rohlik-orders-forecasting-ensemble-voting.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from prettytable import PrettyTable\n",
    "from category_encoders import OneHotEncoder, MEstimateEncoder, CatBoostEncoder\n",
    "from sklearn import set_config\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from category_encoders import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.base import clone\n",
    "from sklearn.preprocessing import FunctionTransformer, StandardScaler, PowerTransformer, LabelEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor\n",
    "from sklearn.ensemble import HistGradientBoostingRegressor, GradientBoostingRegressor\n",
    "from sklearn.ensemble import VotingRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "from scipy.spatial.distance import squareform\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from lightgbm import LGBMRegressor\n",
    "from catboost import CatBoostRegressor\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score, classification_report, roc_curve, confusion_matrix, log_loss, mean_absolute_error, mean_absolute_percentage_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import warnings\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.pandas.set_option('display.max_columns',None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5895ddf1",
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    "tags": []
   },
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c2269d77",
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   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "efa81754",
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   "outputs": [],
   "source": [
    "train = pd.read_csv('/kaggle/input/rohlik-orders-forecasting-challenge/train.csv', index_col='id')\n",
    "test = pd.read_csv('/kaggle/input/rohlik-orders-forecasting-challenge/test.csv', index_col = 'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d9032672",
   "metadata": {
    "execution": {
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     "status": "completed"
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   "outputs": [],
   "source": [
    "data = pd.concat([train, test], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f0680d9",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>warehouse</th>\n",
       "      <th>date</th>\n",
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      "text/plain": [
       "                    warehouse        date  orders holiday_name  holiday  \\\n",
       "id                                                                        \n",
       "Prague_1_2020-12-05  Prague_1  2020-12-05  6895.0          NaN        0   \n",
       "Prague_1_2020-12-06  Prague_1  2020-12-06  6584.0          NaN        0   \n",
       "Prague_1_2020-12-07  Prague_1  2020-12-07  7030.0          NaN        0   \n",
       "Prague_1_2020-12-08  Prague_1  2020-12-08  6550.0          NaN        0   \n",
       "Prague_1_2020-12-09  Prague_1  2020-12-09  6910.0          NaN        0   \n",
       "\n",
       "                     shutdown  mini_shutdown  shops_closed  \\\n",
       "id                                                           \n",
       "Prague_1_2020-12-05       0.0            0.0             0   \n",
       "Prague_1_2020-12-06       0.0            0.0             0   \n",
       "Prague_1_2020-12-07       0.0            0.0             0   \n",
       "Prague_1_2020-12-08       0.0            0.0             0   \n",
       "Prague_1_2020-12-09       0.0            0.0             0   \n",
       "\n",
       "                     winter_school_holidays  school_holidays  blackout  \\\n",
       "id                                                                       \n",
       "Prague_1_2020-12-05                       0                0       0.0   \n",
       "Prague_1_2020-12-06                       0                0       0.0   \n",
       "Prague_1_2020-12-07                       0                0       0.0   \n",
       "Prague_1_2020-12-08                       0                0       0.0   \n",
       "Prague_1_2020-12-09                       0                0       0.0   \n",
       "\n",
       "                     mov_change  frankfurt_shutdown  precipitation  snow  \\\n",
       "id                                                                         \n",
       "Prague_1_2020-12-05         0.0                 0.0            0.0   0.0   \n",
       "Prague_1_2020-12-06         0.0                 0.0            0.0   0.0   \n",
       "Prague_1_2020-12-07         0.0                 0.0            0.0   0.0   \n",
       "Prague_1_2020-12-08         0.0                 0.0            0.8   0.0   \n",
       "Prague_1_2020-12-09         0.0                 0.0            0.5   0.0   \n",
       "\n",
       "                     user_activity_1  user_activity_2  \n",
       "id                                                     \n",
       "Prague_1_2020-12-05           1722.0          32575.0  \n",
       "Prague_1_2020-12-06           1688.0          32507.0  \n",
       "Prague_1_2020-12-07           1696.0          32552.0  \n",
       "Prague_1_2020-12-08           1681.0          32423.0  \n",
       "Prague_1_2020-12-09           1704.0          32410.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
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   "id": "5bc7431a",
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   "source": [
    "data['holiday_name'] = data['holiday_name'].fillna('None')"
   ]
  },
  {
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   "execution_count": 7,
   "id": "10e5bb44",
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   "outputs": [],
   "source": [
    "# Dictionary mapping cities to their countries\n",
    "city_to_country = {\n",
    "    'Munich_1': 'Germany',\n",
    "    'Frankfurt_1': 'Germany',\n",
    "    'Budapest_1': 'Hungary',\n",
    "    'Brno_1': 'Czech Republic',\n",
    "    'Prague_1': 'Czech Republic',\n",
    "    'Prague_2': 'Czech Republic',\n",
    "    'Prague_3': 'Czech Republic'\n",
    "}\n",
    "\n",
    "data['country'] = data['warehouse'].map(city_to_country)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd2b31fd",
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   },
   "source": [
    "Credit goes to [Sheikh Muhammad Abdullah](https://www.kaggle.com/abdmental01) in his [notebook](https://www.kaggle.com/code/abdmental01/rohlik-starter-v1) for the following function"
   ]
  },
  {
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   "execution_count": 8,
   "id": "3ef65fe8",
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    "execution": {
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   "source": [
    "def Process_Date(Df):\n",
    "    Df['date'] = pd.to_datetime(Df['date'])\n",
    "    Df['year'] = Df['date'].dt.year\n",
    "    Df['day'] = Df['date'].dt.day\n",
    "    Df['month'] = Df['date'].dt.month\n",
    "    Df['quarter'] = Df['date'].dt.quarter\n",
    "    Df['month_name'] = Df['date'].dt.month_name()\n",
    "    Df['day_of_week'] = Df['date'].dt.day_name()\n",
    "    Df['week'] = Df['date'].dt.isocalendar().week\n",
    "    \n",
    "    Df['year_sin'] = np.sin(2 * np.pi * Df['year'])\n",
    "    Df['year_cos'] = np.cos(2 * np.pi * Df['year'])\n",
    "    Df['month_sin'] = np.sin(2 * np.pi * Df['month'] / 12) \n",
    "    Df['month_cos'] = np.cos(2 * np.pi * Df['month'] / 12)\n",
    "    Df['day_sin'] = np.sin(2 * np.pi * Df['day'] / 31)  \n",
    "    Df['day_cos'] = np.cos(2 * np.pi * Df['day'] / 31)\n",
    "    Df['quarter_sin'] = np.sin(2 * np.pi * Df['quarter'] / 4)\n",
    "    Df['quarter_cos'] = np.cos(2 * np.pi * Df['quarter'] / 4)\n",
    "    \n",
    "    Df['group']=(Df['year']-2020)*48+Df['month']*4+Df['day']//7\n",
    "    Df['total_holidays_month'] = Df.groupby(['year', 'month'])['holiday'].transform('sum')\n",
    "    Df['total_shops_closed_week'] = Df.groupby(['year', 'week'])['shops_closed'].transform('sum')\n",
    "    Df['group_sin'] = np.sin(2 * np.pi * Df['group'] / Df['group'].max())\n",
    "    Df['group_cos'] = np.cos(2 * np.pi * Df['group'] / Df['group'].max())\n",
    "    return Df"
   ]
  },
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    "execution": {
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     "iopub.status.busy": "2024-07-29T13:30:52.065666Z",
     "iopub.status.idle": "2024-07-29T13:30:52.115958Z",
     "shell.execute_reply": "2024-07-29T13:30:52.114604Z"
    },
    "papermill": {
     "duration": 0.066883,
     "end_time": "2024-07-29T13:30:52.118942",
     "exception": false,
     "start_time": "2024-07-29T13:30:52.052059",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = Process_Date(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f21c300a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:30:52.145429Z",
     "iopub.status.busy": "2024-07-29T13:30:52.144942Z",
     "iopub.status.idle": "2024-07-29T13:30:52.158868Z",
     "shell.execute_reply": "2024-07-29T13:30:52.157417Z"
    },
    "papermill": {
     "duration": 0.03068,
     "end_time": "2024-07-29T13:30:52.161647",
     "exception": false,
     "start_time": "2024-07-29T13:30:52.130967",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Use only columns we have also in test set\n",
    "data = data[['warehouse', 'date', 'holiday_name', 'holiday', 'shops_closed',\n",
    "       'winter_school_holidays', 'school_holidays', 'year', 'day', 'month',\n",
    "       'month_name', 'day_of_week', 'week', 'year_sin', 'year_cos',\n",
    "       'month_sin', 'month_cos', 'day_sin', 'day_cos', 'group',\n",
    "       'total_holidays_month', 'total_shops_closed_week',\n",
    "       'group_sin', 'group_cos', 'country',\n",
    "       'orders']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "be45bc13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:30:52.187428Z",
     "iopub.status.busy": "2024-07-29T13:30:52.186940Z",
     "iopub.status.idle": "2024-07-29T13:30:52.196132Z",
     "shell.execute_reply": "2024-07-29T13:30:52.194883Z"
    },
    "papermill": {
     "duration": 0.02545,
     "end_time": "2024-07-29T13:30:52.198966",
     "exception": false,
     "start_time": "2024-07-29T13:30:52.173516",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def apply_tfidf_svd(df, text_column, max_features=1000, n_components=10):\n",
    "    vectorizer = TfidfVectorizer(max_features=max_features, stop_words='english')\n",
    "    vectors = vectorizer.fit_transform(df[text_column])\n",
    "    svd = TruncatedSVD(n_components)\n",
    "    x_sv = svd.fit_transform(vectors)\n",
    "    tfidf_df = pd.DataFrame(x_sv)\n",
    "    cols = [(text_column + \"_tfidf_\" + str(f)) for f in tfidf_df.columns.to_list()]\n",
    "    tfidf_df.columns = cols\n",
    "    df = df.reset_index(drop=True)\n",
    "    df = pd.concat([df, tfidf_df], axis=\"columns\")\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f0bc100f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:30:52.225344Z",
     "iopub.status.busy": "2024-07-29T13:30:52.224864Z",
     "iopub.status.idle": "2024-07-29T13:30:52.394767Z",
     "shell.execute_reply": "2024-07-29T13:30:52.392897Z"
    },
    "papermill": {
     "duration": 0.187796,
     "end_time": "2024-07-29T13:30:52.399156",
     "exception": false,
     "start_time": "2024-07-29T13:30:52.211360",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = apply_tfidf_svd(data,'holiday_name')\n",
    "data = data.drop(columns=['holiday_name', 'date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "87c3beb2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:30:52.468435Z",
     "iopub.status.busy": "2024-07-29T13:30:52.467672Z",
     "iopub.status.idle": "2024-07-29T13:30:52.478869Z",
     "shell.execute_reply": "2024-07-29T13:30:52.477616Z"
    },
    "papermill": {
     "duration": 0.044214,
     "end_time": "2024-07-29T13:30:52.481466",
     "exception": false,
     "start_time": "2024-07-29T13:30:52.437252",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Source: https://www.kaggle.com/code/gkanamoto/ensemble-stacking-modified\n",
    "data['holiday_before'] = data['holiday'].shift(1).fillna(0).astype(int)\n",
    "data['holiday_after'] = data['holiday'].shift(-1).fillna(0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "af6ce0aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:30:52.507861Z",
     "iopub.status.busy": "2024-07-29T13:30:52.506478Z",
     "iopub.status.idle": "2024-07-29T13:30:52.518221Z",
     "shell.execute_reply": "2024-07-29T13:30:52.517051Z"
    },
    "papermill": {
     "duration": 0.028009,
     "end_time": "2024-07-29T13:30:52.521271",
     "exception": false,
     "start_time": "2024-07-29T13:30:52.493262",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert some columns to categorical\n",
    "categorical_columns = ['holiday', 'shops_closed', 'winter_school_holidays', 'school_holidays']\n",
    "data[categorical_columns] = data[categorical_columns].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fa2aa38d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:30:52.548785Z",
     "iopub.status.busy": "2024-07-29T13:30:52.547472Z",
     "iopub.status.idle": "2024-07-29T13:30:52.570509Z",
     "shell.execute_reply": "2024-07-29T13:30:52.569119Z"
    },
    "papermill": {
     "duration": 0.039627,
     "end_time": "2024-07-29T13:30:52.573367",
     "exception": false,
     "start_time": "2024-07-29T13:30:52.533740",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create dummy features\n",
    "data = pd.get_dummies(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "724beae9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:30:52.599408Z",
     "iopub.status.busy": "2024-07-29T13:30:52.598911Z",
     "iopub.status.idle": "2024-07-29T13:30:52.612181Z",
     "shell.execute_reply": "2024-07-29T13:30:52.611043Z"
    },
    "papermill": {
     "duration": 0.029917,
     "end_time": "2024-07-29T13:30:52.615168",
     "exception": false,
     "start_time": "2024-07-29T13:30:52.585251",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Rebuild train and test\n",
    "train = data[~data['orders'].isnull()]\n",
    "test = data[data['orders'].isnull()]\n",
    "test = test.drop(columns=['orders'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bfc9178b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:30:52.640977Z",
     "iopub.status.busy": "2024-07-29T13:30:52.640470Z",
     "iopub.status.idle": "2024-07-29T13:30:52.647159Z",
     "shell.execute_reply": "2024-07-29T13:30:52.645934Z"
    },
    "papermill": {
     "duration": 0.023483,
     "end_time": "2024-07-29T13:30:52.650417",
     "exception": false,
     "start_time": "2024-07-29T13:30:52.626934",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Shape: (7340, 65)\n",
      "Test Shape: (397, 64)\n"
     ]
    }
   ],
   "source": [
    "print(f'Train Shape: {train.shape}')\n",
    "print(f'Test Shape: {test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d8ee1ab0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:30:52.676714Z",
     "iopub.status.busy": "2024-07-29T13:30:52.676237Z",
     "iopub.status.idle": "2024-07-29T13:30:52.684833Z",
     "shell.execute_reply": "2024-07-29T13:30:52.683806Z"
    },
    "papermill": {
     "duration": 0.024733,
     "end_time": "2024-07-29T13:30:52.687381",
     "exception": false,
     "start_time": "2024-07-29T13:30:52.662648",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y = train['orders']\n",
    "X = train.drop(columns=['orders'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d7445be9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:30:52.714737Z",
     "iopub.status.busy": "2024-07-29T13:30:52.713414Z",
     "iopub.status.idle": "2024-07-29T13:30:52.720077Z",
     "shell.execute_reply": "2024-07-29T13:30:52.718919Z"
    },
    "papermill": {
     "duration": 0.023532,
     "end_time": "2024-07-29T13:30:52.723027",
     "exception": false,
     "start_time": "2024-07-29T13:30:52.699495",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "seed = 1702\n",
    "np.random.seed(seed)\n",
    "skf = StratifiedKFold(random_state=seed, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96351f62",
   "metadata": {
    "papermill": {
     "duration": 0.011635,
     "end_time": "2024-07-29T13:30:52.746656",
     "exception": false,
     "start_time": "2024-07-29T13:30:52.735021",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6ae25217",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:30:52.772269Z",
     "iopub.status.busy": "2024-07-29T13:30:52.771859Z",
     "iopub.status.idle": "2024-07-29T13:30:52.782709Z",
     "shell.execute_reply": "2024-07-29T13:30:52.781235Z"
    },
    "papermill": {
     "duration": 0.026939,
     "end_time": "2024-07-29T13:30:52.785506",
     "exception": false,
     "start_time": "2024-07-29T13:30:52.758567",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def cross_validation_score(estimator, cv, label=''):\n",
    "    X = train.copy()\n",
    "    y = X.pop('orders')\n",
    "    train_scores, val_scores = [], []\n",
    "    \n",
    "    for fold, (train_idx, val_idx) in enumerate(cv.split(X, y)):\n",
    "        model = clone(estimator)\n",
    "        \n",
    "        X_train = X.iloc[train_idx].reset_index(drop = True)\n",
    "        y_train = y[train_idx]\n",
    "        X_val = X.iloc[val_idx].reset_index(drop = True)\n",
    "        y_val = y[val_idx]\n",
    "        \n",
    "        model.fit(X_train, y_train)\n",
    "        \n",
    "        train_preds = model.predict(X_train)\n",
    "        val_preds = model.predict(X_val)\n",
    "        \n",
    "        \n",
    "        train_score = mean_absolute_percentage_error(y_train, train_preds)\n",
    "        val_score = mean_absolute_percentage_error(y_val, val_preds)\n",
    "        \n",
    "        train_scores.append(train_score)\n",
    "        val_scores.append(val_score)\n",
    "    print(f'Val Score: {np.mean(val_scores):.5f} ± {np.std(val_scores):.5f} | Train Score: {np.mean(train_scores):.5f} ± {np.std(train_scores):.5f} | {label}')\n",
    "    return val_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a3fc0316",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:30:52.811646Z",
     "iopub.status.busy": "2024-07-29T13:30:52.811184Z",
     "iopub.status.idle": "2024-07-29T13:30:52.823229Z",
     "shell.execute_reply": "2024-07-29T13:30:52.822022Z"
    },
    "papermill": {
     "duration": 0.028002,
     "end_time": "2024-07-29T13:30:52.825721",
     "exception": false,
     "start_time": "2024-07-29T13:30:52.797719",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "score_list, oof_list = pd.DataFrame(), pd.DataFrame()\n",
    "\n",
    "models = [\n",
    "    ('rf', RandomForestRegressor(random_state = seed)),\n",
    "    ('xgb', XGBRegressor(random_state = seed)),\n",
    "    ('lgb', LGBMRegressor(random_state = seed)),\n",
    "    ('cb', CatBoostRegressor(random_state = seed, verbose = 0)),\n",
    "    ('ad', AdaBoostRegressor(random_state = seed)),\n",
    "    ('ex', ExtraTreesRegressor(random_state = seed)),\n",
    "    ('gb', GradientBoostingRegressor(random_state = seed)),\n",
    "    ('hgb', HistGradientBoostingRegressor(random_state = seed)),\n",
    "    ('lir', LinearRegression())\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d2235c1c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:30:52.852475Z",
     "iopub.status.busy": "2024-07-29T13:30:52.851361Z",
     "iopub.status.idle": "2024-07-29T13:32:22.775951Z",
     "shell.execute_reply": "2024-07-29T13:32:22.774305Z"
    },
    "papermill": {
     "duration": 89.94299,
     "end_time": "2024-07-29T13:32:22.780861",
     "exception": false,
     "start_time": "2024-07-29T13:30:52.837871",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Score: 0.04147 ± 0.00130 | Train Score: 0.01554 ± 0.00019 | rf\n",
      "Val Score: 0.03486 ± 0.00035 | Train Score: 0.01952 ± 0.00028 | xgb\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004262 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 909\n",
      "[LightGBM] [Info] Number of data points in the train set: 5872, number of used features: 63\n",
      "[LightGBM] [Info] Start training from score 5537.641860\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003645 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 904\n",
      "[LightGBM] [Info] Number of data points in the train set: 5872, number of used features: 63\n",
      "[LightGBM] [Info] Start training from score 5541.095027\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003606 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 901\n",
      "[LightGBM] [Info] Number of data points in the train set: 5872, number of used features: 63\n",
      "[LightGBM] [Info] Start training from score 5530.900204\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003822 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 897\n",
      "[LightGBM] [Info] Number of data points in the train set: 5872, number of used features: 63\n",
      "[LightGBM] [Info] Start training from score 5531.874659\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003683 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 906\n",
      "[LightGBM] [Info] Number of data points in the train set: 5872, number of used features: 63\n",
      "[LightGBM] [Info] Start training from score 5535.707595\n",
      "Val Score: 0.03745 ± 0.00058 | Train Score: 0.03174 ± 0.00035 | lgb\n",
      "Val Score: 0.03372 ± 0.00054 | Train Score: 0.02579 ± 0.00040 | cb\n",
      "Val Score: 0.17195 ± 0.00358 | Train Score: 0.17082 ± 0.00576 | ad\n",
      "Val Score: 0.04251 ± 0.00121 | Train Score: 0.00000 ± 0.00000 | ex\n",
      "Val Score: 0.05429 ± 0.00102 | Train Score: 0.05210 ± 0.00050 | gb\n",
      "Val Score: 0.03743 ± 0.00082 | Train Score: 0.03164 ± 0.00042 | hgb\n",
      "Val Score: 0.09443 ± 0.00185 | Train Score: 0.09338 ± 0.00127 | lir\n"
     ]
    }
   ],
   "source": [
    "for (label, model) in models:\n",
    "    score_list[label] = cross_validation_score(estimator=model,\n",
    "                                                                cv=skf,\n",
    "                                                                label=label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2c39d1b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:32:22.880347Z",
     "iopub.status.busy": "2024-07-29T13:32:22.879612Z",
     "iopub.status.idle": "2024-07-29T13:32:23.689752Z",
     "shell.execute_reply": "2024-07-29T13:32:23.688293Z"
    },
    "papermill": {
     "duration": 0.864184,
     "end_time": "2024-07-29T13:32:23.692922",
     "exception": false,
     "start_time": "2024-07-29T13:32:22.828738",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 4), dpi = 300)\n",
    "sns.barplot(data = score_list.reindex((-1 * score_list).mean().sort_values(ascending=False).index, axis = 1), orient = 'h')\n",
    "plt.title('Score Comparison', weight = 'bold', size = 18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3fe574f",
   "metadata": {
    "papermill": {
     "duration": 0.014314,
     "end_time": "2024-07-29T13:32:23.721663",
     "exception": false,
     "start_time": "2024-07-29T13:32:23.707349",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d5c6194d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:32:23.753228Z",
     "iopub.status.busy": "2024-07-29T13:32:23.752741Z",
     "iopub.status.idle": "2024-07-29T13:32:23.759399Z",
     "shell.execute_reply": "2024-07-29T13:32:23.758227Z"
    },
    "papermill": {
     "duration": 0.02534,
     "end_time": "2024-07-29T13:32:23.761955",
     "exception": false,
     "start_time": "2024-07-29T13:32:23.736615",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# We use the best three models\n",
    "voting = VotingRegressor(estimators=[\n",
    "    ('cb', CatBoostRegressor(random_state = seed, verbose = 0)),\n",
    "    ('xgb', XGBRegressor(random_state = seed)),\n",
    "    ('hgb', HistGradientBoostingRegressor(random_state = seed))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a166eea4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:32:23.792775Z",
     "iopub.status.busy": "2024-07-29T13:32:23.792219Z",
     "iopub.status.idle": "2024-07-29T13:32:50.274287Z",
     "shell.execute_reply": "2024-07-29T13:32:50.272599Z"
    },
    "papermill": {
     "duration": 26.500802,
     "end_time": "2024-07-29T13:32:50.277028",
     "exception": false,
     "start_time": "2024-07-29T13:32:23.776226",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val Score: 0.03293 ± 0.00054 | Train Score: 0.02394 ± 0.00014 | voter\n"
     ]
    }
   ],
   "source": [
    "_ = cross_validation_score(estimator=voting, cv=skf, label='voter')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84bc3136",
   "metadata": {
    "papermill": {
     "duration": 0.013832,
     "end_time": "2024-07-29T13:32:50.305140",
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     "start_time": "2024-07-29T13:32:50.291308",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Prediction and Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bb73f0f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:32:50.336600Z",
     "iopub.status.busy": "2024-07-29T13:32:50.336133Z",
     "iopub.status.idle": "2024-07-29T13:32:55.923504Z",
     "shell.execute_reply": "2024-07-29T13:32:55.922119Z"
    },
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     "duration": 5.606632,
     "end_time": "2024-07-29T13:32:55.926228",
     "exception": false,
     "start_time": "2024-07-29T13:32:50.319596",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingRegressor(estimators=[(&#x27;cb&#x27;,\n",
       "                             &lt;catboost.core.CatBoostRegressor object at 0x7cfb6688fca0&gt;),\n",
       "                            (&#x27;xgb&#x27;,\n",
       "                             XGBRegressor(base_score=None, booster=None,\n",
       "                                          callbacks=None,\n",
       "                                          colsample_bylevel=None,\n",
       "                                          colsample_bynode=None,\n",
       "                                          colsample_bytree=None, device=None,\n",
       "                                          early_stopping_rounds=None,\n",
       "                                          enable_categorical=False,\n",
       "                                          eval_metric=None, feature_types=None,\n",
       "                                          gamma=None, grow_policy=None,...\n",
       "                                          interaction_constraints=None,\n",
       "                                          learning_rate=None, max_bin=None,\n",
       "                                          max_cat_threshold=None,\n",
       "                                          max_cat_to_onehot=None,\n",
       "                                          max_delta_step=None, max_depth=None,\n",
       "                                          max_leaves=None,\n",
       "                                          min_child_weight=None, missing=nan,\n",
       "                                          monotone_constraints=None,\n",
       "                                          multi_strategy=None,\n",
       "                                          n_estimators=None, n_jobs=None,\n",
       "                                          num_parallel_tree=None,\n",
       "                                          random_state=1702, ...)),\n",
       "                            (&#x27;hgb&#x27;,\n",
       "                             HistGradientBoostingRegressor(random_state=1702))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingRegressor</label><div class=\"sk-toggleable__content\"><pre>VotingRegressor(estimators=[(&#x27;cb&#x27;,\n",
       "                             &lt;catboost.core.CatBoostRegressor object at 0x7cfb6688fca0&gt;),\n",
       "                            (&#x27;xgb&#x27;,\n",
       "                             XGBRegressor(base_score=None, booster=None,\n",
       "                                          callbacks=None,\n",
       "                                          colsample_bylevel=None,\n",
       "                                          colsample_bynode=None,\n",
       "                                          colsample_bytree=None, device=None,\n",
       "                                          early_stopping_rounds=None,\n",
       "                                          enable_categorical=False,\n",
       "                                          eval_metric=None, feature_types=None,\n",
       "                                          gamma=None, grow_policy=None,...\n",
       "                                          interaction_constraints=None,\n",
       "                                          learning_rate=None, max_bin=None,\n",
       "                                          max_cat_threshold=None,\n",
       "                                          max_cat_to_onehot=None,\n",
       "                                          max_delta_step=None, max_depth=None,\n",
       "                                          max_leaves=None,\n",
       "                                          min_child_weight=None, missing=nan,\n",
       "                                          monotone_constraints=None,\n",
       "                                          multi_strategy=None,\n",
       "                                          n_estimators=None, n_jobs=None,\n",
       "                                          num_parallel_tree=None,\n",
       "                                          random_state=1702, ...)),\n",
       "                            (&#x27;hgb&#x27;,\n",
       "                             HistGradientBoostingRegressor(random_state=1702))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>cb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CatBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>&lt;catboost.core.CatBoostRegressor object at 0x7cfb6688fca0&gt;</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>xgb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=1702, ...)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>hgb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">HistGradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>HistGradientBoostingRegressor(random_state=1702)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "VotingRegressor(estimators=[('cb',\n",
       "                             <catboost.core.CatBoostRegressor object at 0x7cfb6688fca0>),\n",
       "                            ('xgb',\n",
       "                             XGBRegressor(base_score=None, booster=None,\n",
       "                                          callbacks=None,\n",
       "                                          colsample_bylevel=None,\n",
       "                                          colsample_bynode=None,\n",
       "                                          colsample_bytree=None, device=None,\n",
       "                                          early_stopping_rounds=None,\n",
       "                                          enable_categorical=False,\n",
       "                                          eval_metric=None, feature_types=None,\n",
       "                                          gamma=None, grow_policy=None,...\n",
       "                                          interaction_constraints=None,\n",
       "                                          learning_rate=None, max_bin=None,\n",
       "                                          max_cat_threshold=None,\n",
       "                                          max_cat_to_onehot=None,\n",
       "                                          max_delta_step=None, max_depth=None,\n",
       "                                          max_leaves=None,\n",
       "                                          min_child_weight=None, missing=nan,\n",
       "                                          monotone_constraints=None,\n",
       "                                          multi_strategy=None,\n",
       "                                          n_estimators=None, n_jobs=None,\n",
       "                                          num_parallel_tree=None,\n",
       "                                          random_state=1702, ...)),\n",
       "                            ('hgb',\n",
       "                             HistGradientBoostingRegressor(random_state=1702))])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0139fb25",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:32:55.957719Z",
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     "shell.execute_reply": "2024-07-29T13:32:55.965225Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission = test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d0f7f157",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:32:56.002066Z",
     "iopub.status.busy": "2024-07-29T13:32:56.000958Z",
     "iopub.status.idle": "2024-07-29T13:32:56.046521Z",
     "shell.execute_reply": "2024-07-29T13:32:56.045367Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "preds = voting.predict(submission)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5d339f74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:32:56.081972Z",
     "iopub.status.busy": "2024-07-29T13:32:56.081476Z",
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     "shell.execute_reply": "2024-07-29T13:32:56.087209Z"
    },
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     "start_time": "2024-07-29T13:32:56.064740",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission['orders'] = preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7d6c46c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:32:56.124257Z",
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     "status": "completed"
    },
    "tags": []
   },
   "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>orders</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Prague_1_2024-03-16</th>\n",
       "      <td>10465.090216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prague_1_2024-03-17</th>\n",
       "      <td>10156.058332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prague_1_2024-03-18</th>\n",
       "      <td>9905.335073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prague_1_2024-03-19</th>\n",
       "      <td>9642.656961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prague_1_2024-03-20</th>\n",
       "      <td>9664.049660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budapest_1_2024-05-11</th>\n",
       "      <td>7371.787144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budapest_1_2024-05-12</th>\n",
       "      <td>6846.783014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budapest_1_2024-05-13</th>\n",
       "      <td>7107.026902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budapest_1_2024-05-14</th>\n",
       "      <td>7154.501201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Budapest_1_2024-05-15</th>\n",
       "      <td>7059.823571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>397 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             orders\n",
       "id                                 \n",
       "Prague_1_2024-03-16    10465.090216\n",
       "Prague_1_2024-03-17    10156.058332\n",
       "Prague_1_2024-03-18     9905.335073\n",
       "Prague_1_2024-03-19     9642.656961\n",
       "Prague_1_2024-03-20     9664.049660\n",
       "...                             ...\n",
       "Budapest_1_2024-05-11   7371.787144\n",
       "Budapest_1_2024-05-12   6846.783014\n",
       "Budapest_1_2024-05-13   7107.026902\n",
       "Budapest_1_2024-05-14   7154.501201\n",
       "Budapest_1_2024-05-15   7059.823571\n",
       "\n",
       "[397 rows x 1 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub = pd.read_csv('/kaggle/input/rohlik-orders-forecasting-challenge/solution_example.csv', index_col='id')\n",
    "sub['orders'] = preds\n",
    "sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c66e7058",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T13:32:56.179727Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sub[['orders']].to_csv('submission.csv')"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
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