model_training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "usedcars_df = pd.read_parquet(r'data/transformed_data.parquet')\n",
    "usedcars_df = usedcars_df.drop(columns=['offer_description'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the data into train (60%) and temporary set (40%)\n",
    "train_df, temp_df = train_test_split(usedcars_df, test_size=0.4, random_state=1)\n",
    "\n",
    "# Split the temporary set into validation (50%) and test (50%) sets\n",
    "valid_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "y_train = train_df.price_in_euro.values\n",
    "y_valid = valid_df.price_in_euro.values\n",
    "y_test = test_df.price_in_euro.values\n",
    "\n",
    "full_train_df = train_df.copy()\n",
    "full_valid_df = valid_df.copy()\n",
    "full_test_df = test_df.copy()\n",
    "\n",
    "del train_df['price_in_euro']\n",
    "del valid_df['price_in_euro']\n",
    "del test_df['price_in_euro']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns = [\"brand\", \"model\", \"color\", \"registration_date\", \"transmission_type\", \"fuel_type\"]\n",
    "numerical_columns = [\"power_ps\", \"fuel_consumption_l_100km\", \"mileage_in_km\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns = [\"brand\", \"model\", \"color\", \"registration_date\", \"transmission_type\", \"fuel_type\"]\n",
    "\n",
    "# Create a dictionary to store unique values for each categorical column\n",
    "unique_categorical_values = {}\n",
    "\n",
    "for column in categorical_columns:\n",
    "    unique_values = usedcars_df[column].unique()\n",
    "    unique_categorical_values[column] = unique_values\n",
    "\n",
    "# Now save this dictionary using pickle\n",
    "import pickle\n",
    "\n",
    "with open(r'data/unique_categorical_values.pkl', 'wb') as handle:\n",
    "    pickle.dump(unique_categorical_values, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(117084, 9) (39028, 9) (39029, 9)\n"
     ]
    }
   ],
   "source": [
    "print(train_df[categorical_columns + numerical_columns].shape, valid_df[categorical_columns + numerical_columns].shape, test_df[categorical_columns + numerical_columns].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "dv = DictVectorizer(sparse=False)\n",
    "\n",
    "\n",
    "train_dict = train_df[categorical_columns + numerical_columns].to_dict(orient='records')\n",
    "\n",
    "valid_dict = valid_df[categorical_columns + numerical_columns].to_dict(orient='records')\n",
    "\n",
    "test_dict = test_df[categorical_columns + numerical_columns].to_dict(orient='records')\n",
    "\n",
    "X_train = dv.fit_transform(train_dict)\n",
    "X_valid = dv.transform(valid_dict)\n",
    "X_test = dv.transform(test_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('dict_vectorizer.pkl', 'wb') as f:\n",
    "    pickle.dump(dv, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 243 candidates, totalling 729 fits\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-84837439.185 total time=   0.2s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-88242239.397 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-92529537.303 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-33659980.938 total time=   0.2s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-33704582.898 total time=   0.2s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-35596288.167 total time=   0.2s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-22246184.976 total time=   0.4s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-20955510.781 total time=   0.4s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-22773495.400 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-81735824.598 total time=   0.2s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-85038202.717 total time=   0.2s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-89160052.343 total time=   0.2s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-28786247.424 total time=   0.3s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-28513403.135 total time=   0.4s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-30299215.249 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-19365282.193 total time=   0.6s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-17925573.125 total time=   0.6s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-19886469.168 total time=   0.5s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-79765067.375 total time=   0.1s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-82850819.413 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-86751746.806 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-26201966.532 total time=   0.5s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-25816445.989 total time=   0.4s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-27584989.568 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-17818239.155 total time=   0.6s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-16594373.763 total time=   0.6s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-18390278.145 total time=   0.6s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-62695820.978 total time=   0.1s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-65317474.339 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-68420537.592 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-23545293.496 total time=   0.2s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-22217313.592 total time=   0.2s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-24061670.770 total time=   0.2s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-18913160.378 total time=   0.4s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-17126834.340 total time=   0.3s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-19038877.598 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-58750737.481 total time=   0.1s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-60972532.884 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-63977763.254 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-20330620.970 total time=   0.3s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-18840332.649 total time=   0.3s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-20674196.928 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-16988301.969 total time=   0.6s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-15280776.031 total time=   0.5s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-16949717.663 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-56213166.365 total time=   0.1s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-58184839.525 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-60883871.698 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-18746622.139 total time=   0.4s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-17623121.590 total time=   0.4s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-19203290.761 total time=   0.3s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-16191193.261 total time=   0.6s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-15029487.766 total time=   0.6s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-16399157.865 total time=   0.6s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-49335076.836 total time=   0.1s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-50914038.189 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-53313061.455 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-20848063.479 total time=   0.2s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-19002991.869 total time=   0.2s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-21056083.236 total time=   0.2s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-18080452.582 total time=   0.4s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-15815318.046 total time=   0.3s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-17813083.428 total time=   0.3s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-45091700.726 total time=   0.1s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-46266011.156 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-48552758.965 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-18520118.016 total time=   0.3s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-16476037.867 total time=   0.3s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-18409354.797 total time=   0.3s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-16596387.092 total time=   0.4s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-14613841.712 total time=   0.4s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-16267537.579 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-42467607.241 total time=   0.1s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-43402504.570 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-45945324.652 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-17149667.370 total time=   0.3s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-15899562.642 total time=   0.3s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-17639566.877 total time=   0.3s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-15947238.630 total time=   0.5s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-14710775.229 total time=   0.5s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-16020530.513 total time=   0.5s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-74460744.886 total time=   0.1s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-77374924.531 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-80914533.322 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-27296520.498 total time=   0.2s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-27127257.918 total time=   0.3s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-28720127.162 total time=   0.3s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-20584745.023 total time=   0.4s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-19210780.527 total time=   0.4s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-21110566.027 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-68615728.993 total time=   0.1s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-71050809.679 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-74484916.676 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-22427272.265 total time=   0.3s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-21407658.608 total time=   0.3s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-23194963.934 total time=   0.3s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-17823481.813 total time=   0.5s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-16034551.289 total time=   0.5s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-18290283.924 total time=   0.5s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-66156518.139 total time=   0.2s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-68285868.920 total time=   0.2s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-71743556.559 total time=   0.2s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-20373000.333 total time=   0.5s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-19401122.452 total time=   0.5s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-20965486.101 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-16543680.637 total time=   0.7s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-15243693.848 total time=   0.7s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-16900604.303 total time=   0.7s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-51096839.051 total time=   0.1s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-53218079.327 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-55710727.913 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-21300871.131 total time=   0.3s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-19745678.724 total time=   0.2s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-21305103.276 total time=   0.3s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-18380949.377 total time=   0.4s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-16719558.598 total time=   0.4s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-18469390.319 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-44068746.259 total time=   0.2s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-44904407.108 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-47416539.651 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-18085137.660 total time=   0.3s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-16438361.362 total time=   0.3s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-18155794.474 total time=   0.3s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-16702891.372 total time=   0.4s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-15034354.155 total time=   0.5s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-16644600.562 total time=   0.5s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-40859829.755 total time=   0.2s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-41888563.875 total time=   0.2s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-44007985.452 total time=   0.2s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-16970743.713 total time=   0.4s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-15283009.398 total time=   0.4s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-16905912.159 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-15952466.608 total time=   0.6s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-14468196.132 total time=   0.6s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-15870433.685 total time=   0.6s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-39145547.761 total time=   0.1s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-40796393.046 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-42319122.372 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-19508787.238 total time=   0.3s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-18135693.512 total time=   0.2s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-19798045.933 total time=   0.2s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-17663785.663 total time=   0.3s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-15908242.828 total time=   0.3s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-17653865.464 total time=   0.3s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-32060733.542 total time=   0.1s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-32323086.156 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-33821118.861 total time=   0.2s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-17495031.605 total time=   0.3s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-15702076.677 total time=   0.3s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-17423581.871 total time=   0.3s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-16442201.825 total time=   0.4s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-14896711.446 total time=   0.4s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-16393199.599 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-29016798.761 total time=   0.2s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-29275537.690 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-30596023.910 total time=   0.2s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-16310275.838 total time=   0.3s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-15063287.125 total time=   0.4s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-16280787.991 total time=   0.3s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-15822287.105 total time=   0.5s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-14660755.900 total time=   0.6s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-15579004.259 total time=   0.6s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-69277264.243 total time=   0.1s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-72019849.644 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-75027724.450 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-25872320.659 total time=   0.3s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-26084703.996 total time=   0.3s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-27511139.588 total time=   0.3s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-20281365.443 total time=   0.5s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-19052635.999 total time=   0.4s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-20903605.815 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-62786720.058 total time=   0.1s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-64620115.855 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-67818395.077 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-20740938.179 total time=   0.3s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-19936974.408 total time=   0.3s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-21876662.763 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-17382048.480 total time=   0.5s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-16036231.404 total time=   0.5s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-17966853.374 total time=   0.6s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-60180633.614 total time=   0.2s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-61473181.566 total time=   0.2s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-64773768.116 total time=   0.2s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-18886135.805 total time=   0.4s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-17781580.166 total time=   0.5s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-19700966.763 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-16251204.300 total time=   0.7s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-15061891.476 total time=   0.7s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-16767687.325 total time=   0.7s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-45572732.234 total time=   0.1s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-47415896.181 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-49278407.830 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-20520063.479 total time=   0.2s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-19239710.589 total time=   0.2s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-21096150.734 total time=   0.3s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-18485233.694 total time=   0.4s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-16401826.161 total time=   0.4s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-18356224.841 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-38044911.412 total time=   0.2s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-38583768.403 total time=   0.2s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-40804756.712 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-17375987.686 total time=   0.4s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-15936813.253 total time=   0.4s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-18038428.850 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-16481877.342 total time=   0.5s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-14869422.594 total time=   0.5s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-16608467.161 total time=   0.5s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-35083038.167 total time=   0.2s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-35051976.228 total time=   0.2s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-37339440.220 total time=   0.2s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-16450930.777 total time=   0.4s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-15178900.885 total time=   0.4s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-16645506.914 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-15994073.661 total time=   0.6s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-14479249.213 total time=   0.5s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-15812115.412 total time=   0.5s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-34290198.136 total time=   0.1s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-35074258.110 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-36929906.885 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-19194277.983 total time=   0.2s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-17319773.919 total time=   0.2s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-19031667.022 total time=   0.2s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-17917409.594 total time=   0.3s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-15600900.292 total time=   0.3s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-17364581.050 total time=   0.3s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-27712971.695 total time=   0.1s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-27330023.010 total time=   0.1s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-29343491.902 total time=   0.1s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-16958511.789 total time=   0.3s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-15153716.536 total time=   0.3s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-17229339.703 total time=   0.3s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-16356896.498 total time=   0.4s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-14499115.707 total time=   0.7s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-16201576.157 total time=   0.5s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-24866441.359 total time=   0.2s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-24199187.025 total time=   0.2s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-26138308.012 total time=   0.2s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-16001850.325 total time=   0.4s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-14663463.551 total time=   0.4s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-16450681.398 total time=   0.4s\n",
      "[CV 1/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-15504277.284 total time=   0.5s\n",
      "[CV 2/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-14331615.113 total time=   0.5s\n",
      "[CV 3/3] END alpha=5, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-15935068.401 total time=   0.6s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-84837470.905 total time=   0.1s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-88242274.861 total time=   0.1s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-92529579.461 total time=   0.1s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-33660041.571 total time=   0.3s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-33704654.202 total time=   0.3s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-35596356.176 total time=   0.3s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-22246235.290 total time=   0.4s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-20975532.342 total time=   0.4s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-22773559.154 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-81735906.092 total time=   0.1s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-85038285.690 total time=   0.1s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-89160139.119 total time=   0.1s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-28786388.854 total time=   0.4s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-28513561.704 total time=   0.4s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-30299390.200 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-19365138.187 total time=   0.5s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-17925691.671 total time=   0.6s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-19886410.111 total time=   0.5s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-79765216.670 total time=   0.3s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-82845028.471 total time=   0.2s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-86751914.033 total time=   0.2s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-26202079.729 total time=   0.4s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-25809357.976 total time=   0.4s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-27585298.221 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-17818164.886 total time=   0.8s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-16613156.766 total time=   0.8s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-18390310.724 total time=   0.7s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-62695863.975 total time=   0.1s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-65317531.337 total time=   0.1s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-68420593.726 total time=   0.1s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-23545342.897 total time=   0.2s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-22217396.922 total time=   0.3s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-24061735.018 total time=   0.3s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-18913211.992 total time=   0.4s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-17126926.560 total time=   0.4s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-19038958.623 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-58750864.467 total time=   0.1s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-60972657.464 total time=   0.1s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-63977900.909 total time=   0.1s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-20330739.441 total time=   0.3s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-18840449.858 total time=   0.4s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-20672928.731 total time=   0.3s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-16988173.098 total time=   0.5s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-15274524.281 total time=   0.5s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-16944900.709 total time=   0.5s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-56213376.558 total time=   0.1s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-58185076.221 total time=   0.1s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-60884107.207 total time=   0.1s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-18752135.904 total time=   0.4s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-17623295.037 total time=   0.4s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-19206923.067 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-16215044.558 total time=   0.6s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-15036832.984 total time=   0.6s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-16427707.005 total time=   0.7s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-49335123.803 total time=   0.1s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-50914088.009 total time=   0.1s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-53313115.593 total time=   0.1s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-20848120.045 total time=   0.2s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-19003074.125 total time=   0.2s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-21056157.364 total time=   0.2s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-18080505.294 total time=   0.4s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-15759011.145 total time=   0.4s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-17813167.209 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-45091831.480 total time=   0.1s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-46266156.522 total time=   0.1s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-48552903.313 total time=   0.1s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-18520165.215 total time=   0.3s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-16476132.062 total time=   0.3s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-18398120.798 total time=   0.3s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-16596399.163 total time=   0.4s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-14614079.549 total time=   0.4s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-16281166.589 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-42467834.193 total time=   0.1s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-43402769.170 total time=   0.1s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-45945564.839 total time=   0.1s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-17149775.883 total time=   0.3s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-15892649.290 total time=   0.3s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-17639739.168 total time=   0.3s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-15920414.843 total time=   0.5s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-14752737.489 total time=   0.5s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-16022183.892 total time=   0.5s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-74460765.473 total time=   0.1s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-77374954.008 total time=   0.1s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-80914557.488 total time=   0.1s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-27296562.789 total time=   0.3s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-27127304.947 total time=   0.3s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-28720174.762 total time=   0.3s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-20584790.725 total time=   0.4s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-19210850.710 total time=   0.4s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-21110623.935 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-68615832.062 total time=   0.1s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-71050927.562 total time=   0.1s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-74485024.994 total time=   0.2s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-22427381.253 total time=   0.4s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-21405854.386 total time=   0.4s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-23195086.176 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-17829476.791 total time=   0.6s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-16032133.172 total time=   0.6s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-18290682.234 total time=   0.6s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-66156748.513 total time=   0.2s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-68286106.444 total time=   0.2s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-71743817.823 total time=   0.2s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-20373525.549 total time=   0.5s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-19420021.008 total time=   0.5s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-20968280.536 total time=   0.5s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-16551773.670 total time=   0.8s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-15271388.294 total time=   0.7s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-16892557.383 total time=   0.8s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-51096872.646 total time=   0.1s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-53218112.839 total time=   0.1s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-55710759.905 total time=   0.2s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-21300897.623 total time=   0.3s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-19745740.021 total time=   0.3s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-21305160.101 total time=   0.3s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-18380992.187 total time=   0.4s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-16719631.496 total time=   0.4s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-18471241.566 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-44068865.338 total time=   0.2s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-44904538.913 total time=   0.2s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-47416688.011 total time=   0.2s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-18085234.394 total time=   0.3s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-16438428.131 total time=   0.3s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-18155931.053 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-16702903.530 total time=   0.5s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-15034391.692 total time=   0.5s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-16644755.387 total time=   0.5s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-40860098.242 total time=   0.2s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-41888850.522 total time=   0.2s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-44008301.959 total time=   0.2s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-16969750.020 total time=   0.4s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-15283121.930 total time=   0.4s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-16921108.467 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-15998677.653 total time=   0.6s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-14503377.883 total time=   0.7s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-15816315.772 total time=   0.6s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-39145581.081 total time=   0.1s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-40796430.930 total time=   0.1s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-42319159.203 total time=   0.1s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-19520811.399 total time=   0.3s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-18135758.767 total time=   0.3s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-19798099.702 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-17733784.855 total time=   0.5s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-15966965.278 total time=   0.5s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-17653923.556 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-32060863.278 total time=   0.1s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-32323223.584 total time=   0.2s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-33821269.468 total time=   0.2s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-17495113.020 total time=   0.3s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-15696767.595 total time=   0.3s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-17423683.792 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-16453306.082 total time=   0.5s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-14908278.639 total time=   0.5s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-16392545.863 total time=   0.5s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-29017058.852 total time=   0.2s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-29275801.335 total time=   0.2s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-30596329.676 total time=   0.2s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-16324930.752 total time=   0.4s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-15109686.482 total time=   0.4s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-16275947.330 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-15849974.578 total time=   0.6s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-14576643.175 total time=   0.5s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-15677262.481 total time=   0.5s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-69277279.862 total time=   0.1s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-72019882.616 total time=   0.1s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-75027740.835 total time=   0.1s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-25872356.141 total time=   0.3s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-26084747.317 total time=   0.3s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-27511180.700 total time=   0.3s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-20281411.764 total time=   0.4s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-19052701.555 total time=   0.4s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-20903663.847 total time=   0.7s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-62786815.184 total time=   0.2s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-64620233.715 total time=   0.2s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-67818502.635 total time=   0.1s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-20741261.489 total time=   0.4s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-19938059.602 total time=   0.4s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-21876400.358 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-17357053.955 total time=   0.6s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-16037396.370 total time=   0.6s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-17979167.257 total time=   0.6s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-60180865.450 total time=   0.2s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-61473429.459 total time=   0.2s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-64774073.467 total time=   0.2s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-18912010.708 total time=   0.5s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-17782672.955 total time=   0.5s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-19701155.664 total time=   0.5s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-16373318.713 total time=   0.7s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-15059908.712 total time=   0.8s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-16782477.751 total time=   1.0s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-45572767.954 total time=   0.2s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-47415932.564 total time=   0.2s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-49278443.132 total time=   0.1s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-20520101.446 total time=   0.3s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-19239768.083 total time=   0.2s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-21096198.676 total time=   0.3s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-18485271.957 total time=   0.4s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-16467691.696 total time=   0.5s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-18356290.390 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-38045018.734 total time=   0.2s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-38583894.564 total time=   0.2s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-40804882.687 total time=   0.2s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-17376063.275 total time=   0.3s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-15936898.956 total time=   0.3s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-18023161.311 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-16508285.535 total time=   0.5s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-14868876.574 total time=   0.5s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-16562225.540 total time=   0.5s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-35083277.977 total time=   0.2s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-35052243.633 total time=   0.2s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-37364397.168 total time=   0.3s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-16384697.534 total time=   0.5s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-15187321.618 total time=   0.8s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-16634251.476 total time=   0.5s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-15845642.493 total time=   0.9s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-14560256.140 total time=   0.9s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-15818307.045 total time=   0.7s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-34290229.215 total time=   0.1s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-35074290.712 total time=   0.1s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-36929940.019 total time=   0.1s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-19194317.279 total time=   0.2s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-17312568.593 total time=   0.3s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-19031726.198 total time=   0.3s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-17917464.269 total time=   0.4s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-15662982.410 total time=   0.4s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-17364681.779 total time=   0.5s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-27713076.589 total time=   0.2s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-27330130.672 total time=   0.2s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-29343613.656 total time=   0.2s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-16958538.348 total time=   0.3s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-15227755.647 total time=   0.3s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-17221740.820 total time=   0.3s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-16389641.170 total time=   0.4s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-14434533.524 total time=   0.4s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-16195426.507 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-24867348.705 total time=   0.2s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-24199442.160 total time=   0.2s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-26138535.209 total time=   0.2s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-16166802.477 total time=   0.4s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-14686780.432 total time=   0.4s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-16450693.100 total time=   0.4s\n",
      "[CV 1/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-15648470.343 total time=   0.5s\n",
      "[CV 2/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-14321250.832 total time=   0.5s\n",
      "[CV 3/3] END alpha=10, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-15890490.610 total time=   0.5s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-84837513.828 total time=   0.1s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-88242303.795 total time=   0.1s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-92529624.112 total time=   0.1s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-33660098.771 total time=   0.3s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-33704727.062 total time=   0.3s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-35596425.981 total time=   0.3s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-22246283.986 total time=   0.4s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-20975606.141 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-22773620.480 total time=   0.5s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-81735993.223 total time=   0.1s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-85038363.896 total time=   0.1s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-89160220.418 total time=   0.1s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-28786537.131 total time=   0.3s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-28513724.288 total time=   0.3s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-30299556.653 total time=   0.3s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-19365247.630 total time=   0.5s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-17925805.259 total time=   0.6s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-19886982.898 total time=   0.6s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-79765360.983 total time=   0.2s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-82845205.896 total time=   0.2s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-86752080.084 total time=   0.1s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-26202345.574 total time=   0.4s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-25809654.678 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-27599582.443 total time=   0.4s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-17818298.689 total time=   0.6s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-16610423.828 total time=   0.6s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-18397335.607 total time=   0.8s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-62695909.935 total time=   0.1s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-65317584.236 total time=   0.1s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-68420648.871 total time=   0.2s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-23545396.228 total time=   0.3s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-22217482.667 total time=   0.3s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-24061800.293 total time=   0.3s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-18913265.565 total time=   0.4s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-17127018.218 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-19039037.813 total time=   0.4s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-58750988.061 total time=   0.1s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-60972785.224 total time=   0.1s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-63978027.730 total time=   0.1s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-20348220.882 total time=   0.3s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-18840567.422 total time=   0.3s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-20673060.596 total time=   0.3s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-16884513.885 total time=   0.5s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-15277234.393 total time=   0.5s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-16973672.097 total time=   0.5s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-56213578.882 total time=   0.2s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-58185309.858 total time=   0.2s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-60884354.933 total time=   0.1s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-18752317.879 total time=   0.4s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-17543587.828 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-19207166.853 total time=   0.4s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-16208650.952 total time=   0.6s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-14917229.309 total time=   0.6s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-16427048.719 total time=   0.6s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-49335167.463 total time=   0.1s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-50914142.653 total time=   0.1s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-53313174.246 total time=   0.1s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-20848171.904 total time=   0.4s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-19003155.290 total time=   0.3s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-21056224.422 total time=   0.3s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-18080559.527 total time=   0.4s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-15759094.005 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-17813247.678 total time=   0.4s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-45091962.251 total time=   0.1s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-46266295.906 total time=   0.1s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-48553055.858 total time=   0.1s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-18520208.950 total time=   0.3s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-16474661.969 total time=   0.3s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-18398234.342 total time=   0.3s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-16602294.466 total time=   0.6s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-14672130.106 total time=   0.5s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-16281286.057 total time=   0.5s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-42468056.466 total time=   0.2s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-43403033.336 total time=   0.1s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-45945805.774 total time=   0.2s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-17145673.118 total time=   0.4s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-15892949.455 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-17640718.877 total time=   0.4s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-15914318.605 total time=   0.5s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-14744198.174 total time=   0.6s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.3, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-16068436.535 total time=   0.6s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-74460788.846 total time=   0.1s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-77374979.768 total time=   0.1s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-80914584.405 total time=   0.1s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-27296600.256 total time=   0.3s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-27127354.615 total time=   0.3s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-28720225.380 total time=   0.3s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-20584832.440 total time=   0.4s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-19210921.312 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-21110678.743 total time=   0.5s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-68615935.042 total time=   0.2s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-71051027.800 total time=   0.1s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-74487657.878 total time=   0.1s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-22428370.405 total time=   0.4s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-21405976.536 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-23196209.653 total time=   0.3s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-17778781.040 total time=   0.6s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-16032223.637 total time=   0.6s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-18294090.402 total time=   0.6s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-66155253.209 total time=   0.2s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-68286343.777 total time=   0.2s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-71744078.507 total time=   0.2s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-20372782.090 total time=   0.5s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-19418934.692 total time=   0.5s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-20968501.556 total time=   0.5s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-16551230.513 total time=   0.8s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-15236692.854 total time=   0.8s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-16914904.865 total time=   0.8s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-51096903.253 total time=   0.1s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-53218156.312 total time=   0.1s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-55710804.704 total time=   0.1s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-21300925.700 total time=   0.3s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-19745801.047 total time=   0.3s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-21305221.176 total time=   0.3s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-18381037.674 total time=   0.4s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-16719706.176 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-18471318.710 total time=   0.4s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-44068980.427 total time=   0.2s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-44904686.217 total time=   0.2s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-47416829.326 total time=   0.2s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-18085333.544 total time=   0.3s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-16438498.839 total time=   0.3s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-18044443.750 total time=   0.4s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-16705935.943 total time=   0.5s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-15034431.153 total time=   0.5s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-16647380.823 total time=   0.5s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-40860366.276 total time=   0.2s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-41889136.648 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-44008620.806 total time=   0.2s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-16943797.524 total time=   0.5s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-15319039.118 total time=   0.5s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-17066023.530 total time=   0.5s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-16048630.572 total time=   0.7s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-14467754.816 total time=   0.8s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-15943078.049 total time=   0.7s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-39145611.425 total time=   0.1s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-40796472.817 total time=   0.1s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-42319196.380 total time=   0.1s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-19610517.850 total time=   0.2s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-18135820.070 total time=   0.2s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-19798148.926 total time=   0.2s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-17735155.394 total time=   0.4s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-15967028.527 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-17653977.386 total time=   0.4s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-32060989.819 total time=   0.2s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-32323356.603 total time=   0.2s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-33821423.245 total time=   0.2s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-17501534.783 total time=   0.3s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-15696844.742 total time=   0.3s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-17423784.568 total time=   0.3s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-16445443.932 total time=   0.4s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-14904229.155 total time=   0.5s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-16432658.022 total time=   0.4s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-29017315.574 total time=   0.2s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-29276066.315 total time=   0.2s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-30622067.902 total time=   0.2s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-16324900.936 total time=   0.4s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-15062933.300 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-16269266.766 total time=   0.4s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-15841352.569 total time=   0.6s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-14590406.011 total time=   0.6s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.5, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-15621447.088 total time=   0.6s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-69277311.577 total time=   0.1s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-72019910.324 total time=   0.1s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=10;, score=-75027792.296 total time=   0.1s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-25872386.783 total time=   0.3s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-26084790.502 total time=   0.3s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=50;, score=-27511224.343 total time=   0.3s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-20281457.215 total time=   0.4s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-19052762.580 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=3, n_estimators=100;, score=-20903720.590 total time=   0.4s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-62786900.940 total time=   0.2s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-64620340.347 total time=   0.1s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=10;, score=-67818613.020 total time=   0.2s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-20741345.347 total time=   0.4s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-19938168.987 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=50;, score=-21876521.430 total time=   0.4s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-17357131.248 total time=   0.6s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-16061672.742 total time=   0.6s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=5, n_estimators=100;, score=-17957030.796 total time=   0.6s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-60159601.907 total time=   0.2s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-61478337.904 total time=   0.2s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=10;, score=-64774358.767 total time=   0.2s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-18884924.227 total time=   0.5s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-17761519.568 total time=   0.5s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=50;, score=-19658256.935 total time=   0.5s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-16284056.157 total time=   0.8s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-15066695.977 total time=   0.8s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.05, max_depth=7, n_estimators=100;, score=-16710624.692 total time=   0.7s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-45572803.141 total time=   0.1s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-47415970.236 total time=   0.1s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=10;, score=-49278473.983 total time=   0.1s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-20520138.759 total time=   0.3s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-19239828.769 total time=   0.3s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=50;, score=-21096245.196 total time=   0.2s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-18405041.495 total time=   0.4s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-16467780.987 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=3, n_estimators=100;, score=-18480433.294 total time=   0.4s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-38045130.904 total time=   0.1s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-38584025.461 total time=   0.1s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=10;, score=-40805012.478 total time=   0.2s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-17376140.032 total time=   0.3s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-15936985.857 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=50;, score=-18040906.285 total time=   0.4s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-16497351.639 total time=   0.5s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-14868924.238 total time=   0.5s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=5, n_estimators=100;, score=-16583153.697 total time=   0.5s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-35083518.113 total time=   0.2s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-35051518.232 total time=   0.2s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=10;, score=-37364706.042 total time=   0.2s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-16533426.648 total time=   0.5s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-15189175.027 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=50;, score=-16635974.076 total time=   0.4s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-15986709.986 total time=   0.6s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-14602697.929 total time=   0.6s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.1, max_depth=7, n_estimators=100;, score=-15800780.181 total time=   0.6s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-34290260.867 total time=   0.1s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-35074329.891 total time=   0.2s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=10;, score=-36929973.896 total time=   0.1s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-19194357.127 total time=   0.3s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-17312637.005 total time=   0.3s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=50;, score=-19031780.905 total time=   0.2s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-17917520.025 total time=   0.4s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-15663039.770 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=3, n_estimators=100;, score=-17363122.768 total time=   0.4s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-27713183.690 total time=   0.2s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-27330242.811 total time=   0.2s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=10;, score=-29343730.342 total time=   0.2s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-16958565.362 total time=   0.3s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-15220524.826 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=50;, score=-17221828.759 total time=   0.3s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-16389072.552 total time=   0.5s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-14577953.628 total time=   0.5s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=5, n_estimators=100;, score=-16188356.254 total time=   0.5s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-24867566.395 total time=   0.2s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-24199590.759 total time=   0.2s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=10;, score=-26123275.036 total time=   0.2s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-16230278.643 total time=   0.4s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-14787339.689 total time=   0.4s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=50;, score=-16487337.773 total time=   0.4s\n",
      "[CV 1/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-15834870.253 total time=   0.5s\n",
      "[CV 2/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-14394674.678 total time=   0.6s\n",
      "[CV 3/3] END alpha=15, colsample_bytree=0.7, learning_rate=0.15, max_depth=7, n_estimators=100;, score=-16003983.494 total time=   0.5s\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "\n",
    "# Assuming your data is already prepared and split into X_train, X_valid, y_train, y_valid\n",
    "\n",
    "# Define the parameter grid\n",
    "param_grid = {\n",
    "    'colsample_bytree': [0.3, 0.5, 0.7],\n",
    "    'learning_rate': [0.05, 0.1, 0.15],\n",
    "    'max_depth': [3, 5, 7],\n",
    "    'alpha': [5, 10, 15],\n",
    "    'n_estimators': [10, 50, 100]\n",
    "}\n",
    "\n",
    "# Initialize the XGBoost Regressor\n",
    "xgb_model = xgb.XGBRegressor(objective='reg:squarederror')\n",
    "\n",
    "# Set up GridSearchCV\n",
    "grid_search = GridSearchCV(estimator=xgb_model, param_grid=param_grid, \n",
    "                           scoring='neg_mean_squared_error', cv=3, verbose=3)\n",
    "\n",
    "# Fit the model\n",
    "grid_search.fit(X_train[:10000], y_train[:10000])\n",
    "\n",
    "# Best performing model\n",
    "best_model = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters found: {'alpha': 5, 'colsample_bytree': 0.7, 'learning_rate': 0.15, 'max_depth': 7, 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "best_params = grid_search.best_params_\n",
    "\n",
    "print(\"Best parameters found:\", best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(alpha=5, base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.7, 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=0.15, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "             num_parallel_tree=None, ...)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(alpha=5, base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.7, 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=0.15, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "             num_parallel_tree=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(alpha=5, base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.7, 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=0.15, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "             num_parallel_tree=None, ...)"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_reg = xgb.XGBRegressor(\n",
    "    alpha=5,\n",
    "    colsample_bytree=0.7,\n",
    "    learning_rate=0.15,\n",
    "    max_depth=7,\n",
    "    n_estimators=100,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "# Fit the regressor to the training set\n",
    "xg_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = xg_reg.predict(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 2330.59\n",
      "MSE: 12960635.68\n",
      "RMSE: 3600.09\n",
      "R-squared: 0.90\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "import numpy as np\n",
    "\n",
    "mae = mean_absolute_error(y_valid, y_pred)\n",
    "mse = mean_squared_error(y_valid, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_valid, y_pred)\n",
    "\n",
    "print(f\"MAE: {mae:.2f}\")\n",
    "print(f\"MSE: {mse:.2f}\")\n",
    "print(f\"RMSE: {rmse:.2f}\")\n",
    "print(f\"R-squared: {r2:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Calculate residuals\n",
    "residuals = y_valid - y_pred\n",
    "\n",
    "# 1. Scatter Plot of Actual vs. Predicted Values\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_valid, y_pred, alpha=0.5)\n",
    "plt.plot([y_valid.min(), y_valid.max()], [y_valid.min(), y_valid.max()], 'k--', lw=2)\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Predicted')\n",
    "plt.title('Actual vs. Predicted')\n",
    "plt.show()\n",
    "\n",
    "# 2. Residual Plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_pred, residuals, alpha=0.5)\n",
    "plt.axhline(y=0, color='k', linestyle='--')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Residuals')\n",
    "plt.title('Residuals vs. Predicted')\n",
    "plt.show()\n",
    "\n",
    "# 3. Histogram of Residuals\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(residuals, bins=50, kde=True)\n",
    "plt.xlabel('Residuals')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Residuals')\n",
    "plt.show()\n",
    "\n",
    "# 4. Q-Q Plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "stats.probplot(residuals, dist=\"norm\", plot=plt)\n",
    "plt.title('Q-Q Plot')\n",
    "plt.show()\n",
    "\n",
    "# 5. Predicted vs. Residuals Plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_pred, residuals, alpha=0.5)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Residuals')\n",
    "plt.axhline(0, color='red', linestyle='--')\n",
    "plt.title('Predicted vs. Residuals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model to a file\n",
    "with open(r'model/best_xgboost_model.pkl', 'wb') as file:\n",
    "    pickle.dump(xg_reg, file)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mlzoomcamp",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}