final_postprocessed_model.ipynb

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    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "\n",
    "import datetime\n",
    "import lightgbm as lgb\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import random\n",
    "import time\n",
    "\n",
    "from copy import deepcopy\n",
    "from scipy.stats import rankdata\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "def seed_everything(seed=13):\n",
    "    random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    \n",
    "    \n",
    "def read_from_disk(path, filename):\n",
    "    with open(os.path.join(path, filename), 'rb') as handle:\n",
    "        return pickle.load(handle)\n",
    "    \n",
    "    \n",
    "def save_to_disk(obj, filename):\n",
    "    with open(filename, 'wb') as handle:\n",
    "        pickle.dump(obj, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "def timedelta(seconds):\n",
    "    return str(datetime.timedelta(seconds=seconds)).split('.')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fraud-stack-lgbm-26', 'ieee-fraud-detection', 'fraud-kirill-postprocessing']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('../input')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506691, 446)\n"
     ]
    }
   ],
   "source": [
    "df_test_postprocessed = read_from_disk('../input/fraud-kirill-postprocessing', 'df_test_postprocessed.pkl')\n",
    "print(df_test_postprocessed.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_cols = ['card1','card2','card3','card4','card5','card6', 'day_first_trans', 'first_trans_amt', 'addr1', 'id_01', 'DeviceInfo', 'P_emaildomain']\n",
    "\n",
    "def postprocess_y(df, y):\n",
    "    df1 = df.copy()\n",
    "    df1['prediction'] = y\n",
    "    y_new = df1.merge(df1.groupby(list_cols, as_index=False).agg({'prediction':'mean'}), how='left', on=list_cols)['prediction_y'].values\n",
    "    del df1\n",
    "    return y_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.read_csv('../input/fraud-stack-lgbm-26/submission.csv')\n",
    "y_new = postprocess_y(df_test_postprocessed, sub['isFraud'].values)\n",
    "sub['isFraud'] = y_new\n",
    "sub.to_csv('submission.csv', index=False)"
   ]
  }
 ],
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