Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train1 = pd.read_csv('./model/train_1.csv')\n",
    "test1 = pd.read_csv('./model/test_1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'TERMINALNO', 'TRIP_ID', 'user_mean_speed',\n",
       "       'user_var_speed', 'user_max_speed', 'user_mean_height',\n",
       "       'user_var_height', 'user_mean_direction', 'hour_5_per', 'hour_6_per',\n",
       "       'hour_10_per', 'hour_15_per', 'hour_7_per', 'hour_8_per', 'hour_9_per',\n",
       "       'hour_17_per', 'hour_18_per', 'hour_11_per', 'hour_12_per',\n",
       "       'hour_13_per', 'hour_14_per', 'hour_0_per', 'hour_1_per', 'hour_2_per',\n",
       "       'hour_3_per', 'hour_4_per', 'hour_count_max', 'hour_count_mean',\n",
       "       'hour_count_std', 'hour_count_skew', 'night__day_delta',\n",
       "       'night_count_max', 'night_count_mean', 'night_count_std',\n",
       "       'night_count_skew', 'user_distance', 'Y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_use = ['TRIP_ID', 'user_mean_speed', 'user_var_speed',\n",
    "       'user_max_speed', 'user_mean_height', 'user_var_height',\n",
    "       'user_mean_direction', 'hour_5_per', 'hour_6_per', 'hour_10_per',\n",
    "       'hour_15_per', 'hour_7_per', 'hour_8_per', 'hour_9_per', 'hour_17_per',\n",
    "        'hour_11_per', 'hour_12_per', 'hour_13_per',#'hour_18_per',\n",
    "       'hour_14_per', 'hour_0_per', 'hour_1_per', 'hour_2_per', 'hour_3_per',\n",
    "       'hour_4_per',  'hour_count_mean', 'hour_count_std',#'hour_count_max',\n",
    "       'hour_count_skew', 'night__day_delta', #'night_count_max',\n",
    "       'night_count_mean', 'night_count_std', 'night_count_skew',\n",
    "       'user_distance']\n",
    "train = train1[feature_use].fillna(-1)\n",
    "label = train1['Y']\n",
    "test = test1[feature_use].fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x = train1[feature_use].fillna(-1)\n",
    "train_y = train1['Y']\n",
    "test = test1[feature_use].fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_cols(m):\n",
    "    col_max = m.max(axis=0)\n",
    "    col_min = m.min(axis=0)\n",
    "    return (m - col_min) / (col_max - col_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x_disorder = normalize_cols(train_x)\n",
    "test_features = normalize_cols(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set batch size for training\n",
    "# 设置批量大小\n",
    "batch_size = 100\n",
    "\n",
    "# Set random seed to make results reproducible\n",
    "# 为了后面复现，为numpy和tensorflow设置随机种子\n",
    "seed = 3\n",
    "np.random.seed(seed)\n",
    "tf.set_random_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_train01 = 'data/dm/train.csv'\n",
    "path_test01 = 'data/dm/test.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>TERMINALNO</th>\n",
       "      <th>TIME</th>\n",
       "      <th>TRIP_ID</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>DIRECTION</th>\n",
       "      <th>HEIGHT</th>\n",
       "      <th>SPEED</th>\n",
       "      <th>CALLSTATE</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1476923580</td>\n",
       "      <td>1</td>\n",
       "      <td>122.985168</td>\n",
       "      <td>41.103741</td>\n",
       "      <td>12</td>\n",
       "      <td>39.402588</td>\n",
       "      <td>2.15</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1476923640</td>\n",
       "      <td>1</td>\n",
       "      <td>122.984398</td>\n",
       "      <td>41.104904</td>\n",
       "      <td>24</td>\n",
       "      <td>39.311157</td>\n",
       "      <td>4.11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TERMINALNO        TIME  TRIP_ID   LONGITUDE   LATITUDE  DIRECTION  \\\n",
       "0           1  1476923580        1  122.985168  41.103741         12   \n",
       "1           1  1476923640        1  122.984398  41.104904         24   \n",
       "\n",
       "      HEIGHT  SPEED  CALLSTATE    Y  \n",
       "0  39.402588   2.15          0  0.0  \n",
       "1  39.311157   4.11          0  0.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train= pd.read_csv(path_train01)\n",
    "train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import time\n",
    "def timestamp_datetime( value):\n",
    "    fmt = '%Y-%m-%d %H:%M:%S'\n",
    "\n",
    "    value = time.localtime(value)\n",
    "    dt = time.strftime(fmt, value)\n",
    "    return dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "an integer is required (got type str)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-06168bd385d2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'TIME'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'TIME'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtimestamp_datetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'TIME'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'TIME'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mF:\\pycharm\\anaconda3.5\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[0;32m   2549\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2550\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masobject\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2551\u001b[1;33m                 \u001b[0mmapped\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2552\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2553\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/src/inference.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m<ipython-input-19-06168bd385d2>\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'TIME'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'TIME'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtimestamp_datetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'TIME'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'TIME'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-18-eecc26e1054a>\u001b[0m in \u001b[0;36mtimestamp_datetime\u001b[1;34m(value)\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mfmt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'%Y-%m-%d %H:%M:%S'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlocaltime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[0mdt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfmt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mdt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: an integer is required (got type str)"
     ]
    }
   ],
   "source": [
    "train['TIME'] = train['TIME'].apply(lambda x: timestamp_datetime(x), 1)\n",
    "train['TIME'] = train['TIME'].apply(lambda x: str(x)[:13], 1)\n",
    "train.head(20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TERMINALNO</th>\n",
       "      <th>TIME</th>\n",
       "      <th>TRIP_ID</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>DIRECTION</th>\n",
       "      <th>HEIGHT</th>\n",
       "      <th>SPEED</th>\n",
       "      <th>CALLSTATE</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-10-20 08</td>\n",
       "      <td>1</td>\n",
       "      <td>122.985168</td>\n",
       "      <td>41.103741</td>\n",
       "      <td>12</td>\n",
       "      <td>39.402588</td>\n",
       "      <td>2.15</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-10-20 08</td>\n",
       "      <td>1</td>\n",
       "      <td>122.984398</td>\n",
       "      <td>41.104904</td>\n",
       "      <td>24</td>\n",
       "      <td>39.311157</td>\n",
       "      <td>4.11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TERMINALNO           TIME  TRIP_ID   LONGITUDE   LATITUDE  DIRECTION  \\\n",
       "0           1  2016-10-20 08        1  122.985168  41.103741         12   \n",
       "1           1  2016-10-20 08        1  122.984398  41.104904         24   \n",
       "\n",
       "      HEIGHT  SPEED  CALLSTATE    Y  \n",
       "0  39.402588   2.15          0  0.0  \n",
       "1  39.311157   4.11          0  0.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = train.sort_values(by=[\"TERMINALNO\", 'TIME'])\n",
    "train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>TERMINALNO</th>\n",
       "      <th>TIME</th>\n",
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       "   TERMINALNO           TIME  TRIP_ID   LONGITUDE   LATITUDE  DIRECTION  \\\n",
       "0           1  2016-10-20 08        1  122.985168  41.103741         12   \n",
       "1           1  2016-10-20 08        1  122.984398  41.104904         24   \n",
       "\n",
       "      HEIGHT  SPEED  CALLSTATE    Y hour  \n",
       "0  39.402588   2.15          0  0.0   08  \n",
       "1  39.311157   4.11          0  0.0   08  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.index = range(len(train))\n",
    "train['hour'] = train.TIME.apply(lambda x: str(x)[11:13], 1)\n",
    "train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "   TERMINALNO           TIME  TRIP_ID   LONGITUDE   LATITUDE  DIRECTION  \\\n",
       "0           1  2016-10-20 08        1  122.985168  41.103741         12   \n",
       "1           1  2016-10-20 08        1  122.984398  41.104904         24   \n",
       "\n",
       "      HEIGHT  SPEED  CALLSTATE    Y  hour  \n",
       "0  39.402588   2.15          0  0.0     8  \n",
       "1  39.311157   4.11          0  0.0     8  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['hour'] = train['hour'].astype(int)\n",
    "train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['hour_0'] = train.hour.apply(lambda x: 1 if x == 0 else 0, 1)\n",
    "train['hour_1'] = train.hour.apply(lambda x: 1 if x == 1 else 0, 1)\n",
    "train['hour_2'] = train.hour.apply(lambda x: 1 if x == 2 else 0, 1)\n",
    "train['hour_3'] = train.hour.apply(lambda x: 1 if x == 3 else 0, 1)\n",
    "train['hour_4'] = train.hour.apply(lambda x: 1 if x == 4 else 0, 1)\n",
    "train['hour_21'] = train.hour.apply(lambda x: 1 if x == 21 else 0, 1)\n",
    "train['hour_22'] = train.hour.apply(lambda x: 1 if x == 22 else 0, 1)\n",
    "train['hour_23'] = train.hour.apply(lambda x: 1 if x == 23 else 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "   TERMINALNO           TIME  TRIP_ID   LONGITUDE   LATITUDE  DIRECTION  \\\n",
       "0           1  2016-10-20 08        1  122.985168  41.103741         12   \n",
       "1           1  2016-10-20 08        1  122.984398  41.104904         24   \n",
       "\n",
       "      HEIGHT  SPEED  CALLSTATE    Y  hour  hour_0  hour_1  hour_2  hour_3  \\\n",
       "0  39.402588   2.15          0  0.0     8       0       0       0       0   \n",
       "1  39.311157   4.11          0  0.0     8       0       0       0       0   \n",
       "\n",
       "   hour_4  hour_21  hour_22  hour_23  \n",
       "0       0        0        0        0  \n",
       "1       0        0        0        0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   TERMINALNO           TIME  TRIP_ID  LONGITUDE  LATITUDE  DIRECTION  HEIGHT  \\\n",
       "0           1  2016-10-20 08       10         10        10         10      10   \n",
       "1           1  2016-10-20 13       20         20        20         20      20   \n",
       "\n",
       "   SPEED  CALLSTATE   Y  hour  hour_0  hour_1  hour_2  hour_3  hour_4  \\\n",
       "0     10         10  10    10      10      10      10      10      10   \n",
       "1     20         20  20    20      20      20      20      20      20   \n",
       "\n",
       "   hour_21  hour_22  hour_23  \n",
       "0       10       10       10  \n",
       "1       20       20       20  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_hour = train.groupby(['TERMINALNO', 'TIME'], as_index=False).count()\n",
    "train_hour.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   TERMINALNO        TIME  TRIP_ID  LONGITUDE  LATITUDE  DIRECTION  HEIGHT  \\\n",
       "0           1  2016-10-20       10         10        10         10      10   \n",
       "1           1  2016-10-20       20         20        20         20      20   \n",
       "\n",
       "   SPEED  CALLSTATE   Y  hour  hour_0  hour_1  hour_2  hour_3  hour_4  \\\n",
       "0     10         10  10    10      10      10      10      10      10   \n",
       "1     20         20  20    20      20      20      20      20      20   \n",
       "\n",
       "   hour_21  hour_22  hour_23  \n",
       "0       10       10       10  \n",
       "1       20       20       20  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_hour.TIME = train_hour.TIME.apply(lambda x: str(x)[:10], 1)\n",
    "train_hour.head(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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       "      <th>hour_22</th>\n",
       "      <th>hour_23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-10-20</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-10-20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TERMINALNO        TIME  TRIP_ID  LONGITUDE  LATITUDE  DIRECTION  HEIGHT  \\\n",
       "0           1  2016-10-20       10         10        10         10      10   \n",
       "1           1  2016-10-20       20         20        20         20      20   \n",
       "\n",
       "   SPEED  CALLSTATE   Y  hour  hour_0  hour_1  hour_2  hour_3  hour_4  \\\n",
       "0     10         10  10    10      10      10      10      10      10   \n",
       "1     20         20  20    20      20      20      20      20      20   \n",
       "\n",
       "   hour_21  hour_22  hour_23  \n",
       "0       10       10       10  \n",
       "1       20       20       20  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_day = train_hour.groupby(\n",
    "            ['TERMINALNO', 'TIME'], as_index=False).count()\n",
    "train_hour.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\pycharm\\anaconda3.5\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: using a dict on a Series for aggregation\n",
      "is deprecated and will be removed in a future version\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TERMINALNO</th>\n",
       "      <th>hour_count_max</th>\n",
       "      <th>hour_count_mean</th>\n",
       "      <th>hour_count_std</th>\n",
       "      <th>hour_count_skew</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
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       "      <td>1.923538</td>\n",
       "      <td>1.517474</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.777460</td>\n",
       "      <td>1.760201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2.272727</td>\n",
       "      <td>0.882735</td>\n",
       "      <td>0.317196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4.083333</td>\n",
       "      <td>2.810963</td>\n",
       "      <td>0.929589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>1.815339</td>\n",
       "      <td>0.335076</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TERMINALNO  hour_count_max  hour_count_mean  hour_count_std  \\\n",
       "0           1               6         2.800000        1.923538   \n",
       "1           2               9         3.000000        2.777460   \n",
       "2           3               4         2.272727        0.882735   \n",
       "3           4              10         4.083333        2.810963   \n",
       "4           5               7         3.750000        1.815339   \n",
       "\n",
       "   hour_count_skew  \n",
       "0         1.517474  \n",
       "1         1.760201  \n",
       "2         0.317196  \n",
       "3         0.929589  \n",
       "4         0.335076  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_hour_count = train_day.groupby('TERMINALNO')['LONGITUDE'].agg(\n",
    "            {\"hour_count_max\": \"max\",  \"hour_count_mean\": \"mean\", \"hour_count_std\": \"std\",\n",
    "             \"hour_count_skew\": \"skew\"}).reset_index()\n",
    "train_hour_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>TERMINALNO</th>\n",
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       "      <td>2</td>\n",
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       "      <td>41.103439</td>\n",
       "      <td>17</td>\n",
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       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TERMINALNO           TIME  TRIP_ID   LONGITUDE   LATITUDE  DIRECTION  \\\n",
       "0           1  2016-10-20 08        1  122.985168  41.103741         12   \n",
       "1           1  2016-10-20 13        2  122.977905  41.103439         17   \n",
       "\n",
       "      HEIGHT  SPEED  CALLSTATE    Y  hour  hour_0  hour_1  hour_2  hour_3  \\\n",
       "0  39.402588   2.15          0  0.0     8       0       0       0       0   \n",
       "1  60.467529   1.42          4  0.0    13       0       0       0       0   \n",
       "\n",
       "   hour_4  hour_21  hour_22  hour_23  \n",
       "0       0        0        0        0  \n",
       "1       0        0        0        0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_hour_first = train.groupby(\n",
    "            ['TERMINALNO', 'TIME'], as_index=False).first()\n",
    "train_hour_first.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   TERMINALNO        TIME  TRIP_ID   LONGITUDE   LATITUDE  DIRECTION  \\\n",
       "0           1  2016-10-20        1  122.985168  41.103741         12   \n",
       "1           1  2016-10-20        2  122.977905  41.103439         17   \n",
       "\n",
       "      HEIGHT  SPEED  CALLSTATE    Y  hour  hour_0  hour_1  hour_2  hour_3  \\\n",
       "0  39.402588   2.15          0  0.0     8       0       0       0       0   \n",
       "1  60.467529   1.42          4  0.0    13       0       0       0       0   \n",
       "\n",
       "   hour_4  hour_21  hour_22  hour_23  \n",
       "0       0        0        0        0  \n",
       "1       0        0        0        0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_hour_first.TIME = train_hour_first.TIME.apply(\n",
    "            lambda x: str(x)[:10], 1)\n",
    "train_hour_first.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2016-10-28</td>\n",
       "      <td>64</td>\n",
       "      <td>368.964805</td>\n",
       "      <td>123.348171</td>\n",
       "      <td>497</td>\n",
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       "      <td>30.589999</td>\n",
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      ],
      "text/plain": [
       "   TERMINALNO        TIME  TRIP_ID   LONGITUDE    LATITUDE  DIRECTION  \\\n",
       "0           1  2016-10-20        3  245.963073   82.207180         29   \n",
       "1           1  2016-10-28       64  368.964805  123.348171        497   \n",
       "\n",
       "       HEIGHT      SPEED  CALLSTATE    Y  hour  hour_0  hour_1  hour_2  \\\n",
       "0   99.870117   3.570000          4  0.0    21       0       0       0   \n",
       "1  134.364502  30.589999          0  0.0    63       0       0       0   \n",
       "\n",
       "   hour_3  hour_4  hour_21  hour_22  hour_23  \n",
       "0       0       0        0        0        0  \n",
       "1       0       0        0        1        1  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_day_sum = train_hour_first.groupby(\n",
    "            ['TERMINALNO', 'TIME'], as_index=False).sum()\n",
    "train_day_sum.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "      <th>hour_4</th>\n",
       "      <th>hour_21</th>\n",
       "      <th>hour_22</th>\n",
       "      <th>hour_23</th>\n",
       "      <th>hour_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-10-20</td>\n",
       "      <td>3</td>\n",
       "      <td>245.963073</td>\n",
       "      <td>82.207180</td>\n",
       "      <td>29</td>\n",
       "      <td>99.870117</td>\n",
       "      <td>3.570000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</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>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-10-28</td>\n",
       "      <td>64</td>\n",
       "      <td>368.964805</td>\n",
       "      <td>123.348171</td>\n",
       "      <td>497</td>\n",
       "      <td>134.364502</td>\n",
       "      <td>30.589999</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63</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>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TERMINALNO        TIME  TRIP_ID   LONGITUDE    LATITUDE  DIRECTION  \\\n",
       "0           1  2016-10-20        3  245.963073   82.207180         29   \n",
       "1           1  2016-10-28       64  368.964805  123.348171        497   \n",
       "\n",
       "       HEIGHT      SPEED  CALLSTATE    Y  hour  hour_0  hour_1  hour_2  \\\n",
       "0   99.870117   3.570000          4  0.0    21       0       0       0   \n",
       "1  134.364502  30.589999          0  0.0    63       0       0       0   \n",
       "\n",
       "   hour_3  hour_4  hour_21  hour_22  hour_23  hour_count  \n",
       "0       0       0        0        0        0           2  \n",
       "1       0       0        0        1        1           3  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_day_sum['hour_count'] = train_day['LONGITUDE']\n",
    "train_day_sum.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TERMINALNO</th>\n",
       "      <th>TIME</th>\n",
       "      <th>TRIP_ID</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>DIRECTION</th>\n",
       "      <th>HEIGHT</th>\n",
       "      <th>SPEED</th>\n",
       "      <th>CALLSTATE</th>\n",
       "      <th>Y</th>\n",
       "      <th>...</th>\n",
       "      <th>hour_0</th>\n",
       "      <th>hour_1</th>\n",
       "      <th>hour_2</th>\n",
       "      <th>hour_3</th>\n",
       "      <th>hour_4</th>\n",
       "      <th>hour_21</th>\n",
       "      <th>hour_22</th>\n",
       "      <th>hour_23</th>\n",
       "      <th>hour_count</th>\n",
       "      <th>night_drive_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>2016-10-28</td>\n",
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       "      <td>368.964805</td>\n",
       "      <td>123.348171</td>\n",
       "      <td>497</td>\n",
       "      <td>134.364502</td>\n",
       "      <td>30.589999</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TERMINALNO        TIME  TRIP_ID   LONGITUDE    LATITUDE  DIRECTION  \\\n",
       "0           1  2016-10-20        3  245.963073   82.207180         29   \n",
       "1           1  2016-10-28       64  368.964805  123.348171        497   \n",
       "\n",
       "       HEIGHT      SPEED  CALLSTATE    Y        ...          hour_0  hour_1  \\\n",
       "0   99.870117   3.570000          4  0.0        ...               0       0   \n",
       "1  134.364502  30.589999          0  0.0        ...               0       0   \n",
       "\n",
       "   hour_2  hour_3  hour_4  hour_21  hour_22  hour_23  hour_count  \\\n",
       "0       0       0       0        0        0        0           2   \n",
       "1       0       0       0        0        1        1           3   \n",
       "\n",
       "   night_drive_count  \n",
       "0                  0  \n",
       "1                  2  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_day_sum['night_drive_count'] = train_day_sum.apply(lambda x: x['hour_0'] + x['hour_1'] +\n",
    "                                                                           x['hour_2'] + x['hour_3'] + x[\n",
    "                                                                               'hour_4'] +\n",
    "                                                                           x['hour_21'] + x['hour_22'] + x[\n",
    "                                                                            'hour_23'], 1)\n",
    "train_day_sum.head(2)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>TERMINALNO</th>\n",
       "      <th>TIME</th>\n",
       "      <th>TRIP_ID</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>DIRECTION</th>\n",
       "      <th>HEIGHT</th>\n",
       "      <th>SPEED</th>\n",
       "      <th>CALLSTATE</th>\n",
       "      <th>Y</th>\n",
       "      <th>...</th>\n",
       "      <th>hour_1</th>\n",
       "      <th>hour_2</th>\n",
       "      <th>hour_3</th>\n",
       "      <th>hour_4</th>\n",
       "      <th>hour_21</th>\n",
       "      <th>hour_22</th>\n",
       "      <th>hour_23</th>\n",
       "      <th>hour_count</th>\n",
       "      <th>night_drive_count</th>\n",
       "      <th>night_delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-10-20</td>\n",
       "      <td>3</td>\n",
       "      <td>245.963073</td>\n",
       "      <td>82.207180</td>\n",
       "      <td>29</td>\n",
       "      <td>99.870117</td>\n",
       "      <td>3.570000</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-10-28</td>\n",
       "      <td>64</td>\n",
       "      <td>368.964805</td>\n",
       "      <td>123.348171</td>\n",
       "      <td>497</td>\n",
       "      <td>134.364502</td>\n",
       "      <td>30.589999</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TERMINALNO        TIME  TRIP_ID   LONGITUDE    LATITUDE  DIRECTION  \\\n",
       "0           1  2016-10-20        3  245.963073   82.207180         29   \n",
       "1           1  2016-10-28       64  368.964805  123.348171        497   \n",
       "\n",
       "       HEIGHT      SPEED  CALLSTATE    Y     ...       hour_1  hour_2  hour_3  \\\n",
       "0   99.870117   3.570000          4  0.0     ...            0       0       0   \n",
       "1  134.364502  30.589999          0  0.0     ...            0       0       0   \n",
       "\n",
       "   hour_4  hour_21  hour_22  hour_23  hour_count  night_drive_count  \\\n",
       "0       0        0        0        0           2                  0   \n",
       "1       0        0        1        1           3                  2   \n",
       "\n",
       "   night_delta  \n",
       "0     0.000000  \n",
       "1     0.666667  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_day_sum['night_delta'] = train_day_sum['night_drive_count'] / \\\n",
    "                                       train_day_sum['hour_count']\n",
    "train_day_sum.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer  \n",
    "from sklearn.feature_extraction.text import CountVectorizer  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus=[\"I come to China to travel\", \n",
    "    \"This is a car polupar in China\",          \n",
    "    \"I love tea and Apple \",   \n",
    "    \"The work is to write some papers in science\"] \n",
    "\n",
    "vectorizer=CountVectorizer()\n",
    "\n",
    "transformer = TfidfTransformer()\n",
    "tfidf = transformer.fit_transform(vectorizer.fit_transform(corpus))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 4)\t0.4424621378947393\n",
      "  (0, 15)\t0.697684463383976\n",
      "  (0, 3)\t0.348842231691988\n",
      "  (0, 16)\t0.4424621378947393\n",
      "  (1, 3)\t0.3574550433419527\n",
      "  (1, 14)\t0.45338639737285463\n",
      "  (1, 6)\t0.3574550433419527\n",
      "  (1, 2)\t0.45338639737285463\n",
      "  (1, 9)\t0.45338639737285463\n",
      "  (1, 5)\t0.3574550433419527\n",
      "  (2, 7)\t0.5\n",
      "  (2, 12)\t0.5\n",
      "  (2, 0)\t0.5\n",
      "  (2, 1)\t0.5\n",
      "  (3, 15)\t0.2811316284405006\n",
      "  (3, 6)\t0.2811316284405006\n",
      "  (3, 5)\t0.2811316284405006\n",
      "  (3, 13)\t0.3565798233381452\n",
      "  (3, 17)\t0.3565798233381452\n",
      "  (3, 18)\t0.3565798233381452\n",
      "  (3, 11)\t0.3565798233381452\n",
      "  (3, 8)\t0.3565798233381452\n",
      "  (3, 10)\t0.3565798233381452\n"
     ]
    }
   ],
   "source": [
    "print(tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "等价于：向量化，TF-IDF与标准化\n",
    "'''\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf2 = TfidfVectorizer()\n",
    "re = tfidf2.fit_transform(corpus)\n",
    "print(re)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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