更新2-神经网络.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "train=pd.read_csv('case2_training.csv')\n",
    "train=train.copy()\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Region</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Apartment</th>\n",
       "      <th>Beds</th>\n",
       "      <th>Review</th>\n",
       "      <th>Pic Quality</th>\n",
       "      <th>Price</th>\n",
       "      <th>Accept</th>\n",
       "      <th>normalized_price_per_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "      <td>5</td>\n",
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       "      <td>1</td>\n",
       "      <td>4.526975</td>\n",
       "      <td>0.739994</td>\n",
       "      <td>230.552998</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.991450</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.752735</td>\n",
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       "      <td>1</td>\n",
       "      <td>-1.993190</td>\n",
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      "text/plain": [
       "   ID  Region  Date  Weekday  Apartment  Beds    Review  Pic Quality  \\\n",
       "0   1       4    32        5          1     1  4.526975     0.739994   \n",
       "1   2       2    87        4          1     1  4.548455     0.862642   \n",
       "2   3       5   277        5          1     2  4.965732     0.515548   \n",
       "3   4       3   246        2          1     1  4.752735     0.561547   \n",
       "4   5       1   155        2          1     2  3.890960     0.817142   \n",
       "\n",
       "        Price  Accept  normalized_price_per_review  \n",
       "0  230.552998       0                    -0.991450  \n",
       "1  348.203425       0                     0.324016  \n",
       "2  430.462327       0                     0.844153  \n",
       "3  149.305433       1                    -1.993190  \n",
       "4  165.264184       1                    -1.425473  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#构造新特征\n",
    "#price_per_review：\n",
    "def price_per_review(df):\n",
    "    df['price_per_review'] =  (df[\"Price\"])/ (df[\"Review\"])\n",
    "\n",
    "price_per_review(train)\n",
    "\n",
    "\n",
    "train['normalized_price_per_review'] = (train['price_per_review'] - train['price_per_review'].mean()) / (train['price_per_review'].std())\n",
    "del train['price_per_review']\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Date</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Apartment</th>\n",
       "      <th>Beds</th>\n",
       "      <th>Review</th>\n",
       "      <th>Pic Quality</th>\n",
       "      <th>Price</th>\n",
       "      <th>Accept</th>\n",
       "      <th>normalized_price_per_review</th>\n",
       "      <th>normalized_perbed_price</th>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.752735</td>\n",
       "      <td>0.561547</td>\n",
       "      <td>149.305433</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.993190</td>\n",
       "      <td>-1.031145</td>\n",
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       "      <td>4</td>\n",
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      "text/plain": [
       "   ID  Region  Date  Weekday  Apartment  Beds    Review  Pic Quality  \\\n",
       "0   1       4    32        5          1     1  4.526975     0.739994   \n",
       "1   2       2    87        4          1     1  4.548455     0.862642   \n",
       "2   3       5   277        5          1     2  4.965732     0.515548   \n",
       "3   4       3   246        2          1     1  4.752735     0.561547   \n",
       "4   5       1   155        2          1     2  3.890960     0.817142   \n",
       "\n",
       "        Price  Accept  normalized_price_per_review  normalized_perbed_price  \n",
       "0  230.552998       0                    -0.991450                -0.070105  \n",
       "1  348.203425       0                     0.324016                 1.321527  \n",
       "2  430.462327       0                     0.844153                -0.251340  \n",
       "3  149.305433       1                    -1.993190                -1.031145  \n",
       "4  165.264184       1                    -1.425473                -1.819792  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#构造新特征\n",
    "#price_per_bed：单位bed的价格\n",
    "def price_per_bed(df):\n",
    "    df['price_per_bad'] =  (df[\"Price\"])/ (df[\"Beds\"])\n",
    "\n",
    "price_per_bed(train)\n",
    "\n",
    "\n",
    "train['normalized_perbed_price'] = (train['price_per_bad'] - train['price_per_bad'].mean()) / (train['price_per_bad'].std())\n",
    "del train['price_per_bad']\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Weekday</th>\n",
       "      <th>Apartment</th>\n",
       "      <th>Beds</th>\n",
       "      <th>Review</th>\n",
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       "      <th>Accept</th>\n",
       "      <th>normalized_price_per_review</th>\n",
       "      <th>normalized_perbed_price</th>\n",
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      "text/plain": [
       "   ID  Region  Date  Weekday  Apartment  Beds    Review  Pic Quality  \\\n",
       "0   1       4    32        5          1     1  4.526975     0.739994   \n",
       "1   2       2    87        4          1     1  4.548455     0.862642   \n",
       "2   3       5   277        5          1     2  4.965732     0.515548   \n",
       "3   4       3   246        2          1     1  4.752735     0.561547   \n",
       "4   5       1   155        2          1     2  3.890960     0.817142   \n",
       "\n",
       "        Price  Accept  normalized_price_per_review  normalized_perbed_price  \\\n",
       "0  230.552998       0                    -0.991450                -0.070105   \n",
       "1  348.203425       0                     0.324016                 1.321527   \n",
       "2  430.462327       0                     0.844153                -0.251340   \n",
       "3  149.305433       1                    -1.993190                -1.031145   \n",
       "4  165.264184       1                    -1.425473                -1.819792   \n",
       "\n",
       "   rev_mul_picqual  \n",
       "0         3.349935  \n",
       "1         3.923689  \n",
       "2         2.560073  \n",
       "3         2.668886  \n",
       "4         3.179466  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#构造新特征\n",
    "#rev_mul_picqual\n",
    "def rev_mul_picqual(df):\n",
    "    df['rev_mul_picqual'] =  (df[\"Review\"])* (df[\"Pic Quality\"])\n",
    "\n",
    "rev_mul_picqual(train)\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Region</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Beds</th>\n",
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       "      <td>3</td>\n",
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       "      <td>246</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.752735</td>\n",
       "      <td>0.561547</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.993190</td>\n",
       "      <td>-1.031145</td>\n",
       "      <td>2.668886</td>\n",
       "      <td>-1.809686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>155</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3.890960</td>\n",
       "      <td>0.817142</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.425473</td>\n",
       "      <td>-1.819792</td>\n",
       "      <td>3.179466</td>\n",
       "      <td>-1.637429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region  Date  Weekday  Beds    Review  Pic Quality  Accept  \\\n",
       "0       4    32        5     1  4.526975     0.739994       0   \n",
       "1       2    87        4     1  4.548455     0.862642       0   \n",
       "2       5   277        5     2  4.965732     0.515548       0   \n",
       "3       3   246        2     1  4.752735     0.561547       1   \n",
       "4       1   155        2     2  3.890960     0.817142       1   \n",
       "\n",
       "   normalized_price_per_review  normalized_perbed_price  rev_mul_picqual  \\\n",
       "0                    -0.991450                -0.070105         3.349935   \n",
       "1                     0.324016                 1.321527         3.923689   \n",
       "2                     0.844153                -0.251340         2.560073   \n",
       "3                    -1.993190                -1.031145         2.668886   \n",
       "4                    -1.425473                -1.819792         3.179466   \n",
       "\n",
       "   normalized_price  \n",
       "0         -0.932707  \n",
       "1          0.337202  \n",
       "2          1.225097  \n",
       "3         -1.809686  \n",
       "4         -1.637429  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['normalized_price'] = (train['Price'] - train['Price'].mean()) / (train['Price'].std())\n",
    "#train['normalized_price'] = (train['Price'] - train['Price'].mean()) / (train['Price'].max() - train['Price'].mean())\n",
    "#train['normalized_Date'] = (train['Date'] - train['Date'].mean()) / (train['Date'].std())\n",
    "#train['normalized_review'] = (train['Review'] - train['Review'].mean()) / (train['Review'].std())\n",
    "\n",
    "\n",
    "#del train['Date']\n",
    "del train['ID']\n",
    "#del train['Review']                                                                           \n",
    "del train['Apartment']\n",
    "#del train['Weekday']\n",
    "del train['Price']\n",
    "#del train['Beds']\n",
    "                                                                           \n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Beds</th>\n",
       "      <th>Review</th>\n",
       "      <th>Pic Quality</th>\n",
       "      <th>Accept</th>\n",
       "      <th>normalized_price_per_review</th>\n",
       "      <th>normalized_perbed_price</th>\n",
       "      <th>rev_mul_picqual</th>\n",
       "      <th>normalized_price</th>\n",
       "      <th>date_label</th>\n",
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       "    <tr>\n",
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       "      <td>3.179466</td>\n",
       "      <td>-1.637429</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region  Weekday  Beds    Review  Pic Quality  Accept  \\\n",
       "0       4        5     1  4.526975     0.739994       0   \n",
       "1       2        4     1  4.548455     0.862642       0   \n",
       "2       5        5     2  4.965732     0.515548       0   \n",
       "3       3        2     1  4.752735     0.561547       1   \n",
       "4       1        2     2  3.890960     0.817142       1   \n",
       "\n",
       "   normalized_price_per_review  normalized_perbed_price  rev_mul_picqual  \\\n",
       "0                    -0.991450                -0.070105         3.349935   \n",
       "1                     0.324016                 1.321527         3.923689   \n",
       "2                     0.844153                -0.251340         2.560073   \n",
       "3                    -1.993190                -1.031145         2.668886   \n",
       "4                    -1.425473                -1.819792         3.179466   \n",
       "\n",
       "   normalized_price  date_label  \n",
       "0         -0.932707           1  \n",
       "1          0.337202           1  \n",
       "2          1.225097           4  \n",
       "3         -1.809686           3  \n",
       "4         -1.637429           2  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def assign_label(hour):\n",
    "    if hour >=0 and hour < 92:\n",
    "        return 1\n",
    "    elif hour >= 92 and hour <183:\n",
    "        return 2\n",
    "    elif hour >=183 and hour <275 :\n",
    "        return 3\n",
    "    elif hour >= 275 and hour < 366:\n",
    "        return 4   \n",
    "    \n",
    "train[\"date_label\"] = train[\"Date\"].apply(assign_label)\n",
    "#train[\"date_label\"]=train[\"date_label\"].astype('int')\n",
    "del train['Date']\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#cat_columns = ['Region']\n",
    "#dummy_df = pd.get_dummies(train[cat_columns])\n",
    "#train = pd.concat([train, dummy_df], axis=1)\n",
    "#train = train.drop(cat_columns, axis=1)\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Beds</th>\n",
       "      <th>Review</th>\n",
       "      <th>Pic Quality</th>\n",
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       "      <th>normalized_price</th>\n",
       "      <th>date_label</th>\n",
       "      <th>Region_1</th>\n",
       "      <th>Region_2</th>\n",
       "      <th>Region_3</th>\n",
       "      <th>Region_4</th>\n",
       "      <th>Region_5</th>\n",
       "      <th>Region_6</th>\n",
       "      <th>Region_7</th>\n",
       "      <th>Region_8</th>\n",
       "      <th>Region_9</th>\n",
       "      <th>Region_10</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Weekday  Beds    Review  Pic Quality  Accept  normalized_price_per_review  \\\n",
       "0        5     1  4.526975     0.739994       0                    -0.991450   \n",
       "1        4     1  4.548455     0.862642       0                     0.324016   \n",
       "2        5     2  4.965732     0.515548       0                     0.844153   \n",
       "3        2     1  4.752735     0.561547       1                    -1.993190   \n",
       "4        2     2  3.890960     0.817142       1                    -1.425473   \n",
       "\n",
       "   normalized_perbed_price  rev_mul_picqual  normalized_price  date_label  \\\n",
       "0                -0.070105         3.349935         -0.932707           1   \n",
       "1                 1.321527         3.923689          0.337202           1   \n",
       "2                -0.251340         2.560073          1.225097           4   \n",
       "3                -1.031145         2.668886         -1.809686           3   \n",
       "4                -1.819792         3.179466         -1.637429           2   \n",
       "\n",
       "   Region_1  Region_2  Region_3  Region_4  Region_5  Region_6  Region_7  \\\n",
       "0         0         0         0         1         0         0         0   \n",
       "1         0         1         0         0         0         0         0   \n",
       "2         0         0         0         0         1         0         0   \n",
       "3         0         0         1         0         0         0         0   \n",
       "4         1         0         0         0         0         0         0   \n",
       "\n",
       "   Region_8  Region_9  Region_10  \n",
       "0         0         0          0  \n",
       "1         0         0          0  \n",
       "2         0         0          0  \n",
       "3         0         0          0  \n",
       "4         0         0          0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将原训练集里代表类别的特征‘Region’独热编码处理\n",
    "dum=pd.get_dummies(train['Region'],prefix=\"Region\")\n",
    "train=pd.concat([train,dum],axis=1)\n",
    "del train['Region']\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2</td>\n",
       "      <td>4.965732</td>\n",
       "      <td>0.515548</td>\n",
       "      <td>0</td>\n",
       "      <td>0.844153</td>\n",
       "      <td>-0.251340</td>\n",
       "      <td>2.560073</td>\n",
       "      <td>1.225097</td>\n",
       "      <td>4</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4.752735</td>\n",
       "      <td>0.561547</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.993190</td>\n",
       "      <td>-1.031145</td>\n",
       "      <td>2.668886</td>\n",
       "      <td>-1.809686</td>\n",
       "      <td>3</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>1</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",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3.890960</td>\n",
       "      <td>0.817142</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.425473</td>\n",
       "      <td>-1.819792</td>\n",
       "      <td>3.179466</td>\n",
       "      <td>-1.637429</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Beds    Review  Pic Quality  Accept  normalized_price_per_review  \\\n",
       "0     1  4.526975     0.739994       0                    -0.991450   \n",
       "1     1  4.548455     0.862642       0                     0.324016   \n",
       "2     2  4.965732     0.515548       0                     0.844153   \n",
       "3     1  4.752735     0.561547       1                    -1.993190   \n",
       "4     2  3.890960     0.817142       1                    -1.425473   \n",
       "\n",
       "   normalized_perbed_price  rev_mul_picqual  normalized_price  date_label  \\\n",
       "0                -0.070105         3.349935         -0.932707           1   \n",
       "1                 1.321527         3.923689          0.337202           1   \n",
       "2                -0.251340         2.560073          1.225097           4   \n",
       "3                -1.031145         2.668886         -1.809686           3   \n",
       "4                -1.819792         3.179466         -1.637429           2   \n",
       "\n",
       "   Region_1  ...  Region_8  Region_9  Region_10  Weekday_1  Weekday_2  \\\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          1   \n",
       "4         1  ...         0         0          0          0          1   \n",
       "\n",
       "   Weekday_3  Weekday_4  Weekday_5  Weekday_6  Weekday_7  \n",
       "0          0          0          1          0          0  \n",
       "1          0          1          0          0          0  \n",
       "2          0          0          1          0          0  \n",
       "3          0          0          0          0          0  \n",
       "4          0          0          0          0          0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将原训练集里代表类别的特征‘Weekday’独热编码处理\n",
    "dum=pd.get_dummies(train['Weekday'],prefix=\"Weekday\")\n",
    "train=pd.concat([train,dum],axis=1)\n",
    "del train['Weekday']\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Beds</th>\n",
       "      <th>Review</th>\n",
       "      <th>Pic Quality</th>\n",
       "      <th>Accept</th>\n",
       "      <th>normalized_price_per_review</th>\n",
       "      <th>normalized_perbed_price</th>\n",
       "      <th>rev_mul_picqual</th>\n",
       "      <th>normalized_price</th>\n",
       "      <th>Region_1</th>\n",
       "      <th>Region_2</th>\n",
       "      <th>...</th>\n",
       "      <th>Weekday_2</th>\n",
       "      <th>Weekday_3</th>\n",
       "      <th>Weekday_4</th>\n",
       "      <th>Weekday_5</th>\n",
       "      <th>Weekday_6</th>\n",
       "      <th>Weekday_7</th>\n",
       "      <th>date_label_1</th>\n",
       "      <th>date_label_2</th>\n",
       "      <th>date_label_3</th>\n",
       "      <th>date_label_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.526975</td>\n",
       "      <td>0.739994</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.991450</td>\n",
       "      <td>-0.070105</td>\n",
       "      <td>3.349935</td>\n",
       "      <td>-0.932707</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.548455</td>\n",
       "      <td>0.862642</td>\n",
       "      <td>0</td>\n",
       "      <td>0.324016</td>\n",
       "      <td>1.321527</td>\n",
       "      <td>3.923689</td>\n",
       "      <td>0.337202</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4.965732</td>\n",
       "      <td>0.515548</td>\n",
       "      <td>0</td>\n",
       "      <td>0.844153</td>\n",
       "      <td>-0.251340</td>\n",
       "      <td>2.560073</td>\n",
       "      <td>1.225097</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4.752735</td>\n",
       "      <td>0.561547</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.993190</td>\n",
       "      <td>-1.031145</td>\n",
       "      <td>2.668886</td>\n",
       "      <td>-1.809686</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3.890960</td>\n",
       "      <td>0.817142</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.425473</td>\n",
       "      <td>-1.819792</td>\n",
       "      <td>3.179466</td>\n",
       "      <td>-1.637429</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Beds    Review  Pic Quality  Accept  normalized_price_per_review  \\\n",
       "0     1  4.526975     0.739994       0                    -0.991450   \n",
       "1     1  4.548455     0.862642       0                     0.324016   \n",
       "2     2  4.965732     0.515548       0                     0.844153   \n",
       "3     1  4.752735     0.561547       1                    -1.993190   \n",
       "4     2  3.890960     0.817142       1                    -1.425473   \n",
       "\n",
       "   normalized_perbed_price  rev_mul_picqual  normalized_price  Region_1  \\\n",
       "0                -0.070105         3.349935         -0.932707         0   \n",
       "1                 1.321527         3.923689          0.337202         0   \n",
       "2                -0.251340         2.560073          1.225097         0   \n",
       "3                -1.031145         2.668886         -1.809686         0   \n",
       "4                -1.819792         3.179466         -1.637429         1   \n",
       "\n",
       "   Region_2  ...  Weekday_2  Weekday_3  Weekday_4  Weekday_5  Weekday_6  \\\n",
       "0         0  ...          0          0          0          1          0   \n",
       "1         1  ...          0          0          1          0          0   \n",
       "2         0  ...          0          0          0          1          0   \n",
       "3         0  ...          1          0          0          0          0   \n",
       "4         0  ...          1          0          0          0          0   \n",
       "\n",
       "   Weekday_7  date_label_1  date_label_2  date_label_3  date_label_4  \n",
       "0          0             1             0             0             0  \n",
       "1          0             1             0             0             0  \n",
       "2          0             0             0             0             1  \n",
       "3          0             0             0             1             0  \n",
       "4          0             0             1             0             0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将原训练集里代表类别的特征‘date_label’独热编码处理\n",
    "dum=pd.get_dummies(train['date_label'],prefix=\"date_label\")\n",
    "train=pd.concat([train,dum],axis=1)\n",
    "del train['date_label']\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数集平衡"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn import model_selection\n",
    "#features =train.columns.drop(\"Accept\")\n",
    "x=train\n",
    "x=train.drop(['Accept'],axis=1)\n",
    "y=train['Accept']\n",
    "x_train,x_test,y_train,y_test=model_selection.train_test_split(x,y,test_size=0.3,random_state=1234)\n",
    "over_samples=SMOTE(random_state=1234)\n",
    "over_samples_x,over_samples_y=over_samples.fit_sample(x_train,y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "              beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "              hidden_layer_sizes=(2, 20, 10), learning_rate='constant',\n",
       "              learning_rate_init=0.01, max_fun=15000, max_iter=200,\n",
       "              momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n",
       "              power_t=0.5, random_state=None, shuffle=True, solver='adam',\n",
       "              tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "              warm_start=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "\n",
    "\n",
    "mlp=MLPClassifier(hidden_layer_sizes=(2,20,10),\n",
    "                  activation='relu',\n",
    "                  learning_rate='constant',\n",
    "                  learning_rate_init=0.01\n",
    "                  )\n",
    "mlp.fit(over_samples_x,over_samples_y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC均值：0.825933\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score,accuracy_score, \\\n",
    "                            precision_score,recall_score, roc_auc_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores=cross_val_score(mlp,over_samples_x,over_samples_y,cv=5,scoring='roc_auc')\n",
    "print('AUC均值：%.6f\\n'%(scores.mean()))"
   ]
  }
 ],
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