KMODE .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c77db1ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: kmodes in c:\\programdata\\anaconda3\\lib\\site-packages (0.11.1)\n",
      "Requirement already satisfied: scikit-learn>=0.22.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from kmodes) (0.24.2)\n",
      "Requirement already satisfied: scipy>=0.13.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from kmodes) (1.7.1)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from kmodes) (1.1.0)\n",
      "Requirement already satisfied: numpy>=1.10.4 in c:\\programdata\\anaconda3\\lib\\site-packages (from kmodes) (1.20.3)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn>=0.22.0->kmodes) (2.2.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install kmodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6be0767e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "96088ded",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "def data_loader():\n",
    "    train = pd.read_csv('train.csv')\n",
    "    test = pd.read_csv('test.csv')\n",
    "    sub = pd.read_csv('SampleSubmission.csv')\n",
    "    #policy = pd.read_csv('policies.csv')\n",
    "    #data_dict = pd.read_csv('DataDictionary.csv')\n",
    "    return train,test, sub,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9f27a0d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 144 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train,test, sub,  = data_loader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9beef36f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([train, test]).reset_index(drop=True)\n",
    "ntrain = train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e4fe1af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from kmodes.kmodes import KModes\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "802233b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>claim_id</th>\n",
       "      <th>policy_number</th>\n",
       "      <th>claim_number</th>\n",
       "      <th>sum_insured</th>\n",
       "      <th>product</th>\n",
       "      <th>agent</th>\n",
       "      <th>class_of_business</th>\n",
       "      <th>risk_type</th>\n",
       "      <th>loss_date</th>\n",
       "      <th>client_type</th>\n",
       "      <th>renewal_frequency</th>\n",
       "      <th>primary_cause</th>\n",
       "      <th>secondary_cause</th>\n",
       "      <th>branch</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_JZ7QWRHH57RO</td>\n",
       "      <td>pol000009680</td>\n",
       "      <td>claim0011359</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>prod00027</td>\n",
       "      <td>ag00068</td>\n",
       "      <td>cob00031</td>\n",
       "      <td>rt00006</td>\n",
       "      <td>2021-05-13</td>\n",
       "      <td>ct0003</td>\n",
       "      <td>rf0001</td>\n",
       "      <td>pc0007</td>\n",
       "      <td>sc00022</td>\n",
       "      <td>br00006</td>\n",
       "      <td>82150.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_KQ5TJMVLJ4EP</td>\n",
       "      <td>pol000006869</td>\n",
       "      <td>claim0005272</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>prod00005</td>\n",
       "      <td>ag00226</td>\n",
       "      <td>cob00010</td>\n",
       "      <td>rt00032</td>\n",
       "      <td>2018-05-13</td>\n",
       "      <td>ct0003</td>\n",
       "      <td>rf0001</td>\n",
       "      <td>pc0007</td>\n",
       "      <td>sc00023</td>\n",
       "      <td>br00002</td>\n",
       "      <td>3780.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_NYHI7WJGGIAE</td>\n",
       "      <td>pol000006135</td>\n",
       "      <td>claim0004147</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>prod00029</td>\n",
       "      <td>ag00037</td>\n",
       "      <td>cob00010</td>\n",
       "      <td>rt00026</td>\n",
       "      <td>2018-08-18</td>\n",
       "      <td>ct0003</td>\n",
       "      <td>rf0001</td>\n",
       "      <td>pc0007</td>\n",
       "      <td>sc00024</td>\n",
       "      <td>br00002</td>\n",
       "      <td>2196.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_ROMMDCXYUXN5</td>\n",
       "      <td>pol000008991</td>\n",
       "      <td>claim0009962</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>prod00005</td>\n",
       "      <td>ag00037</td>\n",
       "      <td>cob00009</td>\n",
       "      <td>rt00032</td>\n",
       "      <td>2018-12-20</td>\n",
       "      <td>ct0003</td>\n",
       "      <td>rf0001</td>\n",
       "      <td>pc0007</td>\n",
       "      <td>sc00021</td>\n",
       "      <td>br00001</td>\n",
       "      <td>105.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_2OTD9NX8L73D</td>\n",
       "      <td>pol000007768</td>\n",
       "      <td>claim0007346</td>\n",
       "      <td>26850.0</td>\n",
       "      <td>prod00015</td>\n",
       "      <td>ag00226</td>\n",
       "      <td>cob00034</td>\n",
       "      <td>rt00023</td>\n",
       "      <td>2018-11-07</td>\n",
       "      <td>ct0001</td>\n",
       "      <td>rf0001</td>\n",
       "      <td>pc0007</td>\n",
       "      <td>sc00021</td>\n",
       "      <td>br00001</td>\n",
       "      <td>1605.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          claim_id policy_number  claim_number  sum_insured    product  \\\n",
       "0  ID_JZ7QWRHH57RO  pol000009680  claim0011359     300000.0  prod00027   \n",
       "1  ID_KQ5TJMVLJ4EP  pol000006869  claim0005272      16000.0  prod00005   \n",
       "2  ID_NYHI7WJGGIAE  pol000006135  claim0004147       3000.0  prod00029   \n",
       "3  ID_ROMMDCXYUXN5  pol000008991  claim0009962       6000.0  prod00005   \n",
       "4  ID_2OTD9NX8L73D  pol000007768  claim0007346      26850.0  prod00015   \n",
       "\n",
       "     agent class_of_business risk_type   loss_date client_type  \\\n",
       "0  ag00068          cob00031   rt00006  2021-05-13      ct0003   \n",
       "1  ag00226          cob00010   rt00032  2018-05-13      ct0003   \n",
       "2  ag00037          cob00010   rt00026  2018-08-18      ct0003   \n",
       "3  ag00037          cob00009   rt00032  2018-12-20      ct0003   \n",
       "4  ag00226          cob00034   rt00023  2018-11-07      ct0001   \n",
       "\n",
       "  renewal_frequency primary_cause secondary_cause   branch    target  \n",
       "0            rf0001        pc0007         sc00022  br00006  82150.00  \n",
       "1            rf0001        pc0007         sc00023  br00002   3780.24  \n",
       "2            rf0001        pc0007         sc00024  br00002   2196.50  \n",
       "3            rf0001        pc0007         sc00021  br00001    105.67  \n",
       "4            rf0001        pc0007         sc00021  br00001   1605.64  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "23df614f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\AppData\\Local\\Temp/ipykernel_3756/3915202171.py:24: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n",
      "  df[attr] = getattr(df['loss_date'].dt, attr)\n"
     ]
    }
   ],
   "source": [
    "df['risk_business'] = df['class_of_business'] + '_' + df['risk_type']\n",
    "df['product_agent'] = df['agent'] + '_' + df['product']\n",
    "df['renwal_business'] = df['renewal_frequency'] + '_' + df['class_of_business']\n",
    "df['product_class'] = df['class_of_business'] + '_' + df['product']\n",
    "\n",
    "#New Features\n",
    "df['claim_policy'] = df['claim_number'] + '_' + df['policy_number']\n",
    "df['business_agent'] = df['class_of_business'] + '_' + df['agent']\n",
    "df['primary_risk'] = df['primary_cause'] + '_' + df['risk_type']\n",
    "df['secondary_risk'] = df['secondary_cause'] + '_' + df['risk_type']\n",
    "\n",
    "df['year'] = [2022 - int(i.split('-')[0])  for i in df.loss_date]\n",
    "df['cause'] = df['secondary_cause'] + '_' + df['primary_cause']\n",
    "df['product_class'] = df['class_of_business'] + '_' + df['product']\n",
    "\n",
    "id_claim = [i.split('m')[-1] for i in df['claim_number']]\n",
    "df['id_claim'] = id_claim\n",
    "df['idclaim_freq'] = df['id_claim'].map(df['id_claim'].value_counts())\n",
    "df = df.drop('id_claim', axis = 1)\n",
    "\n",
    "df['loss_date'] = pd.to_datetime(df['loss_date'], format='%Y-%m-%d')\n",
    "\n",
    "for attr in ['day', 'month', 'week', 'dayofweek', 'weekofyear', 'days_in_month', 'is_month_start', 'is_month_end', 'dayofyear']:\n",
    "    df[attr] = getattr(df['loss_date'].dt, attr)\n",
    "df['is_weekend'] = (df['dayofweek'] >= 5)*1\n",
    "df['fortnight'] = df['day']%15\n",
    "df['which_fortnight'] = df['day']//15\n",
    "\n",
    "df['branch_product'] = pd.Series(df.branch + '_' + df['product'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4140ddbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run 1, iteration: 1/100, moves: 2690, cost: 18350.0\n",
      "Run 1, iteration: 2/100, moves: 15, cost: 18350.0\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run 2, iteration: 1/100, moves: 71, cost: 18350.0\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run 3, iteration: 1/100, moves: 0, cost: 19532.0\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run 4, iteration: 1/100, moves: 1, cost: 22422.0\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run 5, iteration: 1/100, moves: 2255, cost: 19500.0\n",
      "Run 5, iteration: 2/100, moves: 916, cost: 19500.0\n",
      "Best run was number 1\n"
     ]
    }
   ],
   "source": [
    "data = df[['risk_type', 'product_class', 'cause']]\n",
    "\n",
    "kmode = KModes(n_clusters=3, init = \"random\", n_init = 5, verbose=1)\n",
    "clusters = kmode.fit_predict(data)\n",
    "#clusters\n",
    "\n",
    "data.insert(0, \"Cluster\", clusters, True)\n",
    "\n",
    "\n",
    "pd.DataFrame(data)['Cluster'].value_counts()\n",
    "\n",
    "df[data.columns.to_list()] = data\n",
    "\n",
    "df = df.drop(['risk_type', 'product_class', 'cause'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db85e3e3",
   "metadata": {},
   "source": [
    "df = df[['sum_insured', 'product', 'class_of_business', 'risk_type', 'client_type', \n",
    "    'renewal_frequency', 'primary_cause', 'secondary_cause', 'branch','renwal_business', 'product_class', \n",
    "         'cause', 'year', 'idclaim_freq','Cluster',\n",
    "    'day', 'month', 'week', 'dayofweek', 'weekofyear', 'days_in_month', \n",
    "    'fortnight', 'which_fortnight', 'branch_product']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "76a55590",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('target', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "eacaf345",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['sum_insured', 'product', 'class_of_business', 'client_type', \n",
    "    'renewal_frequency', 'primary_cause', 'secondary_cause', 'branch','renwal_business',\n",
    "         'year', 'idclaim_freq','Cluster',\n",
    "    'day', 'month', 'week', 'dayofweek', 'weekofyear', 'days_in_month', \n",
    "    'fortnight', 'which_fortnight', 'branch_product','secondary_risk', 'primary_risk', 'claim_policy', 'business_agent']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "485e9779",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder,OrdinalEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "61917ec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [feat for feat in list(df) if feat != 'target' ]\n",
    "cat_feat = np.where(df[features].dtypes != float)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b6a9d1cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "to_encode = [val for index, val in enumerate(features) if index in cat_feat]\n",
    "df[to_encode] = df[to_encode].apply(LabelEncoder().fit_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "cd620a13",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_test = df[train.shape[0]:].reset_index(drop = True)\n",
    "new_train = df[:train.shape[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "554f8b1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn import metrics\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "52184299",
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost import CatBoostRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "\n",
    "#lgbm_model = CatBoostRegressor(iterations=3000, random_state = 2000)\n",
    "\n",
    "\n",
    "fold = model_selection.KFold(n_splits=10)\n",
    "\n",
    "def metric(y_val, pred):\n",
    "    return metrics.mean_absolute_error(y_val, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "9f1cc05c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm_model = LGBMRegressor(n_estimators=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "38172f07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "def modelling_tree(algorithm,yy,X, test_df):\n",
    "    cv_score=[]\n",
    "    test_pred=[]\n",
    "    train_score=[]\n",
    "    xx = []\n",
    "    for train_index, test_index in fold.split(X,pd.Series(yy)):\n",
    "        \n",
    "        X_trainx, X_val = X.iloc[train_index], X.iloc[test_index]\n",
    "        y_trainx, y_val = pd.Series(yy).iloc[train_index], pd.Series(yy).iloc[test_index]\n",
    "\n",
    "        algorithm.fit(X_trainx,y_trainx, eval_set=[(X_trainx,y_trainx),(X_val, y_val)])\n",
    "        \n",
    "        train_preds=algorithm.predict(X_trainx)\n",
    "        \n",
    "        train_score.append(metric(y_trainx, train_preds))\n",
    "        \n",
    "        val_preds=algorithm.predict(X_val)\n",
    "        cv_score.append(metric(y_val, val_preds))\n",
    "\n",
    "        test_p = algorithm.predict(test_df)\n",
    "        test_pred.append(test_p)\n",
    "\n",
    "\n",
    "    print(f'Training RMSE Score {np.mean(train_score)}')\n",
    "    print(f'Validation RMSE Score {np.mean(cv_score)}')\n",
    "    \n",
    "    return test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "1fa7e14e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\ttraining's l2: 11.724\tvalid_1's l2: 12.5764\n",
      "[2]\ttraining's l2: 11.1935\tvalid_1's l2: 12.1789\n",
      "[3]\ttraining's l2: 10.748\tvalid_1's l2: 11.8671\n",
      "[4]\ttraining's l2: 10.4169\tvalid_1's l2: 11.6281\n",
      "[5]\ttraining's l2: 10.1035\tvalid_1's l2: 11.4189\n",
      "[6]\ttraining's l2: 9.83145\tvalid_1's l2: 11.2274\n",
      "[7]\ttraining's l2: 9.59811\tvalid_1's l2: 11.0946\n",
      "[8]\ttraining's l2: 9.40626\tvalid_1's l2: 10.9487\n",
      "[9]\ttraining's l2: 9.24723\tvalid_1's l2: 10.8675\n",
      "[10]\ttraining's l2: 9.09167\tvalid_1's l2: 10.8073\n",
      "[11]\ttraining's l2: 8.96512\tvalid_1's l2: 10.7358\n",
      "[12]\ttraining's l2: 8.85292\tvalid_1's l2: 10.695\n",
      "[13]\ttraining's l2: 8.75273\tvalid_1's l2: 10.6435\n",
      "[14]\ttraining's l2: 8.66083\tvalid_1's l2: 10.6088\n",
      "[15]\ttraining's l2: 8.56939\tvalid_1's l2: 10.5689\n",
      "[16]\ttraining's l2: 8.48569\tvalid_1's l2: 10.5408\n",
      "[17]\ttraining's l2: 8.40623\tvalid_1's l2: 10.5214\n",
      "[18]\ttraining's l2: 8.33397\tvalid_1's l2: 10.5067\n",
      "[19]\ttraining's l2: 8.27052\tvalid_1's l2: 10.4907\n",
      "[20]\ttraining's l2: 8.20056\tvalid_1's l2: 10.4909\n",
      "[21]\ttraining's l2: 8.13643\tvalid_1's l2: 10.488\n",
      "[22]\ttraining's l2: 8.07074\tvalid_1's l2: 10.4708\n",
      "[23]\ttraining's l2: 8.02159\tvalid_1's l2: 10.4404\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[229]\ttraining's l2: 4.37218\tvalid_1's l2: 10.3978\n",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[467]\ttraining's l2: 2.79204\tvalid_1's l2: 10.5531\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[693]\ttraining's l2: 1.9852\tvalid_1's l2: 10.7387\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[928]\ttraining's l2: 1.43484\tvalid_1's l2: 10.9155\n",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[131]\ttraining's l2: 5.59065\tvalid_1's l2: 8.19985\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[151]\ttraining's l2: 5.36673\tvalid_1's l2: 8.90631\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[397]\ttraining's l2: 3.23359\tvalid_1's l2: 9.16912\n",
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      "[730]\ttraining's l2: 1.94044\tvalid_1's l2: 9.63252\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[898]\ttraining's l2: 1.53949\tvalid_1's l2: 9.7921\n",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
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      "[70]\ttraining's l2: 6.61864\tvalid_1's l2: 8.6482\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[637]\ttraining's l2: 2.17087\tvalid_1's l2: 9.42038\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[153]\ttraining's l2: 5.3223\tvalid_1's l2: 8.91631\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[651]\ttraining's l2: 2.11775\tvalid_1's l2: 9.60101\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[156]\ttraining's l2: 5.19456\tvalid_1's l2: 9.01132\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[653]\ttraining's l2: 2.11373\tvalid_1's l2: 9.6872\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[224]\ttraining's l2: 4.49585\tvalid_1's l2: 8.73092\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[386]\ttraining's l2: 3.26381\tvalid_1's l2: 8.85655\n",
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      "[713]\ttraining's l2: 1.94927\tvalid_1's l2: 9.2226\n",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[76]\ttraining's l2: 6.51138\tvalid_1's l2: 9.14144\n",
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      "[246]\ttraining's l2: 4.24069\tvalid_1's l2: 9.34245\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[419]\ttraining's l2: 3.06162\tvalid_1's l2: 9.52295\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[635]\ttraining's l2: 2.19906\tvalid_1's l2: 9.67757\n",
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      "[832]\ttraining's l2: 1.66476\tvalid_1's l2: 9.90075\n",
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      "[1]\ttraining's l2: 11.7115\tvalid_1's l2: 12.5912\n",
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     ]
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    {
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     "output_type": "stream",
     "text": [
      "[232]\ttraining's l2: 4.32704\tvalid_1's l2: 9.41176\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[402]\ttraining's l2: 3.1387\tvalid_1's l2: 9.73395\n",
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      "[573]\ttraining's l2: 2.34971\tvalid_1's l2: 9.99625\n",
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      "[747]\ttraining's l2: 1.81011\tvalid_1's l2: 10.1714\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[909]\ttraining's l2: 1.42962\tvalid_1's l2: 10.2961\n",
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    {
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     "output_type": "stream",
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      "[74]\ttraining's l2: 6.54617\tvalid_1's l2: 9.4623\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[565]\ttraining's l2: 2.4019\tvalid_1's l2: 9.96384\n",
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     "output_type": "stream",
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      "[736]\ttraining's l2: 1.87566\tvalid_1's l2: 10.1398\n",
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      "[781]\ttraining's l2: 1.76325\tvalid_1's l2: 10.1846\n",
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      "[783]\ttraining's l2: 1.75807\tvalid_1's l2: 10.1832\n",
      "[784]\ttraining's l2: 1.75656\tvalid_1's l2: 10.1838\n",
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      "[788]\ttraining's l2: 1.74779\tvalid_1's l2: 10.1884\n",
      "[789]\ttraining's l2: 1.74581\tvalid_1's l2: 10.1882\n",
      "[790]\ttraining's l2: 1.74333\tvalid_1's l2: 10.1957\n",
      "[791]\ttraining's l2: 1.74093\tvalid_1's l2: 10.1987\n",
      "[792]\ttraining's l2: 1.73749\tvalid_1's l2: 10.1996\n",
      "[793]\ttraining's l2: 1.73453\tvalid_1's l2: 10.1974\n",
      "[794]\ttraining's l2: 1.733\tvalid_1's l2: 10.1982\n",
      "[795]\ttraining's l2: 1.73013\tvalid_1's l2: 10.1985\n",
      "[796]\ttraining's l2: 1.72746\tvalid_1's l2: 10.1963\n",
      "[797]\ttraining's l2: 1.72485\tvalid_1's l2: 10.2\n",
      "[798]\ttraining's l2: 1.72345\tvalid_1's l2: 10.2002\n",
      "[799]\ttraining's l2: 1.72228\tvalid_1's l2: 10.1986\n",
      "[800]\ttraining's l2: 1.71988\tvalid_1's l2: 10.1991\n",
      "[801]\ttraining's l2: 1.71685\tvalid_1's l2: 10.1982\n",
      "[802]\ttraining's l2: 1.71432\tvalid_1's l2: 10.2016\n",
      "[803]\ttraining's l2: 1.71253\tvalid_1's l2: 10.1989\n",
      "[804]\ttraining's l2: 1.70954\tvalid_1's l2: 10.1971\n",
      "[805]\ttraining's l2: 1.70743\tvalid_1's l2: 10.1958\n",
      "[806]\ttraining's l2: 1.70643\tvalid_1's l2: 10.1963\n",
      "[807]\ttraining's l2: 1.7035\tvalid_1's l2: 10.1994\n",
      "[808]\ttraining's l2: 1.70117\tvalid_1's l2: 10.1976\n",
      "[809]\ttraining's l2: 1.69869\tvalid_1's l2: 10.2002\n",
      "[810]\ttraining's l2: 1.6959\tvalid_1's l2: 10.202\n",
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      "[870]\ttraining's l2: 1.57559\tvalid_1's l2: 10.2716\n",
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      "[879]\ttraining's l2: 1.55739\tvalid_1's l2: 10.2778\n",
      "[880]\ttraining's l2: 1.55522\tvalid_1's l2: 10.2766\n",
      "[881]\ttraining's l2: 1.55184\tvalid_1's l2: 10.2754\n",
      "[882]\ttraining's l2: 1.54979\tvalid_1's l2: 10.2762\n",
      "[883]\ttraining's l2: 1.54869\tvalid_1's l2: 10.2774\n",
      "[884]\ttraining's l2: 1.54662\tvalid_1's l2: 10.2759\n",
      "[885]\ttraining's l2: 1.54528\tvalid_1's l2: 10.2781\n",
      "[886]\ttraining's l2: 1.54249\tvalid_1's l2: 10.2845\n",
      "[887]\ttraining's l2: 1.54007\tvalid_1's l2: 10.2834\n",
      "[888]\ttraining's l2: 1.53823\tvalid_1's l2: 10.2824\n",
      "[889]\ttraining's l2: 1.5351\tvalid_1's l2: 10.2841\n",
      "[890]\ttraining's l2: 1.53386\tvalid_1's l2: 10.2845\n",
      "[891]\ttraining's l2: 1.53075\tvalid_1's l2: 10.2839\n",
      "[892]\ttraining's l2: 1.52858\tvalid_1's l2: 10.2859\n",
      "[893]\ttraining's l2: 1.52685\tvalid_1's l2: 10.2895\n",
      "[894]\ttraining's l2: 1.5253\tvalid_1's l2: 10.2904\n",
      "[895]\ttraining's l2: 1.52152\tvalid_1's l2: 10.291\n",
      "[896]\ttraining's l2: 1.51878\tvalid_1's l2: 10.2891\n",
      "[897]\ttraining's l2: 1.51619\tvalid_1's l2: 10.2877\n",
      "[898]\ttraining's l2: 1.51448\tvalid_1's l2: 10.2892\n",
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      "[900]\ttraining's l2: 1.50953\tvalid_1's l2: 10.285\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[901]\ttraining's l2: 1.50818\tvalid_1's l2: 10.2846\n",
      "[902]\ttraining's l2: 1.50579\tvalid_1's l2: 10.2846\n",
      "[903]\ttraining's l2: 1.50366\tvalid_1's l2: 10.2865\n",
      "[904]\ttraining's l2: 1.50122\tvalid_1's l2: 10.2875\n",
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      "[906]\ttraining's l2: 1.49601\tvalid_1's l2: 10.2862\n",
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      "[911]\ttraining's l2: 1.4862\tvalid_1's l2: 10.2933\n",
      "[912]\ttraining's l2: 1.48446\tvalid_1's l2: 10.2918\n",
      "[913]\ttraining's l2: 1.48315\tvalid_1's l2: 10.2939\n",
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      "[915]\ttraining's l2: 1.47944\tvalid_1's l2: 10.2939\n",
      "[916]\ttraining's l2: 1.47819\tvalid_1's l2: 10.2977\n",
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      "[922]\ttraining's l2: 1.4645\tvalid_1's l2: 10.3057\n",
      "[923]\ttraining's l2: 1.46296\tvalid_1's l2: 10.304\n",
      "[924]\ttraining's l2: 1.46033\tvalid_1's l2: 10.3032\n",
      "[925]\ttraining's l2: 1.4584\tvalid_1's l2: 10.3009\n",
      "[926]\ttraining's l2: 1.45625\tvalid_1's l2: 10.3015\n",
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      "[937]\ttraining's l2: 1.43417\tvalid_1's l2: 10.3075\n",
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      "[940]\ttraining's l2: 1.42898\tvalid_1's l2: 10.3091\n",
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      "[942]\ttraining's l2: 1.42601\tvalid_1's l2: 10.3081\n",
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      "[945]\ttraining's l2: 1.42016\tvalid_1's l2: 10.317\n",
      "[946]\ttraining's l2: 1.41863\tvalid_1's l2: 10.3177\n",
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      "[948]\ttraining's l2: 1.4148\tvalid_1's l2: 10.3214\n",
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      "[987]\ttraining's l2: 1.35059\tvalid_1's l2: 10.3556\n",
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      "[993]\ttraining's l2: 1.34156\tvalid_1's l2: 10.3595\n",
      "[994]\ttraining's l2: 1.34001\tvalid_1's l2: 10.3612\n",
      "[995]\ttraining's l2: 1.33715\tvalid_1's l2: 10.358\n",
      "[996]\ttraining's l2: 1.33466\tvalid_1's l2: 10.3598\n",
      "[997]\ttraining's l2: 1.33355\tvalid_1's l2: 10.3559\n",
      "[998]\ttraining's l2: 1.33163\tvalid_1's l2: 10.3569\n",
      "[999]\ttraining's l2: 1.32961\tvalid_1's l2: 10.3581\n",
      "[1000]\ttraining's l2: 1.32785\tvalid_1's l2: 10.3579\n",
      "Training RMSE Score 0.7801077291047709\n",
      "Validation RMSE Score 2.218579152581809\n",
      "Wall time: 30.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "test_pred = modelling_tree(algorithm = lgbm_model, yy = np.log1p(train.target), X = new_train, test_df = new_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58caa2c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Shrink model to first 677 iterations.\n",
    "Training RMSE Score 1.7742831513487345\n",
    "Validation RMSE Score 2.0662827964790167\n",
    "Wall time: 2min 27s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea87a6f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Training RMSE Score 1.7708831781810859\n",
    "Validation RMSE Score 2.068340989095058\n",
    "Wall time: 1min 20s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7dd06a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_sub = np.mean(np.exp(test_pred) +1, axis = 0)\n",
    "sub['target'] = cat_sub\n",
    "sub['target'].describe()\n",
    "sub.to_csv('1st_model.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "487f5ace",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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