model-3.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: You are using pip version 20.1; however, version 20.1.1 is available.\r\n",
      "You should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "# installing catboost\n",
    "# Catboost == 0.22 was the version of catboost at the start of this competition\n",
    "!pip install catboost==0.22 --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "import joblib\n",
    "\n",
    "from tqdm import tqdm, tqdm_notebook\n",
    "from functools import reduce\n",
    "from time import time\n",
    "\n",
    "from catboost import CatBoostRegressor, CatBoostClassifier\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "pd.set_option('display.max_rows', 1000)           \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Loading data\n",
    "train = pd.read_csv('./input/Train.csv')\n",
    "test = pd.read_csv('./input/Test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "HBox(children=(FloatProgress(value=0.0, max=6.0), HTML(value='')))"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "data=pd.concat([train,test]).reset_index(drop=True)\n",
    "df = data[['ID', 'location', 'target']]\n",
    "for c in tqdm_notebook(['temp', 'precip', 'rel_humidity', 'wind_dir','wind_spd', 'atmos_press']):\n",
    "    tmp = data[c].str.split(',', expand=True)\n",
    "    tmp.columns = [c + '_' + str(x) for x in tmp.columns]\n",
    "    df = pd.concat([df, tmp], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "1dd94a4878124b9da7520f92481a7914",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "obj_cols = [c for c in df.select_dtypes('object').columns if c not in ['ID', 'location']]\n",
    "for c in tqdm_notebook(obj_cols):\n",
    "    df[c] = pd.to_numeric(df[c], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Feature interaction functions\n",
    "# There are 4 types of interactions: product interactions, division interactions, sum interactions and divide interactions\n",
    "\n",
    "def add_prod_interacts(df, inter_cols):   \n",
    "    def apply_interacts(x, inter_cols):\n",
    "        cols = [x + '_prod_' + c for c in inter_cols[inter_cols.index(x)+1:]]\n",
    "        interacts_df[cols] = pd.concat([df[x] * df[c] for c in inter_cols[inter_cols.index(x)+1:]], axis=1)\n",
    "    \n",
    "    interacts_df = pd.DataFrame()\n",
    "    _ = df[inter_cols[:-1]].apply(lambda x: apply_interacts(x.name, inter_cols))\n",
    "    df = pd.concat([df, interacts_df], axis=1)\n",
    "    return df\n",
    "\n",
    "\n",
    "def add_div_interacts(df, inter_cols):   \n",
    "    def apply_interacts(x, inter_cols):\n",
    "        cols = [x + '_div_' + c for c in inter_cols[inter_cols.index(x)+1:]]\n",
    "        interacts_df[cols] = pd.concat([df[x] / df[c] for c in inter_cols[inter_cols.index(x)+1:]], axis=1)\n",
    "    \n",
    "    interacts_df = pd.DataFrame()\n",
    "    _ = df[inter_cols[:-1]].apply(lambda x: apply_interacts(x.name, inter_cols))\n",
    "    df = pd.concat([df, interacts_df], axis=1)\n",
    "\n",
    "    return df\n",
    "\n",
    "def add_sum_interacts(df, inter_cols):   \n",
    "    def apply_interacts(x, inter_cols):\n",
    "        cols = [x + '_sum_' + c for c in inter_cols[inter_cols.index(x)+1:]]\n",
    "        interacts_df[cols] = pd.concat([df[x] + df[c] for c in inter_cols[inter_cols.index(x)+1:]], axis=1)\n",
    "    \n",
    "    interacts_df = pd.DataFrame()\n",
    "    _ = df[inter_cols[:-1]].apply(lambda x: apply_interacts(x.name, inter_cols))\n",
    "    df = pd.concat([df, interacts_df], axis=1)\n",
    "\n",
    "    return df\n",
    "\n",
    "def add_diff_interacts(df, inter_cols):   \n",
    "    def apply_interacts(x, inter_cols):\n",
    "        cols = [x + '_diff_' + c for c in inter_cols[inter_cols.index(x)+1:]]\n",
    "        interacts_df[cols] = pd.concat([df[x] - df[c] for c in inter_cols[inter_cols.index(x)+1:]], axis=1)\n",
    "    \n",
    "    interacts_df = pd.DataFrame()\n",
    "    _ = df[inter_cols[:-1]].apply(lambda x: apply_interacts(x.name, inter_cols))\n",
    "    df = pd.concat([df, interacts_df], axis=1)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "concerned_cols = df.columns[3:729]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['temp_0', 'temp_1', 'temp_2', 'temp_3', 'temp_4', 'temp_5', 'temp_6',\n",
       "       'temp_7', 'temp_8', 'temp_9',\n",
       "       ...\n",
       "       'atmos_press_111', 'atmos_press_112', 'atmos_press_113',\n",
       "       'atmos_press_114', 'atmos_press_115', 'atmos_press_116',\n",
       "       'atmos_press_117', 'atmos_press_118', 'atmos_press_119',\n",
       "       'atmos_press_120'],\n",
       "      dtype='object', length=726)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concerned_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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    {
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     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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     "text": [
      "\n"
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   ],
   "source": [
    "orig_cols_dict = {}\n",
    "added_cols_mean = []\n",
    "added_cols_max = []\n",
    "added_cols_min = []\n",
    "added_cols_range = []\n",
    "\n",
    "weather_cols = ['temp', 'precip', 'rel_humidity', 'wind_dir','wind_spd', 'atmos_press']\n",
    "for w in tqdm_notebook(weather_cols):\n",
    "    selected_cols = [c for c in df.columns if w in c]\n",
    "    orig_cols_dict[w] = pd.Series(selected_cols)\n",
    "    df[f'mean_{w}'] = df[selected_cols].mean(axis=1)\n",
    "    added_cols_mean.append(f'mean_{w}')\n",
    "    df[f'max_{w}'] = df[selected_cols].max(axis=1)\n",
    "    added_cols_min.append(f'max_{w}')\n",
    "    df[f'min_{w}'] = df[selected_cols].min(axis=1)\n",
    "    added_cols_max.append(f'min_{w}')\n",
    "    df[f'range_{w}'] = df[f'max_{w}'] - df[f'min_{w}']\n",
    "    df[f'std_{w}'] = df[selected_cols].std(axis=1)\n",
    "    df[f'median_{w}'] = df[selected_cols].median(axis=1)\n",
    "    df[f'skew_{w}'] = df[selected_cols].skew(axis=1)\n",
    "    df[f'kurt_{w}'] = df[selected_cols].kurt(axis=1)\n",
    "for num_cols in tqdm_notebook([added_cols_mean, added_cols_max, added_cols_min]):\n",
    "    df = add_prod_interacts(df, num_cols)\n",
    "    df = add_div_interacts(df, num_cols)\n",
    "    df = add_sum_interacts(df, num_cols)\n",
    "    df = add_diff_interacts(df, num_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "weather_cols = ['temp', 'precip', 'rel_humidity', 'wind_dir','wind_spd', 'atmos_press']\n",
    "for w in tqdm_notebook(weather_cols):\n",
    "    tmp_df = pd.DataFrame()\n",
    "    tmp_df['weather_col_orig'] = orig_cols_dict[w]\n",
    "    tmp_df['hours_since_start'] = tmp_df['weather_col_orig'].apply(lambda x: x.split('_')[-1])\n",
    "    tmp_df['hours_since_start'] = tmp_df['hours_since_start'].astype('int')\n",
    "    tmp_df['hour_of_day'] = tmp_df['hours_since_start']%24\n",
    "    for hour in range(0, 24):\n",
    "        selected_cols = tmp_df[tmp_df['hour_of_day'] == hour]['weather_col_orig'].tolist()\n",
    "        df[f'{w}_hour_{hour}_mean'] = df[selected_cols].mean(axis=1)\n",
    "        df[f'{w}_hour_{hour}_min'] = df[selected_cols].min(axis=1)\n",
    "        df[f'{w}_hour_{hour}_max'] = df[selected_cols].max(axis=1)\n",
    "        df[f'{w}_hour_{hour}_range'] = df[f'{w}_hour_{hour}_max'] - df[f'{w}_hour_{hour}_min']\n",
    "        df[f'{w}_hour_{hour}_skew'] = df[selected_cols].skew()\n",
    "        df[f'{w}_hour_{hour}_kurt'] = df[selected_cols].kurt()\n",
    "        if hour - 3 > 0 and hour % 3 == 0:\n",
    "            df[f'{w}_hour_{hour}_prev_hour_mean_diff'] = df[selected_cols].mean(axis=1)-df[f'{w}_hour_{hour-3}_mean']\n",
    "        if hour - 5 > 0 and hour % 3 == 0:\n",
    "            df[f'{w}_hour_{hour}_prev_hour_mean_diff_5'] = df[selected_cols].mean(axis=1)-df[f'{w}_hour_{hour-5}_mean']\n",
    "        if hour - 6 > 0 and hour % 6 == 0:\n",
    "            df[f'{w}_hour_{hour}_prev_hour_max_diff_6'] = df[selected_cols].max(axis=1)-df[f'{w}_hour_{hour-6}_max']\n",
    "        if hour - 4 > 0 and hour % 2 == 0:\n",
    "            df[f'{w}_hour_{hour}_prev_hour_max_diff_4'] = df[selected_cols].max(axis=1)-df[f'{w}_hour_{hour-4}_max']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
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     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "weather_cols = ['temp', 'precip', 'rel_humidity', 'wind_dir','wind_spd', 'atmos_press']\n",
    "for w in tqdm_notebook(weather_cols):\n",
    "    tmp_df = pd.DataFrame()\n",
    "    tmp_df['weather_col_orig'] = orig_cols_dict[w]\n",
    "    tmp_df['hours_since_start'] = tmp_df['weather_col_orig'].apply(lambda x: x.split('_')[-1])\n",
    "    tmp_df['hours_since_start'] = tmp_df['hours_since_start'].astype('int')\n",
    "    tmp_df['hour_of_day'] = tmp_df['hours_since_start']%6\n",
    "    for hour in range(0, 6):\n",
    "        selected_cols = tmp_df[tmp_df['hour_of_day'] == hour]['weather_col_orig'].tolist()\n",
    "        df[f'{w}6_hour_{hour}_mean'] = df[selected_cols].mean(axis=1)\n",
    "        df[f'{w}6_hour_{hour}_min'] = df[selected_cols].min(axis=1)\n",
    "        df[f'{w}6_hour_{hour}_max'] = df[selected_cols].max(axis=1)\n",
    "        df[f'{w}6_hour_{hour}_range'] = df[f'{w}_hour_{hour}_max'] - df[f'{w}_hour_{hour}_min']\n",
    "        if hour - 3 > 0 and hour % 3 == 0:\n",
    "            df[f'{w}6_hour_{hour}_prev_{hour}_mean_diff'] = df[f'{w}6_hour_{hour}_mean'] - df[f'{w}6_hour_{hour-3}_mean']"
   ]
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     "text": [
      "\n",
      "\n"
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   ],
   "source": [
    "for w in tqdm_notebook(weather_cols):\n",
    "    tmp_df = pd.DataFrame()\n",
    "    tmp_df['weather_col_orig'] = orig_cols_dict[w]\n",
    "    tmp_df['hours_since_start'] = tmp_df['weather_col_orig'].apply(lambda x: x.split('_')[-1])\n",
    "    tmp_df['hours_since_start'] = tmp_df['hours_since_start'].astype('int')\n",
    "    tmp_df['hour_of_day'] = tmp_df['hours_since_start']%3\n",
    "    for hour in tqdm_notebook(tmp_df['hour_of_day'].unique()):\n",
    "        selected_cols = tmp_df[tmp_df['hour_of_day'] == hour]['weather_col_orig'].tolist()\n",
    "        df[f'{w}3_hour_{hour}_mean'] = df[selected_cols].mean(axis=1)\n",
    "        if hour > 3:\n",
    "            df[f'{w}3_hour_diff_{hour}_{hour-3}'] = df[f'{w}3_hour_{hour}_mean'] - df[f'{w}3_hour_{hour-3}_mean']"
   ]
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      "\n"
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     "text": [
      "\n",
      "\n"
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   ],
   "source": [
    "for w in tqdm_notebook(weather_cols):\n",
    "    tmp_df = pd.DataFrame()\n",
    "    tmp_df['weather_col_orig'] = orig_cols_dict[w]\n",
    "    tmp_df['hours_since_start'] = tmp_df['weather_col_orig'].apply(lambda x: x.split('_')[-1])\n",
    "    tmp_df['hours_since_start'] = tmp_df['hours_since_start'].astype('int')\n",
    "    tmp_df['hour_of_day'] = tmp_df['hours_since_start']//8\n",
    "    for hour in tqdm_notebook(tmp_df['hour_of_day'].unique()):\n",
    "        selected_cols = tmp_df[tmp_df['hour_of_day'] == hour]['weather_col_orig'].tolist()\n",
    "        df[f'{w}_cont_hours_8_{hour}_mean'] = df[selected_cols].mean(axis=1)\n",
    "        df[f'{w}_cont_hours_8_{hour}_min'] = df[selected_cols].min(axis=1)\n",
    "        df[f'{w}_cont_hours_8_{hour}_max'] = df[selected_cols].max(axis=1)\n",
    "        df[f'{w}_cont_hours_8_{hour}_range'] = df[f'{w}_cont_hours_8_{hour}_max'] - df[f'{w}_cont_hours_8_{hour}_min']\n",
    "        if hour > 0:\n",
    "            df[f'{w}_cont_hours_8_hour_diff_{hour}'] = df[f'{w}_cont_hours_8_{hour}_mean'] - df[f'{w}_cont_hours_8_{hour-1}_mean']\n",
    "        if hour > 2:\n",
    "            df[f'{w}_cont_hours_8_hour_diff_{hour}'] = df[f'{w}_cont_hours_8_{hour}_mean'] - df[f'{w}_cont_hours_8_{hour-3}_mean']\n",
    "        if hour > 4:\n",
    "            df[f'{w}_cont_hours_8_hour_diff_{hour}'] = df[f'{w}_cont_hours_8_{hour}_mean'] - df[f'{w}_cont_hours_8_{hour-5}_mean']"
   ]
  },
  {
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   "execution_count": 15,
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     "metadata": {},
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     "text": [
      "\n"
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     "name": "stdout",
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     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for w in tqdm_notebook(weather_cols):\n",
    "    tmp_df = pd.DataFrame()\n",
    "    tmp_df['weather_col_orig'] = orig_cols_dict[w]\n",
    "    tmp_df['hours_since_start'] = tmp_df['weather_col_orig'].apply(lambda x: x.split('_')[-1])\n",
    "    tmp_df['hours_since_start'] = tmp_df['hours_since_start'].astype('int')\n",
    "    tmp_df['hour_of_day'] = tmp_df['hours_since_start']//24\n",
    "    for hour in tqdm_notebook(tmp_df['hour_of_day'].unique()):\n",
    "        selected_cols = tmp_df[tmp_df['hour_of_day'] == hour]['weather_col_orig'].tolist()\n",
    "        df[f'{w}_cont_hours_24_{hour}_mean'] = df[selected_cols].mean(axis=1)\n",
    "        df[f'{w}_cont_hours_24_{hour}_max'] = df[selected_cols].max(axis=1)\n",
    "        df[f'{w}_cont_hours_24_{hour}_min'] = df[selected_cols].min(axis=1)\n",
    "        if hour > 0:\n",
    "            df[f'{w}_cont_hours_24_hour_diff_1_{hour}'] = df[f'{w}_cont_hours_24_{hour}_mean'] / df[f'{w}_cont_hours_24_{hour-1}_mean']\n",
    "        if hour > 1:\n",
    "            df[f'{w}_cont_hours_24_hour_diff_2_{hour}'] = df[f'{w}_cont_hours_24_{hour}_mean'] / df[f'{w}_cont_hours_24_{hour-2}_mean']\n",
    "        if hour > 2:\n",
    "            df[f'{w}_cont_hours_24_hour_diff_3_{hour}'] = df[f'{w}_cont_hours_24_{hour}_mean'] / df[f'{w}_cont_hours_24_{hour-3}_mean']\n",
    "        if hour > 3:\n",
    "            df[f'{w}_cont_hours_24_hour_diff_4_{hour}'] = df[f'{w}_cont_hours_24_{hour}_mean'] / df[f'{w}_cont_hours_24_{hour-4}_mean']\n",
    "        if hour > 4:\n",
    "            df[f'{w}_cont_hours_24_hour_diff_5_{hour}'] = df[f'{w}_cont_hours_24_{hour}_mean'] / df[f'{w}_cont_hours_24_{hour-5}_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2791"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ID_COL, TARGET_COL = 'ID', 'target'\n",
    "features = [c for c in df.columns if c not in [ID_COL, TARGET_COL]]\n",
    "len(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "drop_features = ['location',\n",
    " 'loc_altitude',\n",
    " 'km2',\n",
    " 'aspect',\n",
    " 'dist_trunk',\n",
    " 'dist_primary',\n",
    " 'dist_secondary',\n",
    " 'dist_tertiary',\n",
    " 'dist_unclassified',\n",
    " 'dist_residential',\n",
    " 'popn',\n",
    " 'hh',\n",
    " 'hh_cook_charcoal',\n",
    " 'hh_cook_firewood',]\n",
    "other_features = [c for c in features if c not in drop_features]\n",
    "df[other_features] = df[other_features].apply(lambda x: pd.qcut(x, 20, labels=False, duplicates='drop'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train=df[df.target.notnull()].reset_index(drop=True)\n",
    "test=df[df.target.isna()].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        C\n",
       "1        D\n",
       "2        A\n",
       "3        A\n",
       "4        A\n",
       "        ..\n",
       "15534    A\n",
       "15535    E\n",
       "15536    D\n",
       "15537    D\n",
       "15538    C\n",
       "Name: location, Length: 15539, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['location']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>ID</th>\n",
       "      <th>location</th>\n",
       "      <th>target</th>\n",
       "      <th>temp_0</th>\n",
       "      <th>temp_1</th>\n",
       "      <th>temp_2</th>\n",
       "      <th>temp_3</th>\n",
       "      <th>temp_4</th>\n",
       "      <th>temp_5</th>\n",
       "      <th>temp_6</th>\n",
       "      <th>...</th>\n",
       "      <th>atmos_press_cont_hours_24_hour_diff_3_4</th>\n",
       "      <th>atmos_press_cont_hours_24_hour_diff_4_4</th>\n",
       "      <th>atmos_press_cont_hours_24_5_mean</th>\n",
       "      <th>atmos_press_cont_hours_24_5_max</th>\n",
       "      <th>atmos_press_cont_hours_24_5_min</th>\n",
       "      <th>atmos_press_cont_hours_24_hour_diff_1_5</th>\n",
       "      <th>atmos_press_cont_hours_24_hour_diff_2_5</th>\n",
       "      <th>atmos_press_cont_hours_24_hour_diff_3_5</th>\n",
       "      <th>atmos_press_cont_hours_24_hour_diff_4_5</th>\n",
       "      <th>atmos_press_cont_hours_24_hour_diff_5_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_train_0</td>\n",
       "      <td>C</td>\n",
       "      <td>45.126304</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_train_1</td>\n",
       "      <td>D</td>\n",
       "      <td>79.131702</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_train_10</td>\n",
       "      <td>A</td>\n",
       "      <td>32.661304</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_train_100</td>\n",
       "      <td>A</td>\n",
       "      <td>53.850238</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_train_1000</td>\n",
       "      <td>A</td>\n",
       "      <td>177.418750</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 2793 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID location      target  temp_0  temp_1  temp_2  temp_3  temp_4  \\\n",
       "0     ID_train_0        C   45.126304     NaN     NaN     NaN     NaN     NaN   \n",
       "1     ID_train_1        D   79.131702    10.0     9.0     7.0     7.0     7.0   \n",
       "2    ID_train_10        A   32.661304    18.0    17.0    19.0    16.0     9.0   \n",
       "3   ID_train_100        A   53.850238    11.0    13.0    14.0    15.0    15.0   \n",
       "4  ID_train_1000        A  177.418750     9.0     8.0     8.0     8.0     6.0   \n",
       "\n",
       "   temp_5  temp_6  ...  atmos_press_cont_hours_24_hour_diff_3_4  \\\n",
       "0     NaN     NaN  ...                                      NaN   \n",
       "1     5.0     3.0  ...                                     18.0   \n",
       "2     9.0    10.0  ...                                      0.0   \n",
       "3    14.0    13.0  ...                                     17.0   \n",
       "4     4.0     2.0  ...                                      NaN   \n",
       "\n",
       "   atmos_press_cont_hours_24_hour_diff_4_4  atmos_press_cont_hours_24_5_mean  \\\n",
       "0                                      NaN                               1.0   \n",
       "1                                     18.0                              16.0   \n",
       "2                                      0.0                               8.0   \n",
       "3                                      9.0                               9.0   \n",
       "4                                      NaN                               NaN   \n",
       "\n",
       "   atmos_press_cont_hours_24_5_max  atmos_press_cont_hours_24_5_min  \\\n",
       "0                              1.0                              1.0   \n",
       "1                             16.0                             16.0   \n",
       "2                              8.0                              8.0   \n",
       "3                              9.0                              9.0   \n",
       "4                              NaN                              NaN   \n",
       "\n",
       "   atmos_press_cont_hours_24_hour_diff_1_5  \\\n",
       "0                                     10.0   \n",
       "1                                      9.0   \n",
       "2                                     16.0   \n",
       "3                                     14.0   \n",
       "4                                      NaN   \n",
       "\n",
       "   atmos_press_cont_hours_24_hour_diff_2_5  \\\n",
       "0                                      NaN   \n",
       "1                                     14.0   \n",
       "2                                     11.0   \n",
       "3                                      8.0   \n",
       "4                                      NaN   \n",
       "\n",
       "   atmos_press_cont_hours_24_hour_diff_3_5  \\\n",
       "0                                      NaN   \n",
       "1                                     13.0   \n",
       "2                                     12.0   \n",
       "3                                     14.0   \n",
       "4                                      NaN   \n",
       "\n",
       "   atmos_press_cont_hours_24_hour_diff_4_5  \\\n",
       "0                                      NaN   \n",
       "1                                     16.0   \n",
       "2                                      6.0   \n",
       "3                                     17.0   \n",
       "4                                      NaN   \n",
       "\n",
       "   atmos_press_cont_hours_24_hour_diff_5_5  \n",
       "0                                      NaN  \n",
       "1                                     16.0  \n",
       "2                                      3.0  \n",
       "3                                     12.0  \n",
       "4                                      NaN  \n",
       "\n",
       "[5 rows x 2793 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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   "outputs": [
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>location</th>\n",
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       "      <td>ID_test_1</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
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       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
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       "      <td>7.0</td>\n",
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       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_test_100</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
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       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_test_1000</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
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       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
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      ],
      "text/plain": [
       "             ID location  target  temp_0  temp_1  temp_2  temp_3  temp_4  \\\n",
       "0     ID_test_0        C     NaN    12.0    11.0    10.0     8.0     7.0   \n",
       "1     ID_test_1        C     NaN     2.0     4.0     7.0    11.0    13.0   \n",
       "2    ID_test_10        D     NaN    14.0    14.0    13.0    12.0    10.0   \n",
       "3   ID_test_100        C     NaN     2.0     6.0    10.0    12.0    14.0   \n",
       "4  ID_test_1000        D     NaN     0.0     1.0     4.0     8.0    12.0   \n",
       "\n",
       "   temp_5  temp_6  ...  atmos_press_cont_hours_24_hour_diff_3_4  \\\n",
       "0     4.0     2.0  ...                                      8.0   \n",
       "1    14.0    15.0  ...                                      7.0   \n",
       "2     8.0     8.0  ...                                      6.0   \n",
       "3    15.0    16.0  ...                                      9.0   \n",
       "4    14.0    16.0  ...                                     18.0   \n",
       "\n",
       "   atmos_press_cont_hours_24_hour_diff_4_4  atmos_press_cont_hours_24_5_mean  \\\n",
       "0                                      9.0                               0.0   \n",
       "1                                     14.0                               2.0   \n",
       "2                                      7.0                              15.0   \n",
       "3                                      5.0                               2.0   \n",
       "4                                     17.0                              17.0   \n",
       "\n",
       "   atmos_press_cont_hours_24_5_max  atmos_press_cont_hours_24_5_min  \\\n",
       "0                              0.0                              0.0   \n",
       "1                              2.0                              2.0   \n",
       "2                             15.0                             15.0   \n",
       "3                              2.0                              2.0   \n",
       "4                             17.0                             17.0   \n",
       "\n",
       "   atmos_press_cont_hours_24_hour_diff_1_5  \\\n",
       "0                                      4.0   \n",
       "1                                     13.0   \n",
       "2                                      7.0   \n",
       "3                                     18.0   \n",
       "4                                     12.0   \n",
       "\n",
       "   atmos_press_cont_hours_24_hour_diff_2_5  \\\n",
       "0                                      7.0   \n",
       "1                                     10.0   \n",
       "2                                      8.0   \n",
       "3                                     13.0   \n",
       "4                                      9.0   \n",
       "\n",
       "   atmos_press_cont_hours_24_hour_diff_3_5  \\\n",
       "0                                      3.0   \n",
       "1                                     12.0   \n",
       "2                                      7.0   \n",
       "3                                     16.0   \n",
       "4                                     15.0   \n",
       "\n",
       "   atmos_press_cont_hours_24_hour_diff_4_5  \\\n",
       "0                                      4.0   \n",
       "1                                     11.0   \n",
       "2                                      6.0   \n",
       "3                                     16.0   \n",
       "4                                     17.0   \n",
       "\n",
       "   atmos_press_cont_hours_24_hour_diff_5_5  \n",
       "0                                      4.0  \n",
       "1                                     15.0  \n",
       "2                                      6.0  \n",
       "3                                     14.0  \n",
       "4                                     16.0  \n",
       "\n",
       "[5 rows x 2793 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
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    }
   ],
   "source": [
    "\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# Creating a list of test ids in the order that they will be trained\n",
    "testA =  test[test.location == 'A']\n",
    "testB =  test[test.location == 'B']\n",
    "testC =  test[test.location == 'C']\n",
    "testD =  test[test.location == 'D']\n",
    "testE =  test[test.location == 'E']\n",
    "\n",
    "tA, tD, tE, tBC = testA.ID, testD.ID, testE.ID, test[(test.location == 'B') | (test.location == 'C')].ID\n",
    "test_id = pd.concat([tA, tD, tE, tBC])"
   ]
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     "text": [
      "\n",
      "CPU times: user 5h 24min 56s, sys: 22min 21s, total: 5h 47min 18s\n",
      "Wall time: 1h 36min 16s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Creating a list to hold predictions per seed\n",
    "predzz = []\n",
    "for i in tqdm_notebook(range(20)):\n",
    "  # Creating a list to hold predictions per location\n",
    "  # Training model per location per seed\n",
    "  predictions = []\n",
    "  for area in tqdm_notebook(['A', 'D', 'E'], leave = False):\n",
    "    # Separating training data per location\n",
    "    X = train[train.location == area]\n",
    "    y = X.target\n",
    "    X = X.drop(['ID', 'location', 'target'], axis = 1)\n",
    "\n",
    "    # Shuffling data\n",
    "    X, y = shuffle(X, y, random_state = 0)\n",
    "\n",
    "\n",
    "    # Separating testing data per location\n",
    "    tes = test[test.location == area]\n",
    "    tes = tes.drop(['ID', 'location'], axis = 1)\n",
    "\n",
    "    # Training the model and making predictions per seed, per location\n",
    "    preds = CatBoostRegressor(verbose = False, random_seed = i).fit(X, y).predict(tes)\n",
    "    predictions.extend(preds)\n",
    "\n",
    "  X = train[(train.location == 'B') | (train.location == 'C')]\n",
    "  y = X.target\n",
    "  X = X.drop(['ID', 'location', 'target'], axis = 1)\n",
    "  X, y = shuffle(X, y, random_state = 0)\n",
    "\n",
    "  tes = test[(test.location == 'B') | (test.location == 'C')]\n",
    "  tes = tes.drop(['ID', 'location'], axis = 1)\n",
    "  preds = CatBoostRegressor(verbose = False, random_seed = i).fit(X, y).predict(tes)\n",
    "  predictions.extend(preds)\n",
    "\n",
    "  predzz.append(predictions)\n",
    "\n",
    "# Averaging the predictions\n",
    "preds_avg = np.mean(predzz, axis = 0)\n",
    "\n",
    "# Post processing of the predictions\n",
    "predz = [((((((((((x-0.85)*1.015)-0.85)*1.012)-0.75)*1.0095)-0.55)*1.0065)-0.8)*1.007) for x in preds_avg]\n",
    "predzz = [((x-0.85)*1.015) for x in predz]\n",
    "\n",
    "# Creating a submission file\n",
    "sub_df = pd.DataFrame({'ID': test_id, 'target': predzz})\n",
    "sub_df.to_csv('model_3.csv', index = False)"
   ]
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