet Trim Ki...   \n",
       "\n",
       "          search_term  relevance  \n",
       "0       angle bracket       3.00  \n",
       "1           l bracket       2.50  \n",
       "2           deck over       3.00  \n",
       "3    rain shower head       2.33  \n",
       "4  shower only faucet       2.67  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_uid</th>\n",
       "      <th>product_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>Not only do angles make joints stronger, they ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100002</td>\n",
       "      <td>BEHR Premium Textured DECKOVER is an innovativ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100003</td>\n",
       "      <td>Classic architecture meets contemporary design...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100004</td>\n",
       "      <td>The Grape Solar 265-Watt Polycrystalline PV So...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100005</td>\n",
       "      <td>Update your bathroom with the Delta Vero Singl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   product_uid                                product_description\n",
       "0       100001  Not only do angles make joints stronger, they ...\n",
       "1       100002  BEHR Premium Textured DECKOVER is an innovativ...\n",
       "2       100003  Classic architecture meets contemporary design...\n",
       "3       100004  The Grape Solar 265-Watt Polycrystalline PV So...\n",
       "4       100005  Update your bathroom with the Delta Vero Singl..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_desc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>product_title</th>\n",
       "      <th>product_uid</th>\n",
       "      <th>relevance</th>\n",
       "      <th>search_term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Simpson Strong-Tie 12-Gauge Angle</td>\n",
       "      <td>100001</td>\n",
       "      <td>3.00</td>\n",
       "      <td>angle bracket</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Simpson Strong-Tie 12-Gauge Angle</td>\n",
       "      <td>100001</td>\n",
       "      <td>2.50</td>\n",
       "      <td>l bracket</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>BEHR Premium Textured DeckOver 1-gal. #SC-141 ...</td>\n",
       "      <td>100002</td>\n",
       "      <td>3.00</td>\n",
       "      <td>deck over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>Delta Vero 1-Handle Shower Only Faucet Trim Ki...</td>\n",
       "      <td>100005</td>\n",
       "      <td>2.33</td>\n",
       "      <td>rain shower head</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>Delta Vero 1-Handle Shower Only Faucet Trim Ki...</td>\n",
       "      <td>100005</td>\n",
       "      <td>2.67</td>\n",
       "      <td>shower only faucet</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                      product_title  product_uid  \\\n",
       "0   2                  Simpson Strong-Tie 12-Gauge Angle       100001   \n",
       "1   3                  Simpson Strong-Tie 12-Gauge Angle       100001   \n",
       "2   9  BEHR Premium Textured DeckOver 1-gal. #SC-141 ...       100002   \n",
       "3  16  Delta Vero 1-Handle Shower Only Faucet Trim Ki...       100005   \n",
       "4  17  Delta Vero 1-Handle Shower Only Faucet Trim Ki...       100005   \n",
       "\n",
       "   relevance         search_term  \n",
       "0       3.00       angle bracket  \n",
       "1       2.50           l bracket  \n",
       "2       3.00           deck over  \n",
       "3       2.33    rain shower head  \n",
       "4       2.67  shower only faucet  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#合并测试集与训练集，便于统一文本预处理\n",
    "#PANDAS 数据合并与重塑（concat篇）：https://blog.csdn.net/stevenkwong/article/details/52528616\n",
    "#PANDAS 数据合并与重塑（join/merge篇）:https://blog.csdn.net/stevenkwong/article/details/52540605\n",
    "df_all = pd.concat((df_train, df_test), axis=0, ignore_index=True)\n",
    "df_all.head()\n",
    "#print(df_all.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>product_title</th>\n",
       "      <th>product_uid</th>\n",
       "      <th>relevance</th>\n",
       "      <th>search_term</th>\n",
       "      <th>product_description</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Simpson Strong-Tie 12-Gauge Angle</td>\n",
       "      <td>100001</td>\n",
       "      <td>3.00</td>\n",
       "      <td>angle bracket</td>\n",
       "      <td>Not only do angles make joints stronger, they ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Simpson Strong-Tie 12-Gauge Angle</td>\n",
       "      <td>100001</td>\n",
       "      <td>2.50</td>\n",
       "      <td>l bracket</td>\n",
       "      <td>Not only do angles make joints stronger, they ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>BEHR Premium Textured DeckOver 1-gal. #SC-141 ...</td>\n",
       "      <td>100002</td>\n",
       "      <td>3.00</td>\n",
       "      <td>deck over</td>\n",
       "      <td>BEHR Premium Textured DECKOVER is an innovativ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>Delta Vero 1-Handle Shower Only Faucet Trim Ki...</td>\n",
       "      <td>100005</td>\n",
       "      <td>2.33</td>\n",
       "      <td>rain shower head</td>\n",
       "      <td>Update your bathroom with the Delta Vero Singl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>Delta Vero 1-Handle Shower Only Faucet Trim Ki...</td>\n",
       "      <td>100005</td>\n",
       "      <td>2.67</td>\n",
       "      <td>shower only faucet</td>\n",
       "      <td>Update your bathroom with the Delta Vero Singl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                      product_title  product_uid  \\\n",
       "0   2                  Simpson Strong-Tie 12-Gauge Angle       100001   \n",
       "1   3                  Simpson Strong-Tie 12-Gauge Angle       100001   \n",
       "2   9  BEHR Premium Textured DeckOver 1-gal. #SC-141 ...       100002   \n",
       "3  16  Delta Vero 1-Handle Shower Only Faucet Trim Ki...       100005   \n",
       "4  17  Delta Vero 1-Handle Shower Only Faucet Trim Ki...       100005   \n",
       "\n",
       "   relevance         search_term  \\\n",
       "0       3.00       angle bracket   \n",
       "1       2.50           l bracket   \n",
       "2       3.00           deck over   \n",
       "3       2.33    rain shower head   \n",
       "4       2.67  shower only faucet   \n",
       "\n",
       "                                 product_description  \n",
       "0  Not only do angles make joints stronger, they ...  \n",
       "1  Not only do angles make joints stronger, they ...  \n",
       "2  BEHR Premium Textured DECKOVER is an innovativ...  \n",
       "3  Update your bathroom with the Delta Vero Singl...  \n",
       "4  Update your bathroom with the Delta Vero Singl...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#把描述信息加入表，how='left'表示左边全部保留，on表示以什么为基准对齐\n",
    "df_all = pd.merge(df_all, df_desc, how='left', on='product_uid')\n",
    "df_all.head()\n",
    "#print(\"******************************\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2) 文本预处理，把表格的语句处理为计算机能懂的格式，这里使用NLTK\n",
    "stemmer = SnowballStemmer('english')\n",
    "#这里简单处理，只提取了词干\n",
    "def str_stemmer(s):\n",
    "    return \" \".join([stemmer.stem(word) for word in s.lower().split()])\n",
    "#计算\"关键词次数\"\n",
    "def str_common_word(str1, str2):\n",
    "    return sum(int(str2.find(word)>=0) for word in str1.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#把每一个column都跑一遍，用str_stemmer清洁所有的文本内容\n",
    "df_all['search_term'] = df_all['search_term'].map(lambda x: str_stemmer(x))\n",
    "df_all['product_title'] = df_all['product_title'].map(lambda x: str_stemmer(x))\n",
    "df_all['product_description'] = df_all['product_description'].map(lambda x: str_stemmer(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3） 自制文本特征\n",
    "#关键字的长度\n",
    "df_all['len_of_query'] = df_all['search_term'].map(lambda x: len(x.split())).astype(np.int64)\n",
    "#搜索词与标题中有多少关键字重合\n",
    "df_all['commons_in_title'] = df_all.apply(lambda x:str_common_word(x['search_term'], x['product_title']), axis=1)\n",
    "#搜索词与描述中有多少关键字重合\n",
    "#df_all['commons_in_desc'] = df_all.apply(lambda x: str_common_word(x['search_term'], x['product_description'], axis = 1)\n",
    "df_all['commons_in_desc'] = df_all.apply(lambda x: str_common_word(x['search_term'], x['product_description']), axis=1)\n",
    "#搞完之后，我们把不能被『机器学习模型』处理的column给drop掉,这一步太偷懒了\n",
    "df_all = df_all.drop(['search_term', 'product_title', 'product_description'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>100005</td>\n",
       "      <td>2.33</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>100005</td>\n",
       "      <td>2.67</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18</td>\n",
       "      <td>100006</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20</td>\n",
       "      <td>100006</td>\n",
       "      <td>2.67</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>21</td>\n",
       "      <td>100006</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>23</td>\n",
       "      <td>100007</td>\n",
       "      <td>2.67</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>27</td>\n",
       "      <td>100009</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>34</td>\n",
       "      <td>100010</td>\n",
       "      <td>2.67</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>35</td>\n",
       "      <td>100011</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>37</td>\n",
       "      <td>100011</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>38</td>\n",
       "      <td>100011</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>48</td>\n",
       "      <td>100012</td>\n",
       "      <td>2.67</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>51</td>\n",
       "      <td>100013</td>\n",
       "      <td>2.67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>65</td>\n",
       "      <td>100016</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>69</td>\n",
       "      <td>100017</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>75</td>\n",
       "      <td>100017</td>\n",
       "      <td>1.67</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>81</td>\n",
       "      <td>100017</td>\n",
       "      <td>2.33</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id  product_uid  relevance  len_of_query  commons_in_title  \\\n",
       "0    2       100001       3.00             2                 1   \n",
       "1    3       100001       2.50             2                 1   \n",
       "2    9       100002       3.00             2                 1   \n",
       "3   16       100005       2.33             3                 1   \n",
       "4   17       100005       2.67             3                 3   \n",
       "5   18       100006       3.00             2                 1   \n",
       "6   20       100006       2.67             3                 2   \n",
       "7   21       100006       3.00             1                 1   \n",
       "8   23       100007       2.67             2                 2   \n",
       "9   27       100009       3.00             2                 2   \n",
       "10  34       100010       2.67             2                 1   \n",
       "11  35       100011       3.00             5                 4   \n",
       "12  37       100011       3.00             2                 2   \n",
       "13  38       100011       2.00             2                 1   \n",
       "14  48       100012       2.67             6                 4   \n",
       "15  51       100013       2.67             1                 1   \n",
       "16  65       100016       3.00             2                 2   \n",
       "17  69       100017       1.00             2                 0   \n",
       "18  75       100017       1.67             4                 0   \n",
       "19  81       100017       2.33             2                 0   \n",
       "\n",
       "    commons_in_desc  \n",
       "0                 1  \n",
       "1                 1  \n",
       "2                 1  \n",
       "3                 1  \n",
       "4                 2  \n",
       "5                 2  \n",
       "6                 2  \n",
       "7                 1  \n",
       "8                 2  \n",
       "9                 2  \n",
       "10                2  \n",
       "11                5  \n",
       "12                2  \n",
       "13                1  \n",
       "14                3  \n",
       "15                1  \n",
       "16                2  \n",
       "17                1  \n",
       "18                0  \n",
       "19                0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "###################\n"
     ]
    }
   ],
   "source": [
    "#4) 重塑训练／测试集合， 刚才把train与test合并了，现在分开\n",
    "df_train = df_all.loc[df_train.index]\n",
    "df_test = df_all.loc[df_test.index]\n",
    "#记录id，后续上传能用\n",
    "test_ids = df_test['id']\n",
    "#分离出y_train\n",
    "y_train = df_train['relevance'].values\n",
    "#删除原集合中的label\n",
    "X_train = df_train.drop(['id', 'relevance'], axis=1).values\n",
    "X_test = df_test.drop(['id', 'relevance'], axis=1).values\n",
    "print(\"###################\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11144ed30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#5)建立模型：Ridge模型\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "%matplotlib inline\n",
    "params = [1, 3, 4, 6, 7, 8,]\n",
    "test_scores = []\n",
    "for param in params:\n",
    "    clf = RandomForestRegressor(n_estimators=30, max_depth=param)\n",
    "    test_score = np.sqrt(-cross_val_score(clf, X_train, y_train, cv=5, scoring='neg_mean_squared_error'))\n",
    "    test_scores.append(np.mean(test_score))\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(params, test_scores)\n",
    "plt.title(\"Param vs CV Error\");\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=7,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=30, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#6) 上传结果\n",
    "rf = RandomForestRegressor(n_estimators=30, max_depth=7)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame({\"id\": test_ids, \"relevance\": y_pred}).to_csv('submission123.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}