data.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/78/78/50ef81a903eccc4e90e278a143c9a0530f05199f6221d2e1b21025852982/pandas-0.23.4-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (14.6MB)\n",
      "\u001b[K    100% |████████████████████████████████| 14.7MB 553kB/s ta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.9.0 in /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages (from pandas) (1.14.5)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages (from pandas) (2.7.3)\n",
      "Requirement already satisfied: pytz>=2011k in /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages (from pandas) (2018.5)\n",
      "Requirement already satisfied: six>=1.5 in /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages (from python-dateutil>=2.5.0->pandas) (1.11.0)\n",
      "Installing collected packages: pandas\n",
      "Successfully installed pandas-0.23.4\n",
      "\u001b[33mYou are using pip version 18.0, however version 18.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "    use_ID  mer_ID  loc_ID      time\n",
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "koubei_train = pd.read_csv(\"data/ijcai2016_koubei_train.csv\")\n",
    "koubei_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1081724, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "koubei_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict = {}\n",
    "exist_dict = {}\n",
    "mer_loc_count = {}\n",
    "merchant_count = 0\n",
    "merchant_dict = {}\n",
    "open_interval_dict = {}\n",
    "time_interval_dict = {}\n",
    "openning_time_dict = {}\n",
    "last_time_dict = {}\n",
    "location_count = {}\n",
    "count = 2000\n",
    "# sort by time\n",
    "koubei_train.sort_values(\"time\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "         use_ID  mer_ID  loc_ID      time\n",
       "400965   727382     210     410  20150701\n",
       "271666   958880    5231     337  20150701\n",
       "275360  1976144    5231     337  20150701\n",
       "275364  1976144    5231     337  20150701\n",
       "401005   727382     210     410  20150701\n",
       "401015   727382     210     410  20150701\n",
       "400985   674494     210     410  20150701\n",
       "400946   727382     210     410  20150701\n",
       "271665   958880    5231     337  20150701\n",
       "400979   674494     210     410  20150701\n",
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       "625484   930608     625     130  20150701\n",
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       "490479   358324     216     407  20150701\n",
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       "791149  1847956    5231     254  20150702\n",
       "442238   831782    5231     461  20150702\n",
       "273040    77744    5231     337  20150702\n",
       "442422    79200    5231     461  20150702\n",
       "421666    53877    5231     371  20150702"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "koubei_train.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in koubei_train.iterrows():\n",
    "    # get location count\n",
    "    if row[\"loc_ID\"] not in location_count:\n",
    "        location_count[row[\"loc_ID\"]] = 1\n",
    "    else:\n",
    "        location_count[row[\"loc_ID\"]] += 1\n",
    "    \n",
    "    # get merchant location dict & open interval & time interval\n",
    "    if (row[\"mer_ID\"], row[\"loc_ID\"]) not in mer_loc_count:\n",
    "        mer_loc_count[(row[\"mer_ID\"], row[\"loc_ID\"])] = 1\n",
    "        open_interval_dict[(row[\"mer_ID\"], row[\"loc_ID\"])] = row[\"time\"]\n",
    "        time_interval_dict[(row[\"mer_ID\"], row[\"loc_ID\"], row[\"time\"])] = 0\n",
    "    else:\n",
    "        mer_loc_count[(row[\"mer_ID\"], row[\"loc_ID\"])] += 1\n",
    "        # get time interval between this (m,l) and open_interval\n",
    "        time_interval_dict[(row[\"mer_ID\"], row[\"loc_ID\"], row[\"time\"])] = row[\"time\"] - open_interval_dict[(row[\"mer_ID\"], row[\"loc_ID\"])]\n",
    "    \n",
    "    # get openning time\n",
    "    if (row[\"mer_ID\"], row[\"loc_ID\"]) not in openning_time_dict:\n",
    "        openning_time_dict[(row[\"mer_ID\"], row[\"loc_ID\"])] = row[\"time\"]\n",
    "    else:\n",
    "        pass\n",
    "    # get last purchase time (increased by time, just update time to get last purchase time)\n",
    "    last_time_dict[(row[\"mer_ID\"], row[\"loc_ID\"])] = row[\"time\"]\n",
    "\n",
    "#     count -= 1\n",
    "#     if count <= 0:\n",
    "#         break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "         use_ID  mer_ID  loc_ID      time\n",
       "400965   727382     210     410  20150701\n",
       "271666   958880    5231     337  20150701\n",
       "275360  1976144    5231     337  20150701\n",
       "275364  1976144    5231     337  20150701\n",
       "401005   727382     210     410  20150701"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "koubei_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(210, 410) 189\n",
      "(5231, 337) 6338\n",
      "(625, 130) 158\n",
      "(216, 407) 182\n",
      "(5231, 197) 4669\n",
      "(5231, 254) 2503\n"
     ]
    }
   ],
   "source": [
    "# mer_loc_count\n",
    "count = 5\n",
    "for (k, v) in mer_loc_count.items():\n",
    "    print(k, v)\n",
    "    count -= 1\n",
    "    if count < 0:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "410 5357\n",
      "337 24988\n",
      "130 3873\n",
      "407 527\n",
      "197 19205\n",
      "254 14082\n"
     ]
    }
   ],
   "source": [
    "# location_count\n",
    "count = 5\n",
    "for (k, v) in location_count.items():\n",
    "    print(k, v)\n",
    "    count -= 1\n",
    "    if count < 0:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# mer_loc_popularity dict\n",
    "mer_loc_popularity = {}\n",
    "for (k, v) in mer_loc_count.items():\n",
    "    ratio = v / location_count[(k[1])]\n",
    "    mer_loc_popularity[k] = ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "387\n"
     ]
    }
   ],
   "source": [
    "# date interval \n",
    "from datetime import date\n",
    "def date_interval(date1, date2):\n",
    "\n",
    "    date1 = date(int(str(date1)[:4]), int(str(date1)[4:6]), int(str(date1)[6:]))\n",
    "    date2 = date(int(str(date2)[:4]), int(str(date2)[4:6]), int(str(date2)[6:]))\n",
    "    return abs(date2 - date1).days\n",
    "print(date_interval(20180910, 20170819))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# average purchase interval\n",
    "avg_pur_interval = {}\n",
    "for (k, v) in mer_loc_count.items():\n",
    "    openning_date = int(openning_time_dict[k])\n",
    "    last_date = int(last_time_dict[k])\n",
    "    average_interval = (date_interval(last_date, openning_date) / v)\n",
    "    avg_pur_interval[k] = average_interval\n",
    "#     print(\"openning_date: {}, last_date: {}, mer_loc_count: {}, average_interval: {}\".format(openning_date, last_date, v, average_interval))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(columns=['mer_id', 'loc_id', 'mer_loc_records', 'popularity', 'openning_date', 'last_purchase_date', 'average_purchase_interval'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mer_id</th>\n",
       "      <th>loc_id</th>\n",
       "      <th>mer_loc_records</th>\n",
       "      <th>popularity</th>\n",
       "      <th>openning_date</th>\n",
       "      <th>last_purchase_date</th>\n",
       "      <th>average_purchase_interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [mer_id, loc_id, mer_loc_records, popularity, openning_date, last_purchase_date, average_purchase_interval]\n",
       "Index: []"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    }
   ],
   "source": [
    "mer_loc_id = 1\n",
    "for (k, v) in mer_loc_count.items():\n",
    "    mer_id = k[0]\n",
    "    loc_id = k[1]\n",
    "    mer_loc_records = v\n",
    "    popularity = mer_loc_popularity[k]\n",
    "    openning_date = openning_time_dict[k]\n",
    "    last_purchase_date = last_time_dict[k]\n",
    "    average_purchase_interval = avg_pur_interval[k]\n",
    "    df = df.append(pd.DataFrame(\n",
    "        {\n",
    "            'mer_id': mer_id,\n",
    "            'loc_id': loc_id,\n",
    "            'mer_loc_records': mer_loc_records,\n",
    "            'popularity': popularity,\n",
    "            'openning_date': openning_date,\n",
    "            'last_purchase_date': last_purchase_date,\n",
    "            'average_purchase_interval': average_purchase_interval\n",
    "        }, index = [mer_loc_id]))\n",
    "    mer_loc_id += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  mer_id loc_id mer_loc_records  popularity openning_date last_purchase_date  \\\n",
       "1    210    410             189    0.035281      20150701           20151130   \n",
       "2   5231    337            6338    0.253642      20150701           20151130   \n",
       "3    625    130             158    0.040795      20150701           20151130   \n",
       "4    216    407             182    0.345351      20150701           20151130   \n",
       "5   5231    197            4669    0.243114      20150702           20151130   \n",
       "6   5231    254            2503    0.177745      20150702           20151130   \n",
       "1    210    410             189    0.035281      20150701           20151130   \n",
       "2   5231    337            6338    0.253642      20150701           20151130   \n",
       "3    625    130             158    0.040795      20150701           20151130   \n",
       "4    216    407             182    0.345351      20150701           20151130   \n",
       "5   5231    197            4669    0.243114      20150702           20151130   \n",
       "6   5231    254            2503    0.177745      20150702           20151130   \n",
       "1    210    410             189    0.035281      20150701           20151130   \n",
       "2   5231    337            6338    0.253642      20150701           20151130   \n",
       "3    625    130             158    0.040795      20150701           20151130   \n",
       "4    216    407             182    0.345351      20150701           20151130   \n",
       "5   5231    197            4669    0.243114      20150702           20151130   \n",
       "6   5231    254            2503    0.177745      20150702           20151130   \n",
       "7   5231    461            6950    0.442140      20150702           20151130   \n",
       "8   5231    371            9641    0.849802      20150702           20151130   \n",
       "\n",
       "   average_purchase_interval  \n",
       "1                   0.804233  \n",
       "2                   0.023982  \n",
       "3                   0.962025  \n",
       "4                   0.835165  \n",
       "5                   0.032341  \n",
       "6                   0.060328  \n",
       "1                   0.804233  \n",
       "2                   0.023982  \n",
       "3                   0.962025  \n",
       "4                   0.835165  \n",
       "5                   0.032341  \n",
       "6                   0.060328  \n",
       "1                   0.804233  \n",
       "2                   0.023982  \n",
       "3                   0.962025  \n",
       "4                   0.835165  \n",
       "5                   0.032341  \n",
       "6                   0.060328  \n",
       "7                   0.021727  \n",
       "8                   0.015662  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_csv(\"mer_loc_data.csv\", encoding='utf-8')\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting sklearn\n",
      "  Downloading https://files.pythonhosted.org/packages/1e/7a/dbb3be0ce9bd5c8b7e3d87328e79063f8b263b2b1bfa4774cb1147bfcd3f/sklearn-0.0.tar.gz\n",
      "Collecting scikit-learn (from sklearn)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/72/c8/7a449014b506d4fed3d7fef5b86c0210ff882a13da82626a12ce9f7db616/scikit_learn-0.20.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (7.8MB)\n",
      "\u001b[K    100% |████████████████████████████████| 7.8MB 12kB/s ta 0:00:014\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.8.2 in /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages (from scikit-learn->sklearn) (1.14.5)\n",
      "Requirement already satisfied: scipy>=0.13.3 in /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages (from scikit-learn->sklearn) (1.1.0)\n",
      "Building wheels for collected packages: sklearn\n",
      "  Running setup.py bdist_wheel for sklearn ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /Users/starwe/Library/Caches/pip/wheels/76/03/bb/589d421d27431bcd2c6da284d5f2286c8e3b2ea3cf1594c074\n",
      "Successfully built sklearn\n",
      "Installing collected packages: scikit-learn, sklearn\n",
      "Successfully installed scikit-learn-0.20.0 sklearn-0.0\n",
      "\u001b[33mYou are using pip version 18.0, however version 18.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.externals import joblib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "file = open('mer_loc_data.csv' , 'r')\n",
    "\n",
    "data = [line.strip().split(',') for line in file]\n",
    "feature = np.array([[float(x) for x in row[1:]] for row in data[1:]])\n",
    "\n",
    "clf = KMeans(n_clusters=30)\n",
    "s = clf.fit(feature)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>401015</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401005</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        674494  727382  930608  958880\n",
       "400965       0       1       0       0\n",
       "400950       1       0       0       0\n",
       "625484       0       0       1       0\n",
       "400976       1       0       0       0\n",
       "400979       1       0       0       0\n",
       "271665       0       0       0       1\n",
       "400985       1       0       0       0\n",
       "400946       0       1       0       0\n",
       "401015       0       1       0       0\n",
       "401005       0       1       0       0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(koubei_train[\"use_ID\"].head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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