Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>msno</th>\n",
       "      <th>is_churn</th>\n",
       "      <th>city</th>\n",
       "      <th>bd</th>\n",
       "      <th>gender</th>\n",
       "      <th>registered_via</th>\n",
       "      <th>registration_init_time</th>\n",
       "      <th>expiration_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>waLDQMmcOu2jLDaV1ddDkgCrB/jl6sD66Xzs0Vqax1Y=</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>female</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20050406.0</td>\n",
       "      <td>20170907.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>QA7uiXy8vIbUSPOkCf9RwQ3FsT8jVq2OxDr8zqa7bRQ=</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>male</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20050407.0</td>\n",
       "      <td>20170321.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fGwBva6hikQmTJzrbz/2Ezjm5Cth5jZUNvXigKK2AFA=</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>female</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20051016.0</td>\n",
       "      <td>20170203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mT5V8rEpa+8wuqi6x0DoVd3H5icMKkE9Prt49UlmK+4=</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>female</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20051102.0</td>\n",
       "      <td>20170926.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XaPhtGLk/5UvvOYHcONTwsnH97P4eGECeq+BARGItRw=</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>male</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20051228.0</td>\n",
       "      <td>20170927.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           msno  is_churn  city    bd  gender  \\\n",
       "0  waLDQMmcOu2jLDaV1ddDkgCrB/jl6sD66Xzs0Vqax1Y=         1  18.0  36.0  female   \n",
       "1  QA7uiXy8vIbUSPOkCf9RwQ3FsT8jVq2OxDr8zqa7bRQ=         1  10.0  38.0    male   \n",
       "2  fGwBva6hikQmTJzrbz/2Ezjm5Cth5jZUNvXigKK2AFA=         1  11.0  27.0  female   \n",
       "3  mT5V8rEpa+8wuqi6x0DoVd3H5icMKkE9Prt49UlmK+4=         1  13.0  23.0  female   \n",
       "4  XaPhtGLk/5UvvOYHcONTwsnH97P4eGECeq+BARGItRw=         1   3.0  27.0    male   \n",
       "\n",
       "   registered_via  registration_init_time  expiration_date  \n",
       "0             9.0              20050406.0       20170907.0  \n",
       "1             9.0              20050407.0       20170321.0  \n",
       "2             9.0              20051016.0       20170203.0  \n",
       "3             9.0              20051102.0       20170926.0  \n",
       "4             9.0              20051228.0       20170927.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "import gc\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "\n",
    "train_df = pd.read_csv(\n",
    "    './data/train.csv',\n",
    "    dtype={\n",
    "        'msno': str,\n",
    "        'is_churn': bool,\n",
    "    })\n",
    "members_df = pd.read_csv(\n",
    "    './data/members.csv',\n",
    "    dtype={\n",
    "     'registered_via' : np.uint8,\n",
    "     'gender' : 'category',\n",
    "    },\n",
    ")\n",
    "train_df = pd.merge(train_df, members_df, how='left', on='msno')\n",
    "del members_df\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bradgordon/.virtualenvs/wsdm/lib/python3.5/site-packages/pandas/core/groupby.py:4036: FutureWarning: using a dict with renaming is deprecated and will be removed in a future version\n",
      "  return super(DataFrameGroupBy, self).aggregate(arg, *args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>msno</th>\n",
       "      <th>is_churn</th>\n",
       "      <th>city</th>\n",
       "      <th>bd</th>\n",
       "      <th>gender</th>\n",
       "      <th>registered_via</th>\n",
       "      <th>registration_init_time</th>\n",
       "      <th>expiration_date</th>\n",
       "      <th>plan_net_worth_x</th>\n",
       "      <th>cancel_times_x</th>\n",
       "      <th>...</th>\n",
       "      <th>plan_net_worth_y</th>\n",
       "      <th>cancel_times_y</th>\n",
       "      <th>total_actual_payment_y</th>\n",
       "      <th>mean_payment_each_transaction_y</th>\n",
       "      <th>total_order_y</th>\n",
       "      <th>plan_net_worth</th>\n",
       "      <th>cancel_times</th>\n",
       "      <th>total_actual_payment</th>\n",
       "      <th>mean_payment_each_transaction</th>\n",
       "      <th>total_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>waLDQMmcOu2jLDaV1ddDkgCrB/jl6sD66Xzs0Vqax1Y=</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>female</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20050406.0</td>\n",
       "      <td>20170907.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>74.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>74.500000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>QA7uiXy8vIbUSPOkCf9RwQ3FsT8jVq2OxDr8zqa7bRQ=</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>male</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20050407.0</td>\n",
       "      <td>20170321.0</td>\n",
       "      <td>3309.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3309.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3458.0</td>\n",
       "      <td>150.347826</td>\n",
       "      <td>23</td>\n",
       "      <td>3309.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3458.0</td>\n",
       "      <td>150.347826</td>\n",
       "      <td>23</td>\n",
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       "    <tr>\n",
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       "      <td>fGwBva6hikQmTJzrbz/2Ezjm5Cth5jZUNvXigKK2AFA=</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>female</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20051016.0</td>\n",
       "      <td>20170203.0</td>\n",
       "      <td>1492.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1492.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1492.0</td>\n",
       "      <td>149.200000</td>\n",
       "      <td>10</td>\n",
       "      <td>1492.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1492.0</td>\n",
       "      <td>149.200000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mT5V8rEpa+8wuqi6x0DoVd3H5icMKkE9Prt49UlmK+4=</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>female</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20051102.0</td>\n",
       "      <td>20170926.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>252.0</td>\n",
       "      <td>0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>252.0</td>\n",
       "      <td>0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XaPhtGLk/5UvvOYHcONTwsnH97P4eGECeq+BARGItRw=</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>male</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20051228.0</td>\n",
       "      <td>20170927.0</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           msno  is_churn  city    bd  gender  \\\n",
       "0  waLDQMmcOu2jLDaV1ddDkgCrB/jl6sD66Xzs0Vqax1Y=         1  18.0  36.0  female   \n",
       "1  QA7uiXy8vIbUSPOkCf9RwQ3FsT8jVq2OxDr8zqa7bRQ=         1  10.0  38.0    male   \n",
       "2  fGwBva6hikQmTJzrbz/2Ezjm5Cth5jZUNvXigKK2AFA=         1  11.0  27.0  female   \n",
       "3  mT5V8rEpa+8wuqi6x0DoVd3H5icMKkE9Prt49UlmK+4=         1  13.0  23.0  female   \n",
       "4  XaPhtGLk/5UvvOYHcONTwsnH97P4eGECeq+BARGItRw=         1   3.0  27.0    male   \n",
       "\n",
       "   registered_via  registration_init_time  expiration_date  plan_net_worth_x  \\\n",
       "0             9.0              20050406.0       20170907.0             149.0   \n",
       "1             9.0              20050407.0       20170321.0            3309.0   \n",
       "2             9.0              20051016.0       20170203.0            1492.0   \n",
       "3             9.0              20051102.0       20170926.0             252.0   \n",
       "4             9.0              20051228.0       20170927.0            1272.0   \n",
       "\n",
       "   cancel_times_x     ...       plan_net_worth_y  cancel_times_y  \\\n",
       "0               0     ...                  149.0               0   \n",
       "1               2     ...                 3309.0               2   \n",
       "2               1     ...                 1492.0               1   \n",
       "3               0     ...                  252.0               0   \n",
       "4               0     ...                 1272.0               0   \n",
       "\n",
       "   total_actual_payment_y  mean_payment_each_transaction_y  total_order_y  \\\n",
       "0                   149.0                        74.500000              2   \n",
       "1                  3458.0                       150.347826             23   \n",
       "2                  1492.0                       149.200000             10   \n",
       "3                   252.0                       126.000000              2   \n",
       "4                  1272.0                       159.000000              8   \n",
       "\n",
       "   plan_net_worth  cancel_times  total_actual_payment  \\\n",
       "0           149.0             0                 149.0   \n",
       "1          3309.0             2                3458.0   \n",
       "2          1492.0             1                1492.0   \n",
       "3           252.0             0                 252.0   \n",
       "4          1272.0             0                1272.0   \n",
       "\n",
       "   mean_payment_each_transaction  total_order  \n",
       "0                      74.500000            2  \n",
       "1                     150.347826           23  \n",
       "2                     149.200000           10  \n",
       "3                     126.000000            2  \n",
       "4                     159.000000            8  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions_df = pd.read_csv(\n",
    "    './data/transactions.csv',\n",
    "    dtype={\n",
    "        'payment_method': np.uint8,\n",
    "        'payment_plan_days': np.uint8,\n",
    "        'plan_list_price': np.uint8,\n",
    "        'actual_amount_paid': np.uint8,\n",
    "        'is_auto_renew': np.uint8,\n",
    "        'is_cancel': np.bool,\n",
    "    },\n",
    ")\n",
    "transactions_df = pd.merge(train_df, transactions_df, how='left', on='msno')\n",
    "grouped = transactions_df.copy().groupby('msno')\n",
    "\n",
    "stats_df = grouped.agg({\n",
    "    'msno': {'total_order': 'count'},\n",
    "    'plan_list_price': {'plan_net_worth': 'sum'},\n",
    "    'actual_amount_paid': {\n",
    "        'mean_payment_each_transaction': 'mean',\n",
    "        'total_actual_payment': 'sum',\n",
    "    },\n",
    "    'is_cancel': {'cancel_times': lambda x: sum(x == 1)},\n",
    "})\n",
    "stats_df.columns = stats_df.columns.droplevel(0)\n",
    "stats_df.reset_index(inplace=True)\n",
    "train_df = pd.merge(train_df, stats_df, how='left', on='msno')\n",
    "\n",
    "del transactions_df, stats_df\n",
    "\n",
    "# In the description the bd column is said to be in a very wide range \n",
    "#So I decided to clip it just to store it as a smaller type\n",
    "train_df['bd'].clip(0,100) \n",
    "train_df['bd'].fillna(0,inplace=True)\n",
    "train_df['bd'].astype(np.uint8,inplace=True)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "del train_df['cancel_times_x'], train_df['cancel_times_y'], train_df['plan_net_worth_x'], train_df['plan_net_worth_y'], train_df['total_actual_payment_x'], train_df['total_actual_payment_y'], train_df['mean_payment_each_transaction_x'], train_df['mean_payment_each_transaction_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['msno', 'is_churn', 'city', 'bd', 'gender', 'registered_via',\n",
       "       'registration_init_time', 'expiration_date', 'plan_net_worth',\n",
       "       'cancel_times', 'total_actual_payment', 'mean_payment_each_transaction',\n",
       "       'total_order'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del train_df['total_order_x'], train_df['total_order_y']\n",
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dTrain = xgb.DMatrix(train_df)\n",
    "params = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}\n",
    "num_round = 2\n",
    "\n",
    "Y = train_df.is_churn\n",
    "del train_df['msno']\n",
    "del train_df['is_churn']\n",
    "# print ('running cross validation')\n",
    "# # do cross validation, this will print result out as\n",
    "# # [iteration]  metric_name:mean_value+std_value\n",
    "# # std_value is standard deviation of the metric\n",
    "# xgb.cv(param, dtrain, num_round, nfold=5,\n",
    "#        metrics={'error'}, seed = 0,\n",
    "#        callbacks=[xgb.callback.print_evaluation(show_stdv=True)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.describe()\n",
    "del train_df['gender']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression()\n",
    "train_df = train_df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(train_df, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  3.76399629e-07,   9.50288542e-07,  -1.21944718e-07,\n",
       "         -7.37131402e-06,   7.28340117e-06,  -3.10552042e-04,\n",
       "          7.63793333e-08,  -3.24074521e-04,  -1.49364506e-05,\n",
       "         -2.94370845e-06]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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