LGMB Log Transform Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "00fb976e",
   "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": 2,
   "id": "f80f5a62",
   "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": 3,
   "id": "bc238e4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 67.6 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train,test, sub,  = data_loader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d6263a85",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([train, test]).reset_index(drop=True)\n",
    "ntrain = train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c1a2ea8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ntrain = train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9b93a869",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\AppData\\Local\\Temp/ipykernel_21308/1164735024.py:26: 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",
    "\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",
    "\n",
    "df['year'] = [int(i.split('-')[0]) - 2018 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": 7,
   "id": "b1753c36",
   "metadata": {},
   "outputs": [],
   "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',\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": 8,
   "id": "2361da6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = df.drop(['claim_id', 'loss_date', 'target','client_type', 'risk_business'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fc320346",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "23c741c6",
   "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": 11,
   "id": "cb254bfb",
   "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": 12,
   "id": "a552fef3",
   "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": 13,
   "id": "86fff604",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn import metrics\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "322dd55c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightgbm import LGBMRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "34a6cfcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm_model = LGBMRegressor(n_estimators=80)\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": 26,
   "id": "4516c3a0",
   "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",
    "    \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",
    "        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": 27,
   "id": "5c38f5c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\ttraining's l2: 11.7254\tvalid_1's l2: 12.532\n",
      "[2]\ttraining's l2: 11.1921\tvalid_1's l2: 12.1159\n",
      "[3]\ttraining's l2: 10.7608\tvalid_1's l2: 11.8072\n",
      "[4]\ttraining's l2: 10.4074\tvalid_1's l2: 11.551\n",
      "[5]\ttraining's l2: 10.0966\tvalid_1's l2: 11.3137\n",
      "[6]\ttraining's l2: 9.82208\tvalid_1's l2: 11.1657\n",
      "[7]\ttraining's l2: 9.59446\tvalid_1's l2: 11.0516\n",
      "[8]\ttraining's l2: 9.40113\tvalid_1's l2: 10.9286\n",
      "[9]\ttraining's l2: 9.22866\tvalid_1's l2: 10.8371\n",
      "[10]\ttraining's l2: 9.09329\tvalid_1's l2: 10.7896\n",
      "[11]\ttraining's l2: 8.9592\tvalid_1's l2: 10.7096\n",
      "[12]\ttraining's l2: 8.85047\tvalid_1's l2: 10.6811\n",
      "[13]\ttraining's l2: 8.75\tvalid_1's l2: 10.6468\n",
      "[14]\ttraining's l2: 8.65128\tvalid_1's l2: 10.623\n",
      "[15]\ttraining's l2: 8.56387\tvalid_1's l2: 10.5961\n",
      "[16]\ttraining's l2: 8.48134\tvalid_1's l2: 10.5656\n",
      "[17]\ttraining's l2: 8.40157\tvalid_1's l2: 10.5562\n",
      "[18]\ttraining's l2: 8.32319\tvalid_1's l2: 10.5367\n",
      "[19]\ttraining's l2: 8.25218\tvalid_1's l2: 10.4936\n",
      "[20]\ttraining's l2: 8.19416\tvalid_1's l2: 10.4714\n",
      "[21]\ttraining's l2: 8.1328\tvalid_1's l2: 10.4459\n",
      "[22]\ttraining's l2: 8.06613\tvalid_1's l2: 10.4547\n",
      "[23]\ttraining's l2: 8.01045\tvalid_1's l2: 10.4436\n",
      "[24]\ttraining's l2: 7.95547\tvalid_1's l2: 10.4323\n",
      "[25]\ttraining's l2: 7.90372\tvalid_1's l2: 10.4204\n",
      "[26]\ttraining's l2: 7.84939\tvalid_1's l2: 10.426\n",
      "[27]\ttraining's l2: 7.7997\tvalid_1's l2: 10.4198\n",
      "[28]\ttraining's l2: 7.7629\tvalid_1's l2: 10.4147\n",
      "[29]\ttraining's l2: 7.72316\tvalid_1's l2: 10.3973\n",
      "[30]\ttraining's l2: 7.67222\tvalid_1's l2: 10.3832\n",
      "[31]\ttraining's l2: 7.62776\tvalid_1's l2: 10.3833\n",
      "[32]\ttraining's l2: 7.58231\tvalid_1's l2: 10.3632\n",
      "[33]\ttraining's l2: 7.54375\tvalid_1's l2: 10.367\n",
      "[34]\ttraining's l2: 7.51147\tvalid_1's l2: 10.3508\n",
      "[35]\ttraining's l2: 7.46812\tvalid_1's l2: 10.3278\n",
      "[36]\ttraining's l2: 7.4273\tvalid_1's l2: 10.34\n",
      "[37]\ttraining's l2: 7.38244\tvalid_1's l2: 10.3548\n",
      "[38]\ttraining's l2: 7.34687\tvalid_1's l2: 10.3479\n",
      "[39]\ttraining's l2: 7.30733\tvalid_1's l2: 10.3269\n",
      "[40]\ttraining's l2: 7.27765\tvalid_1's l2: 10.3304\n",
      "[41]\ttraining's l2: 7.24707\tvalid_1's l2: 10.333\n",
      "[42]\ttraining's l2: 7.21607\tvalid_1's l2: 10.3257\n",
      "[43]\ttraining's l2: 7.18383\tvalid_1's l2: 10.3325\n",
      "[44]\ttraining's l2: 7.15516\tvalid_1's l2: 10.3332\n",
      "[45]\ttraining's l2: 7.13095\tvalid_1's l2: 10.3292\n",
      "[46]\ttraining's l2: 7.10157\tvalid_1's l2: 10.3256\n",
      "[47]\ttraining's l2: 7.07728\tvalid_1's l2: 10.3271\n",
      "[48]\ttraining's l2: 7.05072\tvalid_1's l2: 10.3218\n",
      "[49]\ttraining's l2: 7.02368\tvalid_1's l2: 10.3101\n",
      "[50]\ttraining's l2: 6.99762\tvalid_1's l2: 10.3061\n",
      "[51]\ttraining's l2: 6.97601\tvalid_1's l2: 10.3012\n",
      "[52]\ttraining's l2: 6.94844\tvalid_1's l2: 10.3041\n",
      "[53]\ttraining's l2: 6.92836\tvalid_1's l2: 10.2915\n",
      "[54]\ttraining's l2: 6.90253\tvalid_1's l2: 10.299\n",
      "[55]\ttraining's l2: 6.87899\tvalid_1's l2: 10.2971\n",
      "[56]\ttraining's l2: 6.85519\tvalid_1's l2: 10.2942\n",
      "[57]\ttraining's l2: 6.82307\tvalid_1's l2: 10.3146\n",
      "[58]\ttraining's l2: 6.80021\tvalid_1's l2: 10.3317\n",
      "[59]\ttraining's l2: 6.77714\tvalid_1's l2: 10.3253\n",
      "[60]\ttraining's l2: 6.75855\tvalid_1's l2: 10.315\n",
      "[61]\ttraining's l2: 6.73368\tvalid_1's l2: 10.3179\n",
      "[62]\ttraining's l2: 6.70951\tvalid_1's l2: 10.3115\n",
      "[63]\ttraining's l2: 6.68741\tvalid_1's l2: 10.3215\n",
      "[64]\ttraining's l2: 6.66398\tvalid_1's l2: 10.3146\n",
      "[65]\ttraining's l2: 6.6445\tvalid_1's l2: 10.3153\n",
      "[66]\ttraining's l2: 6.62362\tvalid_1's l2: 10.3046\n",
      "[67]\ttraining's l2: 6.60256\tvalid_1's l2: 10.3099\n",
      "[68]\ttraining's l2: 6.57659\tvalid_1's l2: 10.3044\n",
      "[69]\ttraining's l2: 6.55694\tvalid_1's l2: 10.2932\n",
      "[70]\ttraining's l2: 6.53998\tvalid_1's l2: 10.3057\n",
      "[71]\ttraining's l2: 6.52411\tvalid_1's l2: 10.3171\n",
      "[72]\ttraining's l2: 6.50563\tvalid_1's l2: 10.3037\n",
      "[73]\ttraining's l2: 6.4845\tvalid_1's l2: 10.3154\n",
      "[74]\ttraining's l2: 6.46793\tvalid_1's l2: 10.3066\n",
      "[75]\ttraining's l2: 6.44599\tvalid_1's l2: 10.3003\n",
      "[76]\ttraining's l2: 6.42836\tvalid_1's l2: 10.3043\n",
      "[77]\ttraining's l2: 6.41321\tvalid_1's l2: 10.2998\n",
      "[78]\ttraining's l2: 6.39929\tvalid_1's l2: 10.3158\n",
      "[79]\ttraining's l2: 6.38512\tvalid_1's l2: 10.3203\n",
      "[80]\ttraining's l2: 6.36383\tvalid_1's l2: 10.3114\n",
      "[1]\ttraining's l2: 11.8617\tvalid_1's l2: 11.4167\n",
      "[2]\ttraining's l2: 11.355\tvalid_1's l2: 10.9372\n",
      "[3]\ttraining's l2: 10.9633\tvalid_1's l2: 10.5578\n",
      "[4]\ttraining's l2: 10.5963\tvalid_1's l2: 10.1892\n",
      "[5]\ttraining's l2: 10.2966\tvalid_1's l2: 9.90857\n",
      "[6]\ttraining's l2: 10.0434\tvalid_1's l2: 9.69635\n",
      "[7]\ttraining's l2: 9.82154\tvalid_1's l2: 9.51203\n",
      "[8]\ttraining's l2: 9.63682\tvalid_1's l2: 9.32538\n",
      "[9]\ttraining's l2: 9.47265\tvalid_1's l2: 9.16302\n",
      "[10]\ttraining's l2: 9.33095\tvalid_1's l2: 9.00548\n",
      "[11]\ttraining's l2: 9.20213\tvalid_1's l2: 8.92133\n",
      "[12]\ttraining's l2: 9.08705\tvalid_1's l2: 8.87432\n",
      "[13]\ttraining's l2: 8.98775\tvalid_1's l2: 8.78051\n",
      "[14]\ttraining's l2: 8.89327\tvalid_1's l2: 8.72592\n",
      "[15]\ttraining's l2: 8.7959\tvalid_1's l2: 8.68011\n",
      "[16]\ttraining's l2: 8.70368\tvalid_1's l2: 8.6359\n",
      "[17]\ttraining's l2: 8.62091\tvalid_1's l2: 8.56327\n",
      "[18]\ttraining's l2: 8.5437\tvalid_1's l2: 8.52537\n",
      "[19]\ttraining's l2: 8.47349\tvalid_1's l2: 8.48399\n",
      "[20]\ttraining's l2: 8.403\tvalid_1's l2: 8.4413\n",
      "[21]\ttraining's l2: 8.34106\tvalid_1's l2: 8.41764\n",
      "[22]\ttraining's l2: 8.28088\tvalid_1's l2: 8.39505\n",
      "[23]\ttraining's l2: 8.21869\tvalid_1's l2: 8.36926\n",
      "[24]\ttraining's l2: 8.16225\tvalid_1's l2: 8.3371\n",
      "[25]\ttraining's l2: 8.10521\tvalid_1's l2: 8.29649\n",
      "[26]\ttraining's l2: 8.05804\tvalid_1's l2: 8.30048\n",
      "[27]\ttraining's l2: 8.0155\tvalid_1's l2: 8.28418\n",
      "[28]\ttraining's l2: 7.96487\tvalid_1's l2: 8.25942\n",
      "[29]\ttraining's l2: 7.92035\tvalid_1's l2: 8.24657\n",
      "[30]\ttraining's l2: 7.87784\tvalid_1's l2: 8.22556\n",
      "[31]\ttraining's l2: 7.83351\tvalid_1's l2: 8.21081\n",
      "[32]\ttraining's l2: 7.78686\tvalid_1's l2: 8.2249\n",
      "[33]\ttraining's l2: 7.73895\tvalid_1's l2: 8.22906\n",
      "[34]\ttraining's l2: 7.69809\tvalid_1's l2: 8.23102\n",
      "[35]\ttraining's l2: 7.65653\tvalid_1's l2: 8.23421\n",
      "[36]\ttraining's l2: 7.62154\tvalid_1's l2: 8.24173\n",
      "[37]\ttraining's l2: 7.58724\tvalid_1's l2: 8.24401\n",
      "[38]\ttraining's l2: 7.55609\tvalid_1's l2: 8.2429\n",
      "[39]\ttraining's l2: 7.5272\tvalid_1's l2: 8.23433\n",
      "[40]\ttraining's l2: 7.49489\tvalid_1's l2: 8.24089\n",
      "[41]\ttraining's l2: 7.4537\tvalid_1's l2: 8.23091\n",
      "[42]\ttraining's l2: 7.41537\tvalid_1's l2: 8.21079\n",
      "[43]\ttraining's l2: 7.38613\tvalid_1's l2: 8.21265\n",
      "[44]\ttraining's l2: 7.36034\tvalid_1's l2: 8.20739\n",
      "[45]\ttraining's l2: 7.3331\tvalid_1's l2: 8.21239\n",
      "[46]\ttraining's l2: 7.2963\tvalid_1's l2: 8.19595\n",
      "[47]\ttraining's l2: 7.26553\tvalid_1's l2: 8.18284\n",
      "[48]\ttraining's l2: 7.23763\tvalid_1's l2: 8.17923\n",
      "[49]\ttraining's l2: 7.20713\tvalid_1's l2: 8.15069\n",
      "[50]\ttraining's l2: 7.18199\tvalid_1's l2: 8.14201\n",
      "[51]\ttraining's l2: 7.1596\tvalid_1's l2: 8.15463\n",
      "[52]\ttraining's l2: 7.13003\tvalid_1's l2: 8.16594\n",
      "[53]\ttraining's l2: 7.10406\tvalid_1's l2: 8.17035\n",
      "[54]\ttraining's l2: 7.07776\tvalid_1's l2: 8.16293\n",
      "[55]\ttraining's l2: 7.05419\tvalid_1's l2: 8.15783\n",
      "[56]\ttraining's l2: 7.03146\tvalid_1's l2: 8.16344\n",
      "[57]\ttraining's l2: 7.01108\tvalid_1's l2: 8.15742\n",
      "[58]\ttraining's l2: 6.99479\tvalid_1's l2: 8.16487\n",
      "[59]\ttraining's l2: 6.97363\tvalid_1's l2: 8.1656\n",
      "[60]\ttraining's l2: 6.95584\tvalid_1's l2: 8.17813\n",
      "[61]\ttraining's l2: 6.93156\tvalid_1's l2: 8.16922\n",
      "[62]\ttraining's l2: 6.91455\tvalid_1's l2: 8.15847\n",
      "[63]\ttraining's l2: 6.89226\tvalid_1's l2: 8.15451\n",
      "[64]\ttraining's l2: 6.86974\tvalid_1's l2: 8.1583\n",
      "[65]\ttraining's l2: 6.85406\tvalid_1's l2: 8.15432\n",
      "[66]\ttraining's l2: 6.82732\tvalid_1's l2: 8.15376\n",
      "[67]\ttraining's l2: 6.80802\tvalid_1's l2: 8.14773\n",
      "[68]\ttraining's l2: 6.78142\tvalid_1's l2: 8.1472\n",
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      "[7]\ttraining's l2: 9.79018\tvalid_1's l2: 9.72623\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "[61]\ttraining's l2: 6.90999\tvalid_1's l2: 8.71136\n",
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      "[1]\ttraining's l2: 11.8615\tvalid_1's l2: 11.2372\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\ttraining's l2: 11.8268\tvalid_1's l2: 11.6443\n",
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      "[10]\ttraining's l2: 9.17426\tvalid_1's l2: 10.3135\n",
      "[11]\ttraining's l2: 9.05205\tvalid_1's l2: 10.2008\n",
      "[12]\ttraining's l2: 8.92425\tvalid_1's l2: 10.1171\n",
      "[13]\ttraining's l2: 8.81552\tvalid_1's l2: 10.0506\n",
      "[14]\ttraining's l2: 8.72058\tvalid_1's l2: 9.98481\n",
      "[15]\ttraining's l2: 8.6289\tvalid_1's l2: 9.89938\n",
      "[16]\ttraining's l2: 8.53488\tvalid_1's l2: 9.87937\n",
      "[17]\ttraining's l2: 8.45525\tvalid_1's l2: 9.81957\n",
      "[18]\ttraining's l2: 8.37632\tvalid_1's l2: 9.75422\n",
      "[19]\ttraining's l2: 8.30795\tvalid_1's l2: 9.72355\n",
      "[20]\ttraining's l2: 8.24508\tvalid_1's l2: 9.69558\n",
      "[21]\ttraining's l2: 8.18302\tvalid_1's l2: 9.68444\n",
      "[22]\ttraining's l2: 8.12777\tvalid_1's l2: 9.66029\n",
      "[23]\ttraining's l2: 8.07445\tvalid_1's l2: 9.6511\n",
      "[24]\ttraining's l2: 8.01534\tvalid_1's l2: 9.58407\n",
      "[25]\ttraining's l2: 7.95951\tvalid_1's l2: 9.57607\n",
      "[26]\ttraining's l2: 7.90968\tvalid_1's l2: 9.56217\n",
      "[27]\ttraining's l2: 7.86228\tvalid_1's l2: 9.52111\n",
      "[28]\ttraining's l2: 7.81194\tvalid_1's l2: 9.50254\n",
      "[29]\ttraining's l2: 7.76337\tvalid_1's l2: 9.48927\n",
      "[30]\ttraining's l2: 7.71639\tvalid_1's l2: 9.46858\n",
      "[31]\ttraining's l2: 7.67752\tvalid_1's l2: 9.4501\n",
      "[32]\ttraining's l2: 7.63223\tvalid_1's l2: 9.42619\n",
      "[33]\ttraining's l2: 7.59514\tvalid_1's l2: 9.41786\n",
      "[34]\ttraining's l2: 7.56\tvalid_1's l2: 9.38796\n",
      "[35]\ttraining's l2: 7.52146\tvalid_1's l2: 9.39191\n",
      "[36]\ttraining's l2: 7.48192\tvalid_1's l2: 9.36477\n",
      "[37]\ttraining's l2: 7.44688\tvalid_1's l2: 9.34239\n",
      "[38]\ttraining's l2: 7.4193\tvalid_1's l2: 9.33859\n",
      "[39]\ttraining's l2: 7.39208\tvalid_1's l2: 9.33403\n",
      "[40]\ttraining's l2: 7.3628\tvalid_1's l2: 9.33394\n",
      "[41]\ttraining's l2: 7.32559\tvalid_1's l2: 9.33304\n",
      "[42]\ttraining's l2: 7.29737\tvalid_1's l2: 9.34281\n",
      "[43]\ttraining's l2: 7.26607\tvalid_1's l2: 9.3342\n",
      "[44]\ttraining's l2: 7.24236\tvalid_1's l2: 9.32356\n",
      "[45]\ttraining's l2: 7.21317\tvalid_1's l2: 9.31908\n",
      "[46]\ttraining's l2: 7.18316\tvalid_1's l2: 9.30722\n",
      "[47]\ttraining's l2: 7.15971\tvalid_1's l2: 9.30324\n",
      "[48]\ttraining's l2: 7.13472\tvalid_1's l2: 9.29361\n",
      "[49]\ttraining's l2: 7.11088\tvalid_1's l2: 9.30621\n",
      "[50]\ttraining's l2: 7.08805\tvalid_1's l2: 9.30937\n",
      "[51]\ttraining's l2: 7.06491\tvalid_1's l2: 9.30473\n",
      "[52]\ttraining's l2: 7.038\tvalid_1's l2: 9.28782\n",
      "[53]\ttraining's l2: 7.00699\tvalid_1's l2: 9.27669\n",
      "[54]\ttraining's l2: 6.97181\tvalid_1's l2: 9.26627\n",
      "[55]\ttraining's l2: 6.94818\tvalid_1's l2: 9.26649\n",
      "[56]\ttraining's l2: 6.92589\tvalid_1's l2: 9.26566\n",
      "[57]\ttraining's l2: 6.90601\tvalid_1's l2: 9.26314\n",
      "[58]\ttraining's l2: 6.87874\tvalid_1's l2: 9.25896\n",
      "[59]\ttraining's l2: 6.85208\tvalid_1's l2: 9.23058\n",
      "[60]\ttraining's l2: 6.83218\tvalid_1's l2: 9.24124\n",
      "[61]\ttraining's l2: 6.80794\tvalid_1's l2: 9.23502\n",
      "[62]\ttraining's l2: 6.77135\tvalid_1's l2: 9.23583\n",
      "[63]\ttraining's l2: 6.7531\tvalid_1's l2: 9.23198\n",
      "[64]\ttraining's l2: 6.729\tvalid_1's l2: 9.24302\n",
      "[65]\ttraining's l2: 6.70656\tvalid_1's l2: 9.22471\n",
      "[66]\ttraining's l2: 6.68604\tvalid_1's l2: 9.22365\n",
      "[67]\ttraining's l2: 6.66338\tvalid_1's l2: 9.21613\n",
      "[68]\ttraining's l2: 6.64402\tvalid_1's l2: 9.21646\n",
      "[69]\ttraining's l2: 6.62077\tvalid_1's l2: 9.20545\n",
      "[70]\ttraining's l2: 6.59671\tvalid_1's l2: 9.20567\n",
      "[71]\ttraining's l2: 6.58144\tvalid_1's l2: 9.21088\n",
      "[72]\ttraining's l2: 6.56682\tvalid_1's l2: 9.20443\n",
      "[73]\ttraining's l2: 6.54122\tvalid_1's l2: 9.22234\n",
      "[74]\ttraining's l2: 6.52434\tvalid_1's l2: 9.21073\n",
      "[75]\ttraining's l2: 6.50532\tvalid_1's l2: 9.20045\n",
      "[76]\ttraining's l2: 6.48257\tvalid_1's l2: 9.20189\n",
      "[77]\ttraining's l2: 6.46405\tvalid_1's l2: 9.20517\n",
      "[78]\ttraining's l2: 6.44913\tvalid_1's l2: 9.21453\n",
      "[79]\ttraining's l2: 6.42849\tvalid_1's l2: 9.21379\n",
      "[80]\ttraining's l2: 6.40855\tvalid_1's l2: 9.20762\n",
      "[1]\ttraining's l2: 11.7738\tvalid_1's l2: 12.0175\n",
      "[2]\ttraining's l2: 11.2733\tvalid_1's l2: 11.6109\n",
      "[3]\ttraining's l2: 10.8446\tvalid_1's l2: 11.2856\n",
      "[4]\ttraining's l2: 10.5006\tvalid_1's l2: 11.0102\n",
      "[5]\ttraining's l2: 10.1962\tvalid_1's l2: 10.7832\n",
      "[6]\ttraining's l2: 9.91936\tvalid_1's l2: 10.5733\n",
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      "[8]\ttraining's l2: 9.5084\tvalid_1's l2: 10.3352\n",
      "[9]\ttraining's l2: 9.32635\tvalid_1's l2: 10.2136\n",
      "[10]\ttraining's l2: 9.1744\tvalid_1's l2: 10.113\n",
      "[11]\ttraining's l2: 9.04851\tvalid_1's l2: 10.0379\n",
      "[12]\ttraining's l2: 8.93232\tvalid_1's l2: 9.98776\n",
      "[13]\ttraining's l2: 8.83041\tvalid_1's l2: 9.9085\n",
      "[14]\ttraining's l2: 8.73229\tvalid_1's l2: 9.85505\n",
      "[15]\ttraining's l2: 8.63678\tvalid_1's l2: 9.83342\n",
      "[16]\ttraining's l2: 8.54727\tvalid_1's l2: 9.80073\n",
      "[17]\ttraining's l2: 8.47016\tvalid_1's l2: 9.78563\n",
      "[18]\ttraining's l2: 8.39402\tvalid_1's l2: 9.76173\n",
      "[19]\ttraining's l2: 8.32122\tvalid_1's l2: 9.72255\n",
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      "[21]\ttraining's l2: 8.19111\tvalid_1's l2: 9.71173\n",
      "[22]\ttraining's l2: 8.12839\tvalid_1's l2: 9.69708\n",
      "[23]\ttraining's l2: 8.07347\tvalid_1's l2: 9.67455\n",
      "[24]\ttraining's l2: 8.01716\tvalid_1's l2: 9.67086\n",
      "[25]\ttraining's l2: 7.95911\tvalid_1's l2: 9.66255\n",
      "[26]\ttraining's l2: 7.9124\tvalid_1's l2: 9.66621\n",
      "[27]\ttraining's l2: 7.85965\tvalid_1's l2: 9.65652\n",
      "[28]\ttraining's l2: 7.81168\tvalid_1's l2: 9.64555\n",
      "[29]\ttraining's l2: 7.75949\tvalid_1's l2: 9.62381\n",
      "[30]\ttraining's l2: 7.71615\tvalid_1's l2: 9.6099\n",
      "[31]\ttraining's l2: 7.67019\tvalid_1's l2: 9.577\n",
      "[32]\ttraining's l2: 7.62407\tvalid_1's l2: 9.56885\n",
      "[33]\ttraining's l2: 7.58436\tvalid_1's l2: 9.55949\n",
      "[34]\ttraining's l2: 7.54148\tvalid_1's l2: 9.55417\n",
      "[35]\ttraining's l2: 7.50474\tvalid_1's l2: 9.5424\n",
      "[36]\ttraining's l2: 7.47122\tvalid_1's l2: 9.54418\n",
      "[37]\ttraining's l2: 7.44149\tvalid_1's l2: 9.52807\n",
      "[38]\ttraining's l2: 7.40356\tvalid_1's l2: 9.51952\n",
      "[39]\ttraining's l2: 7.37266\tvalid_1's l2: 9.51556\n",
      "[40]\ttraining's l2: 7.34105\tvalid_1's l2: 9.49334\n",
      "[41]\ttraining's l2: 7.30987\tvalid_1's l2: 9.49104\n",
      "[42]\ttraining's l2: 7.27894\tvalid_1's l2: 9.48094\n",
      "[43]\ttraining's l2: 7.25222\tvalid_1's l2: 9.47177\n",
      "[44]\ttraining's l2: 7.22173\tvalid_1's l2: 9.47669\n",
      "[45]\ttraining's l2: 7.19645\tvalid_1's l2: 9.48019\n",
      "[46]\ttraining's l2: 7.17012\tvalid_1's l2: 9.48273\n",
      "[47]\ttraining's l2: 7.14332\tvalid_1's l2: 9.48464\n",
      "[48]\ttraining's l2: 7.11832\tvalid_1's l2: 9.47896\n",
      "[49]\ttraining's l2: 7.09705\tvalid_1's l2: 9.47581\n",
      "[50]\ttraining's l2: 7.07642\tvalid_1's l2: 9.48494\n",
      "[51]\ttraining's l2: 7.05017\tvalid_1's l2: 9.48876\n",
      "[52]\ttraining's l2: 7.02984\tvalid_1's l2: 9.48486\n",
      "[53]\ttraining's l2: 7.00626\tvalid_1's l2: 9.48946\n",
      "[54]\ttraining's l2: 6.98193\tvalid_1's l2: 9.48162\n",
      "[55]\ttraining's l2: 6.94603\tvalid_1's l2: 9.47377\n",
      "[56]\ttraining's l2: 6.9231\tvalid_1's l2: 9.47359\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[57]\ttraining's l2: 6.89898\tvalid_1's l2: 9.46634\n",
      "[58]\ttraining's l2: 6.87479\tvalid_1's l2: 9.46777\n",
      "[59]\ttraining's l2: 6.84242\tvalid_1's l2: 9.46742\n",
      "[60]\ttraining's l2: 6.8212\tvalid_1's l2: 9.46057\n",
      "[61]\ttraining's l2: 6.79198\tvalid_1's l2: 9.46542\n",
      "[62]\ttraining's l2: 6.77494\tvalid_1's l2: 9.46597\n",
      "[63]\ttraining's l2: 6.75493\tvalid_1's l2: 9.46653\n",
      "[64]\ttraining's l2: 6.73\tvalid_1's l2: 9.47152\n",
      "[65]\ttraining's l2: 6.70673\tvalid_1's l2: 9.47823\n",
      "[66]\ttraining's l2: 6.68847\tvalid_1's l2: 9.47285\n",
      "[67]\ttraining's l2: 6.67159\tvalid_1's l2: 9.46817\n",
      "[68]\ttraining's l2: 6.64832\tvalid_1's l2: 9.47247\n",
      "[69]\ttraining's l2: 6.63026\tvalid_1's l2: 9.47359\n",
      "[70]\ttraining's l2: 6.61081\tvalid_1's l2: 9.4697\n",
      "[71]\ttraining's l2: 6.59041\tvalid_1's l2: 9.4539\n",
      "[72]\ttraining's l2: 6.57736\tvalid_1's l2: 9.45266\n",
      "[73]\ttraining's l2: 6.55846\tvalid_1's l2: 9.44367\n",
      "[74]\ttraining's l2: 6.54066\tvalid_1's l2: 9.43805\n",
      "[75]\ttraining's l2: 6.51731\tvalid_1's l2: 9.42736\n",
      "[76]\ttraining's l2: 6.49697\tvalid_1's l2: 9.42792\n",
      "[77]\ttraining's l2: 6.48085\tvalid_1's l2: 9.43124\n",
      "[78]\ttraining's l2: 6.46307\tvalid_1's l2: 9.43683\n",
      "[79]\ttraining's l2: 6.44398\tvalid_1's l2: 9.43084\n",
      "[80]\ttraining's l2: 6.42818\tvalid_1's l2: 9.43527\n",
      "Training RMSE Score 1.779220089530956\n",
      "Validation RMSE Score 2.0725185159435653\n",
      "Wall time: 3.9 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": 28,
   "id": "6892e39b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_sub = np.mean(np.exp(test_pred) +1, axis = 0)\n",
    "sub['target'] = cat_sub\n",
    "sub['target'].describe()\n",
    "\n",
    "sub.to_csv('3rd_model.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a0eb313",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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